Instruction: What field is the article from?

Input:
Title: A Systems-Theoretical Formalization of Closed Systems
Abstract: There is a lack of formalism for some key foundational concepts in systems
engineering. One of the most recently acknowledged deficits is the inadequacy
of systems engineering practices for engineering intelligent systems. In our
previous works, we proposed that closed systems precepts could be used to
accomplish a required paradigm shift for the systems engineering of intelligent
systems. However, to enable such a shift, formal foundations for closed systems
precepts that expand the theory of systems engineering are needed. The concept
of closure is a critical concept in the formalism underlying closed systems
precepts. In this paper, we provide formal, systems- and information-theoretic
definitions of closure to identify and distinguish different types of closed
systems. Then, we assert a mathematical framework to evaluate the subjective
formation of the boundaries and constraints of such systems. Finally, we argue
that engineering an intelligent system can benefit from appropriate closed and
open systems paradigms on multiple levels of abstraction of the system. In the
main, this framework will provide the necessary fundamentals to aid in systems
engineering of intelligent systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: RLHF and IIA: Perverse Incentives
Abstract: Existing algorithms for reinforcement learning from human feedback (RLHF) can
incentivize responses at odds with preferences because they are based on models
that assume independence of irrelevant alternatives (IIA). The perverse
incentives induced by IIA give rise to egregious behavior when innovating on
query formats or learning algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Enhancing Trajectory Prediction through Self-Supervised Waypoint Noise Prediction
Abstract: Trajectory prediction is an important task that involves modeling the
indeterminate nature of traffic actors to forecast future trajectories given
the observed trajectory sequences. However, current methods confine themselves
to presumed data manifolds, assuming that trajectories strictly adhere to these
manifolds, resulting in overly simplified predictions. To this end, we propose
a novel approach called SSWNP (Self-Supervised Waypoint Noise Prediction). In
our approach, we first create clean and noise-augmented views of past observed
trajectories across the spatial domain of waypoints. We then compel the
trajectory prediction model to maintain spatial consistency between predictions
from these two views, in addition to the trajectory prediction task.
Introducing the noise-augmented view mitigates the model's reliance on a narrow
interpretation of the data manifold, enabling it to learn more plausible and
diverse representations. We also predict the noise present in the two views of
past observed trajectories as an auxiliary self-supervised task, enhancing the
model's understanding of the underlying representation and future predictions.
Empirical evidence demonstrates that the incorporation of SSWNP into the model
learning process significantly improves performance, even in noisy
environments, when compared to baseline methods. Our approach can complement
existing trajectory prediction methods. To showcase the effectiveness of our
approach, we conducted extensive experiments on three datasets: NBA Sports VU,
ETH-UCY, and TrajNet++, with experimental results highlighting the substantial
improvement achieved in trajectory prediction tasks.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Automatic Bug Detection in Games using LSTM Networks
Abstract: We introduced a new framework to detect perceptual bugs using a Long
Short-Term Memory (LSTM) network, which detects bugs in video games as
anomalies. The detected buggy frames are then clustered to determine the
category of the occurred bug. The framework was evaluated on two First Person
Shooter (FPS) games. Results show the effectiveness of the framework.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: NeRFiller: Completing Scenes via Generative 3D Inpainting
Abstract: We propose NeRFiller, an approach that completes missing portions of a 3D
capture via generative 3D inpainting using off-the-shelf 2D visual generative
models. Often parts of a captured 3D scene or object are missing due to mesh
reconstruction failures or a lack of observations (e.g., contact regions, such
as the bottom of objects, or hard-to-reach areas). We approach this challenging
3D inpainting problem by leveraging a 2D inpainting diffusion model. We
identify a surprising behavior of these models, where they generate more 3D
consistent inpaints when images form a 2$\times$2 grid, and show how to
generalize this behavior to more than four images. We then present an iterative
framework to distill these inpainted regions into a single consistent 3D scene.
In contrast to related works, we focus on completing scenes rather than
deleting foreground objects, and our approach does not require tight 2D object
masks or text. We compare our approach to relevant baselines adapted to our
setting on a variety of scenes, where NeRFiller creates the most 3D consistent
and plausible scene completions. Our project page is at
https://ethanweber.me/nerfiller.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: HAL 9000: Skynet's Risk Manager
Abstract: Intrusion Tolerant Systems (ITSs) are a necessary component for
cyber-services/infrastructures. Additionally, as cyberattacks follow a
multi-domain attack surface, a similar defensive approach should be applied,
namely, the use of an evolving multi-disciplinary solution that combines ITS,
cybersecurity and Artificial Intelligence (AI). With the increased popularity
of AI solutions, due to Big Data use-case scenarios and decision support and
automation scenarios, new opportunities to apply Machine Learning (ML)
algorithms have emerged, namely ITS empowerment. Using ML algorithms, an ITS
can augment its intrusion tolerance capability, by learning from previous
attacks and from known vulnerabilities. As such, this work's contribution is
twofold: (1) an ITS architecture (Skynet) based on the state-of-the-art and
incorporates new components to increase its intrusion tolerance capability and
its adaptability to new adversaries; (2) an improved Risk Manager design that
leverages AI to improve ITSs by automatically assessing OS risks to intrusions,
and advise with safer configurations. One of the reasons that intrusions are
successful is due to bad configurations or slow adaptability to new threats.
This can be caused by the dependency that systems have for human intervention.
One of the characteristics in Skynet and HAL 9000 design is the removal of
human intervention. Being fully automatized lowers the chance of successful
intrusions caused by human error. Our experiments using Skynet, shows that HAL
is able to choose 15% safer configurations than the state-of-the-art risk
manager.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks
Abstract: This paper studies the problem of CPRP, concept prerequisite relation
prediction, which is a fundamental task in using AI for education. CPRP is
usually formulated into a link-prediction task on a relationship graph of
concepts and solved by training the graph neural network (GNN) model. However,
current directed GNNs fail to manage graph isomorphism which refers to the
invariance of non-isomorphic graphs, reducing the expressivity of resulting
representations. We present a permutation-equivariant directed GNN model by
introducing the Weisfeiler-Lehman test into directed GNN learning. Our method
is then used for CPRP and evaluated on three public datasets. The experimental
results show that our model delivers better prediction performance than the
state-of-the-art methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Re-evaluating Retrosynthesis Algorithms with Syntheseus
Abstract: The planning of how to synthesize molecules, also known as retrosynthesis,
has been a growing focus of the machine learning and chemistry communities in
recent years. Despite the appearance of steady progress, we argue that
imperfect benchmarks and inconsistent comparisons mask systematic shortcomings
of existing techniques. To remedy this, we present a benchmarking library
called syntheseus which promotes best practice by default, enabling consistent
meaningful evaluation of single-step and multi-step retrosynthesis algorithms.
We use syntheseus to re-evaluate a number of previous retrosynthesis
algorithms, and find that the ranking of state-of-the-art models changes when
evaluated carefully. We end with guidance for future works in this area.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Regularization by Texts for Latent Diffusion Inverse Solvers
Abstract: The recent advent of diffusion models has led to significant progress in
solving inverse problems, leveraging these models as effective generative
priors. Nonetheless, challenges related to the ill-posed nature of such
problems remain, often due to inherent ambiguities in measurements. Drawing
inspiration from the human ability to resolve visual ambiguities through
perceptual biases, here we introduce a novel latent diffusion inverse solver by
incorporating regularization by texts (TReg). Specifically, TReg applies the
textual description of the preconception of the solution during the reverse
sampling phase, of which description isndynamically reinforced through
null-text optimization for adaptive negation. Our comprehensive experimental
results demonstrate that TReg successfully mitigates ambiguity in latent
diffusion inverse solvers, enhancing their effectiveness and accuracy.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic
Abstract: In this work, we approach the problem of Qur'anic information retrieval (IR)
in Arabic and English. Using the latest state-of-the-art methods in neural IR,
we research what helps to tackle this task more efficiently. Training retrieval
models requires a lot of data, which is difficult to obtain for training
in-domain. Therefore, we commence with training on a large amount of general
domain data and then continue training on in-domain data. To handle the lack of
in-domain data, we employed a data augmentation technique, which considerably
improved results in MRR@10 and NDCG@5 metrics, setting the state-of-the-art in
Qur'anic IR for both English and Arabic. The absence of an Islamic corpus and
domain-specific model for IR task in English motivated us to address this lack
of resources and take preliminary steps of the Islamic corpus compilation and
domain-specific language model (LM) pre-training, which helped to improve the
performance of the retrieval models that use the domain-specific LM as the
shared backbone. We examined several language models (LMs) in Arabic to select
one that efficiently deals with the Qur'anic IR task. Besides transferring
successful experiments from English to Arabic, we conducted additional
experiments with retrieval task in Arabic to amortize the scarcity of general
domain datasets used to train the retrieval models. Handling Qur'anic IR task
combining English and Arabic allowed us to enhance the comparison and share
valuable insights across models and languages.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Deriving Comprehensible Theories from Probabilistic Circuits
Abstract: The field of Explainable AI (XAI) is seeking to shed light on the inner
workings of complex AI models and uncover the rationale behind their decisions.
One of the models gaining attention are probabilistic circuits (PCs), which are
a general and unified framework for tractable probabilistic models that support
efficient computation of various probabilistic queries. Probabilistic circuits
guarantee inference that is polynomial in the size of the circuit. In this
paper, we improve the explainability of probabilistic circuits by computing a
comprehensible, readable logical theory that covers the high-density regions
generated by a PC. To achieve this, pruning approaches based on generative
significance are used in a new method called PUTPUT (Probabilistic circuit
Understanding Through Pruning Underlying logical Theories). The method is
applied to a real world use case where music playlists are automatically
generated and expressed as readable (database) queries. Evaluation shows that
this approach can effectively produce a comprehensible logical theory that
describes the high-density regions of a PC and outperforms state of the art
methods when exploring the performance-comprehensibility trade-off.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: PixLore: A Dataset-driven Approach to Rich Image Captioning
Abstract: In the domain of vision-language integration, generating detailed image
captions poses a significant challenge due to the lack of a curated and rich
dataset. This study introduces PixLore, a novel method that leverages Querying
Transformers through the fine-tuning of the BLIP-2 model using the LoRa method
on a standard commercial GPU. Our approach, which involves training on a
carefully assembled dataset from state-of-the-art Computer Vision models
combined and augmented by ChatGPT, addresses the question of whether intricate
image understanding can be achieved with an ensemble of smaller-scale models.
Comparative evaluations against major models such as GPT-4 and Google Bard
demonstrate that PixLore-2.7B, despite having considerably fewer parameters, is
rated higher than the existing State-of-the-Art models in over half of the
assessments. This research not only presents a groundbreaking approach but also
highlights the importance of well-curated datasets in enhancing the performance
of smaller models.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A novel post-hoc explanation comparison metric and applications
Abstract: Explanatory systems make the behavior of machine learning models more
transparent, but are often inconsistent. To quantify the differences between
explanatory systems, this paper presents the Shreyan Distance, a novel metric
based on the weighted difference between ranked feature importance lists
produced by such systems. This paper uses the Shreyan Distance to compare two
explanatory systems, SHAP and LIME, for both regression and classification
learning tasks. Because we find that the average Shreyan Distance varies
significantly between these two tasks, we conclude that consistency between
explainers not only depends on inherent properties of the explainers
themselves, but also the type of learning task. This paper further contributes
the XAISuite library, which integrates the Shreyan distance algorithm into
machine learning pipelines.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge
Abstract: Speech recognition systems driven by DNNs have revolutionized human-computer
interaction through voice interfaces, which significantly facilitate our daily
lives. However, the growing popularity of these systems also raises special
concerns on their security, particularly regarding backdoor attacks. A backdoor
attack inserts one or more hidden backdoors into a DNN model during its
training process, such that it does not affect the model's performance on
benign inputs, but forces the model to produce an adversary-desired output if a
specific trigger is present in the model input. Despite the initial success of
current audio backdoor attacks, they suffer from the following limitations: (i)
Most of them require sufficient knowledge, which limits their widespread
adoption. (ii) They are not stealthy enough, thus easy to be detected by
humans. (iii) Most of them cannot attack live speech, reducing their
practicality. To address these problems, in this paper, we propose FlowMur, a
stealthy and practical audio backdoor attack that can be launched with limited
knowledge. FlowMur constructs an auxiliary dataset and a surrogate model to
augment adversary knowledge. To achieve dynamicity, it formulates trigger
generation as an optimization problem and optimizes the trigger over different
attachment positions. To enhance stealthiness, we propose an adaptive data
poisoning method according to Signal-to-Noise Ratio (SNR). Furthermore, ambient
noise is incorporated into the process of trigger generation and data poisoning
to make FlowMur robust to ambient noise and improve its practicality. Extensive
experiments conducted on two datasets demonstrate that FlowMur achieves high
attack performance in both digital and physical settings while remaining
resilient to state-of-the-art defenses. In particular, a human study confirms
that triggers generated by FlowMur are not easily detected by participants.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: A General Neural Causal Model for Interactive Recommendation
Abstract: Survivor bias in observational data leads the optimization of recommender
systems towards local optima. Currently most solutions re-mines existing
human-system collaboration patterns to maximize longer-term satisfaction by
reinforcement learning. However, from the causal perspective, mitigating
survivor effects requires answering a counterfactual problem, which is
generally unidentifiable and inestimable. In this work, we propose a neural
causal model to achieve counterfactual inference. Specifically, we first build
a learnable structural causal model based on its available graphical
representations which qualitatively characterizes the preference transitions.
Mitigation of the survivor bias is achieved though counterfactual consistency.
To identify the consistency, we use the Gumbel-max function as structural
constrains. To estimate the consistency, we apply reinforcement optimizations,
and use Gumbel-Softmax as a trade-off to get a differentiable function. Both
theoretical and empirical studies demonstrate the effectiveness of our
solution.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: An Investigation of Darwiche and Pearl's Postulates for Iterated Belief Update
Abstract: Belief revision and update, two significant types of belief change, both
focus on how an agent modify her beliefs in presence of new information. The
most striking difference between them is that the former studies the change of
beliefs in a static world while the latter concentrates on a
dynamically-changing world. The famous AGM and KM postulates were proposed to
capture rational belief revision and update, respectively. However, both of
them are too permissive to exclude some unreasonable changes in the iteration.
In response to this weakness, the DP postulates and its extensions for iterated
belief revision were presented. Furthermore, Rodrigues integrated these
postulates in belief update. Unfortunately, his approach does not meet the
basic requirement of iterated belief update. This paper is intended to solve
this problem of Rodrigues's approach. Firstly, we present a modification of the
original KM postulates based on belief states. Subsequently, we migrate several
well-known postulates for iterated belief revision to iterated belief update.
Moreover, we provide the exact semantic characterizations based on partial
preorders for each of the proposed postulates. Finally, we analyze the
compatibility between the above iterated postulates and the KM postulates for
belief update.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
Abstract: The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
Abstract: Predicting stock market is vital for investors and policymakers, acting as a
barometer of the economic health. We leverage social media data, a potent
source of public sentiment, in tandem with macroeconomic indicators as
government-compiled statistics, to refine stock market predictions. However,
prior research using tweet data for stock market prediction faces three
challenges. First, the quality of tweets varies widely. While many are filled
with noise and irrelevant details, only a few genuinely mirror the actual
market scenario. Second, solely focusing on the historical data of a particular
stock without considering its sector can lead to oversight. Stocks within the
same industry often exhibit correlated price behaviors. Lastly, simply
forecasting the direction of price movement without assessing its magnitude is
of limited value, as the extent of the rise or fall truly determines
profitability. In this paper, diverging from the conventional methods, we
pioneer an ECON. The framework has following advantages: First, ECON has an
adept tweets filter that efficiently extracts and decodes the vast array of
tweet data. Second, ECON discerns multi-level relationships among stocks,
sectors, and macroeconomic factors through a self-aware mechanism in semantic
space. Third, ECON offers enhanced accuracy in predicting substantial stock
price fluctuations by capitalizing on stock price movement. We showcase the
state-of-the-art performance of our proposed model using a dataset,
specifically curated by us, for predicting stock market movements and
volatility.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Responsible Emergent Multi-Agent Behavior
Abstract: Responsible AI has risen to the forefront of the AI research community. As
neural network-based learning algorithms continue to permeate real-world
applications, the field of Responsible AI has played a large role in ensuring
that such systems maintain a high-level of human-compatibility. Despite this
progress, the state of the art in Responsible AI has ignored one crucial point:
human problems are multi-agent problems. Predominant approaches largely
consider the performance of a single AI system in isolation, but human problems
are, by their very nature, multi-agent. From driving in traffic to negotiating
economic policy, human problem-solving involves interaction and the interplay
of the actions and motives of multiple individuals.
  This dissertation develops the study of responsible emergent multi-agent
behavior, illustrating how researchers and practitioners can better understand
and shape multi-agent learning with respect to three pillars of Responsible AI:
interpretability, fairness, and robustness. First, I investigate multi-agent
interpretability, presenting novel techniques for understanding emergent
multi-agent behavior at multiple levels of granularity. With respect to
low-level interpretability, I examine the extent to which implicit
communication emerges as an aid to coordination in multi-agent populations. I
introduce a novel curriculum-driven method for learning high-performing
policies in difficult, sparse reward environments and show through a measure of
position-based social influence that multi-agent teams that learn sophisticated
coordination strategies exchange significantly more information through
implicit signals than lesser-coordinated agents. Then, at a high-level, I study
concept-based interpretability in the context of multi-agent learning. I
propose a novel method for learning intrinsically interpretable, concept-based
policies and show that it enables...

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Abstract: Machine learning is currently undergoing an explosion in capability,
popularity, and sophistication. However, one of the major barriers to
widespread acceptance of machine learning (ML) is trustworthiness: most ML
models operate as black boxes, their inner workings opaque and mysterious, and
it can be difficult to trust their conclusions without understanding how those
conclusions are reached. Explainability is therefore a key aspect of improving
trustworthiness: the ability to better understand, interpret, and anticipate
the behaviour of ML models. To this end, we propose SMILE, a new method that
builds on previous approaches by making use of statistical distance measures to
improve explainability while remaining applicable to a wide range of input data
domains.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity
Abstract: To address the challenge of increasing network size, researchers have
developed sparse models through network pruning. However, maintaining model
accuracy while achieving significant speedups on general computing devices
remains an open problem. In this paper, we present a novel mobile inference
acceleration framework SparseByteNN, which leverages fine-grained kernel
sparsity to achieve real-time execution as well as high accuracy. Our framework
consists of two parts: (a) A fine-grained kernel sparsity schema with a
sparsity granularity between structured pruning and unstructured pruning. It
designs multiple sparse patterns for different operators. Combined with our
proposed whole network rearrangement strategy, the schema achieves a high
compression rate and high precision at the same time. (b) Inference engine
co-optimized with the sparse pattern. The conventional wisdom is that this
reduction in theoretical FLOPs does not translate into real-world efficiency
gains. We aim to correct this misconception by introducing a family of
efficient sparse kernels for ARM and WebAssembly. Equipped with our efficient
implementation of sparse primitives, we show that sparse versions of
MobileNet-v1 outperform strong dense baselines on the efficiency-accuracy
curve. Experimental results on Qualcomm 855 show that for 30% sparse
MobileNet-v1, SparseByteNN achieves 1.27x speedup over the dense version and
1.29x speedup over the state-of-the-art sparse inference engine MNN with a
slight accuracy drop of 0.224%. The source code of SparseByteNN will be
available at https://github.com/lswzjuer/SparseByteNN

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Survey on Detection of LLMs-Generated Content
Abstract: The burgeoning capabilities of advanced large language models (LLMs) such as
ChatGPT have led to an increase in synthetic content generation with
implications across a variety of sectors, including media, cybersecurity,
public discourse, and education. As such, the ability to detect LLMs-generated
content has become of paramount importance. We aim to provide a detailed
overview of existing detection strategies and benchmarks, scrutinizing their
differences and identifying key challenges and prospects in the field,
advocating for more adaptable and robust models to enhance detection accuracy.
We also posit the necessity for a multi-faceted approach to defend against
various attacks to counter the rapidly advancing capabilities of LLMs. To the
best of our knowledge, this work is the first comprehensive survey on the
detection in the era of LLMs. We hope it will provide a broad understanding of
the current landscape of LLMs-generated content detection, offering a guiding
reference for researchers and practitioners striving to uphold the integrity of
digital information in an era increasingly dominated by synthetic content. The
relevant papers are summarized and will be consistently updated at
https://github.com/Xianjun-Yang/Awesome_papers_on_LLMs_detection.git.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learning to Act without Actions
Abstract: Pre-training large models on vast amounts of web data has proven to be an
effective approach for obtaining powerful, general models in several domains,
including language and vision. However, this paradigm has not yet taken hold in
deep reinforcement learning (RL). This gap is due to the fact that the most
abundant form of embodied behavioral data on the web consists of videos, which
do not include the action labels required by existing methods for training
policies from offline data. We introduce Latent Action Policies from
Observation (LAPO), a method to infer latent actions and, consequently,
latent-action policies purely from action-free demonstrations. Our experiments
on challenging procedurally-generated environments show that LAPO can act as an
effective pre-training method to obtain RL policies that can then be rapidly
fine-tuned to expert-level performance. Our approach serves as a key stepping
stone to enabling the pre-training of powerful, generalist RL models on the
vast amounts of action-free demonstrations readily available on the web.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: GLaMM: Pixel Grounding Large Multimodal Model
Abstract: Large Multimodal Models (LMMs) extend Large Language Models to the vision
domain. Initial efforts towards LMMs used holistic images and text prompts to
generate ungrounded textual responses. Very recently, region-level LMMs have
been used to generate visually grounded responses. However, they are limited to
only referring a single object category at a time, require users to specify the
regions in inputs, or cannot offer dense pixel-wise object grounding. In this
work, we present Grounding LMM (GLaMM), the first model that can generate
natural language responses seamlessly intertwined with corresponding object
segmentation masks. GLaMM not only grounds objects appearing in the
conversations but is flexible enough to accept both textual and optional visual
prompts (region of interest) as input. This empowers users to interact with the
model at various levels of granularity, both in textual and visual domains. Due
to the lack of standard benchmarks for the novel setting of generating visually
grounded detailed conversations, we introduce a comprehensive evaluation
protocol with our curated grounded conversations. Our proposed Grounded
Conversation Generation (GCG) task requires densely grounded concepts in
natural scenes at a large-scale. To this end, we propose a densely annotated
Grounding-anything Dataset (GranD) using our proposed automated annotation
pipeline that encompasses 7.5M unique concepts grounded in a total of 810M
regions available with segmentation masks. Besides GCG, GLaMM also performs
effectively on several downstream tasks e.g., referring expression
segmentation, image and region-level captioning and vision-language
conversations. Project Page: https://mbzuai-oryx.github.io/groundingLMM.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Peer attention enhances student learning
Abstract: Human visual attention is susceptible to social influences. In education,
peer effects impact student learning, but their precise role in modulating
attention remains unclear. Our experiment (N=311) demonstrates that displaying
peer visual attention regions when students watch online course videos enhances
their focus and engagement. However, students retain adaptability in following
peer attention cues. Overall, guided peer attention improves learning
experiences and outcomes. These findings elucidate how peer visual attention
shapes students' gaze patterns, deepening understanding of peer influence on
learning. They also offer insights into designing adaptive online learning
interventions leveraging peer attention modelling to optimize student
attentiveness and success.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Plagiarism and AI Assistance Misuse in Web Programming: Unfair Benefits and Characteristics
Abstract: In programming education, plagiarism and misuse of artificial intelligence
(AI) assistance are emerging issues. However, not many relevant studies are
focused on web programming. We plan to develop automated tools to help
instructors identify both misconducts. To fully understand the issues, we
conducted a controlled experiment to observe the unfair benefits and the
characteristics. We compared student performance in completing web programming
tasks independently, with a submission to plagiarize, and with the help of AI
assistance (ChatGPT). Our study shows that students who are involved in such
misconducts get comparable test marks with less completion time. Plagiarized
submissions are similar to the independent ones except in trivial aspects such
as color and identifier names. AI-assisted submissions are more complex, making
them less readable. Students believe AI assistance could be useful given proper
acknowledgment of the use, although they are not convinced with readability and
correctness of the solutions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Neural Collage Transfer: Artistic Reconstruction via Material Manipulation
Abstract: Collage is a creative art form that uses diverse material scraps as a base
unit to compose a single image. Although pixel-wise generation techniques can
reproduce a target image in collage style, it is not a suitable method due to
the solid stroke-by-stroke nature of the collage form. While some previous
works for stroke-based rendering produced decent sketches and paintings,
collages have received much less attention in research despite their popularity
as a style. In this paper, we propose a method for learning to make collages
via reinforcement learning without the need for demonstrations or collage
artwork data. We design the collage Markov Decision Process (MDP), which allows
the agent to handle various materials and propose a model-based soft
actor-critic to mitigate the agent's training burden derived from the
sophisticated dynamics of collage. Moreover, we devise additional techniques
such as active material selection and complexity-based multi-scale collage to
handle target images at any size and enhance the results' aesthetics by placing
relatively more scraps in areas of high complexity. Experimental results show
that the trained agent appropriately selected and pasted materials to
regenerate the target image into a collage and obtained a higher evaluation
score on content and style than pixel-wise generation methods. Code is
available at https://github.com/northadventure/CollageRL.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Identifying Reasons for Bias: An Argumentation-Based Approach
Abstract: As algorithmic decision-making systems become more prevalent in society,
ensuring the fairness of these systems is becoming increasingly important.
Whilst there has been substantial research in building fair algorithmic
decision-making systems, the majority of these methods require access to the
training data, including personal characteristics, and are not transparent
regarding which individuals are classified unfairly. In this paper, we propose
a novel model-agnostic argumentation-based method to determine why an
individual is classified differently in comparison to similar individuals. Our
method uses a quantitative argumentation framework to represent attribute-value
pairs of an individual and of those similar to them, and uses a well-known
semantics to identify the attribute-value pairs in the individual contributing
most to their different classification. We evaluate our method on two datasets
commonly used in the fairness literature and illustrate its effectiveness in
the identification of bias.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape
Abstract: The creation of lifelike speech-driven 3D facial animation requires a natural
and precise synchronization between audio input and facial expressions.
However, existing works still fail to render shapes with flexible head poses
and natural facial details (e.g., wrinkles). This limitation is mainly due to
two aspects: 1) Collecting training set with detailed 3D facial shapes is
highly expensive. This scarcity of detailed shape annotations hinders the
training of models with expressive facial animation. 2) Compared to mouth
movement, the head pose is much less correlated to speech content.
Consequently, concurrent modeling of both mouth movement and head pose yields
the lack of facial movement controllability. To address these challenges, we
introduce VividTalker, a new framework designed to facilitate speech-driven 3D
facial animation characterized by flexible head pose and natural facial
details. Specifically, we explicitly disentangle facial animation into head
pose and mouth movement and encode them separately into discrete latent spaces.
Then, these attributes are generated through an autoregressive process
leveraging a window-based Transformer architecture. To augment the richness of
3D facial animation, we construct a new 3D dataset with detailed shapes and
learn to synthesize facial details in line with speech content. Extensive
quantitative and qualitative experiments demonstrate that VividTalker
outperforms state-of-the-art methods, resulting in vivid and realistic
speech-driven 3D facial animation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles
Abstract: Exploration in dynamic and uncertain real-world environments is an open
problem in robotics and constitutes a foundational capability of autonomous
systems operating in most of the real world. While 3D exploration planning has
been extensively studied, the environments are assumed static or only reactive
collision avoidance is carried out. We propose a novel approach to not only
avoid dynamic obstacles but also include them in the plan itself, to exploit
the dynamic environment in the agent's favor. The proposed planner, Dynamic
Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with
respect to dynamic obstacles. To thoroughly evaluate exploration planners in
such settings we propose a new enhanced benchmark suite with several dynamic
environments, including large-scale outdoor environments. DAEP outperform
state-of-the-art planners in dynamic and large-scale environments. DAEP is
shown to be more effective at both exploration and collision avoidance.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: A GAN Approach for Node Embedding in Heterogeneous Graphs Using Subgraph Sampling
Abstract: Our research addresses class imbalance issues in heterogeneous graphs using
graph neural networks (GNNs). We propose a novel method combining the strengths
of Generative Adversarial Networks (GANs) with GNNs, creating synthetic nodes
and edges that effectively balance the dataset. This approach directly targets
and rectifies imbalances at the data level. The proposed framework resolves
issues such as neglecting graph structures during data generation and creating
synthetic structures usable with GNN-based classifiers in downstream tasks. It
processes node and edge information concurrently, improving edge balance
through node augmentation and subgraph sampling. Additionally, our framework
integrates a threshold strategy, aiding in determining optimal edge thresholds
during training without time-consuming parameter adjustments. Experiments on
the Amazon and Yelp Review datasets highlight the effectiveness of the
framework we proposed, especially in minority node identification, where it
consistently outperforms baseline models across key performance metrics,
demonstrating its potential in the field.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Moral Foundations of Large Language Models
Abstract: Moral foundations theory (MFT) is a psychological assessment tool that
decomposes human moral reasoning into five factors, including care/harm,
liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary
in the weight they place on these dimensions when making moral decisions, in
part due to their cultural upbringing and political ideology. As large language
models (LLMs) are trained on datasets collected from the internet, they may
reflect the biases that are present in such corpora. This paper uses MFT as a
lens to analyze whether popular LLMs have acquired a bias towards a particular
set of moral values. We analyze known LLMs and find they exhibit particular
moral foundations, and show how these relate to human moral foundations and
political affiliations. We also measure the consistency of these biases, or
whether they vary strongly depending on the context of how the model is
prompted. Finally, we show that we can adversarially select prompts that
encourage the moral to exhibit a particular set of moral foundations, and that
this can affect the model's behavior on downstream tasks. These findings help
illustrate the potential risks and unintended consequences of LLMs assuming a
particular moral stance.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Decentralized Personalized Online Federated Learning
Abstract: Vanilla federated learning does not support learning in an online
environment, learning a personalized model on each client, and learning in a
decentralized setting. There are existing methods extending federated learning
in each of the three aspects. However, some important applications on
enterprise edge servers (e.g. online item recommendation at global scale)
involve the three aspects at the same time. Therefore, we propose a new
learning setting \textit{Decentralized Personalized Online Federated Learning}
that considers all the three aspects at the same time.
  In this new setting for learning, the first technical challenge is how to
aggregate the shared model parameters from neighboring clients to obtain a
personalized local model with good performance on each client. We propose to
directly learn an aggregation by optimizing the performance of the local model
with respect to the aggregation weights. This not only improves personalization
of each local model but also helps the local model adapting to potential data
shift by intelligently incorporating the right amount of information from its
neighbors. The second challenge is how to select the neighbors for each client.
We propose a peer selection method based on the learned aggregation weights
enabling each client to select the most helpful neighbors and reduce
communication cost at the same time. We verify the effectiveness and robustness
of our proposed method on three real-world item recommendation datasets and one
air quality prediction dataset.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Large Language Models for Autonomous Driving: Real-World Experiments
Abstract: Autonomous driving systems are increasingly popular in today's technological
landscape, where vehicles with partial automation have already been widely
available on the market, and the full automation era with ``driverless''
capabilities is near the horizon. However, accurately understanding humans'
commands, particularly for autonomous vehicles that have only passengers
instead of drivers, and achieving a high level of personalization remain
challenging tasks in the development of autonomous driving systems. In this
paper, we introduce a Large Language Model (LLM)-based framework Talk-to-Drive
(Talk2Drive) to process verbal commands from humans and make autonomous driving
decisions with contextual information, satisfying their personalized
preferences for safety, efficiency, and comfort. First, a speech recognition
module is developed for Talk2Drive to interpret verbal inputs from humans to
textual instructions, which are then sent to LLMs for reasoning. Then,
appropriate commands for the Electrical Control Unit (ECU) are generated,
achieving a 100\% success rate in executing codes. Real-world experiments show
that our framework can substantially reduce the takeover rate for a diverse
range of drivers by up to 90.1\%. To the best of our knowledge, Talk2Drive
marks the first instance of employing an LLM-based system in a real-world
autonomous driving environment.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: The Rise of Creative Machines: Exploring the Impact of Generative AI
Abstract: This study looks at how generative artificial intelligence (AI) can
revolutionize marketing, product development, and research. It discusses the
latest developments in the field, easy-to-use resources, and moral and social
hazards. In addition to addressing mitigating techniques for issues like
prejudice and disinformation, the debate emphasizes the significance of
responsible development through continual stakeholder communication and ethical
principles.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: NLQxform: A Language Model-based Question to SPARQL Transformer
Abstract: In recent years, scholarly data has grown dramatically in terms of both scale
and complexity. It becomes increasingly challenging to retrieve information
from scholarly knowledge graphs that include large-scale heterogeneous
relationships, such as authorship, affiliation, and citation, between various
types of entities, e.g., scholars, papers, and organizations. As part of the
Scholarly QALD Challenge, this paper presents a question-answering (QA) system
called NLQxform, which provides an easy-to-use natural language interface to
facilitate accessing scholarly knowledge graphs. NLQxform allows users to
express their complex query intentions in natural language questions. A
transformer-based language model, i.e., BART, is employed to translate
questions into standard SPARQL queries, which can be evaluated to retrieve the
required information. According to the public leaderboard of the Scholarly QALD
Challenge at ISWC 2023 (Task 1: DBLP-QUAD - Knowledge Graph Question Answering
over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA
task, demonstrating the competitiveness of the system.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ABIGX: A Unified Framework for eXplainable Fault Detection and Classification
Abstract: For explainable fault detection and classification (FDC), this paper proposes
a unified framework, ABIGX (Adversarial fault reconstruction-Based Integrated
Gradient eXplanation). ABIGX is derived from the essentials of previous
successful fault diagnosis methods, contribution plots (CP) and
reconstruction-based contribution (RBC). It is the first explanation framework
that provides variable contributions for the general FDC models. The core part
of ABIGX is the adversarial fault reconstruction (AFR) method, which rethinks
the FR from the perspective of adversarial attack and generalizes to fault
classification models with a new fault index. For fault classification, we put
forward a new problem of fault class smearing, which intrinsically hinders the
correct explanation. We prove that ABIGX effectively mitigates this problem and
outperforms the existing gradient-based explanation methods. For fault
detection, we theoretically bridge ABIGX with conventional fault diagnosis
methods by proving that CP and RBC are the linear specifications of ABIGX. The
experiments evaluate the explanations of FDC by quantitative metrics and
intuitive illustrations, the results of which show the general superiority of
ABIGX to other advanced explanation methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
Abstract: Compressing large language models (LLMs), often consisting of billions of
parameters, provides faster inference, smaller memory footprints, and enables
local deployment. Two standard compression techniques are pruning and
quantization, with the former eliminating redundant connections in model layers
and the latter representing model parameters with fewer bits. The key tradeoff
is between the degree of compression and the impact on the quality of the
compressed model. Existing research on LLM compression primarily focuses on
performance in terms of general metrics like perplexity or downstream task
accuracy. More fine-grained metrics, such as those measuring parametric
knowledge, remain significantly underexplored. To help bridge this gap, we
present a comprehensive analysis across multiple model families (ENCODER,
ENCODER-DECODER, and DECODER) using the LAMA and LM-HARNESS benchmarks in order
to systematically quantify the effect of commonly employed compression
techniques on model performance. A particular focus is on tradeoffs involving
parametric knowledge, with the goal of providing practitioners with practical
insights to help make informed decisions on compression. We release our
codebase1 to enable further research.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
Abstract: Segmentation and tracking of unseen object instances in discrete frames pose
a significant challenge in dynamic industrial robotic contexts, such as
distribution warehouses. Here, robots must handle object rearrangement,
including shifting, removal, and partial occlusion by new items, and track
these items after substantial temporal gaps. The task is further complicated
when robots encounter objects not learned in their training sets, which
requires the ability to segment and track previously unseen items. Considering
that continuous observation is often inaccessible in such settings, our task
involves working with a discrete set of frames separated by indefinite periods
during which substantial changes to the scene may occur. This task also
translates to domestic robotic applications, such as rearrangement of objects
on a table. To address these demanding challenges, we introduce new synthetic
and real-world datasets that replicate these industrial and household
scenarios. We also propose a novel paradigm for joint segmentation and tracking
in discrete frames along with a transformer module that facilitates efficient
inter-frame communication. The experiments we conduct show that our approach
significantly outperforms recent methods. For additional results and videos,
please visit \href{https://sites.google.com/view/stow-corl23}{website}. Code
and dataset will be released.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Abstract: The difficulty of identifying the physical model of complex systems has led
to exploring methods that do not rely on such complex modeling of the systems.
Deep reinforcement learning has been the pioneer for solving this problem
without the need for relying on the physical model of complex systems by just
interacting with it. However, it uses a black-box learning approach that makes
it difficult to be applied within real-world and safety-critical systems
without providing explanations of the actions derived by the model.
Furthermore, an open research question in deep reinforcement learning is how to
focus the policy learning of critical decisions within a sparse domain. This
paper proposes a novel approach for the use of deep reinforcement learning in
safety-critical systems. It combines the advantages of probabilistic modeling
and reinforcement learning with the added benefits of interpretability and
works in collaboration and synchronization with conventional decision-making
strategies. The BC-SRLA is activated in specific situations which are
identified autonomously through the fused information of probabilistic model
and reinforcement learning, such as abnormal conditions or when the system is
near-to-failure. Further, it is initialized with a baseline policy using policy
cloning to allow minimum interactions with the environment to address the
challenges associated with using RL in safety-critical industries. The
effectiveness of the BC-SRLA is demonstrated through a case study in
maintenance applied to turbofan engines, where it shows superior performance to
the prior art and other baselines.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DTL: Disentangled Transfer Learning for Visual Recognition
Abstract: When pre-trained models become rapidly larger, the cost of fine-tuning on
downstream tasks steadily increases, too. To economically fine-tune these
models, parameter-efficient transfer learning (PETL) is proposed, which only
tunes a tiny subset of trainable parameters to efficiently learn quality
representations. However, current PETL methods are facing the dilemma that
during training the GPU memory footprint is not effectively reduced as
trainable parameters. PETL will likely fail, too, if the full fine-tuning
encounters the out-of-GPU-memory issue. This phenomenon happens because
trainable parameters from these methods are generally entangled with the
backbone, such that a lot of intermediate states have to be stored in GPU
memory for gradient propagation. To alleviate this problem, we introduce
Disentangled Transfer Learning (DTL), which disentangles the trainable
parameters from the backbone using a lightweight Compact Side Network (CSN). By
progressively extracting task-specific information with a few low-rank linear
mappings and appropriately adding the information back to the backbone, CSN
effectively realizes knowledge transfer in various downstream tasks. We
conducted extensive experiments to validate the effectiveness of our method.
The proposed method not only reduces a large amount of GPU memory usage and
trainable parameters, but also outperforms existing PETL methods by a
significant margin in accuracy, achieving new state-of-the-art on several
standard benchmarks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: LSA64: An Argentinian Sign Language Dataset
Abstract: Automatic sign language recognition is a research area that encompasses
human-computer interaction, computer vision and machine learning. Robust
automatic recognition of sign language could assist in the translation process
and the integration of hearing-impaired people, as well as the teaching of sign
language to the hearing population. Sign languages differ significantly in
different countries and even regions, and their syntax and semantics are
different as well from those of written languages. While the techniques for
automatic sign language recognition are mostly the same for different
languages, training a recognition system for a new language requires having an
entire dataset for that language. This paper presents a dataset of 64 signs
from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains
3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first
step towards building a comprehensive research-level dataset of Argentinian
signs, specifically tailored to sign language recognition or other machine
learning tasks. The subjects that performed the signs wore colored gloves to
ease the hand tracking and segmentation steps, allowing experiments on the
dataset to focus specifically on the recognition of signs. We also present a
pre-processed version of the dataset, from which we computed statistics of
movement, position and handshape of the signs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Abstract: Time series anomaly detection is challenging due to the complexity and
variety of patterns that can occur. One major difficulty arises from modeling
time-dependent relationships to find contextual anomalies while maintaining
detection accuracy for point anomalies. In this paper, we propose a framework
for unsupervised time series anomaly detection that utilizes point-based and
sequence-based reconstruction models. The point-based model attempts to
quantify point anomalies, and the sequence-based model attempts to quantify
both point and contextual anomalies. Under the formulation that the observed
time point is a two-stage deviated value from a nominal time point, we
introduce a nominality score calculated from the ratio of a combined value of
the reconstruction errors. We derive an induced anomaly score by further
integrating the nominality score and anomaly score, then theoretically prove
the superiority of the induced anomaly score over the original anomaly score
under certain conditions. Extensive studies conducted on several public
datasets show that the proposed framework outperforms most state-of-the-art
baselines for time series anomaly detection.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Promoting Counterfactual Robustness through Diversity
Abstract: Counterfactual explanations shed light on the decisions of black-box models
by explaining how an input can be altered to obtain a favourable decision from
the model (e.g., when a loan application has been rejected). However, as noted
recently, counterfactual explainers may lack robustness in the sense that a
minor change in the input can cause a major change in the explanation. This can
cause confusion on the user side and open the door for adversarial attacks. In
this paper, we study some sources of non-robustness. While there are
fundamental reasons for why an explainer that returns a single counterfactual
cannot be robust in all instances, we show that some interesting robustness
guarantees can be given by reporting multiple rather than a single
counterfactual. Unfortunately, the number of counterfactuals that need to be
reported for the theoretical guarantees to hold can be prohibitively large. We
therefore propose an approximation algorithm that uses a diversity criterion to
select a feasible number of most relevant explanations and study its robustness
empirically. Our experiments indicate that our method improves the
state-of-the-art in generating robust explanations, while maintaining other
desirable properties and providing competitive computational performance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Differentiable Visual Computing for Inverse Problems and Machine Learning
Abstract: Originally designed for applications in computer graphics, visual computing
(VC) methods synthesize information about physical and virtual worlds, using
prescribed algorithms optimized for spatial computing. VC is used to analyze
geometry, physically simulate solids, fluids, and other media, and render the
world via optical techniques. These fine-tuned computations that operate
explicitly on a given input solve so-called forward problems, VC excels at. By
contrast, deep learning (DL) allows for the construction of general algorithmic
models, side stepping the need for a purely first principles-based approach to
problem solving. DL is powered by highly parameterized neural network
architectures -- universal function approximators -- and gradient-based search
algorithms which can efficiently search that large parameter space for optimal
models. This approach is predicated by neural network differentiability, the
requirement that analytic derivatives of a given problem's task metric can be
computed with respect to neural network's parameters. Neural networks excel
when an explicit model is not known, and neural network training solves an
inverse problem in which a model is computed from data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: PrivateLoRA For Efficient Privacy Preserving LLM
Abstract: End users face a choice between privacy and efficiency in current Large
Language Model (LLM) service paradigms. In cloud-based paradigms, users are
forced to compromise data locality for generation quality and processing speed.
Conversely, edge device paradigms maintain data locality but fail to deliver
satisfactory performance. In this work, we propose a novel LLM service paradigm
that distributes privacy-sensitive computation on edge devices and shared
computation in the cloud. Only activations are transmitted between the central
cloud and edge devices to ensure data locality. Our core innovation,
PrivateLoRA, addresses the challenging communication overhead by exploiting the
low rank of residual activations, achieving over 95% communication reduction.
Consequently, PrivateLoRA effectively maintains data locality and is extremely
resource efficient. Under standard 5G networks, PrivateLoRA achieves throughput
over 300% of device-only solutions for 7B models and over 80% of an A100 GPU
for 33B models. PrivateLoRA also provides tuning performance comparable to LoRA
for advanced personalization. Our approach democratizes access to
state-of-the-art generative AI for edge devices, paving the way for more
tailored LLM experiences for the general public. To our knowledge, our proposed
framework is the first efficient and privacy-preserving LLM solution in the
literature.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: LLM as an Art Director (LaDi): Using LLMs to improve Text-to-Media Generators
Abstract: Recent advancements in text-to-image generation have revolutionized numerous
fields, including art and cinema, by automating the generation of high-quality,
context-aware images and video. However, the utility of these technologies is
often limited by the inadequacy of text prompts in guiding the generator to
produce artistically coherent and subject-relevant images. In this paper, We
describe the techniques that can be used to make Large Language Models (LLMs)
act as Art Directors that enhance image and video generation. We describe our
unified system for this called "LaDi". We explore how LaDi integrates multiple
techniques for augmenting the capabilities of text-to-image generators (T2Is)
and text-to-video generators (T2Vs), with a focus on constrained decoding,
intelligent prompting, fine-tuning, and retrieval. LaDi and these techniques
are being used today in apps and platforms developed by Plai Labs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Machine Learning For An Explainable Cost Prediction of Medical Insurance
Abstract: Predictive modeling in healthcare continues to be an active actuarial
research topic as more insurance companies aim to maximize the potential of
Machine Learning approaches to increase their productivity and efficiency. In
this paper, the authors deployed three regression-based ensemble ML models that
combine variations of decision trees through Extreme Gradient Boosting,
Gradient-boosting Machine, and Random Forest) methods in predicting medical
insurance costs. Explainable Artificial Intelligence methods SHapley Additive
exPlanations and Individual Conditional Expectation plots were deployed to
discover and explain the key determinant factors that influence medical
insurance premium prices in the dataset. The dataset used comprised 986 records
and is publicly available in the KAGGLE repository. The models were evaluated
using four performance evaluation metrics, including R-squared, Mean Absolute
Error, Root Mean Squared Error, and Mean Absolute Percentage Error. The results
show that all models produced impressive outcomes; however, the XGBoost model
achieved a better overall performance although it also expanded more
computational resources, while the RF model recorded a lesser prediction error
and consumed far fewer computing resources than the XGBoost model. Furthermore,
we compared the outcome of both XAi methods in identifying the key determinant
features that influenced the PremiumPrices for each model and whereas both XAi
methods produced similar outcomes, we found that the ICE plots showed in more
detail the interactions between each variable than the SHAP analysis which
seemed to be more high-level. It is the aim of the authors that the
contributions of this study will help policymakers, insurers, and potential
medical insurance buyers in their decision-making process for selecting the
right policies that meet their specific needs.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Orca 2: Teaching Small Language Models How to Reason
Abstract: Orca 1 learns from rich signals, such as explanation traces, allowing it to
outperform conventional instruction-tuned models on benchmarks like BigBench
Hard and AGIEval. In Orca 2, we continue exploring how improved training
signals can enhance smaller LMs' reasoning abilities. Research on training
small LMs has often relied on imitation learning to replicate the output of
more capable models. We contend that excessive emphasis on imitation may
restrict the potential of smaller models. We seek to teach small LMs to employ
different solution strategies for different tasks, potentially different from
the one used by the larger model. For example, while larger models might
provide a direct answer to a complex task, smaller models may not have the same
capacity. In Orca 2, we teach the model various reasoning techniques
(step-by-step, recall then generate, recall-reason-generate, direct answer,
etc.). More crucially, we aim to help the model learn to determine the most
effective solution strategy for each task. We evaluate Orca 2 using a
comprehensive set of 15 diverse benchmarks (corresponding to approximately 100
tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of
similar size and attains performance levels similar or better to those of
models 5-10x larger, as assessed on complex tasks that test advanced reasoning
abilities in zero-shot settings. make Orca 2 weights publicly available at
aka.ms/orca-lm to support research on the development, evaluation, and
alignment of smaller LMs

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: MELA: Multilingual Evaluation of Linguistic Acceptability
Abstract: Recent benchmarks for Large Language Models (LLMs) have mostly focused on
application-driven tasks such as complex reasoning and code generation, and
this has led to a scarcity in purely linguistic evaluation of LLMs. Against
this background, we introduce Multilingual Evaluation of Linguistic
Acceptability -- MELA, the first multilingual benchmark on linguistic
acceptability with 48K samples covering 10 languages from a diverse set of
language families. We establish baselines of commonly used LLMs along with
supervised models, and conduct cross-lingual transfer and multi-task learning
experiments with XLM-R. In pursuit of multilingual interpretability, we analyze
the weights of fine-tuned XLM-R to explore the possibility of identifying
transfer difficulty between languages. Our results show that ChatGPT benefits
much from in-context examples but still lags behind fine-tuned XLM-R, while the
performance of GPT-4 is on par with fine-tuned XLM-R even in zero-shot setting.
Cross-lingual and multi-task learning experiments show that unlike semantic
tasks, in-language training data is crucial in acceptability judgements.
Results in layerwise probing indicate that the upper layers of XLM-R become a
task-specific but language-agnostic region for multilingual acceptability
judgment. We also introduce the concept of conflicting weight, which could be a
potential indicator for the difficulty of cross-lingual transfer between
languages. Our data will be available at https://github.com/sjtu-compling/MELA.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Music Recommendation on Spotify using Deep Learning
Abstract: Hosting about 50 million songs and 4 billion playlists, there is an enormous
amount of data generated at Spotify every single day - upwards of 600 gigabytes
of data (harvard.edu). Since the algorithms that Spotify uses in recommendation
systems is proprietary and confidential, code for big data analytics and
recommendation can only be speculated. However, it is widely theorized that
Spotify uses two main strategies to target users' playlists and personalized
mixes that are infamous for their retention - exploration and exploitation
(kaggle.com). This paper aims to appropriate filtering using the approach of
deep learning for maximum user likeability. The architecture achieves 98.57%
and 80% training and validation accuracy respectively.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Beyond Words: A Mathematical Framework for Interpreting Large Language Models
Abstract: Large language models (LLMs) are powerful AI tools that can generate and
comprehend natural language text and other complex information. However, the
field lacks a mathematical framework to systematically describe, compare and
improve LLMs. We propose Hex a framework that clarifies key terms and concepts
in LLM research, such as hallucinations, alignment, self-verification and
chain-of-thought reasoning. The Hex framework offers a precise and consistent
way to characterize LLMs, identify their strengths and weaknesses, and
integrate new findings. Using Hex, we differentiate chain-of-thought reasoning
from chain-of-thought prompting and establish the conditions under which they
are equivalent. This distinction clarifies the basic assumptions behind
chain-of-thought prompting and its implications for methods that use it, such
as self-verification and prompt programming.
  Our goal is to provide a formal framework for LLMs that can help both
researchers and practitioners explore new possibilities for generative AI. We
do not claim to have a definitive solution, but rather a tool for opening up
new research avenues. We argue that our formal definitions and results are
crucial for advancing the discussion on how to build generative AI systems that
are safe, reliable, fair and robust, especially in domains like healthcare and
software engineering.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Artificial intelligence and the limits of the humanities
Abstract: The complexity of cultures in the modern world is now beyond human
comprehension. Cognitive sciences cast doubts on the traditional explanations
based on mental models. The core subjects in humanities may lose their
importance. Humanities have to adapt to the digital age. New, interdisciplinary
branches of humanities emerge. Instant access to information will be replaced
by instant access to knowledge. Understanding the cognitive limitations of
humans and the opportunities opened by the development of artificial
intelligence and interdisciplinary research necessary to address global
challenges is the key to the revitalization of humanities. Artificial
intelligence will radically change humanities, from art to political sciences
and philosophy, making these disciplines attractive to students and enabling
them to go beyond current limitations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Meta Prompting for AGI Systems
Abstract: This paper presents an in-depth exploration of Meta Prompting, a novel
technique that revolutionizes the way large language models (LLMs), multi-modal
foundation models, and AI systems approach problem-solving and data
interpretation. Meta Prompting, rooted in type theory and category theory,
prioritizes the structure and syntax of information, providing a unique
framework that transcends traditional content-focused methods. We delve into
the formal definitions of Meta Prompting, contrasting it with Few-Shot
Prompting, and highlight its applicability and superiority in various AI
applications.
  Key to this exploration is the expansion of Meta Prompting into the realm of
complex reasoning. Here, we demonstrate how this technique adeptly breaks down
intricate problems into manageable sub-problems, facilitating a step-by-step,
detailed approach to problem-solving. This method proves especially
advantageous in terms of token efficiency and offering a fair comparison in
problem-solving scenarios, standing out against few-shot example approaches.
  Furthermore, the paper breaks new ground by extending Meta Prompting into
multi-modal foundation model settings. This extension addresses the integration
of diverse data types, such as images, audio, and video, within the structured
framework of Meta Prompting, highlighting both the challenges and the vast
potential of this approach in handling complex, multi-faceted data (The code is
available at https://github.com/meta-prompting/meta-prompting).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario
Abstract: Visual question answering (VQA) is a fundamental and essential AI task, and
VQA-based disaster scenario understanding is a hot research topic. For
instance, we can ask questions about a disaster image by the VQA model and the
answer can help identify whether anyone or anything is affected by the
disaster. However, previous VQA models for disaster damage assessment have some
shortcomings, such as limited candidate answer space, monotonous question
types, and limited answering capability of existing models. In this paper, we
propose a zero-shot VQA model named Zero-shot VQA for Flood Disaster Damage
Assessment (ZFDDA). It is a VQA model for damage assessment without
pre-training. Also, with flood disaster as the main research object, we build a
Freestyle Flood Disaster Image Question Answering dataset (FFD-IQA) to evaluate
our VQA model. This new dataset expands the question types to include
free-form, multiple-choice, and yes-no questions. At the same time, we expand
the size of the previous dataset to contain a total of 2,058 images and 22,422
question-meta ground truth pairs. Most importantly, our model uses
well-designed chain of thought (CoT) demonstrations to unlock the potential of
the large language model, allowing zero-shot VQA to show better performance in
disaster scenarios. The experimental results show that the accuracy in
answering complex questions is greatly improved with CoT prompts. Our study
provides a research basis for subsequent research of VQA for other disaster
scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Forms of Understanding of XAI-Explanations
Abstract: Explainability has become an important topic in computer science and
artificial intelligence, leading to a subfield called Explainable Artificial
Intelligence (XAI). The goal of providing or seeking explanations is to achieve
(better) 'understanding' on the part of the explainee. However, what it means
to 'understand' is still not clearly defined, and the concept itself is rarely
the subject of scientific investigation. This conceptual article aims to
present a model of forms of understanding in the context of XAI and beyond.
From an interdisciplinary perspective bringing together computer science,
linguistics, sociology, and psychology, a definition of understanding and its
forms, assessment, and dynamics during the process of giving everyday
explanations are explored. Two types of understanding are considered as
possible outcomes of explanations, namely enabledness, 'knowing how' to do or
decide something, and comprehension, 'knowing that' -- both in different
degrees (from shallow to deep). Explanations regularly start with shallow
understanding in a specific domain and can lead to deep comprehension and
enabledness of the explanandum, which we see as a prerequisite for human users
to gain agency. In this process, the increase of comprehension and enabledness
are highly interdependent. Against the background of this systematization,
special challenges of understanding in XAI are discussed.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research
Abstract: Agricultural price prediction is crucial for farmers, policymakers, and other
stakeholders in the agricultural sector. However, it is a challenging task due
to the complex and dynamic nature of agricultural markets. Machine learning
algorithms have the potential to revolutionize agricultural price prediction by
improving accuracy, real-time prediction, customization, and integration. This
paper reviews recent research on machine learning algorithms for agricultural
price prediction. We discuss the importance of agriculture in developing
countries and the problems associated with crop price falls. We then identify
the challenges of predicting agricultural prices and highlight how machine
learning algorithms can support better prediction. Next, we present a
comprehensive analysis of recent research, discussing the strengths and
weaknesses of various machine learning techniques. We conclude that machine
learning has the potential to revolutionize agricultural price prediction, but
further research is essential to address the limitations and challenges
associated with this approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Common (good) practices measuring trust in HRI
Abstract: Trust in robots is widely believed to be imperative for the adoption of
robots into people's daily lives. It is, therefore, understandable that the
literature of the last few decades focuses on measuring how much people trust
robots -- and more generally, any agent - to foster such trust in these
technologies. Researchers have been exploring how people trust robot in
different ways, such as measuring trust on human-robot interactions (HRI) based
on textual descriptions or images without any physical contact, during and
after interacting with the technology. Nevertheless, trust is a complex
behaviour, and it is affected and depends on several factors, including those
related to the interacting agents (e.g. humans, robots, pets), itself (e.g.
capabilities, reliability), the context (e.g. task), and the environment (e.g.
public spaces vs private spaces vs working spaces). In general, most
roboticists agree that insufficient levels of trust lead to a risk of
disengagement while over-trust in technology can cause over-reliance and
inherit dangers, for example, in emergency situations. It is, therefore, very
important that the research community has access to reliable methods to measure
people's trust in robots and technology. In this position paper, we outline
current methods and their strengths, identify (some) weakly covered aspects and
discuss the potential for covering a more comprehensive amount of factors
influencing trust in HRI.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: KEN: Kernel Extensions using Natural Language
Abstract: The ability to modify and extend an operating system is an important feature
for improving a system's security, reliability, and performance. The extended
Berkeley Packet Filters (eBPF) ecosystem has emerged as the standard mechanism
for extending the Linux kernel and has recently been ported to Windows. eBPF
programs inject new logic into the kernel that the system will execute before
or after existing logic. While the eBPF ecosystem provides a flexible mechanism
for kernel extension, it is difficult for developers to write eBPF programs
today. An eBPF developer must have deep knowledge of the internals of the
operating system to determine where to place logic and cope with programming
limitations on the control flow and data accesses of their eBPF program
enforced by the eBPF verifier. This paper presents KEN, an alternative
framework that alleviates the difficulty of writing an eBPF program by allowing
Kernel Extensions to be written in Natural language. KEN uses recent advances
in large language models (LLMs) to synthesize an eBPF program given a user's
English language prompt. To ensure that LLM's output is semantically equivalent
to the user's prompt, KEN employs a combination of LLM-empowered program
comprehension, symbolic execution, and a series of feedback loops. KEN's key
novelty is the combination of these techniques. In particular, the system uses
symbolic execution in a novel structure that allows it to combine the results
of program synthesis and program comprehension and build on the recent success
that LLMs have shown for each of these tasks individually. To evaluate KEN, we
developed a new corpus of natural language prompts for eBPF programs. We show
that KEN produces correct eBPF programs on 80% which is an improvement of a
factor of 2.67 compared to an LLM-empowered program synthesis baseline.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
Abstract: Knowledge graphs (KGs), which consist of triples, are inherently incomplete
and always require completion procedure to predict missing triples. In
real-world scenarios, KGs are distributed across clients, complicating
completion tasks due to privacy restrictions. Many frameworks have been
proposed to address the issue of federated knowledge graph completion. However,
the existing frameworks, including FedE, FedR, and FEKG, have certain
limitations. = FedE poses a risk of information leakage, FedR's optimization
efficacy diminishes when there is minimal overlap among relations, and FKGE
suffers from computational costs and mode collapse issues. To address these
issues, we propose a novel method, i.e., Federated Latent Embedding Sharing
Tensor factorization (FLEST), which is a novel approach using federated tensor
factorization for KG completion. FLEST decompose the embedding matrix and
enables sharing of latent dictionary embeddings to lower privacy risks.
Empirical results demonstrate FLEST's effectiveness and efficiency, offering a
balanced solution between performance and privacy. FLEST expands the
application of federated tensor factorization in KG completion tasks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Multi-scale Diffusion Denoised Smoothing
Abstract: Along with recent diffusion models, randomized smoothing has become one of a
few tangible approaches that offers adversarial robustness to models at scale,
e.g., those of large pre-trained models. Specifically, one can perform
randomized smoothing on any classifier via a simple "denoise-and-classify"
pipeline, so-called denoised smoothing, given that an accurate denoiser is
available - such as diffusion model. In this paper, we present scalable methods
to address the current trade-off between certified robustness and accuracy in
denoised smoothing. Our key idea is to "selectively" apply smoothing among
multiple noise scales, coined multi-scale smoothing, which can be efficiently
implemented with a single diffusion model. This approach also suggests a new
objective to compare the collective robustness of multi-scale smoothed
classifiers, and questions which representation of diffusion model would
maximize the objective. To address this, we propose to further fine-tune
diffusion model (a) to perform consistent denoising whenever the original image
is recoverable, but (b) to generate rather diverse outputs otherwise. Our
experiments show that the proposed multi-scale smoothing scheme combined with
diffusion fine-tuning enables strong certified robustness available with high
noise level while maintaining its accuracy close to non-smoothed classifiers.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Multimodal Machine Unlearning
Abstract: Machine Unlearning is the process of removing specific training data samples
and their corresponding effects from an already trained model. It has
significant practical benefits, such as purging private, inaccurate, or
outdated information from trained models without the need for complete
re-training. Unlearning within a multimodal setting presents unique challenges
due to the intrinsic dependencies between different data modalities and the
expensive cost of training on large multimodal datasets and architectures.
Current approaches to machine unlearning have not fully addressed these
challenges. To bridge this gap, we introduce MMUL, a machine unlearning
approach specifically designed for multimodal data and models. MMUL formulates
the multimodal unlearning task by focusing on three key properties: (a):
modality decoupling, which effectively decouples the association between
individual unimodal data points within multimodal inputs marked for deletion,
rendering them as unrelated data points within the model's context, (b):
unimodal knowledge retention, which retains the unimodal representation
capability of the model post-unlearning, and (c): multimodal knowledge
retention, which retains the multimodal representation capability of the model
post-unlearning. MMUL is efficient to train and is not constrained by the
requirement of using a strongly convex loss. Experiments on two multimodal
models and four multimodal benchmark datasets, including vision-language and
graph-language datasets, show that MMUL outperforms existing baselines, gaining
an average improvement of +17.6 points against the best-performing unimodal
baseline in distinguishing between deleted and remaining data. In addition,
MMUL can largely maintain pre-existing knowledge of the original model post
unlearning, with a performance gap of only 0.3 points compared to retraining a
new model from scratch.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Abstract: In the field of algorithmic fairness, significant attention has been put on
group fairness criteria, such as Demographic Parity and Equalized Odds.
Nevertheless, these objectives, measured as global averages, have raised
concerns about persistent local disparities between sensitive groups. In this
work, we address the problem of local fairness, which ensures that the
predictor is unbiased not only in terms of expectations over the whole
population, but also within any subregion of the feature space, unknown at
training time. To enforce this objective, we introduce ROAD, a novel approach
that leverages the Distributionally Robust Optimization (DRO) framework within
a fair adversarial learning objective, where an adversary tries to infer the
sensitive attribute from the predictions. Using an instance-level re-weighting
strategy, ROAD is designed to prioritize inputs that are likely to be locally
unfair, i.e. where the adversary faces the least difficulty in reconstructing
the sensitive attribute. Numerical experiments demonstrate the effectiveness of
our method: it achieves Pareto dominance with respect to local fairness and
accuracy for a given global fairness level across three standard datasets, and
also enhances fairness generalization under distribution shift.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News
Abstract: Large language and vision models have transformed how social movements
scholars identify protest and extract key protest attributes from multi-modal
data such as texts, images, and videos. This article documents how we
fine-tuned two large pretrained transformer models, including longformer and
swin-transformer v2, to infer potential protests in news articles using textual
and imagery data. First, the longformer model was fine-tuned using the Dynamic
of Collective Action (DoCA) Corpus. We matched the New York Times articles with
the DoCA database to obtain a training dataset for downstream tasks. Second,
the swin-transformer v2 models was trained on UCLA-protest imagery data.
UCLA-protest project contains labeled imagery data with information such as
protest, violence, and sign. Both fine-tuned models will be available via
\url{https://github.com/Joshzyj/llvms4protest}. We release this short technical
report for social movement scholars who are interested in using LLVMs to infer
protests in textual and imagery data.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Abstract: The alignment tuning process of large language models (LLMs) typically
involves instruction learning through supervised fine-tuning (SFT) and
preference tuning via reinforcement learning from human feedback (RLHF). A
recent study, LIMA (Zhou et al. 2023), shows that using merely 1K examples for
SFT can achieve significant alignment performance as well, suggesting that the
effect of alignment tuning might be "superficial." This raises questions about
how exactly the alignment tuning transforms a base LLM.
  We analyze the effect of alignment tuning by examining the token distribution
shift between base LLMs and their aligned counterpart. Our findings reveal that
base LLMs and their alignment-tuned versions perform nearly identically in
decoding on the majority of token positions. Most distribution shifts occur
with stylistic tokens. These direct evidence strongly supports the Superficial
Alignment Hypothesis suggested by LIMA.
  Based on these findings, we rethink the alignment of LLMs by posing the
research question: how effectively can we align base LLMs without SFT or RLHF?
To address this, we introduce a simple, tuning-free alignment method, URIAL.
URIAL achieves effective alignment purely through in-context learning (ICL)
with base LLMs, requiring as few as three constant stylistic examples and a
system prompt. We conduct a fine-grained and interpretable evaluation on a
diverse set of examples, named JUST-EVAL-INSTRUCT. Results demonstrate that
base LLMs with URIAL can match or even surpass the performance of LLMs aligned
with SFT or SFT+RLHF. We show that the gap between tuning-free and tuning-based
alignment methods can be significantly reduced through strategic prompting and
ICL. Our findings on the superficial nature of alignment tuning and results
with URIAL suggest that deeper analysis and theoretical understanding of
alignment is crucial to future LLM research.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
Abstract: Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped
facts. Recent advancements have been explored to enhance the semantics of these
facts by incorporating more potent representations, such as hyper-relational
facts. However, these approaches are limited to \emph{atomic facts}, which
describe a single piece of information. This paper extends beyond \emph{atomic
facts} and delves into \emph{nested facts}, represented by quoted triples where
subjects and objects are triples themselves (e.g., ((\emph{BarackObama},
\emph{holds\_position}, \emph{President}), \emph{succeed\_by},
(\emph{DonaldTrump}, \emph{holds\_position}, \emph{President}))). These nested
facts enable the expression of complex semantics like \emph{situations} over
time and \emph{logical patterns} over entities and relations. In response, we
introduce NestE, a novel KG embedding approach that captures the semantics of
both atomic and nested factual knowledge. NestE represents each atomic fact as
a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix
that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
Each element of the matrix is represented as a complex number in the
generalized 4D hypercomplex space, including (spherical) quaternions,
hyperbolic quaternions, and split-quaternions. Through thorough analysis, we
demonstrate the embedding's efficacy in capturing diverse logical patterns over
nested facts, surpassing the confines of first-order logic-like expressions.
Our experimental results showcase NestE's significant performance gains over
current baselines in triple prediction and conditional link prediction. The
code and pre-trained models are open available at
https://github.com/xiongbo010/NestE.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Causality is all you need
Abstract: In the fundamental statistics course, students are taught to remember the
well-known saying: "Correlation is not Causation". Till now, statistics (i.e.,
correlation) have developed various successful frameworks, such as Transformer
and Pre-training large-scale models, which have stacked multiple parallel
self-attention blocks to imitate a wide range of tasks. However, in the
causation community, how to build an integrated causal framework still remains
an untouched domain despite its excellent intervention capabilities. In this
paper, we propose the Causal Graph Routing (CGR) framework, an integrated
causal scheme relying entirely on the intervention mechanisms to reveal the
cause-effect forces hidden in data. Specifically, CGR is composed of a stack of
causal layers. Each layer includes a set of parallel deconfounding blocks from
different causal graphs. We combine these blocks via the concept of the
proposed sufficient cause, which allows the model to dynamically select the
suitable deconfounding methods in each layer. CGR is implemented as the stacked
networks, integrating no confounder, back-door adjustment, front-door
adjustment, and probability of sufficient cause. We evaluate this framework on
two classical tasks of CV and NLP. Experiments show CGR can surpass the current
state-of-the-art methods on both Visual Question Answer and Long Document
Classification tasks. In particular, CGR has great potential in building the
"causal" pre-training large-scale model that effectively generalizes to diverse
tasks. It will improve the machines' comprehension of causal relationships
within a broader semantic space.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning
Abstract: This work provides a comparative analysis illustrating how Deep Learning (DL)
surpasses Machine Learning (ML) in addressing tasks within Internet of Things
(IoT), such as attack classification and device-type identification. Our
approach involves training and evaluating a DL model using a range of diverse
IoT-related datasets, allowing us to gain valuable insights into how adaptable
and practical these models can be when confronted with various IoT
configurations. We initially convert the unstructured network traffic data from
IoT networks, stored in PCAP files, into images by processing the packet data.
This conversion process adapts the data to meet the criteria of DL
classification methods. The experiments showcase the ability of DL to surpass
the constraints tied to manually engineered features, achieving superior
results in attack detection and maintaining comparable outcomes in device-type
identification. Additionally, a notable feature extraction time difference
becomes evident in the experiments: traditional methods require around 29
milliseconds per data packet, while DL accomplishes the same task in just 2.9
milliseconds. The significant time gap, DL's superior performance, and the
recognized limitations of manually engineered features, presents a compelling
call to action within the IoT community. This encourages us to shift from
exploring new IoT features for each dataset to addressing the challenges of
integrating DL into IoT, making it a more efficient solution for real-world IoT
scenarios.

Output: Cryptography and Security