Instruction: What field is the article from?

Input:
Title: Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation
Abstract: The utilisation of deep learning segmentation algorithms that learn complex
organs and tissue patterns and extract essential regions of interest from the
noisy background to improve the visual ability for medical image diagnosis has
achieved impressive results in Medical Image Computing (MIC). This thesis
focuses on retinal blood vessel segmentation tasks, providing an extensive
literature review of deep learning-based medical image segmentation approaches
while comparing the methodologies and empirical performances. The work also
examines the limitations of current state-of-the-art methods by pointing out
the two significant existing limitations: data size constraints and the
dependency on high computational resources. To address such problems, this work
proposes a novel efficient, simple multiview learning framework that
contrastively learns invariant vessel feature representation by comparing with
multiple augmented views by various transformations to overcome data shortage
and improve generalisation ability. Moreover, the hybrid network architecture
integrates the attention mechanism into a Convolutional Neural Network to
further capture complex continuous curvilinear vessel structures. The result
demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining
the highest F1 score of 83.46% and the highest Intersection over Union (IOU)
score of 71.62% with UNet structure, surpassing existing benchmark UNet-based
methods by 1.95% and 2.8%, respectively. The combination of the metrics
indicates the model detects the vessel object accurately with a highly
coincidental location with the ground truth. Moreover, the proposed approach
could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and
such characteristics support the efficient implementation for real-world
applications and deployments.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Probabilistic Copyright Protection Can Fail for Text-to-Image Generative Models
Abstract: The booming use of text-to-image generative models has raised concerns about
their high risk of producing copyright-infringing content. While probabilistic
copyright protection methods provide a probabilistic guarantee against such
infringement, in this paper, we introduce Virtually Assured Amplification
Attack (VA3), a novel online attack framework that exposes the vulnerabilities
of these protection mechanisms. The proposed framework significantly amplifies
the probability of generating infringing content on the sustained interactions
with generative models and a lower-bounded success probability of each
engagement. Our theoretical and experimental results demonstrate the
effectiveness of our approach and highlight the potential risk of implementing
probabilistic copyright protection in practical applications of text-to-image
generative models. Code is available at https://github.com/South7X/VA3.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images
Abstract: This study performs comprehensive evaluation of four neural network
architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion
segmentation from PET/CT images. These networks were trained, validated, and
tested on a diverse, multi-institutional dataset of 611 cases. Internal testing
(88 cases; total metabolic tumor volume (TMTV) range [0.52, 2300] ml) showed
SegResNet as the top performer with a median Dice similarity coefficient (DSC)
of 0.76 and median false positive volume (FPV) of 4.55 ml; all networks had a
median false negative volume (FNV) of 0 ml. On the unseen external test set
(145 cases with TMTV range: [0.10, 2480] ml), SegResNet achieved the best
median DSC of 0.68 and FPV of 21.46 ml, while UNet had the best FNV of 0.41 ml.
We assessed reproducibility of six lesion measures, calculated their prediction
errors, and examined DSC performance in relation to these lesion measures,
offering insights into segmentation accuracy and clinical relevance.
Additionally, we introduced three lesion detection criteria, addressing the
clinical need for identifying lesions, counting them, and segmenting based on
metabolic characteristics. We also performed expert intra-observer variability
analysis revealing the challenges in segmenting ``easy'' vs. ``hard'' cases, to
assist in the development of more resilient segmentation algorithms. Finally,
we performed inter-observer agreement assessment underscoring the importance of
a standardized ground truth segmentation protocol involving multiple expert
annotators. Code is available at:
https://github.com/microsoft/lymphoma-segmentation-dnn

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Multiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization
Abstract: This work addresses the task of weakly-supervised object localization. The
goal is to learn object localization using only image-level class labels, which
are much easier to obtain compared to bounding box annotations. This task is
important because it reduces the need for labor-intensive ground-truth
annotations. However, methods for object localization trained using weak
supervision often suffer from limited accuracy in localization. To address this
challenge and enhance localization accuracy, we propose a multiscale object
localization transformer (MOLT). It comprises multiple object localization
transformers that extract patch embeddings across various scales. Moreover, we
introduce a deep clustering-guided refinement method that further enhances
localization accuracy by utilizing separately extracted image segments. These
segments are obtained by clustering pixels using convolutional neural networks.
Finally, we demonstrate the effectiveness of our proposed method by conducting
experiments on the publicly available ILSVRC-2012 dataset.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Finetuning an LLM on Contextual Knowledge of Classics for Q&A
Abstract: The open-source publishing of large language models (LLMs) has created many
possibilities for how anyone who understands language and has access to a
computer can interact with significant tools of artificial intelligence,
particularly in the context of learning and knowledge dissemination. However,
the utility of these models in specialized fields like Classics is still
largely unexplored. This project is an attempt to merge the knowledge of
Classics with the capabilities of artificial intelligence by finetuning an LLM
to cater to the specific needs of learners and professionals. The goal of this
project is to develop an LLM that not only reproduces contextual knowledge
accurately but also exhibits a consistent "personality" - and, indeed, has
consistent propriety - to appeal to a diverse audience who possess differing
levels of knowledge. A significant portion of this project was dedicated to
refining the dataset, following the principle of "garbage in, garbage out," to
ensure the model generates relevant, useful, and creative responses when given
a prompt (a statement, question, or single word). After training and
evaluation, my model's ability to handle a vast array of different types of
inputs and prompting exceeded expectations for a 355M parameter model, though
its occasional hallucinations (especially when set with a high temperature),
particularly in its assertions about historical events or its own identity,
make it seem somewhat capricious and more work in the form of continuous
finetuning will be undertaken.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: How Well Do Large Language Models Truly Ground?
Abstract: Reliance on the inherent knowledge of Large Language Models (LLMs) can cause
issues such as hallucinations, lack of control, and difficulties in integrating
variable knowledge. To mitigate this, LLMs can be probed to generate responses
by grounding on external context, often given as input (knowledge-augmented
models). Yet, previous research is often confined to a narrow view of the term
"grounding", often only focusing on whether the response contains the correct
answer or not, which does not ensure the reliability of the entire response. To
address this limitation, we introduce a strict definition of grounding: a model
is considered truly grounded when its responses (1) fully utilize necessary
knowledge from the provided context, and (2) don't exceed the knowledge within
the contexts. We introduce a new dataset and a grounding metric to assess this
new definition and perform experiments across 13 LLMs of different sizes and
training methods to provide insights into the factors that influence grounding
performance. Our findings contribute to a better understanding of how to
improve grounding capabilities and suggest an area of improvement toward more
reliable and controllable LLM applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Image and Data Mining in Reticular Chemistry Using GPT-4V
Abstract: The integration of artificial intelligence into scientific research has
reached a new pinnacle with GPT-4V, a large language model featuring enhanced
vision capabilities, accessible through ChatGPT or an API. This study
demonstrates the remarkable ability of GPT-4V to navigate and obtain complex
data for metal-organic frameworks, especially from graphical sources. Our
approach involved an automated process of converting 346 scholarly articles
into 6240 images, which represents a benchmark dataset in this task, followed
by deploying GPT-4V to categorize and analyze these images using natural
language prompts. This methodology enabled GPT-4V to accurately identify and
interpret key plots integral to MOF characterization, such as nitrogen
isotherms, PXRD patterns, and TGA curves, among others, with accuracy and
recall above 93%. The model's proficiency in extracting critical information
from these plots not only underscores its capability in data mining but also
highlights its potential in aiding the creation of comprehensive digital
databases for reticular chemistry. In addition, the extracted nitrogen isotherm
data from the selected literature allowed for a comparison between theoretical
and experimental porosity values for over 200 compounds, highlighting certain
discrepancies and underscoring the importance of integrating computational and
experimental data. This work highlights the potential of AI in accelerating
scientific discovery and innovation, bridging the gap between computational
tools and experimental research, and paving the way for more efficient,
inclusive, and comprehensive scientific inquiry.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
Abstract: Long-term urban mobility predictions play a crucial role in the effective
management of urban facilities and services. Conventionally, urban mobility
data has been structured as spatiotemporal videos, treating longitude and
latitude grids as fundamental pixels. Consequently, video prediction methods,
relying on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs),
have been instrumental in this domain. In our research, we introduce a fresh
perspective on urban mobility prediction. Instead of oversimplifying urban
mobility data as traditional video data, we regard it as a complex multivariate
time series. This perspective involves treating the time-varying values of each
grid in each channel as individual time series, necessitating a thorough
examination of temporal dynamics, cross-variable correlations, and
frequency-domain insights for precise and reliable predictions. To address this
challenge, we present the Super-Multivariate Urban Mobility Transformer
(SUMformer), which utilizes a specially designed attention mechanism to
calculate temporal and cross-variable correlations and reduce computational
costs stemming from a large number of time series. SUMformer also employs
low-frequency filters to extract essential information for long-term
predictions. Furthermore, SUMformer is structured with a temporal patch merge
mechanism, forming a hierarchical framework that enables the capture of
multi-scale correlations. Consequently, it excels in urban mobility pattern
modeling and long-term prediction, outperforming current state-of-the-art
methods across three real-world datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering
Abstract: While large language models (LLMs) are equipped with longer text input
capabilities than before, they are struggling to seek correct information in
long contexts. The "lost in the middle" problem challenges most LLMs, referring
to the dramatic decline in accuracy when correct information is located in the
middle. To overcome this crucial issue, this paper proposes to enhance the
information searching and reflection ability of LLMs in long contexts via
specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA).
Following these tasks, our model excels in focusing more precisely on the
desired information. Experimental results show substantial improvement in
Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7%
absolute gain in shuffled settings, by 21.5% in passage retrieval task. We
release our model, Ziya-Reader to promote related research in the community.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models
Abstract: Generating novel views of an object from a single image is a challenging
task. It requires an understanding of the underlying 3D structure of the object
from an image and rendering high-quality, spatially consistent new views. While
recent methods for view synthesis based on diffusion have shown great progress,
achieving consistency among various view estimates and at the same time abiding
by the desired camera pose remains a critical problem yet to be solved. In this
work, we demonstrate a strikingly simple method, where we utilize a pre-trained
video diffusion model to solve this problem. Our key idea is that synthesizing
a novel view could be reformulated as synthesizing a video of a camera going
around the object of interest -- a scanning video -- which then allows us to
leverage the powerful priors that a video diffusion model would have learned.
Thus, to perform novel-view synthesis, we create a smooth camera trajectory to
the target view that we wish to render, and denoise using both a
view-conditioned diffusion model and a video diffusion model. By doing so, we
obtain a highly consistent novel view synthesis, outperforming the state of the
art.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
Abstract: The springing up of Large Language Models (LLMs) has shifted the community
from single-task-orientated natural language processing (NLP) research to a
holistic end-to-end multi-task learning paradigm. Along this line of research
endeavors in the area, LLM-based prompting methods have attracted much
attention, partially due to the technological advantages brought by prompt
engineering (PE) as well as the underlying NLP principles disclosed by various
prompting methods. Traditional supervised learning usually requires training a
model based on labeled data and then making predictions. In contrast, PE
methods directly use the powerful capabilities of existing LLMs (i.e., GPT-3
and GPT-4) via composing appropriate prompts, especially under few-shot or
zero-shot scenarios. Facing the abundance of studies related to the prompting
and the ever-evolving nature of this field, this article aims to (i) illustrate
a novel perspective to review existing PE methods, within the well-established
communication theory framework; (ii) facilitate a better/deeper understanding
of developing trends of existing PE methods used in four typical tasks; (iii)
shed light on promising research directions for future PE methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Comprehensive Literature Review on Sweet Orange Leaf Diseases
Abstract: Sweet orange leaf diseases are significant to agricultural productivity. Leaf
diseases impact fruit quality in the citrus industry. The apparition of machine
learning makes the development of disease finder. Early detection and diagnosis
are necessary for leaf management. Sweet orange leaf disease-predicting
automated systems have already been developed using different image-processing
techniques. This comprehensive literature review is systematically based on
leaf disease and machine learning methodologies applied to the detection of
damaged leaves via image classification. The benefits and limitations of
different machine learning models, including Vision Transformer (ViT), Neural
Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP,
EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine
learning models tested on various datasets and detected the disease. This
comprehensive review study related to leaf disease compares the performance of
the models; those models' accuracy, precision, recall, etc., were used in the
subsisting studies

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
Abstract: We present SPHINX, a versatile multi-modal large language model (MLLM) with a
joint mixing of model weights, tuning tasks, and visual embeddings. First, for
stronger vision-language alignment, we unfreeze the large language model (LLM)
during pre-training, and introduce a weight mix strategy between LLMs trained
by real-world and synthetic data. By directly integrating the weights from two
domains, the mixed LLM can efficiently incorporate diverse semantics with
favorable robustness. Then, to enable multi-purpose capabilities, we mix a
variety of tasks for joint visual instruction tuning, and design task-specific
instructions to avoid inter-task conflict. In addition to the basic visual
question answering, we include more challenging tasks such as region-level
understanding, caption grounding, document layout detection, and human pose
estimation, contributing to mutual enhancement over different scenarios.
Additionally, we propose to extract comprehensive visual embeddings from
various network architectures, pre-training paradigms, and information
granularity, providing language models with more robust image representations.
Based on our proposed joint mixing, SPHINX exhibits superior multi-modal
understanding capabilities on a wide range of applications. On top of this, we
further propose an efficient strategy aiming to better capture fine-grained
appearances of high-resolution images. With a mixing of different scales and
high-resolution sub-images, SPHINX attains exceptional visual parsing and
reasoning performance on existing evaluation benchmarks. We hope our work may
cast a light on the exploration of joint mixing in future MLLM research. Code
is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Abstract: Uncertainty estimation aims to evaluate the confidence of a trained deep
neural network. However, existing uncertainty estimation approaches rely on
low-dimensional distributional assumptions and thus suffer from the high
dimensionality of latent features. Existing approaches tend to focus on
uncertainty on discrete classification probabilities, which leads to poor
generalizability to uncertainty estimation for other tasks. Moreover, most of
the literature requires seeing the out-of-distribution (OOD) data in the
training for better estimation of uncertainty, which limits the uncertainty
estimation performance in practice because the OOD data are typically unseen.
To overcome these limitations, we propose a new framework using data-adaptive
high-dimensional hypothesis testing for uncertainty estimation, which leverages
the statistical properties of the feature representations. Our method directly
operates on latent representations and thus does not require retraining the
feature encoder under a modified objective. The test statistic relaxes the
feature distribution assumptions to high dimensionality, and it is more
discriminative to uncertainties in the latent representations. We demonstrate
that encoding features with Bayesian neural networks can enhance testing
performance and lead to more accurate uncertainty estimation. We further
introduce a family-wise testing procedure to determine the optimal threshold of
OOD detection, which minimizes the false discovery rate (FDR). Extensive
experiments validate the satisfactory performance of our framework on
uncertainty estimation and task-specific prediction over a variety of
competitors. The experiments on the OOD detection task also show satisfactory
performance of our method when the OOD data are unseen in the training. Codes
are available at https://github.com/HKU-MedAI/bnn_uncertainty.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CRAB: Assessing the Strength of Causal Relationships Between Real-world Events
Abstract: Understanding narratives requires reasoning about the cause-and-effect
relationships between events mentioned in the text. While existing foundation
models yield impressive results in many NLP tasks requiring reasoning, it is
unclear whether they understand the complexity of the underlying network of
causal relationships of events in narratives. In this work, we present CRAB, a
new Causal Reasoning Assessment Benchmark designed to evaluate causal
understanding of events in real-world narratives. CRAB contains fine-grained,
contextual causality annotations for ~2.7K pairs of real-world events that
describe various newsworthy event timelines (e.g., the acquisition of Twitter
by Elon Musk). Using CRAB, we measure the performance of several large language
models, demonstrating that most systems achieve poor performance on the task.
Motivated by classical causal principles, we also analyze the causal structures
of groups of events in CRAB, and find that models perform worse on causal
reasoning when events are derived from complex causal structures compared to
simple linear causal chains. We make our dataset and code available to the
research community.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Intrinsic Harmonization for Illumination-Aware Compositing
Abstract: Despite significant advancements in network-based image harmonization
techniques, there still exists a domain disparity between typical training
pairs and real-world composites encountered during inference. Most existing
methods are trained to reverse global edits made on segmented image regions,
which fail to accurately capture the lighting inconsistencies between the
foreground and background found in composited images. In this work, we
introduce a self-supervised illumination harmonization approach formulated in
the intrinsic image domain. First, we estimate a simple global lighting model
from mid-level vision representations to generate a rough shading for the
foreground region. A network then refines this inferred shading to generate a
harmonious re-shading that aligns with the background scene. In order to match
the color appearance of the foreground and background, we utilize ideas from
prior harmonization approaches to perform parameterized image edits in the
albedo domain. To validate the effectiveness of our approach, we present
results from challenging real-world composites and conduct a user study to
objectively measure the enhanced realism achieved compared to state-of-the-art
harmonization methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: LRM: Large Reconstruction Model for Single Image to 3D
Abstract: We propose the first Large Reconstruction Model (LRM) that predicts the 3D
model of an object from a single input image within just 5 seconds. In contrast
to many previous methods that are trained on small-scale datasets such as
ShapeNet in a category-specific fashion, LRM adopts a highly scalable
transformer-based architecture with 500 million learnable parameters to
directly predict a neural radiance field (NeRF) from the input image. We train
our model in an end-to-end manner on massive multi-view data containing around
1 million objects, including both synthetic renderings from Objaverse and real
captures from MVImgNet. This combination of a high-capacity model and
large-scale training data empowers our model to be highly generalizable and
produce high-quality 3D reconstructions from various testing inputs including
real-world in-the-wild captures and images from generative models. Video demos
and interactable 3D meshes can be found on this website:
https://yiconghong.me/LRM/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?
Abstract: The gameplay of strategic board games such as chess, Go and Hex is often
characterized by combinatorial, relational structures -- capturing distinct
interactions and non-local patterns -- and not just images. Nonetheless, most
common self-play reinforcement learning (RL) approaches simply approximate
policy and value functions using convolutional neural networks (CNN). A key
feature of CNNs is their relational inductive bias towards locality and
translational invariance. In contrast, graph neural networks (GNN) can encode
more complicated and distinct relational structures. Hence, we investigate the
crucial question: Can GNNs, with their ability to encode complex connections,
replace CNNs in self-play reinforcement learning? To this end, we do a
comparison with Hex -- an abstract yet strategically rich board game -- serving
as our experimental platform. Our findings reveal that GNNs excel at dealing
with long range dependency situations in game states and are less prone to
overfitting, but also showing a reduced proficiency in discerning local
patterns. This suggests a potential paradigm shift, signaling the use of
game-specific structures to reshape self-play reinforcement learning.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DALE: Generative Data Augmentation for Low-Resource Legal NLP
Abstract: We present DALE, a novel and effective generative Data Augmentation framework
for low-resource LEgal NLP. DALE addresses the challenges existing frameworks
pose in generating effective data augmentations of legal documents - legal
language, with its specialized vocabulary and complex semantics, morphology,
and syntax, does not benefit from data augmentations that merely rephrase the
source sentence. To address this, DALE, built on an Encoder-Decoder Language
Model, is pre-trained on a novel unsupervised text denoising objective based on
selective masking - our masking strategy exploits the domain-specific language
characteristics of templatized legal documents to mask collocated spans of
text. Denoising these spans helps DALE acquire knowledge about legal concepts,
principles, and language usage. Consequently, it develops the ability to
generate coherent and diverse augmentations with novel contexts. Finally, DALE
performs conditional generation to generate synthetic augmentations for
low-resource Legal NLP tasks. We demonstrate the effectiveness of DALE on 13
datasets spanning 6 tasks and 4 low-resource settings. DALE outperforms all our
baselines, including LLMs, qualitatively and quantitatively, with improvements
of 1%-50%.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration
Abstract: Machine learning models built on training data with multiple modalities can
reveal new insights that are not accessible through unimodal datasets. For
example, cardiac magnetic resonance images (MRIs) and electrocardiograms (ECGs)
are both known to capture useful information about subjects' cardiovascular
health status. A multimodal machine learning model trained from large datasets
can potentially predict the onset of heart-related diseases and provide novel
medical insights about the cardiovascular system. Despite the potential
benefits, it is difficult for medical experts to explore multimodal
representation models without visual aids and to test the predictive
performance of the models on various subpopulations. To address the challenges,
we developed a visual analytics system called Latent Space Explorer. Latent
Space Explorer provides interactive visualizations that enable users to explore
the multimodal representation of subjects, define subgroups of interest,
interactively decode data with different modalities with the selected subjects,
and inspect the accuracy of the embedding in downstream prediction tasks. A
user study was conducted with medical experts and their feedback provided
useful insights into how Latent Space Explorer can help their analysis and
possible new direction for further development in the medical domain.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Vision-based Learning for Drones: A Survey
Abstract: Drones as advanced cyber-physical systems are undergoing a transformative
shift with the advent of vision-based learning, a field that is rapidly gaining
prominence due to its profound impact on drone autonomy and functionality.
Different from existing task-specific surveys, this review offers a
comprehensive overview of vision-based learning in drones, emphasizing its
pivotal role in enhancing their operational capabilities. We start by
elucidating the fundamental principles of vision-based learning, highlighting
how it significantly improves drones' visual perception and decision-making
processes. We then categorize vision-based control methods into indirect,
semi-direct, and end-to-end approaches from the perception-control perspective.
We further explore various applications of vision-based drones with learning
capabilities, ranging from single-agent systems to more complex multi-agent and
heterogeneous system scenarios, and underscore the challenges and innovations
characterizing each area. Finally, we explore open questions and potential
solutions, paving the way for ongoing research and development in this dynamic
and rapidly evolving field. With growing large language models (LLMs) and
embodied intelligence, vision-based learning for drones provides a promising
but challenging road towards artificial general intelligence (AGI) in 3D
physical world.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Global $\mathcal{L}^2$ minimization with certainty via geometrically adapted gradient descent in Deep Learning
Abstract: We consider the gradient descent flow widely used for the minimization of the
$\mathcal{L}^2$ cost function in Deep Learning networks, and introduce two
modified versions; one adapted for the overparametrized setting, and the other
for the underparametrized setting. Both have a clear and natural invariant
geometric meaning, taking into account the pullback vector bundle structure in
the overparametrized, and the pushforward vector bundle structure in the
underparametrized setting. In the overparametrized case, we prove that,
provided that a rank condition holds, all orbits of the modified gradient
descent drive the $\mathcal{L}^2$ cost to its global minimum at a uniform
exponential convergence rate. We point out relations of the latter to
sub-Riemannian geometry.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
Abstract: Asynchronous pipeline model parallelism with a "1F1B" (one forward, one
backward) schedule generates little bubble overhead and always provides quite a
high throughput. However, the "1F1B" schedule inevitably leads to weight
inconsistency and weight staleness issues due to the cross-training of
different mini-batches across GPUs. To simultaneously address these two
problems, in this paper, we propose an optimizer-dependent weight prediction
strategy (a.k.a PipeOptim) for asynchronous pipeline training. The key insight
of our proposal is that we employ a weight prediction strategy in the forward
pass to ensure that each mini-batch uses consistent and staleness-free weights
to compute the forward pass. To be concrete, we first construct the weight
prediction scheme based on the update rule of the used optimizer when training
the deep neural network models. Then throughout the "1F1B" pipelined training,
each mini-batch is mandated to execute weight prediction ahead of the forward
pass, subsequently employing the predicted weights to perform the forward pass.
As a result, PipeOptim 1) inherits the advantage of the "1F1B" schedule and
generates pretty high throughput, and 2) can ensure effective parameter
learning regardless of the type of the used optimizer. To verify the
effectiveness of our proposal, we conducted extensive experimental evaluations
using eight different deep-learning models spanning three machine-learning
tasks including image classification, sentiment analysis, and machine
translation. The experiment results demonstrate that PipeOptim outperforms the
popular pipelined approaches including GPipe, PipeDream, PipeDream-2BW, and
SpecTrain. The code of PipeOptim can be accessible at
https://github.com/guanleics/PipeOptim.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling
Abstract: Humans possess a remarkable ability to integrate auditory and visual
information, enabling a deeper understanding of the surrounding environment.
This early fusion of audio and visual cues, demonstrated through cognitive
psychology and neuroscience research, offers promising potential for developing
multimodal perception models. However, training early fusion architectures
poses significant challenges, as the increased model expressivity requires
robust learning frameworks to harness their enhanced capabilities. In this
paper, we address this challenge by leveraging the masked reconstruction
framework, previously successful in unimodal settings, to train audio-visual
encoders with early fusion. Additionally, we propose an attention-based fusion
module that captures interactions between local audio and visual
representations, enhancing the model's ability to capture fine-grained
interactions. While effective, this procedure can become computationally
intractable, as the number of local representations increases. Thus, to address
the computational complexity, we propose an alternative procedure that
factorizes the local representations before representing audio-visual
interactions. Extensive evaluations on a variety of datasets demonstrate the
superiority of our approach in audio-event classification, visual sound
localization, sound separation, and audio-visual segmentation. These
contributions enable the efficient training of deeply integrated audio-visual
models and significantly advance the usefulness of early fusion architectures.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time Series
Abstract: The phenomenon of Gravitational Wave (GW) analysis has grown in popularity as
technology has advanced and the process of observing gravitational waves has
become more precise. Although the sensitivity and the frequency of observation
of GW signals are constantly improving, the possibility of noise in the
collected GW data remains. In this paper, we propose two new Machine and Deep
learning ensemble approaches (i.e., ShallowWaves and DeepWaves Ensembles) for
detecting different types of noise and patterns in datasets from GW
observatories. Our research also investigates various Machine and Deep Learning
techniques for multi-class classification and provides a comprehensive
benchmark, emphasizing the best results in terms of three commonly used
performance metrics (i.e., accuracy, precision, and recall). We train and test
our models on a dataset consisting of annotated time series from real-world
data collected by the Advanced Laser Interferometer GW Observatory (LIGO). We
empirically show that the best overall accuracy is obtained by the proposed
DeepWaves Ensemble, followed close by the ShallowWaves Ensemble.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Tied-Lora: Enhacing parameter efficiency of LoRA with weight tying
Abstract: We propose Tied-LoRA, a simple paradigm utilizes weight tying and selective
training to further increase parameter efficiency of the Low-rank adaptation
(LoRA) method. Our investigations include all feasible combinations parameter
training/freezing in conjunction with weight tying to identify the optimal
balance between performance and the number of trainable parameters. Through
experiments covering a variety of tasks and two base language models, we
provide analysis revealing trade-offs between efficiency and performance. Our
experiments uncovered a particular Tied-LoRA configuration that stands out by
demonstrating comparable performance across several tasks while employing only
13~\% percent of parameters utilized by the standard LoRA method.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Transformer Based Model for Predicting Rapid Impact Compaction Outcomes: A Case Study of Utapao International Airport
Abstract: This paper introduces a novel deep learning approach to predict the
engineering properties of the ground improved by Rapid Impact Compaction (RIC),
which is a ground improvement technique that uses a drop hammer to compact the
soil and fill layers. The proposed approach uses transformer-based neural
networks to capture the complex nonlinear relationships between the input
features, such as the hammer energy, drop height, and number of blows, and the
output variables, such as the cone resistance. The approach is applied to a
real-world dataset from a trial test section for the new apron construction of
the Utapao International Airport in Thailand. The results show that the
proposed approach outperforms the existing methods in terms of prediction
accuracy and efficiency and provides interpretable attention maps that reveal
the importance of different features for RIC prediction. The paper also
discusses the limitations and future directions of applying deep learning
methods to RIC prediction.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?
Abstract: Despite tremendous advances in AI, it remains a significant challenge to
develop interactive task guidance systems that can offer situated, personalized
guidance and assist humans in various tasks. These systems need to have a
sophisticated understanding of the user as well as the environment, and make
timely accurate decisions on when and what to say. To address this issue, we
created a new multimodal benchmark dataset, Watch, Talk and Guide (WTaG) based
on natural interaction between a human user and a human instructor. We further
proposed two tasks: User and Environment Understanding, and Instructor Decision
Making. We leveraged several foundation models to study to what extent these
models can be quickly adapted to perceptually enabled task guidance. Our
quantitative, qualitative, and human evaluation results show that these models
can demonstrate fair performances in some cases with no task-specific training,
but a fast and reliable adaptation remains a significant challenge. Our
benchmark and baselines will provide a stepping stone for future work on
situated task guidance.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?
Abstract: Despite the commercial abundance of UAVs, aerial data acquisition remains
challenging, and the existing Asia and North America-centric open-source UAV
datasets are small-scale or low-resolution and lack diversity in scene
contextuality. Additionally, the color content of the scenes, solar-zenith
angle, and population density of different geographies influence the data
diversity. These two factors conjointly render suboptimal aerial-visual
perception of the deep neural network (DNN) models trained primarily on the
ground-view data, including the open-world foundational models.
  To pave the way for a transformative era of aerial detection, we present
Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record
synchronized scenes from different perspectives -- ground camera and
drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard
2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million
annotated bounding boxes. This makes MAVREC the largest ground and aerial-view
dataset, and the fourth largest among all drone-based datasets across all
modalities and tasks. Through our extensive benchmarking on MAVREC, we
recognize that augmenting object detectors with ground-view images from the
corresponding geographical location is a superior pre-training strategy for
aerial detection. Building on this strategy, we benchmark MAVREC with a
curriculum-based semi-supervised object detection approach that leverages
labeled (ground and aerial) and unlabeled (only aerial) images to enhance the
aerial detection. We publicly release the MAVREC dataset:
https://mavrec.github.io.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
Abstract: Large Language Models (LLMs) like the GPT and LLaMA families have
demonstrated exceptional capabilities in capturing and condensing critical
contextual information and achieving state-of-the-art performance in the
summarization task. However, community concerns about these models'
hallucination issues continue to rise. LLMs sometimes generate factually
hallucinated summaries, which can be extremely harmful in the clinical domain
NLP tasks (e.g., clinical note summarization), where factually incorrect
statements can lead to critically erroneous diagnoses. Fine-tuning LLMs using
human feedback has shown the promise of aligning LLMs to be factually
consistent during generation, but such training procedure requires high-quality
human-annotated data, which can be extremely expensive to get in the clinical
domain. In this work, we propose a new pipeline using ChatGPT instead of human
experts to generate high-quality feedback data for improving factual
consistency in the clinical note summarization task. We focus specifically on
edit feedback because recent work discusses the shortcomings of human alignment
via preference feedback in complex situations (such as clinical NLP tasks that
require extensive expert knowledge), as well as some advantages of collecting
edit feedback from domain experts. In addition, although GPT has reached the
expert level in many clinical NLP tasks (e.g., USMLE QA), there is not much
previous work discussing whether GPT can generate expert-level edit feedback
for LMs in the clinical note summarization task. We hope to fill this gap.
Finally, our evaluations demonstrate the potential use of GPT edits in human
alignment, especially from a factuality perspective.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
Abstract: We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System
Abstract: Explainable Artificial Intelligence is a concept aimed at making complex
algorithms transparent to users through a uniform solution. Researchers have
highlighted the importance of integrating domain specific contexts to develop
explanations tailored to end users. In this study, we focus on the Schufa
housing scoring system in Germany and investigate how users information needs
and expectations for explanations vary based on their roles. Using the
speculative design approach, we asked business information students to imagine
user interfaces that provide housing credit score explanations from the
perspectives of both tenants and landlords. Our preliminary findings suggest
that although there are general needs that apply to all users, there are also
conflicting needs that depend on the practical realities of their roles and how
credit scores affect them. We contribute to Human centered XAI research by
proposing future research directions that examine users explanatory needs
considering their roles and agencies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Forecasting Post-Wildfire Vegetation Recovery in California using a Convolutional Long Short-Term Memory Tensor Regression Network
Abstract: The study of post-wildfire plant regrowth is essential for developing
successful ecosystem recovery strategies. Prior research mainly examines key
ecological and biogeographical factors influencing post-fire succession. This
research proposes a novel approach for predicting and analyzing post-fire plant
recovery. We develop a Convolutional Long Short-Term Memory Tensor Regression
(ConvLSTMTR) network that predicts future Normalized Difference Vegetation
Index (NDVI) based on short-term plant growth data after fire containment. The
model is trained and tested on 104 major California wildfires occurring between
2013 and 2020, each with burn areas exceeding 3000 acres. The integration of
ConvLSTM with tensor regression enables the calculation of an overall logistic
growth rate k using predicted NDVI. Overall, our k-value predictions
demonstrate impressive performance, with 50% of predictions exhibiting an
absolute error of 0.12 or less, and 75% having an error of 0.24 or less.
Finally, we employ Uniform Manifold Approximation and Projection (UMAP) and KNN
clustering to identify recovery trends, offering insights into regions with
varying rates of recovery. This study pioneers the combined use of tensor
regression and ConvLSTM, and introduces the application of UMAP for clustering
similar wildfires. This advances predictive ecological modeling and could
inform future post-fire vegetation management strategies.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Scalable Knowledge Graph Construction and Inference on Human Genome Variants
Abstract: Real-world knowledge can be represented as a graph consisting of entities and
relationships between the entities. The need for efficient and scalable
solutions arises when dealing with vast genomic data, like RNA-sequencing.
Knowledge graphs offer a powerful approach for various tasks in such
large-scale genomic data, such as analysis and inference. In this work,
variant-level information extracted from the RNA-sequences of vaccine-na\"ive
COVID-19 patients have been represented as a unified, large knowledge graph.
Variant call format (VCF) files containing the variant-level information were
annotated to include further information for each variant. The data records in
the annotated files were then converted to Resource Description Framework (RDF)
triples. Each VCF file obtained had an associated CADD scores file that
contained the raw and Phred-scaled scores for each variant. An ontology was
defined for the VCF and CADD scores files. Using this ontology and the
extracted information, a large, scalable knowledge graph was created. Available
graph storage was then leveraged to query and create datasets for further
downstream tasks. We also present a case study using the knowledge graph and
perform a classification task using graph machine learning. We also draw
comparisons between different Graph Neural Networks (GNNs) for the case study.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Abstract: Imitation learning from a large set of human demonstrations has proved to be
an effective paradigm for building capable robot agents. However, the
demonstrations can be extremely costly and time-consuming to collect. We
introduce MimicGen, a system for automatically synthesizing large-scale, rich
datasets from only a small number of human demonstrations by adapting them to
new contexts. We use MimicGen to generate over 50K demonstrations across 18
tasks with diverse scene configurations, object instances, and robot arms from
just ~200 human demonstrations. We show that robot agents can be effectively
trained on this generated dataset by imitation learning to achieve strong
performance in long-horizon and high-precision tasks, such as multi-part
assembly and coffee preparation, across broad initial state distributions. We
further demonstrate that the effectiveness and utility of MimicGen data compare
favorably to collecting additional human demonstrations, making it a powerful
and economical approach towards scaling up robot learning. Datasets, simulation
environments, videos, and more at https://mimicgen.github.io .

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Hot PATE: Private Aggregation of Distributions for Diverse Task
Abstract: The Private Aggregation of Teacher Ensembles (PATE)
framework~\cite{PapernotAEGT:ICLR2017} is a versatile approach to
privacy-preserving machine learning. In PATE, teacher models are trained on
distinct portions of sensitive data, and their predictions are privately
aggregated to label new training examples for a student model.
  Until now, PATE has primarily been explored with classification-like tasks,
where each example possesses a ground-truth label, and knowledge is transferred
to the student by labeling public examples. Generative AI models, however,
excel in open ended \emph{diverse} tasks with multiple valid responses and
scenarios that may not align with traditional labeled examples. Furthermore,
the knowledge of models is often encapsulated in the response distribution
itself and may be transferred from teachers to student in a more fluid way. We
propose \emph{hot PATE}, tailored for the diverse setting. In hot PATE, each
teacher model produces a response distribution and the aggregation method must
preserve both privacy and diversity of responses. We demonstrate, analytically
and empirically, that hot PATE achieves privacy-utility tradeoffs that are
comparable to, and in diverse settings, significantly surpass, the baseline
``cold'' PATE.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production
Abstract: A significant challenge for predictive maintenance in the pulp-and-paper
industry is the infrequency of paper breaks during the production process. In
this article, operational data is analyzed from a paper manufacturing machine
in which paper breaks are relatively rare but have a high economic impact.
Utilizing a dataset comprising 18,398 instances derived from a quality
assurance protocol, we address the scarcity of break events (124 cases) that
pose a challenge for machine learning predictive models. With the help of
Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority
Oversampling Technique (SMOTE), we implement a novel data augmentation
framework. This method ensures that the synthetic data mirrors the distribution
of the real operational data but also seeks to enhance the performance metrics
of predictive modeling. Before and after the data augmentation, we evaluate
three different machine learning algorithms-Decision Trees (DT), Random Forest
(RF), and Logistic Regression (LR). Utilizing the CTGAN-enhanced dataset, our
study achieved significant improvements in predictive maintenance performance
metrics. The efficacy of CTGAN in addressing data scarcity was evident, with
the models' detection of machine breaks (Class 1) improving by over 30% for
Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression.
With this methodological advancement, this study contributes to industrial
quality control and maintenance scheduling by addressing rare event prediction
in manufacturing processes.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Abstract: Causal inference from longitudinal observational data is a challenging
problem due to the difficulty in correctly identifying the time-dependent
confounders, especially in the presence of latent time-dependent confounders.
Instrumental variable (IV) is a powerful tool for addressing the latent
confounders issue, but the traditional IV technique cannot deal with latent
time-dependent confounders in longitudinal studies. In this work, we propose a
novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal
effect estimation from data with latent time-dependent confounders. At each
time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN)
architecture to infer latent IV, and then uses the inferred latent IV factor
for addressing the confounding bias caused by the latent time-dependent
confounders. We provide a theoretical analysis for the proposed TIFM method
regarding causal effect estimation in longitudinal data. Extensive evaluation
with synthetic datasets demonstrates the effectiveness of TIFM in addressing
causal effect estimation over time. We further apply TIFM to a climate dataset
to showcase the potential of the proposed method in tackling real-world
problems.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Ranking with Slot Constraints
Abstract: We introduce the problem of ranking with slot constraints, which can be used
to model a wide range of application problems -- from college admission with
limited slots for different majors, to composing a stratified cohort of
eligible participants in a medical trial. We show that the conventional
Probability Ranking Principle (PRP) can be highly sub-optimal for
slot-constrained ranking problems, and we devise a new ranking algorithm,
called MatchRank. The goal of MatchRank is to produce rankings that maximize
the number of filled slots if candidates are evaluated by a human decision
maker in the order of the ranking. In this way, MatchRank generalizes the PRP,
and it subsumes the PRP as a special case when there are no slot constraints.
Our theoretical analysis shows that MatchRank has a strong approximation
guarantee without any independence assumptions between slots or candidates.
Furthermore, we show how MatchRank can be implemented efficiently. Beyond the
theoretical guarantees, empirical evaluations show that MatchRank can provide
substantial improvements over a range of synthetic and real-world tasks.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs
Abstract: Despite the recent progress in text summarization made by large language
models (LLMs), they often generate summaries that are factually inconsistent
with original articles, known as "hallucinations" in text generation. Unlike
previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes
but more sophisticated ones, such as imposing cause and effect, adding false
details, overgeneralizing, etc. These hallucinations are challenging to detect
through traditional methods, which poses great challenges for improving the
factual consistency of text summarization. In this paper, we propose an
adversarially DEcoupling method to disentangle the Comprehension and
EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based
efficient training to cover the shortage of sensitivity for true and false in
the training process of LLMs. In this way, LLMs are less confused about
embellishing and understanding; thus, they can execute the instructions more
accurately and have enhanced abilities to distinguish hallucinations.
Experimental results show that DECENT significantly improves the reliability of
text summarization based on LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers
Abstract: Query expansion has been proved to be effective in improving recall and
precision of first-stage retrievers, and yet its influence on a complicated,
state-of-the-art cross-encoder ranker remains under-explored. We first show
that directly applying the expansion techniques in the current literature to
state-of-the-art neural rankers can result in deteriorated zero-shot
performance. To this end, we propose GFF, a pipeline that includes a large
language model and a neural ranker, to Generate, Filter, and Fuse query
expansions more effectively in order to improve the zero-shot ranking metrics
such as nDCG@10. Specifically, GFF first calls an instruction-following
language model to generate query-related keywords through a reasoning chain.
Leveraging self-consistency and reciprocal rank weighting, GFF further filters
and combines the ranking results of each expanded query dynamically. By
utilizing this pipeline, we show that GFF can improve the zero-shot nDCG@10 on
BEIR and TREC DL 2019/2020. We also analyze different modelling choices in the
GFF pipeline and shed light on the future directions in query expansion for
zero-shot neural rankers.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
Abstract: The Geometric Algebra Transformer (GATr) is a versatile architecture for
geometric deep learning based on projective geometric algebra. We generalize
this architecture into a blueprint that allows one to construct a scalable
transformer architecture given any geometric (or Clifford) algebra. We study
versions of this architecture for Euclidean, projective, and conformal
algebras, all of which are suited to represent 3D data, and evaluate them in
theory and practice. The simplest Euclidean architecture is computationally
cheap, but has a smaller symmetry group and is not as sample-efficient, while
the projective model is not sufficiently expressive. Both the conformal algebra
and an improved version of the projective algebra define powerful, performant
architectures.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Hallucination Detection for Grounded Instruction Generation
Abstract: We investigate the problem of generating instructions to guide humans to
navigate in simulated residential environments. A major issue with current
models is hallucination: they generate references to actions or objects that
are inconsistent with what a human follower would perform or encounter along
the described path. We develop a model that detects these hallucinated
references by adopting a model pre-trained on a large corpus of image-text
pairs, and fine-tuning it with a contrastive loss that separates correct
instructions from instructions containing synthesized hallucinations. Our final
model outperforms several baselines, including using word probability estimated
by the instruction-generation model, and supervised models based on LSTM and
Transformer.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: GeoLocator: a location-integrated large multimodal model for inferring geo-privacy
Abstract: Geographic privacy or geo-privacy refers to the keeping private of one's
geographic location, especially the restriction of geographical data maintained
by personal electronic equipment. Geo-privacy is a crucial aspect of personal
security, however often goes unnoticed in daily activities. With the surge in
the use of Large Multimodal Models (LMM), such as GPT-4, for Open Source
Intelligence (OSINT), the potential risks associated with geo-privacy breaches
have intensified. This study develops a location-integrated GPT-4 based model
named GeoLocator and designed four-dimensional experiments to demonstrate its
capability in inferring and identifying the locational information of input
imageries and/or social media contents. Our experiments reveal that GeoLocator
generates specific geographic details with high accuracy and consequently
embeds the risk of the model users exposing geospatial information to the
public unintentionally, highlighting the thread of online data sharing,
information gathering technologies and LLM on geo-privacy. We conclude with the
broader implications of GeoLocator and our findings for individuals and the
community at large, by emphasizing the urgency for enhanced awareness and
protective measures against geo-privacy leakage in the era of advanced AI and
widespread social media usage.
  Keywords: geoprivacy, GPT-4, image comprehension, Large Multimodal Model
(LMM), Open Source Intelligence (OSINT)

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: DevBots can co-design APIs
Abstract: DevBots are automated tools that perform various tasks in order to support
software development. They are a growing trend and have been used in
repositories to automate repetitive tasks, as code generators, and as
collaborators in eliciting requirements and defining architectures. In this
study, we analyzed 24 articles to investigate the state of the art of using
DevBots in software development, trying to understand their characteristics,
identify use cases, learn the relationship between DevBots and conversational
software development, and discuss how prompt engineering can enable
collaboration between human developers and bots. Additionally, we identified a
gap to address by applying prompt engineering to collaborative API design
between human designers and DevBots and proposed an experiment to assess what
approach, between using Retrieval Augmented Generation or not, is more
suitable. Our conclusion is that DevBots can collaborate with human API
designers, but the two approaches have advantages and disadvantages.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
Abstract: In this work, we develop and release Yuan 2.0, a series of large language
models with parameters ranging from 2.1 billion to 102.6 billion. The Localized
Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of
local dependencies of natural language into Attention. A data filtering and
generating system is presented to build pre-training and fine-tuning dataset in
high quality. A distributed training method with non-uniform pipeline parallel,
data parallel, and optimizer parallel is proposed, which greatly reduces the
bandwidth requirements of intra-node communication, and achieves good
performance in large-scale distributed training. Yuan 2.0 models display
impressive ability in code generation, math problem-solving, and chatting
compared with existing models. The latest version of YUAN 2.0, including model
weights and source code, is accessible at Github.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System
Abstract: Deep learning based intrusion detection systems (DL-based IDS) have emerged
as one of the best choices for providing security solutions against various
network intrusion attacks. However, due to the emergence and development of
adversarial deep learning technologies, it becomes challenging for the adoption
of DL models into IDS. In this paper, we propose a novel IDS architecture that
can enhance the robustness of IDS against adversarial attacks by combining
conventional machine learning (ML) models and Deep Learning models. The
proposed DLL-IDS consists of three components: DL-based IDS, adversarial
example (AE) detector, and ML-based IDS. We first develop a novel AE detector
based on the local intrinsic dimensionality (LID). Then, we exploit the low
attack transferability between DL models and ML models to find a robust ML
model that can assist us in determining the maliciousness of AEs. If the input
traffic is detected as an AE, the ML-based IDS will predict the maliciousness
of input traffic, otherwise the DL-based IDS will work for the prediction. The
fusion mechanism can leverage the high prediction accuracy of DL models and low
attack transferability between DL models and ML models to improve the
robustness of the whole system. In our experiments, we observe a significant
improvement in the prediction performance of the IDS when subjected to
adversarial attack, achieving high accuracy with low resource consumption.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: CNL2ASP: converting controlled natural language sentences into ASP
Abstract: Answer Set Programming (ASP) is a popular declarative programming language
for solving hard combinatorial problems. Although ASP has gained widespread
acceptance in academic and industrial contexts, there are certain user groups
who may find it more advantageous to employ a higher-level language that
closely resembles natural language when specifying ASP programs. In this paper,
we propose a novel tool, called CNL2ASP, for translating English sentences
expressed in a controlled natural language (CNL) form into ASP. In particular,
we first provide a definition of the type of sentences allowed by our CNL and
their translation as ASP rules, and then exemplify the usage of the CNL for the
specification of both synthetic and real-world combinatorial problems. Finally,
we report the results of an experimental analysis conducted on the real-world
problems to compare the performance of automatically generated encodings with
the ones written by ASP practitioners, showing that our tool can obtain
satisfactory performance on these benchmarks. Under consideration in Theory and
Practice of Logic Programming (TPLP).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classification
Abstract: Electroencephalography (EEG) and Natural Language Processing (NLP) can be
applied for education to measure students' comprehension in classroom lectures;
currently, the two measures have been used separately. In this work, we propose
a classification framework for predicting students' lecture comprehension in
two tasks: (i) students' confusion after listening to the simulated lecture and
(ii) the correctness of students' responses to the post-lecture assessment. The
proposed framework includes EEG and NLP feature extraction, processing, and
classification. EEG and NLP features are extracted to construct integrated
features obtained from recorded EEG signals and sentence-level syntactic
analysis, which provide information about specific biomarkers and sentence
structures. An ensemble stacking classification method -- a combination of
multiple individual models that produces an enhanced predictive model -- is
studied to learn from the features to make predictions accurately. Furthermore,
we also utilized subjective confusion ratings as another integrated feature to
enhance classification performance. By doing so, experiment results show that
this framework performs better than the baselines, which achieved F1 up to 0.65
for predicting confusion and 0.78 for predicting correctness, highlighting that
utilizing this has helped improve the classification performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Spoken Word2Vec: A Perspective And Some Techniques
Abstract: Text word embeddings that encode distributional semantic features work by
modeling contextual similarities of frequently occurring words. Acoustic word
embeddings, on the other hand, typically encode low-level phonetic
similarities. Semantic embeddings for spoken words have been previously
explored using similar algorithms to Word2Vec, but the resulting vectors still
mainly encoded phonetic rather than semantic features. In this paper, we
examine the assumptions and architectures used in previous works and show
experimentally how Word2Vec algorithms fail to encode distributional semantics
when the input units are acoustically correlated. In addition, previous works
relied on the simplifying assumptions of perfect word segmentation and
clustering by word type. Given these conditions, a trivial solution identical
to text-based embeddings has been overlooked. We follow this simpler path using
automatic word type clustering and examine the effects on the resulting
embeddings, highlighting the true challenges in this task.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learning Machine Morality through Experience and Interaction
Abstract: Increasing interest in ensuring safety of next-generation Artificial
Intelligence (AI) systems calls for novel approaches to embedding morality into
autonomous agents. Traditionally, this has been done by imposing explicit
top-down rules or hard constraints on systems, for example by filtering system
outputs through pre-defined ethical rules. Recently, instead, entirely
bottom-up methods for learning implicit preferences from human behavior have
become increasingly popular, such as those for training and fine-tuning Large
Language Models. In this paper, we provide a systematization of existing
approaches to the problem of introducing morality in machines - modeled as a
continuum, and argue that the majority of popular techniques lie at the
extremes - either being fully hard-coded, or entirely learned, where no
explicit statement of any moral principle is required. Given the relative
strengths and weaknesses of each type of methodology, we argue that more hybrid
solutions are needed to create adaptable and robust, yet more controllable and
interpretable agents.
  In particular, we present three case studies of recent works which use
learning from experience (i.e., Reinforcement Learning) to explicitly provide
moral principles to learning agents - either as intrinsic rewards, moral
logical constraints or textual principles for language models. For example,
using intrinsic rewards in Social Dilemma games, we demonstrate how it is
possible to represent classical moral frameworks for agents. We also present an
overview of the existing work in this area in order to provide empirical
evidence for the potential of this hybrid approach. We then discuss strategies
for evaluating the effectiveness of moral learning agents. Finally, we present
open research questions and implications for the future of AI safety and ethics
which are emerging from this framework.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles
Abstract: This work introduces a preference learning method that ensures adherence to
traffic rules for autonomous vehicles. Our approach incorporates priority
ordering of signal temporal logic (STL) formulas, describing traffic rules,
into a learning framework. By leveraging the parametric weighted signal
temporal logic (PWSTL), we formulate the problem of safety-guaranteed
preference learning based on pairwise comparisons, and propose an approach to
solve this learning problem. Our approach finds a feasible valuation for the
weights of the given PWSTL formula such that, with these weights, preferred
signals have weighted quantitative satisfaction measures greater than their
non-preferred counterparts. The feasible valuation of weights given by our
approach leads to a weighted STL formula which can be used in
correct-and-custom-by-construction controller synthesis. We demonstrate the
performance of our method with human subject studies in two different simulated
driving scenarios involving a stop sign and a pedestrian crossing. Our approach
yields competitive results compared to existing preference learning methods in
terms of capturing preferences, and notably outperforms them when safety is
considered.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Abstract: We consider the inventory management problem, where the goal is to balance
conflicting objectives such as availability and wastage of a large range of
products in a store. We propose a reinforcement learning (RL) approach that
utilises General Value Functions (GVFs) to derive domain-backed inventory
replenishment policies. The inventory replenishment decisions are modelled as a
sequential decision making problem, which is challenging due to uncertain
demand and the existence of aggregate (cross-product) constraints. In existing
literature, GVFs have primarily been used for auxiliary task learning. We use
this capability to train GVFs on domain-critical characteristics such as
prediction of stock-out probability and wastage quantity. Using this domain
expertise for more effective exploration, we train an RL agent to compute the
inventory replenishment quantities for a large range of products (up to 6000 in
the reported experiments), which share aggregate constraints such as the total
weight/volume per delivery. Additionally, we show that the GVF predictions can
be used to provide additional domain-backed insights into the decisions
proposed by the RL agent. Finally, since the environment dynamics are fully
transferred, the trained GVFs can be used for faster adaptation to vastly
different business objectives (for example, due to the start of a promotional
period or due to deployment in a new customer environment).

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A knowledge-driven AutoML architecture
Abstract: This paper proposes a knowledge-driven AutoML architecture for pipeline and
deep feature synthesis. The main goal is to render the AutoML process
explainable and to leverage domain knowledge in the synthesis of pipelines and
features. The architecture explores several novel ideas: first, the
construction of pipelines and deep features is approached in an unified way.
Next, synthesis is driven by a shared knowledge system, interactively queried
as to what pipeline operations to use or features to compute. Lastly, the
synthesis processes takes decisions at runtime using partial solutions and
results of their application on data. Two experiments are conducted to
demonstrate the functionality of a na\"{\i}ve implementation of the proposed
architecture and to discuss its advantages, trade-offs as well as future
potential for AutoML.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Visual Hindsight Self-Imitation Learning for Interactive Navigation
Abstract: Interactive visual navigation tasks, which involve following instructions to
reach and interact with specific targets, are challenging not only because
successful experiences are very rare but also because the complex visual inputs
require a substantial number of samples. Previous methods for these tasks often
rely on intricately designed dense rewards or the use of expensive expert data
for imitation learning. To tackle these challenges, we propose a novel
approach, Visual Hindsight Self-Imitation Learning (VHS) for enhancing sample
efficiency through hindsight goal re-labeling and self-imitation. We also
introduce a prototypical goal embedding method derived from experienced goal
observations, that is particularly effective in vision-based and partially
observable environments. This embedding technique allows the agent to visually
reinterpret its unsuccessful attempts, enabling vision-based goal re-labeling
and self-imitation from enhanced successful experiences. Experimental results
show that VHS outperforms existing techniques in interactive visual navigation
tasks, confirming its superior performance and sample efficiency.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
Abstract: Few-shot named entity recognition (NER) aims to recognize novel named
entities in low-resource domains utilizing existing knowledge. However, the
present few-shot NER models assume that the labeled data are all clean without
noise or outliers, and there are few works focusing on the robustness of the
cross-domain transfer learning ability to textual adversarial attacks in
Few-shot NER. In this work, we comprehensively explore and assess the
robustness of few-shot NER models under textual adversarial attack scenario,
and found the vulnerability of existing few-shot NER models. Furthermore, we
propose a robust two-stage few-shot NER method with Boundary Discrimination and
Correlation Purification (BDCP). Specifically, in the span detection stage, the
entity boundary discriminative module is introduced to provide a highly
distinguishing boundary representation space to detect entity spans. In the
entity typing stage, the correlations between entities and contexts are
purified by minimizing the interference information and facilitating
correlation generalization to alleviate the perturbations caused by textual
adversarial attacks. In addition, we construct adversarial examples for
few-shot NER based on public datasets Few-NERD and Cross-Dataset. Comprehensive
evaluations on those two groups of few-shot NER datasets containing adversarial
examples demonstrate the robustness and superiority of the proposed method.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Abstract: Text-to-video diffusion models have advanced video generation significantly.
However, customizing these models to generate videos with tailored motions
presents a substantial challenge. In specific, they encounter hurdles in (a)
accurately reproducing motion from a target video, and (b) creating diverse
visual variations. For example, straightforward extensions of static image
customization methods to video often lead to intricate entanglements of
appearance and motion data. To tackle this, here we present the Video Motion
Customization (VMC) framework, a novel one-shot tuning approach crafted to
adapt temporal attention layers within video diffusion models. Our approach
introduces a novel motion distillation objective using residual vectors between
consecutive frames as a motion reference. The diffusion process then preserves
low-frequency motion trajectories while mitigating high-frequency
motion-unrelated noise in image space. We validate our method against
state-of-the-art video generative models across diverse real-world motions and
contexts. Our codes, data and the project demo can be found at
https://video-motion-customization.github.io

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Formal concept analysis for evaluating intrinsic dimension of a natural language
Abstract: Some results of a computational experiment for determining the intrinsic
dimension of linguistic varieties for the Bengali and Russian languages are
presented. At the same time, both sets of words and sets of bigrams in these
languages were considered separately. The method used to solve this problem was
based on formal concept analysis algorithms. It was found that the intrinsic
dimensions of these languages are significantly less than the dimensions used
in popular neural network models in natural language processing.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
Abstract: In the quest for unveiling novel categories at test time, we confront the
inherent limitations of traditional supervised recognition models that are
restricted by a predefined category set. While strides have been made in the
realms of self-supervised and open-world learning towards test-time category
discovery, a crucial yet often overlooked question persists: what exactly
delineates a category? In this paper, we conceptualize a category through the
lens of optimization, viewing it as an optimal solution to a well-defined
problem. Harnessing this unique conceptualization, we propose a novel,
efficient and self-supervised method capable of discovering previously unknown
categories at test time. A salient feature of our approach is the assignment of
minimum length category codes to individual data instances, which encapsulates
the implicit category hierarchy prevalent in real-world datasets. This
mechanism affords us enhanced control over category granularity, thereby
equipping our model to handle fine-grained categories adeptly. Experimental
evaluations, bolstered by state-of-the-art benchmark comparisons, testify to
the efficacy of our solution in managing unknown categories at test time.
Furthermore, we fortify our proposition with a theoretical foundation,
providing proof of its optimality. Our code is available at
https://github.com/SarahRastegar/InfoSieve.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Deep Bayesian Reinforcement Learning for Spacecraft Proximity Maneuvers and Docking
Abstract: In the pursuit of autonomous spacecraft proximity maneuvers and docking(PMD),
we introduce a novel Bayesian actor-critic reinforcement learning algorithm to
learn a control policy with the stability guarantee. The PMD task is formulated
as a Markov decision process that reflects the relative dynamic model, the
docking cone and the cost function. Drawing from the principles of Lyapunov
theory, we frame the temporal difference learning as a constrained Gaussian
process regression problem. This innovative approach allows the state-value
function to be expressed as a Lyapunov function, leveraging the Gaussian
process and deep kernel learning. We develop a novel Bayesian quadrature policy
optimization procedure to analytically compute the policy gradient while
integrating Lyapunov-based stability constraints. This integration is pivotal
in satisfying the rigorous safety demands of spaceflight missions. The proposed
algorithm has been experimentally evaluated on a spacecraft air-bearing testbed
and shows impressive and promising performance.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation
Abstract: Offline reinforcement learning leverages pre-collected datasets of
transitions to train policies. It can serve as effective initialization for
online algorithms, enhancing sample efficiency and speeding up convergence.
However, when such datasets are limited in size and quality, offline
pre-training can produce sub-optimal policies and lead to degraded online
reinforcement learning performance. In this paper we propose a model-based data
augmentation strategy to maximize the benefits of offline reinforcement
learning pre-training and reduce the scale of data needed to be effective. Our
approach leverages a world model of the environment trained on the offline
dataset to augment states during offline pre-training. We evaluate our approach
on a variety of MuJoCo robotic tasks and our results show it can jump-start
online fine-tuning and substantially reduce - in some cases by an order of
magnitude - the required number of environment interactions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Causal Interpretation of Self-Attention in Pre-Trained Transformers
Abstract: We propose a causal interpretation of self-attention in the Transformer
neural network architecture. We interpret self-attention as a mechanism that
estimates a structural equation model for a given input sequence of symbols
(tokens). The structural equation model can be interpreted, in turn, as a
causal structure over the input symbols under the specific context of the input
sequence. Importantly, this interpretation remains valid in the presence of
latent confounders. Following this interpretation, we estimate conditional
independence relations between input symbols by calculating partial
correlations between their corresponding representations in the deepest
attention layer. This enables learning the causal structure over an input
sequence using existing constraint-based algorithms. In this sense, existing
pre-trained Transformers can be utilized for zero-shot causal-discovery. We
demonstrate this method by providing causal explanations for the outcomes of
Transformers in two tasks: sentiment classification (NLP) and recommendation.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Multitask Kernel-based Learning with First-Order Logic Constraints
Abstract: In this paper we propose a general framework to integrate supervised and
unsupervised examples with background knowledge expressed by a collection of
first-order logic clauses into kernel machines. In particular, we consider a
multi-task learning scheme where multiple predicates defined on a set of
objects are to be jointly learned from examples, enforcing a set of FOL
constraints on the admissible configurations of their values. The predicates
are defined on the feature spaces, in which the input objects are represented,
and can be either known a priori or approximated by an appropriate kernel-based
learner. A general approach is presented to convert the FOL clauses into a
continuous implementation that can deal with the outputs computed by the
kernel-based predicates. The learning problem is formulated as a
semi-supervised task that requires the optimization in the primal of a loss
function that combines a fitting loss measure on the supervised examples, a
regularization term, and a penalty term that enforces the constraints on both
the supervised and unsupervised examples. Unfortunately, the penalty term is
not convex and it can hinder the optimization process. However, it is possible
to avoid poor solutions by using a two stage learning schema, in which the
supervised examples are learned first and then the constraints are enforced.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Classifying patient voice in social media data using neural networks: A comparison of AI models on different data sources and therapeutic domains
Abstract: It is essential that healthcare professionals and members of the healthcare
community can access and easily understand patient experiences in the real
world, so that care standards can be improved and driven towards personalised
drug treatment. Social media platforms and message boards are deemed suitable
sources of patient experience information, as patients have been observed to
discuss and exchange knowledge, look for and provide support online. This paper
tests the hypothesis that not all online patient experience information can be
treated and collected in the same way, as a result of the inherent differences
in the way individuals talk about their journeys, in different therapeutic
domains and or data sources.
  We used linguistic analysis to understand and identify similarities between
datasets, across patient language, between data sources (Reddit, SocialGist)
and therapeutic domains (cardiovascular, oncology, immunology, neurology). We
detected common vocabulary used by patients in the same therapeutic domain
across data sources, except for immunology patients, who use unique vocabulary
between the two data sources, and compared to all other datasets. We combined
linguistically similar datasets to train classifiers (CNN, transformer) to
accurately identify patient experience posts from social media, a task we refer
to as patient voice classification. The cardiovascular and neurology
transformer classifiers perform the best in their respective comparisons for
the Reddit data source, achieving F1-scores of 0.865 and 1.0 respectively. The
overall best performing classifier is the transformer classifier trained on all
data collected for this experiment, achieving F1-scores ranging between 0.863
and 0.995 across all therapeutic domain and data source specific test datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and Embedding
Abstract: One of the significant barriers to the training of statistical models on
knowledge graphs is the difficulty that scientists have in finding the best
input data to address their prediction goal. In addition to this, a key
challenge is to determine how to manipulate these relational data, which are
often in the form of particular triples (i.e., subject, predicate, object), to
enable the learning process. Currently, many high-quality catalogs of knowledge
graphs, are available. However, their primary goal is the re-usability of these
resources, and their interconnection, in the context of the Semantic Web. This
paper describes the LiveSchema initiative, namely, a first version of a gateway
that has the main scope of leveraging the gold mine of data collected by many
existing catalogs collecting relational data like ontologies and knowledge
graphs. At the current state, LiveSchema contains - 1000 datasets from 4 main
sources and offers some key facilities, which allow to: i) evolving LiveSchema,
by aggregating other source catalogs and repositories as input sources; ii)
querying all the collected resources; iii) transforming each given dataset into
formal concept analysis matrices that enable analysis and visualization
services; iv) generating models and tensors from each given dataset.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
Abstract: Utilizing pre-trained 2D large-scale generative models, recent works are
capable of generating high-quality novel views from a single in-the-wild image.
However, due to the lack of information from multiple views, these works
encounter difficulties in generating controllable novel views. In this paper,
we present DreamComposer, a flexible and scalable framework that can enhance
existing view-aware diffusion models by injecting multi-view conditions.
Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain
3D representations of an object from multiple views. Then, it renders the
latent features of the target view from 3D representations with the multi-view
feature fusion module. Finally the target view features extracted from
multi-view inputs are injected into a pre-trained diffusion model. Experiments
show that DreamComposer is compatible with state-of-the-art diffusion models
for zero-shot novel view synthesis, further enhancing them to generate
high-fidelity novel view images with multi-view conditions, ready for
controllable 3D object reconstruction and various other applications.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Advancing AI Audits for Enhanced AI Governance
Abstract: As artificial intelligence (AI) is integrated into various services and
systems in society, many companies and organizations have proposed AI
principles, policies, and made the related commitments. Conversely, some have
proposed the need for independent audits, arguing that the voluntary principles
adopted by the developers and providers of AI services and systems
insufficiently address risk. This policy recommendation summarizes the issues
related to the auditing of AI services and systems and presents three
recommendations for promoting AI auditing that contribute to sound AI
governance. Recommendation1.Development of institutional design for AI audits.
Recommendation2.Training human resources for AI audits. Recommendation3.
Updating AI audits in accordance with technological progress.
  In this policy recommendation, AI is assumed to be that which recognizes and
predicts data with the last chapter outlining how generative AI should be
audited.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards
Abstract: This paper endeavors to augment the robustness of offline reinforcement
learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent
circumstance in real-world applications. We propose two algorithmic frameworks,
ROAM and ROOM, for robust off-policy evaluation (OPE) and offline policy
optimization (OPO), respectively. Central to our frameworks is the strategic
incorporation of the median-of-means method with offline RL, enabling
straightforward uncertainty estimation for the value function estimator. This
not only adheres to the principle of pessimism in OPO but also adeptly manages
heavy-tailed rewards. Theoretical results and extensive experiments demonstrate
that our two frameworks outperform existing methods on the logged dataset
exhibits heavy-tailed reward distributions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
Abstract: Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated
malware, thereby preventing them from invading computers. As a significant
representation of dynamic malware behavior, the API (Application Programming
Interface) sequence, comprised of consecutive API calls, has progressively
become the dominant feature of dynamic analysis methods. Though there have been
numerous deep learning models for malware detection based on API sequences, the
quality of API call representations produced by those models is limited. These
models cannot generate representations for unknown API calls, which weakens
both the detection performance and the generalization. Further, the concept
drift phenomenon of API calls is prominent. To tackle these issues, we
introduce a prompt engineering-assisted malware dynamic analysis using GPT-4.
In this method, GPT-4 is employed to create explanatory text for each API call
within the API sequence. Afterward, the pre-trained language model BERT is used
to obtain the representation of the text, from which we derive the
representation of the API sequence. Theoretically, this proposed method is
capable of generating representations for all API calls, excluding the
necessity for dataset training during the generation process. Utilizing the
representation, a CNN-based detection model is designed to extract the feature.
We adopt five benchmark datasets to validate the performance of the proposed
model. The experimental results reveal that the proposed detection algorithm
performs better than the state-of-the-art method (TextCNN). Specifically, in
cross-database experiments and few-shot learning experiments, the proposed
model achieves excellent detection performance and almost a 100% recall rate
for malware, verifying its superior generalization performance. The code is
available at: github.com/yan-scnu/Prompted_Dynamic_Detection.

Output: Cryptography and Security