Instruction: What field is the article from?

Prompt:
Title: Color-Emotion Associations in Art: Fuzzy Approach
Abstract: Art objects can evoke certain emotions. Color is a fundamental element of
visual art and plays a significant role in how art is perceived. This paper
introduces a novel approach to classifying emotions in art using Fuzzy Sets. We
employ a fuzzy approach because it aligns well with human judgments' imprecise
and subjective nature. Extensive fuzzy colors (n=120) and a broad emotional
spectrum (n=10) allow for a more human-consistent and context-aware exploration
of emotions inherent in paintings. First, we introduce the fuzzy color
representation model. Then, at the fuzzification stage, we process the Wiki Art
Dataset of paintings tagged with emotions, extracting fuzzy dominant colors
linked to specific emotions. This results in fuzzy color distributions for ten
emotions. Finally, we convert them back to a crisp domain, obtaining a
knowledge base of color-emotion associations in primary colors. Our findings
reveal strong associations between specific emotions and colors; for instance,
gratitude strongly correlates with green, brown, and orange. Other noteworthy
associations include brown and anger, orange with shame, yellow with happiness,
and gray with fear. Using these associations and Jaccard similarity, we can
find the emotions in the arbitrary untagged image. We conducted a 2AFC
experiment involving human subjects to evaluate the proposed method. The
average hit rate of 0.77 indicates a significant correlation between the
method's predictions and human perception. The proposed method is simple to
adapt to art painting retrieval systems. The study contributes to the
theoretical understanding of color-emotion associations in art, offering
valuable insights for various practical applications besides art, like
marketing, design, and psychology.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Co-guiding for Multi-intent Spoken Language Understanding
Abstract: Recent graph-based models for multi-intent SLU have obtained promising
results through modeling the guidance from the prediction of intents to the
decoding of slot filling. However, existing methods (1) only model the
unidirectional guidance from intent to slot, while there are bidirectional
inter-correlations between intent and slot; (2) adopt homogeneous graphs to
model the interactions between the slot semantics nodes and intent label nodes,
which limit the performance. In this paper, we propose a novel model termed
Co-guiding Net, which implements a two-stage framework achieving the mutual
guidances between the two tasks. In the first stage, the initial estimated
labels of both tasks are produced, and then they are leveraged in the second
stage to model the mutual guidances. Specifically, we propose two heterogeneous
graph attention networks working on the proposed two heterogeneous semantics
label graphs, which effectively represent the relations among the semantics
nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which
exploits the single-task and dual-task semantics contrastive relations. For the
first stage, we propose single-task supervised contrastive learning, and for
the second stage, we propose co-guiding supervised contrastive learning, which
considers the two tasks' mutual guidances in the contrastive learning
procedure. Experiment results on multi-intent SLU show that our model
outperforms existing models by a large margin, obtaining a relative improvement
of 21.3% over the previous best model on MixATIS dataset in overall accuracy.
We also evaluate our model on the zero-shot cross-lingual scenario and the
results show that our model can relatively improve the state-of-the-art model
by 33.5% on average in terms of overall accuracy for the total 9 languages.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform
Abstract: Artificial intelligence (AI) refers to the ability of machines or software to
mimic or even surpass human intelligence in a given cognitive task. While
humans learn by both induction and deduction, the success of current AI is
rooted in induction, relying on its ability to detect statistical regularities
in task input -- an ability learnt from a vast amount of training data using
enormous computation resources. We examine the performance of such a
statistical AI in a human task through the lens of four factors, including task
learnability, statistical resource, computation resource, and learning
techniques, and then propose a three-phase visual framework to understand the
evolving relation between AI and jobs. Based on this conceptual framework, we
develop a simple economic model of competition to show the existence of an
inflection point for each occupation. Before AI performance crosses the
inflection point, human workers always benefit from an improvement in AI
performance, but after the inflection point, human workers become worse off
whenever such an improvement occurs. To offer empirical evidence, we first
argue that AI performance has passed the inflection point for the occupation of
translation but not for the occupation of web development. We then study how
the launch of ChatGPT, which led to significant improvement of AI performance
on many tasks, has affected workers in these two occupations on a large online
labor platform. Consistent with the inflection point conjecture, we find that
translators are negatively affected by the shock both in terms of the number of
accepted jobs and the earnings from those jobs, while web developers are
positively affected by the very same shock. Given the potentially large
disruption of AI on employment, more studies on more occupations using data
from different platforms are urgently needed.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: On Computing Makespan-Optimal Solutions for Generalized Sliding-Tile Puzzles
Abstract: In the $15$-puzzle game, $15$ labeled square tiles are reconfigured on a
$4\times 4$ board through an escort, wherein each (time) step, a single tile
neighboring it may slide into it, leaving the space previously occupied by the
tile as the new escort. We study a generalized sliding-tile puzzle (GSTP) in
which (1) there are $1+$ escorts and (2) multiple tiles can move synchronously
in a single time step. Compared with popular discrete multi-agent/robot motion
models, GSTP provides a more accurate model for a broad array of high-utility
applications, including warehouse automation and autonomous garage parking, but
is less studied due to the more involved tile interactions. In this work, we
analyze optimal GSTP solution structures, establishing that computing
makespan-optimal solutions for GSTP is NP-complete and developing polynomial
time algorithms yielding makespans approximating the minimum with expected/high
probability constant factors, assuming randomized start and goal
configurations.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
Abstract: Multi-modal Large Language Models (MLLMs) tuned on machine-generated
instruction-following data have demonstrated remarkable performance in various
multi-modal understanding and generation tasks. However, the hallucinations
inherent in machine-generated data, which could lead to hallucinatory outputs
in MLLMs, remain under-explored. This work aims to investigate various
hallucinations (i.e., object, relation, attribute hallucinations) and mitigate
those hallucinatory toxicities in large-scale machine-generated visual
instruction datasets. Drawing on the human ability to identify factual errors,
we present a novel hallucination detection and elimination framework,
HalluciDoctor, based on the cross-checking paradigm. We use our framework to
identify and eliminate hallucinations in the training data automatically.
Interestingly, HalluciDoctor also indicates that spurious correlations arising
from long-tail object co-occurrences contribute to hallucinations. Based on
that, we execute counterfactual visual instruction expansion to balance data
distribution, thereby enhancing MLLMs' resistance to hallucinations.
Comprehensive experiments on hallucination evaluation benchmarks show that our
method successfully mitigates 44.6% hallucinations relatively and maintains
competitive performance compared to LLaVA.The source code will be released at
\url{https://github.com/Yuqifan1117/HalluciDoctor}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data
Abstract: Automated disease, weed and crop classification with computer vision will be
invaluable in the future of agriculture. However, existing model architectures
like ResNet, EfficientNet and ConvNeXt often underperform on smaller,
specialised datasets typical of such projects. We address this gap with
informed data collection and the development of a new CNN architecture,
PhytNet. Utilising a novel dataset of infrared cocoa tree images, we
demonstrate PhytNet's development and compare its performance with existing
architectures. Data collection was informed by analysis of spectroscopy data,
which provided useful insights into the spectral characteristics of cocoa
trees. Such information could inform future data collection and model
development. Cocoa was chosen as a focal species due to the diverse pathology
of its diseases, which pose significant challenges for detection. ResNet18
showed some signs of overfitting, while EfficientNet variants showed distinct
signs of overfitting. By contrast, PhytNet displayed excellent attention to
relevant features, no overfitting, and an exceptionally low computation cost
(1.19 GFLOPS). As such PhytNet is a promising candidate for rapid disease or
plant classification, or precise localisation of disease symptoms for
autonomous systems.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
Abstract: Single cell analysis of human skeletal muscle (SM) tissue cross-sections is a
fundamental tool for understanding many neuromuscular disorders. For this
analysis to be reliable and reproducible, identification of individual fibres
within microscopy images (segmentation) of SM tissue should be automatic and
precise. Biomedical scientists in this field currently rely on custom tools and
general machine learning (ML) models, both followed by labour intensive and
subjective manual interventions to fine-tune segmentation. We believe that
fully automated, precise, reproducible segmentation is possible by training ML
models. However, in this important biomedical domain, there are currently no
good quality, publicly available annotated imaging datasets available for ML
model training. In this paper we release NCL-SM: a high quality bioimaging
dataset of 46 human SM tissue cross-sections from both healthy control subjects
and from patients with genetically diagnosed muscle pathology. These images
include $>$ 50k manually segmented muscle fibres (myofibres). In addition we
also curated high quality myofibre segmentations, annotating reasons for
rejecting low quality myofibres and low quality regions in SM tissue images,
making these annotations completely ready for downstream analysis. This, we
believe, will pave the way for development of a fully automatic pipeline that
identifies individual myofibres within images of tissue sections and, in
particular, also classifies individual myofibres that are fit for further
analysis.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery
Abstract: Natural protected areas are vital for biodiversity, climate change
mitigation, and supporting ecological processes. Despite their significance,
comprehensive mapping is hindered by a lack of understanding of their
characteristics and a missing land cover class definition. This paper aims to
advance the explanation of the designating patterns forming protected and wild
areas. To this end, we propose a novel framework that uses activation
maximization and a generative adversarial model. With this, we aim to generate
satellite images that, in combination with domain knowledge, are capable of
offering complete and valid explanations for the spatial and spectral patterns
that define the natural authenticity of these regions. Our proposed framework
produces more precise attribution maps pinpointing the designating patterns
forming the natural authenticity of protected areas. Our approach fosters our
understanding of the ecological integrity of the protected natural areas and
may contribute to future monitoring and preservation efforts.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: How Multilingual is Multilingual LLM?
Abstract: Large Language Models (LLMs), trained predominantly on extensive English
data, often exhibit limitations when applied to other languages. Current
research is primarily focused on enhancing the multilingual capabilities of
these models by employing various tuning strategies. Despite their
effectiveness in certain languages, the understanding of the multilingual
abilities of LLMs remains incomplete. This study endeavors to evaluate the
multilingual capacity of LLMs by conducting an exhaustive analysis across 101
languages, and classifies languages with similar characteristics into four
distinct quadrants. By delving into each quadrant, we shed light on the
rationale behind their categorization and offer actionable guidelines for
tuning these languages. Extensive experiments reveal that existing LLMs possess
multilingual capabilities that surpass our expectations, and we can
significantly improve the multilingual performance of LLMs by focusing on these
distinct attributes present in each quadrant.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Verifiable Text Generation with Symbolic References
Abstract: Large language models (LLMs) have demonstrated an impressive ability to
synthesize plausible and fluent text. However they remain vulnerable to
hallucinations, and thus their outputs generally require manual human
verification for high-stakes applications, which can be time-consuming and
difficult. This paper proposes symbolically grounded generation (SymGen) as a
simple approach for enabling easier validation of an LLM's output. SymGen
prompts an LLM to interleave its regular output text with explicit symbolic
references to fields present in some conditioning data (e.g., a table in JSON
format). The references can be used to display the provenance of different
spans of text in the generation, reducing the effort required for manual
verification. Across data-to-text and question answering experiments, we find
that LLMs are able to directly output text that makes use of symbolic
references while maintaining fluency and accuracy.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Artificial Intelligence for reverse engineering: application to detergents using Raman spectroscopy
Abstract: The reverse engineering of a complex mixture, regardless of its nature, has
become significant today. Being able to quickly assess the potential toxicity
of new commercial products in relation to the environment presents a genuine
analytical challenge. The development of digital tools (databases,
chemometrics, machine learning, etc.) and analytical techniques (Raman
spectroscopy, NIR spectroscopy, mass spectrometry, etc.) will allow for the
identification of potential toxic molecules. In this article, we use the
example of detergent products, whose composition can prove dangerous to humans
or the environment, necessitating precise identification and quantification for
quality control and regulation purposes. The combination of various digital
tools (spectral database, mixture database, experimental design, Chemometrics /
Machine Learning algorithm{\ldots}) together with different sample preparation
methods (raw sample, or several concentrated / diluted samples) Raman
spectroscopy, has enabled the identification of the mixture's constituents and
an estimation of its composition. Implementing such strategies across different
analytical tools can result in time savings for pollutant identification and
contamination assessment in various matrices. This strategy is also applicable
in the industrial sector for product or raw material control, as well as for
quality control purposes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Abstract: Large language models (LLMs) provide excellent text-generation capabilities,
but standard prompting and generation methods generally do not lead to
intentional or goal-directed agents and might necessitate considerable prompt
tuning. This becomes particularly apparent in multi-turn conversations: even
the best current LLMs rarely ask clarifying questions, engage in explicit
information gathering, or take actions now that lead to better decisions after
multiple turns. Reinforcement learning has the potential to leverage the
powerful modeling capabilities of LLMs, as well as their internal
representation of textual interactions, to create capable goal-directed
language agents. This can enable intentional and temporally extended
interactions, such as with humans, through coordinated persuasion and carefully
crafted questions, or in goal-directed play through text games to bring about
desired final outcomes. However, enabling this requires the community to
develop stable and reliable reinforcement learning algorithms that can
effectively train LLMs. Developing such algorithms requires tasks that can
gauge progress on algorithm design, provide accessible and reproducible
evaluations for multi-turn interactions, and cover a range of task properties
and challenges in improving reinforcement learning algorithms. Our paper
introduces the LMRL-Gym benchmark for evaluating multi-turn RL for LLMs,
together with an open-source research framework containing a basic toolkit for
getting started on multi-turn RL with offline value-based and policy-based RL
methods. Our benchmark consists of 8 different language tasks, which require
multiple rounds of language interaction and cover a range of tasks in
open-ended dialogue and text games.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree transformation
Abstract: Large-scale language models have made great progress in the field of software
engineering in recent years. They can be used for many code-related tasks such
as code clone detection, code-to-code search, and method name prediction.
However, these large-scale language models based on each code token have
several drawbacks: They are usually large in scale, heavily dependent on
labels, and require a lot of computing power and time to fine-tune new
datasets.Furthermore, code embedding should be performed on the entire code
snippet rather than encoding each code token. The main reason for this is that
encoding each code token would cause model parameter inflation, resulting in a
lot of parameters storing information that we are not very concerned about. In
this paper, we propose a novel framework, called TransformCode, that learns
about code embeddings in a contrastive learning manner. The framework uses the
Transformer encoder as an integral part of the model. We also introduce a novel
data augmentation technique called abstract syntax tree transformation: This
technique applies syntactic and semantic transformations to the original code
snippets to generate more diverse and robust anchor samples. Our proposed
framework is both flexible and adaptable: It can be easily extended to other
downstream tasks that require code representation such as code clone detection
and classification. The framework is also very efficient and scalable: It does
not require a large model or a large amount of training data, and can support
any programming language.Finally, our framework is not limited to unsupervised
learning, but can also be applied to some supervised learning tasks by
incorporating task-specific labels or objectives. To explore the effectiveness
of our framework, we conducted extensive experiments on different software
engineering tasks using different programming languages and multiple datasets.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Generation of Games for Opponent Model Differentiation
Abstract: Protecting against adversarial attacks is a common multiagent problem.
Attackers in the real world are predominantly human actors, and the protection
methods often incorporate opponent models to improve the performance when
facing humans. Previous results show that modeling human behavior can
significantly improve the performance of the algorithms. However, modeling
humans correctly is a complex problem, and the models are often simplified and
assume humans make mistakes according to some distribution or train parameters
for the whole population from which they sample. In this work, we use data
gathered by psychologists who identified personality types that increase the
likelihood of performing malicious acts. However, in the previous work, the
tests on a handmade game could not show strategic differences between the
models. We created a novel model that links its parameters to psychological
traits. We optimized over parametrized games and created games in which the
differences are profound. Our work can help with automatic game generation when
we need a game in which some models will behave differently and to identify
situations in which the models do not align.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Emotion-Oriented Behavior Model Using Deep Learning
Abstract: Emotions, as a fundamental ingredient of any social interaction, lead to
behaviors that represent the effectiveness of the interaction through facial
expressions and gestures in humans. Hence an agent must possess the social and
cognitive abilities to understand human social parameters and behave
accordingly. However, no such emotion-oriented behavior model is presented yet
in the existing research. The emotion prediction may generate appropriate
agents' behaviors for effective interaction using conversation modality.
Considering the importance of emotions, and behaviors, for an agent's social
interaction, an Emotion-based Behavior model is presented in this paper for
Socio-cognitive artificial agents. The proposed model is implemented using
tweets data trained on multiple models like Long Short-Term Memory (LSTM),
Convolution Neural Network (CNN) and Bidirectional Encoder Representations from
Transformers (BERT) for emotion prediction with an average accuracy of 92%, and
55% respectively. Further, using emotion predictions from CNN-LSTM, the
behavior module responds using facial expressions and gestures using Behavioral
Markup Language (BML). The accuracy of emotion-based behavior predictions is
statistically validated using the 2-tailed Pearson correlation on the data
collected from human users through questionnaires. Analysis shows that all
emotion-based behaviors accurately depict human-like gestures and facial
expressions based on the significant correlation at the 0.01 and 0.05 levels.
This study is a steppingstone to a multi-faceted artificial agent interaction
based on emotion-oriented behaviors. Cognition has significance regarding
social interaction among humans.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery
Abstract: Built environment supports all the daily activities and shapes our health.
Leveraging informative street view imagery, previous research has established
the profound correlation between the built environment and chronic,
non-communicable diseases; however, predicting the exposure risk of infectious
diseases remains largely unexplored. The person-to-person contacts and
interactions contribute to the complexity of infectious disease, which is
inherently different from non-communicable diseases. Besides, the complex
relationships between street view imagery and epidemic exposure also hinder
accurate predictions. To address these problems, we construct a regional
mobility graph informed by the gravity model, based on which we propose a
transmission-aware graph convolutional network (GCN) to capture disease
transmission patterns arising from human mobility. Experiments show that the
proposed model significantly outperforms baseline models by 8.54% in weighted
F1, shedding light on a low-cost, scalable approach to assess epidemic exposure
risks from street view imagery.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
Abstract: This study addresses a gap in the utilization of Reinforcement Learning (RL)
and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing
Problem (SVRP) that involves the challenging task of optimizing vehicle routes
under uncertain conditions. We propose a novel end-to-end framework that
comprehensively addresses the key sources of stochasticity in SVRP and utilizes
an RL agent with a simple yet effective architecture and a tailored training
method. Through comparative analysis, our proposed model demonstrates superior
performance compared to a widely adopted state-of-the-art metaheuristic,
achieving a significant 3.43% reduction in travel costs. Furthermore, the model
exhibits robustness across diverse SVRP settings, highlighting its adaptability
and ability to learn optimal routing strategies in varying environments. The
publicly available implementation of our framework serves as a valuable
resource for future research endeavors aimed at advancing RL-based solutions
for SVRP.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: BClean: A Bayesian Data Cleaning System
Abstract: There is a considerable body of work on data cleaning which employs various
principles to rectify erroneous data and transform a dirty dataset into a
cleaner one. One of prevalent approaches is probabilistic methods, including
Bayesian methods. However, existing probabilistic methods often assume a
simplistic distribution (e.g., Gaussian distribution), which is frequently
underfitted in practice, or they necessitate experts to provide a complex prior
distribution (e.g., via a programming language). This requirement is both
labor-intensive and costly, rendering these methods less suitable for
real-world applications. In this paper, we propose BClean, a Bayesian Cleaning
system that features automatic Bayesian network construction and user
interaction. We recast the data cleaning problem as a Bayesian inference that
fully exploits the relationships between attributes in the observed dataset and
any prior information provided by users. To this end, we present an automatic
Bayesian network construction method that extends a structure learning-based
functional dependency discovery method with similarity functions to capture the
relationships between attributes. Furthermore, our system allows users to
modify the generated Bayesian network in order to specify prior information or
correct inaccuracies identified by the automatic generation process. We also
design an effective scoring model (called the compensative scoring model)
necessary for the Bayesian inference. To enhance the efficiency of data
cleaning, we propose several approximation strategies for the Bayesian
inference, including graph partitioning, domain pruning, and pre-detection. By
evaluating on both real-world and synthetic datasets, we demonstrate that
BClean is capable of achieving an F-measure of up to 0.9 in data cleaning,
outperforming existing Bayesian methods by 2% and other data cleaning methods
by 15%.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: The WHY in Business Processes: Discovery of Causal Execution Dependencies
Abstract: A crucial element in predicting the outcomes of process interventions and
making informed decisions about the process is unraveling the genuine
relationships between the execution of process activities. Contemporary process
discovery algorithms exploit time precedence as their main source of model
derivation. Such reliance can sometimes be deceiving from a causal perspective.
This calls for faithful new techniques to discover the true execution
dependencies among the tasks in the process. To this end, our work offers a
systematic approach to the unveiling of the true causal business process by
leveraging an existing causal discovery algorithm over activity timing. In
addition, this work delves into a set of conditions under which process mining
discovery algorithms generate a model that is incongruent with the causal
business process model, and shows how the latter model can be methodologically
employed for a sound analysis of the process. Our methodology searches for such
discrepancies between the two models in the context of three causal patterns,
and derives a new view in which these inconsistencies are annotated over the
mined process model. We demonstrate our methodology employing two open process
mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery
technique. We apply it on a synthesized dataset and on two open benchmark data
sets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
Abstract: Bayesian networks (BNs) are a foundational model in machine learning and
causal inference. Their graphical structure can handle high-dimensional
problems, divide-and-conquering them into a sparse collection of smaller ones;
underlies Judea Pearl's causality; and determines their explainability and
interpretability. Despite their popularity, there are few resources in the
literature on how to compute Shannon's entropy and the Kullback-Leibler (KL)
divergence for BNs under their most common distributional assumptions. In this
paper, we provide computationally efficient algorithms for both by leveraging
BNs' graphical structure, and we illustrate them with a complete set of
numerical examples. In the process, we show it is possible to reduce the
computational complexity of KL from cubic to quadratic for Gaussian BNs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
Abstract: Few-shot object detection, which focuses on detecting novel objects with few
labels, is an emerging challenge in the community. Recent studies show that
adapting a pre-trained model or modified loss function can improve performance.
In this paper, we explore leveraging the power of Contrastive Language-Image
Pre-training (CLIP) and hard negative classification loss in low data setting.
Specifically, we propose Re-scoring using Image-language Similarity for
Few-shot object detection (RISF) which extends Faster R-CNN by introducing
Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss
(BNRL). The former adapts CLIP, which performs zero-shot classification, to
re-score the classification scores of a detector using image-class
similarities, the latter is modified classification loss considering the
punishment for fake backgrounds as well as confusing categories on a
generalized few-shot object detection dataset. Extensive experiments on MS-COCO
and PASCAL VOC show that the proposed RISF substantially outperforms the
state-of-the-art approaches. The code will be available.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Abstract: Data annotated by humans is a source of knowledge by describing the
peculiarities of the problem and therefore fueling the decision process of the
trained model. Unfortunately, the annotation process for subjective natural
language processing (NLP) problems like offensiveness or emotion detection is
often very expensive and time-consuming. One of the inevitable risks is to
spend some of the funds and annotator effort on annotations that do not provide
any additional knowledge about the specific task. To minimize these costs, we
propose a new model-based approach that allows the selection of tasks annotated
individually for each text in a multi-task scenario. The experiments carried
out on three datasets, dozens of NLP tasks, and thousands of annotations show
that our method allows up to 40% reduction in the number of annotations with
negligible loss of knowledge. The results also emphasize the need to collect a
diverse amount of data required to efficiently train a model, depending on the
subjectivity of the annotation task. We also focused on measuring the relation
between subjective tasks by evaluating the model in single-task and multi-task
scenarios. Moreover, for some datasets, training only on the labels predicted
by our model improved the efficiency of task selection as a self-supervised
learning regularization technique.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67
Abstract: Breast cancer molecular subtypes classification plays an import role to sort
patients with divergent prognosis. The biomarkers used are Estrogen Receptor
(ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers
expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB),
HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is
used to classify subtypes, although interlaboratory and interobserver
variations can affect its accuracy, besides being a time-consuming technique.
The Fourier transform infrared micro-spectroscopy may be coupled with deep
learning for cancer evaluation, where there is still a lack of studies for
subtypes and biomarker levels prediction. This study presents a novel 2D deep
learning approach to achieve these predictions. Sixty micro-FTIR images of
320x320 pixels were collected from a human breast biopsies microarray. Data
were clustered by K-means, preprocessed and 32x32 patches were generated using
a fully automated approach. CaReNet-V2, a novel convolutional neural network,
was developed to classify breast cancer (CA) vs adjacent tissue (AT) and
molecular subtypes, and to predict biomarkers level. The clustering method
enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype
were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels,
where borderline values showed lower performance (minimum accuracy of 0.54).
Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2
is a potential technique for breast cancer biopsies evaluation, standing out as
a screening analysis technique and helping to prioritize patients.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
Abstract: Large Language Models (LLMs) have introduced a new era of proficiency in
comprehending complex healthcare and biomedical topics. However, there is a
noticeable lack of models in languages other than English and models that can
interpret multi-modal input, which is crucial for global healthcare
accessibility. In response, this study introduces Qilin-Med-VL, the first
Chinese large vision-language model designed to integrate the analysis of
textual and visual data. Qilin-Med-VL combines a pre-trained Vision Transformer
(ViT) with a foundational LLM. It undergoes a thorough two-stage curriculum
training process that includes feature alignment and instruction tuning. This
method enhances the model's ability to generate medical captions and answer
complex medical queries. We also release ChiMed-VL, a dataset consisting of
more than 1M image-text pairs. This dataset has been carefully curated to
enable detailed and comprehensive interpretation of medical data using various
types of images.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Emergence of Abstract State Representations in Embodied Sequence Modeling
Abstract: Decision making via sequence modeling aims to mimic the success of language
models, where actions taken by an embodied agent are modeled as tokens to
predict. Despite their promising performance, it remains unclear if embodied
sequence modeling leads to the emergence of internal representations that
represent the environmental state information. A model that lacks abstract
state representations would be liable to make decisions based on surface
statistics which fail to generalize. We take the BabyAI environment, a grid
world in which language-conditioned navigation tasks are performed, and build a
sequence modeling Transformer, which takes a language instruction, a sequence
of actions, and environmental observations as its inputs. In order to
investigate the emergence of abstract state representations, we design a
"blindfolded" navigation task, where only the initial environmental layout, the
language instruction, and the action sequence to complete the task are
available for training. Our probing results show that intermediate
environmental layouts can be reasonably reconstructed from the internal
activations of a trained model, and that language instructions play a role in
the reconstruction accuracy. Our results suggest that many key features of
state representations can emerge via embodied sequence modeling, supporting an
optimistic outlook for applications of sequence modeling objectives to more
complex embodied decision-making domains.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ChatGPT and post-test probability
Abstract: Reinforcement learning-based large language models, such as ChatGPT, are
believed to have potential to aid human experts in many domains, including
healthcare. There is, however, little work on ChatGPT's ability to perform a
key task in healthcare: formal, probabilistic medical diagnostic reasoning.
This type of reasoning is used, for example, to update a pre-test probability
to a post-test probability. In this work, we probe ChatGPT's ability to perform
this task. In particular, we ask ChatGPT to give examples of how to use Bayes
rule for medical diagnosis. Our prompts range from queries that use terminology
from pure probability (e.g., requests for a "posterior probability") to queries
that use terminology from the medical diagnosis literature (e.g., requests for
a "post-test probability"). We show how the introduction of medical variable
names leads to an increase in the number of errors that ChatGPT makes. Given
our results, we also show how one can use prompt engineering to facilitate
ChatGPT's partial avoidance of these errors. We discuss our results in light of
recent commentaries on sensitivity and specificity. We also discuss how our
results might inform new research directions for large language models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions
Abstract: Mathematical questioning is crucial for assessing students problem-solving
skills. Since manually creating such questions requires substantial effort,
automatic methods have been explored. Existing state-of-the-art models rely on
fine-tuning strategies and struggle to generate questions that heavily involve
multiple steps of logical and arithmetic reasoning. Meanwhile, large language
models(LLMs) such as ChatGPT have excelled in many NLP tasks involving logical
and arithmetic reasoning. Nonetheless, their applications in generating
educational questions are underutilized, especially in the field of
mathematics. To bridge this gap, we take the first step to conduct an in-depth
analysis of ChatGPT in generating pre-university math questions. Our analysis
is categorized into two main settings: context-aware and context-unaware. In
the context-aware setting, we evaluate ChatGPT on existing math
question-answering benchmarks covering elementary, secondary, and ternary
classes. In the context-unaware setting, we evaluate ChatGPT in generating math
questions for each lesson from pre-university math curriculums that we crawl.
Our crawling results in TopicMath, a comprehensive and novel collection of
pre-university math curriculums collected from 121 math topics and 428 lessons
from elementary, secondary, and tertiary classes. Through this analysis, we aim
to provide insight into the potential of ChatGPT as a math questioner.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Utilizing Language Models for Energy Load Forecasting
Abstract: Energy load forecasting plays a crucial role in optimizing resource
allocation and managing energy consumption in buildings and cities. In this
paper, we propose a novel approach that leverages language models for energy
load forecasting. We employ prompting techniques to convert energy consumption
data into descriptive sentences, enabling fine-tuning of language models. By
adopting an autoregressive generating approach, our proposed method enables
predictions of various horizons of future energy load consumption. Through
extensive experiments on real-world datasets, we demonstrate the effectiveness
and accuracy of our proposed method. Our results indicate that utilizing
language models for energy load forecasting holds promise for enhancing energy
efficiency and facilitating intelligent decision-making in energy systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: AdaDiff: Adaptive Step Selection for Fast Diffusion
Abstract: Diffusion models, as a type of generative models, have achieved impressive
results in generating images and videos conditioned on textual conditions.
However, the generation process of diffusion models involves denoising for
dozens of steps to produce photorealistic images/videos, which is
computationally expensive. Unlike previous methods that design
``one-size-fits-all'' approaches for speed up, we argue denoising steps should
be sample-specific conditioned on the richness of input texts. To this end, we
introduce AdaDiff, a lightweight framework designed to learn instance-specific
step usage policies, which are then used by the diffusion model for generation.
AdaDiff is optimized using a policy gradient method to maximize a carefully
designed reward function, balancing inference time and generation quality. We
conduct experiments on three image generation and two video generation
benchmarks and demonstrate that our approach achieves similar results in terms
of visual quality compared to the baseline using a fixed 50 denoising steps
while reducing inference time by at least 33%, going as high as 40%.
Furthermore, our qualitative analysis shows that our method allocates more
steps to more informative text conditions and fewer steps to simpler text
conditions.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
Abstract: This work investigates the potential of undermining both fairness and
detection performance in abusive language detection. In a dynamic and complex
digital world, it is crucial to investigate the vulnerabilities of these
detection models to adversarial fairness attacks to improve their fairness
robustness. We propose a simple yet effective framework FABLE that leverages
backdoor attacks as they allow targeted control over the fairness and detection
performance. FABLE explores three types of trigger designs (i.e., rare,
artificial, and natural triggers) and novel sampling strategies. Specifically,
the adversary can inject triggers into samples in the minority group with the
favored outcome (i.e., "non-abusive") and flip their labels to the unfavored
outcome, i.e., "abusive". Experiments on benchmark datasets demonstrate the
effectiveness of FABLE attacking fairness and utility in abusive language
detection.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality
Abstract: This paper introduces RACER, the Rational Artificial Intelligence
Car-following model Enhanced by Reality, a cutting-edge deep learning
car-following model, that satisfies partial derivative constraints, designed to
predict Adaptive Cruise Control (ACC) driving behavior while staying
theoretically feasible. Unlike conventional models, RACER effectively
integrates Rational Driving Constraints (RDCs), crucial tenets of actual
driving, resulting in strikingly accurate and realistic predictions. Against
established models like the Optimal Velocity Relative Velocity (OVRV), a
car-following Neural Network (NN), and a car-following Physics-Informed Neural
Network (PINN), RACER excels across key metrics, such as acceleration,
velocity, and spacing. Notably, it displays a perfect adherence to the RDCs,
registering zero violations, in stark contrast to other models. This study
highlights the immense value of incorporating physical constraints within AI
models, especially for augmenting safety measures in transportation. It also
paves the way for future research to test these models against human driving
data, with the potential to guide safer and more rational driving behavior. The
versatility of the proposed model, including its potential to incorporate
additional derivative constraints and broader architectural applications,
enhances its appeal and broadens its impact within the scientific community.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey
Abstract: Object detection and tracking are vital and fundamental tasks for autonomous
driving, aiming at identifying and locating objects from those predefined
categories in a scene. 3D point cloud learning has been attracting more and
more attention among all other forms of self-driving data. Currently, there are
many deep learning methods for 3D object detection. However, the tasks of
object detection and tracking for point clouds still need intensive study due
to the unique characteristics of point cloud data. To help get a good grasp of
the present situation of this research, this paper shows recent advances in
deep learning methods for 3D object detection and tracking.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models
Abstract: In the field of autonomous vehicles (AVs), accurately discerning commander
intent and executing linguistic commands within a visual context presents a
significant challenge. This paper introduces a sophisticated encoder-decoder
framework, developed to address visual grounding in AVs.Our Context-Aware
Visual Grounding (CAVG) model is an advanced system that integrates five core
encoders-Text, Image, Context, and Cross-Modal-with a Multimodal decoder. This
integration enables the CAVG model to adeptly capture contextual semantics and
to learn human emotional features, augmented by state-of-the-art Large Language
Models (LLMs) including GPT-4. The architecture of CAVG is reinforced by the
implementation of multi-head cross-modal attention mechanisms and a
Region-Specific Dynamic (RSD) layer for attention modulation. This
architectural design enables the model to efficiently process and interpret a
range of cross-modal inputs, yielding a comprehensive understanding of the
correlation between verbal commands and corresponding visual scenes. Empirical
evaluations on the Talk2Car dataset, a real-world benchmark, demonstrate that
CAVG establishes new standards in prediction accuracy and operational
efficiency. Notably, the model exhibits exceptional performance even with
limited training data, ranging from 50% to 75% of the full dataset. This
feature highlights its effectiveness and potential for deployment in practical
AV applications. Moreover, CAVG has shown remarkable robustness and
adaptability in challenging scenarios, including long-text command
interpretation, low-light conditions, ambiguous command contexts, inclement
weather conditions, and densely populated urban environments. The code for the
proposed model is available at our Github.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning
Abstract: Street-level bureaucrats interact directly with people on behalf of
government agencies to perform a wide range of functions, including, for
example, administering social services and policing. A key feature of
street-level bureaucracy is that the civil servants, while tasked with
implementing agency policy, are also granted significant discretion in how they
choose to apply that policy in individual cases. Using that discretion could be
beneficial, as it allows for exceptions to policies based on human interactions
and evaluations, but it could also allow biases and inequities to seep into
important domains of societal resource allocation. In this paper, we use
machine learning techniques to understand street-level bureaucrats' behavior.
We leverage a rich dataset that combines demographic and other information on
households with information on which homelessness interventions they were
assigned during a period when assignments were not formulaic. We find that
caseworker decisions in this time are highly predictable overall, and some, but
not all of this predictivity can be captured by simple decision rules. We
theorize that the decisions not captured by the simple decision rules can be
considered applications of caseworker discretion. These discretionary decisions
are far from random in both the characteristics of such households and in terms
of the outcomes of the decisions. Caseworkers typically only apply discretion
to households that would be considered less vulnerable. When they do apply
discretion to assign households to more intensive interventions, the marginal
benefits to those households are significantly higher than would be expected if
the households were chosen at random; there is no similar reduction in marginal
benefit to households that are discretionarily allocated less intensive
interventions, suggesting that caseworkers are improving outcomes using their
knowledge.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Augmenting deep neural networks with symbolic knowledge: Towards trustworthy and interpretable AI for education
Abstract: Artificial neural networks (ANNs) have shown to be amongst the most important
artificial intelligence (AI) techniques in educational applications, providing
adaptive educational services. However, their educational potential is limited
in practice due to three major challenges: i) difficulty in incorporating
symbolic educational knowledge (e.g., causal relationships, and practitioners'
knowledge) in their development, ii) learning and reflecting biases, and iii)
lack of interpretability. Given the high-risk nature of education, the
integration of educational knowledge into ANNs becomes crucial for developing
AI applications that adhere to essential educational restrictions, and provide
interpretability over the predictions. This research argues that the
neural-symbolic family of AI has the potential to address the named challenges.
To this end, it adapts a neural-symbolic AI framework and accordingly develops
an approach called NSAI, that injects and extracts educational knowledge into
and from deep neural networks, for modelling learners computational thinking.
Our findings reveal that the NSAI approach has better generalizability compared
to deep neural networks trained merely on training data, as well as training
data augmented by SMOTE and autoencoder methods. More importantly, unlike the
other models, the NSAI approach prioritises robust representations that capture
causal relationships between input features and output labels, ensuring safety
in learning to avoid spurious correlations and control biases in training data.
Furthermore, the NSAI approach enables the extraction of rules from the learned
network, facilitating interpretation and reasoning about the path to
predictions, as well as refining the initial educational knowledge. These
findings imply that neural-symbolic AI can overcome the limitations of ANNs in
education, enabling trustworthy and interpretable applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Evaluating Large Language Models through Gender and Racial Stereotypes
Abstract: Language Models have ushered a new age of AI gaining traction within the NLP
community as well as amongst the general population. AI's ability to make
predictions, generations and its applications in sensitive decision-making
scenarios, makes it even more important to study these models for possible
biases that may exist and that can be exaggerated. We conduct a quality
comparative study and establish a framework to evaluate language models under
the premise of two kinds of biases: gender and race, in a professional setting.
We find out that while gender bias has reduced immensely in newer models, as
compared to older ones, racial bias still exists.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context
Abstract: With the help of Chain-of-Thought (CoT) prompting, Large Language Models
(LLMs) have achieved remarkable performance on various reasoning tasks.
However, most of them have been evaluated under noise-free context and the
dilemma for LLMs to produce inaccurate results under the noisy context has not
been fully investigated. Existing studies utilize trigger sentences to
encourage LLMs to concentrate on the relevant information but the trigger has
limited effect on final answer prediction. Inspired by interactive CoT method,
where intermediate reasoning steps are promoted by multiple rounds of
interaction between users and LLMs, we propose a novel prompting method, namely
R$^3$ prompting, for CoT reasoning under noisy context. Specifically, R$^3$
prompting interacts with LLMs to perform key sentence extraction, variable
declaration and answer prediction, which corresponds to a thought process of
reviewing, rephrasing and resolving. The responses generated at the last
interaction will perform as hints to guide toward the responses of the next
interaction. Our experiments show that R$^3$ prompting significantly
outperforms existing CoT prompting methods on five reasoning tasks under noisy
context. With GPT-3.5-turbo, we observe 3.7% accuracy improvement on average on
the reasoning tasks under noisy context compared to the most competitive
prompting baseline. More analyses and ablation studies show the robustness and
generalization of R$^3$ prompting method in solving reasoning tasks in LLMs
under noisy context.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Knowledge-driven Autonomous Driving
Abstract: This paper explores the emerging knowledge-driven autonomous driving
technologies. Our investigation highlights the limitations of current
autonomous driving systems, in particular their sensitivity to data bias,
difficulty in handling long-tail scenarios, and lack of interpretability.
Conversely, knowledge-driven methods with the abilities of cognition,
generalization and life-long learning emerge as a promising way to overcome
these challenges. This paper delves into the essence of knowledge-driven
autonomous driving and examines its core components: dataset \& benchmark,
environment, and driver agent. By leveraging large language models, world
models, neural rendering, and other advanced artificial intelligence
techniques, these components collectively contribute to a more holistic,
adaptive, and intelligent autonomous driving system. The paper systematically
organizes and reviews previous research efforts in this area, and provides
insights and guidance for future research and practical applications of
autonomous driving. We will continually share the latest updates on
cutting-edge developments in knowledge-driven autonomous driving along with the
relevant valuable open-source resources at:
\url{https://github.com/PJLab-ADG/awesome-knowledge-driven-AD}.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder
Abstract: Despite its practical importance across a wide range of modalities, recent
advances in self-supervised learning (SSL) have been primarily focused on a few
well-curated domains, e.g., vision and language, often relying on their
domain-specific knowledge. For example, Masked Auto-Encoder (MAE) has become
one of the popular architectures in these domains, but less has explored its
potential in other modalities. In this paper, we develop MAE as a unified,
modality-agnostic SSL framework. In turn, we argue meta-learning as a key to
interpreting MAE as a modality-agnostic learner, and propose enhancements to
MAE from the motivation to jointly improve its SSL across diverse modalities,
coined MetaMAE as a result. Our key idea is to view the mask reconstruction of
MAE as a meta-learning task: masked tokens are predicted by adapting the
Transformer meta-learner through the amortization of unmasked tokens. Based on
this novel interpretation, we propose to integrate two advanced meta-learning
techniques. First, we adapt the amortized latent of the Transformer encoder
using gradient-based meta-learning to enhance the reconstruction. Then, we
maximize the alignment between amortized and adapted latents through task
contrastive learning which guides the Transformer encoder to better encode the
task-specific knowledge. Our experiment demonstrates the superiority of MetaMAE
in the modality-agnostic SSL benchmark (called DABS), significantly
outperforming prior baselines. Code is available at
https://github.com/alinlab/MetaMAE.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
Abstract: Collaborative perception has recently gained significant attention in
autonomous driving, improving perception quality by enabling the exchange of
additional information among vehicles. However, deploying collaborative
perception systems can lead to domain shifts due to diverse environmental
conditions and data heterogeneity among connected and autonomous vehicles
(CAVs). To address these challenges, we propose a unified domain generalization
framework applicable in both training and inference stages of collaborative
perception. In the training phase, we introduce an Amplitude Augmentation
(AmpAug) method to augment low-frequency image variations, broadening the
model's ability to learn across various domains. We also employ a
meta-consistency training scheme to simulate domain shifts, optimizing the
model with a carefully designed consistency loss to encourage domain-invariant
representations. In the inference phase, we introduce an intra-system domain
alignment mechanism to reduce or potentially eliminate the domain discrepancy
among CAVs prior to inference. Comprehensive experiments substantiate the
effectiveness of our method in comparison with the existing state-of-the-art
works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Code Ownership in Open-Source AI Software Security
Abstract: As open-source AI software projects become an integral component in the AI
software development, it is critical to develop a novel methods to ensure and
measure the security of the open-source projects for developers. Code
ownership, pivotal in the evolution of such projects, offers insights into
developer engagement and potential vulnerabilities. In this paper, we leverage
the code ownership metrics to empirically investigate the correlation with the
latent vulnerabilities across five prominent open-source AI software projects.
The findings from the large-scale empirical study suggest a positive
relationship between high-level ownership (characterised by a limited number of
minor contributors) and a decrease in vulnerabilities. Furthermore, we
innovatively introduce the time metrics, anchored on the project's duration,
individual source code file timelines, and the count of impacted releases.
These metrics adeptly categorise distinct phases of open-source AI software
projects and their respective vulnerability intensities. With these novel code
ownership metrics, we have implemented a Python-based command-line application
to aid project curators and quality assurance professionals in evaluating and
benchmarking their on-site projects. We anticipate this work will embark a
continuous research development for securing and measuring open-source AI
project security.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: C-Procgen: Empowering Procgen with Controllable Contexts
Abstract: We present C-Procgen, an enhanced suite of environments on top of the Procgen
benchmark. C-Procgen provides access to over 200 unique game contexts across 16
games. It allows for detailed configuration of environments, ranging from game
mechanics to agent attributes. This makes the procedural generation process,
previously a black-box in Procgen, more transparent and adaptable for various
research needs.The upgrade enhances dynamic context management and
individualized assignments, while maintaining computational efficiency.
C-Procgen's controllable contexts make it applicable in diverse reinforcement
learning research areas, such as learning dynamics analysis, curriculum
learning, and transfer learning. We believe that C-Procgen will fill a gap in
the current literature and offer a valuable toolkit for future works.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation
Abstract: This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech
Translation (AV2AV) framework, where the input and output of the system are
multimodal (i.e., audio and visual speech). With the proposed AV2AV, two key
advantages can be brought: 1) We can perform real-like conversations with
individuals worldwide in a virtual meeting by utilizing our own primary
languages. In contrast to Speech-to-Speech Translation (A2A), which solely
translates between audio modalities, the proposed AV2AV directly translates
between audio-visual speech. This capability enhances the dialogue experience
by presenting synchronized lip movements along with the translated speech. 2)
We can improve the robustness of the spoken language translation system. By
employing the complementary information of audio-visual speech, the system can
effectively translate spoken language even in the presence of acoustic noise,
showcasing robust performance. To mitigate the problem of the absence of a
parallel AV2AV translation dataset, we propose to train our spoken language
translation system with the audio-only dataset of A2A. This is done by learning
unified audio-visual speech representations through self-supervised learning in
advance to train the translation system. Moreover, we propose an AV-Renderer
that can generate raw audio and video in parallel. It is designed with
zero-shot speaker modeling, thus the speaker in source audio-visual speech can
be maintained at the target translated audio-visual speech. The effectiveness
of AV2AV is evaluated with extensive experiments in a many-to-many language
translation setting. The demo page is available on
https://choijeongsoo.github.io/av2av.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Rethinking Benchmark and Contamination for Language Models with Rephrased Samples
Abstract: Large language models are increasingly trained on all the data ever produced
by humans. Many have raised concerns about the trustworthiness of public
benchmarks due to potential contamination in pre-training or fine-tuning
datasets. While most data decontamination efforts apply string matching (e.g.,
n-gram overlap) to remove benchmark data, we show that these methods are
insufficient, and simple variations of test data (e.g., paraphrasing,
translation) can easily bypass these decontamination measures. Furthermore, we
demonstrate that if such variation of test data is not eliminated, a 13B model
can easily overfit a test benchmark and achieve drastically high performance,
on par with GPT-4. We validate such observations in widely used benchmarks such
as MMLU, GSK8k, and HumanEval. To address this growing risk, we propose a
stronger LLM-based decontamination method and apply it to widely used
pre-training and fine-tuning datasets, revealing significant previously unknown
test overlap. For example, in pre-training sets such as RedPajama-Data-1T and
StarCoder-Data, we identified that 8-18\% of the HumanEval benchmark overlaps.
Interestingly, we also find such contamination in synthetic dataset generated
by GPT-3.5/4, suggesting a potential risk of unintentional contamination. We
urge the community to adopt stronger decontamination approaches when using
public benchmarks. Moreover, we call for the community to actively develop
fresh one-time exams to evaluate models accurately. Our decontamination tool is
publicly available at https://github.com/lm-sys/llm-decontaminator.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Tell, don't show: Declarative facts influence how LLMs generalize
Abstract: We examine how large language models (LLMs) generalize from abstract
declarative statements in their training data. As an illustration, consider an
LLM that is prompted to generate weather reports for London in 2050. One
possibility is that the temperatures in the reports match the mean and variance
of reports from 2023 (i.e. matching the statistics of pretraining). Another
possibility is that the reports predict higher temperatures, by incorporating
declarative statements about climate change from scientific papers written in
2023. An example of such a declarative statement is "global temperatures will
increase by $1^{\circ} \mathrm{C}$ by 2050".
  To test the influence of abstract declarative statements, we construct tasks
in which LLMs are finetuned on both declarative and procedural information. We
find that declarative statements influence model predictions, even when they
conflict with procedural information. In particular, finetuning on a
declarative statement $S$ increases the model likelihood for logical
consequences of $S$. The effect of declarative statements is consistent across
three domains: aligning an AI assistant, predicting weather, and predicting
demographic features. Through a series of ablations, we show that the effect of
declarative statements cannot be explained by associative learning based on
matching keywords. Nevertheless, the effect of declarative statements on model
likelihoods is small in absolute terms and increases surprisingly little with
model size (i.e. from 330 million to 175 billion parameters). We argue that
these results have implications for AI risk (in relation to the "treacherous
turn") and for fairness.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Enhancing the Rationale-Input Alignment for Self-explaining Rationalization
Abstract: Rationalization empowers deep learning models with self-explaining
capabilities through a cooperative game, where a generator selects a
semantically consistent subset of the input as a rationale, and a subsequent
predictor makes predictions based on the selected rationale. In this paper, we
discover that rationalization is prone to a problem named \emph{rationale
shift}, which arises from the algorithmic bias of the cooperative game.
Rationale shift refers to a situation where the semantics of the selected
rationale may deviate from the original input, but the predictor still produces
accurate predictions based on the deviation, resulting in a compromised
generator with misleading feedback.
  To address this issue, we first demonstrate the importance of the alignment
between the rationale and the full input through both empirical observations
and theoretical analysis. Subsequently, we introduce a novel approach called
DAR (\textbf{D}iscriminatively \textbf{A}ligned \textbf{R}ationalization),
which utilizes an auxiliary module pretrained on the full input to
discriminatively align the selected rationale and the original input. We
theoretically illustrate how DAR accomplishes the desired alignment, thereby
overcoming the rationale shift problem. The experiments on two widely used
real-world benchmarks show that the proposed method significantly improves the
explanation quality (measured by the overlap between the model-selected
explanation and the human-annotated rationale) as compared to state-of-the-art
techniques. Additionally, results on two synthetic settings further validate
the effectiveness of DAR in addressing the rationale shift problem.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Enhancing Explainability in Mobility Data Science through a combination of methods
Abstract: In the domain of Mobility Data Science, the intricate task of interpreting
models trained on trajectory data, and elucidating the spatio-temporal movement
of entities, has persistently posed significant challenges. Conventional XAI
techniques, although brimming with potential, frequently overlook the distinct
structure and nuances inherent within trajectory data. Observing this
deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI
techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP
(SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct
trajectory visualization, and Permutation Feature Importance (PFI). Unlike
conventional strategies that deploy these methods singularly, our unified
approach capitalizes on the collective efficacy of these techniques, yielding
deeper and more granular insights for models reliant on trajectory data. In
crafting this synthesis, we effectively address the multifaceted essence of
trajectories, achieving not only amplified interpretability but also a nuanced,
contextually rich comprehension of model decisions. To validate and enhance our
framework, we undertook a survey to gauge preferences and reception among
various user demographics. Our findings underscored a dichotomy: professionals
with academic orientations, particularly those in roles like Data Scientist, IT
Expert, and ML Engineer, showcased a profound, technical understanding and
often exhibited a predilection for amalgamated methods for interpretability.
Conversely, end-users or individuals less acquainted with AI and Data Science
showcased simpler inclinations, such as bar plots indicating timestep
significance or visual depictions pinpointing pivotal segments of a vessel's
trajectory.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages
Abstract: Systematic reviews are vital for guiding practice, research, and policy, yet
they are often slow and labour-intensive. Large language models (LLMs) could
offer a way to speed up and automate systematic reviews, but their performance
in such tasks has not been comprehensively evaluated against humans, and no
study has tested GPT-4, the biggest LLM so far. This pre-registered study
evaluates GPT-4's capability in title/abstract screening, full-text review, and
data extraction across various literature types and languages using a
'human-out-of-the-loop' approach. Although GPT-4 had accuracy on par with human
performance in most tasks, results were skewed by chance agreement and dataset
imbalance. After adjusting for these, there was a moderate level of performance
for data extraction, and - barring studies that used highly reliable prompts -
screening performance levelled at none to moderate for different stages and
languages. When screening full-text literature using highly reliable prompts,
GPT-4's performance was 'almost perfect.' Penalising GPT-4 for missing key
studies using highly reliable prompts improved its performance even more. Our
findings indicate that, currently, substantial caution should be used if LLMs
are being used to conduct systematic reviews, but suggest that, for certain
systematic review tasks delivered under reliable prompts, LLMs can rival human
performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Hybrid Focal and Full-Range Attention Based Graph Transformers
Abstract: The paradigm of Transformers using the self-attention mechanism has
manifested its advantage in learning graph-structured data. Yet, Graph
Transformers are capable of modeling full range dependencies but are often
deficient in extracting information from locality. A common practice is to
utilize Message Passing Neural Networks (MPNNs) as an auxiliary to capture
local information, which however are still inadequate for comprehending
substructures. In this paper, we present a purely attention-based architecture,
namely Focal and Full-Range Graph Transformer (FFGT), which can mitigate the
loss of local information in learning global correlations. The core component
of FFGT is a new mechanism of compound attention, which combines the
conventional full-range attention with K-hop focal attention on ego-nets to
aggregate both global and local information. Beyond the scope of canonical
Transformers, the FFGT has the merit of being more substructure-aware. Our
approach enhances the performance of existing Graph Transformers on various
open datasets, while achieves compatible SOTA performance on several Long-Range
Graph Benchmark (LRGB) datasets even with a vanilla transformer. We further
examine influential factors on the optimal focal length of attention via
introducing a novel synthetic dataset based on SBM-PATTERN.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The Hyperdimensional Transform: a Holographic Representation of Functions
Abstract: Integral transforms are invaluable mathematical tools to map functions into
spaces where they are easier to characterize. We introduce the hyperdimensional
transform as a new kind of integral transform. It converts square-integrable
functions into noise-robust, holographic, high-dimensional representations
called hyperdimensional vectors. The central idea is to approximate a function
by a linear combination of random functions. We formally introduce a set of
stochastic, orthogonal basis functions and define the hyperdimensional
transform and its inverse. We discuss general transform-related properties such
as its uniqueness, approximation properties of the inverse transform, and the
representation of integrals and derivatives. The hyperdimensional transform
offers a powerful, flexible framework that connects closely with other integral
transforms, such as the Fourier, Laplace, and fuzzy transforms. Moreover, it
provides theoretical foundations and new insights for the field of
hyperdimensional computing, a computing paradigm that is rapidly gaining
attention for efficient and explainable machine learning algorithms, with
potential applications in statistical modelling and machine learning. In
addition, we provide straightforward and easily understandable code, which can
function as a tutorial and allows for the reproduction of the demonstrated
examples, from computing the transform to solving differential equations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: InteraSSort: Interactive Assortment Planning Using Large Language Models
Abstract: Assortment planning, integral to multiple commercial offerings, is a key
problem studied in e-commerce and retail settings. Numerous variants of the
problem along with their integration into business solutions have been
thoroughly investigated in the existing literature. However, the nuanced
complexities of in-store planning and a lack of optimization proficiency among
store planners with strong domain expertise remain largely overlooked. These
challenges frequently necessitate collaborative efforts with multiple
stakeholders which often lead to prolonged decision-making processes and
significant delays. To mitigate these challenges and capitalize on the
advancements of Large Language Models (LLMs), we propose an interactive
assortment planning framework, InteraSSort that augments LLMs with optimization
tools to assist store planners in making decisions through interactive
conversations. Specifically, we develop a solution featuring a user-friendly
interface that enables users to express their optimization objectives as input
text prompts to InteraSSort and receive tailored optimized solutions as output.
Our framework extends beyond basic functionality by enabling the inclusion of
additional constraints through interactive conversation, facilitating precise
and highly customized decision-making. Extensive experiments demonstrate the
effectiveness of our framework and potential extensions to a broad range of
operations management challenges.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation
Abstract: Neural knowledge-to-text generation models often struggle to faithfully
generate descriptions for the input facts: they may produce hallucinations that
contradict the given facts, or describe facts not present in the input. To
reduce hallucinations, we propose a novel decoding method, TWEAK (Think While
Effectively Articulating Knowledge). TWEAK treats the generated sequences at
each decoding step and its future sequences as hypotheses, and ranks each
generation candidate based on how well their corresponding hypotheses support
the input facts using a Hypothesis Verification Model (HVM). We first
demonstrate the effectiveness of TWEAK by using a Natural Language Inference
(NLI) model as the HVM and report improved faithfulness with minimal impact on
the quality. We then replace the NLI model with our task-specific HVM trained
with a first-of-a-kind dataset, FATE (Fact-Aligned Textual Entailment), which
pairs input facts with their faithful and hallucinated descriptions with the
hallucinated spans marked. The new HVM improves the faithfulness and the
quality further and runs faster. Overall the best TWEAK variants improve on
average 2.22/7.17 points on faithfulness measured by FactKB over WebNLG and
TekGen/GenWiki, respectively, with only 0.14/0.32 points degradation on quality
measured by BERTScore over the same datasets. Since TWEAK is a decoding-only
approach, it can be integrated with any neural generative model without
retraining.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: De-identification of clinical free text using natural language processing: A systematic review of current approaches
Abstract: Background: Electronic health records (EHRs) are a valuable resource for
data-driven medical research. However, the presence of protected health
information (PHI) makes EHRs unsuitable to be shared for research purposes.
De-identification, i.e. the process of removing PHI is a critical step in
making EHR data accessible. Natural language processing has repeatedly
demonstrated its feasibility in automating the de-identification process.
Objectives: Our study aims to provide systematic evidence on how the
de-identification of clinical free text has evolved in the last thirteen years,
and to report on the performances and limitations of the current
state-of-the-art systems. In addition, we aim to identify challenges and
potential research opportunities in this field. Methods: A systematic search in
PubMed, Web of Science and the DBLP was conducted for studies published between
January 2010 and February 2023. Titles and abstracts were examined to identify
the relevant studies. Selected studies were then analysed in-depth, and
information was collected on de-identification methodologies, data sources, and
measured performance. Results: A total of 2125 publications were identified for
the title and abstract screening. 69 studies were found to be relevant. Machine
learning (37 studies) and hybrid (26 studies) approaches are predominant, while
six studies relied only on rules. Majority of the approaches were trained and
evaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most
frequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016
CEGS N-GRID (10 studies) corpora.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Abstract: Accurate net load forecasting is vital for energy planning, aiding decisions
on trade and load distribution. However, assessing the performance of
forecasting models across diverse input variables, like temperature and
humidity, remains challenging, particularly for eliciting a high degree of
trust in the model outcomes. In this context, there is a growing need for
data-driven technological interventions to aid scientists in comprehending how
models react to both noisy and clean input variables, thus shedding light on
complex behaviors and fostering confidence in the outcomes. In this paper, we
present Forte, a visual analytics-based application to explore deep
probabilistic net load forecasting models across various input variables and
understand the error rates for different scenarios. With carefully designed
visual interventions, this web-based interface empowers scientists to derive
insights about model performance by simulating diverse scenarios, facilitating
an informed decision-making process. We discuss observations made using Forte
and demonstrate the effectiveness of visualization techniques to provide
valuable insights into the correlation between weather inputs and net load
forecasts, ultimately advancing grid capabilities by improving trust in
forecasting models.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Abstract: Video-based large language models (Video-LLMs) have been recently introduced,
targeting both fundamental improvements in perception and comprehension, and a
diverse range of user inquiries. In pursuit of the ultimate goal of achieving
artificial general intelligence, a truly intelligent Video-LLM model should not
only see and understand the surroundings, but also possess human-level
commonsense, and make well-informed decisions for the users. To guide the
development of such a model, the establishment of a robust and comprehensive
evaluation system becomes crucial. To this end, this paper proposes
\textit{Video-Bench}, a new comprehensive benchmark along with a toolkit
specifically designed for evaluating Video-LLMs. The benchmark comprises 10
meticulously crafted tasks, evaluating the capabilities of Video-LLMs across
three distinct levels: Video-exclusive Understanding, Prior Knowledge-based
Question-Answering, and Comprehension and Decision-making. In addition, we
introduce an automatic toolkit tailored to process model outputs for various
tasks, facilitating the calculation of metrics and generating convenient final
scores. We evaluate 8 representative Video-LLMs using \textit{Video-Bench}. The
findings reveal that current Video-LLMs still fall considerably short of
achieving human-like comprehension and analysis of real-world videos, offering
valuable insights for future research directions. The benchmark and toolkit are
available at: \url{https://github.com/PKU-YuanGroup/Video-Bench}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Traffic Sign Interpretation in Real Road Scene
Abstract: Most existing traffic sign-related works are dedicated to detecting and
recognizing part of traffic signs individually, which fails to analyze the
global semantic logic among signs and may convey inaccurate traffic
instruction. Following the above issues, we propose a traffic sign
interpretation (TSI) task, which aims to interpret global semantic interrelated
traffic signs (e.g.,~driving instruction-related texts, symbols, and guide
panels) into a natural language for providing accurate instruction support to
autonomous or assistant driving. Meanwhile, we design a multi-task learning
architecture for TSI, which is responsible for detecting and recognizing
various traffic signs and interpreting them into a natural language like a
human. Furthermore, the absence of a public TSI available dataset prompts us to
build a traffic sign interpretation dataset, namely TSI-CN. The dataset
consists of real road scene images, which are captured from the highway and the
urban way in China from a driver's perspective. It contains rich location
labels of texts, symbols, and guide panels, and the corresponding natural
language description labels. Experiments on TSI-CN demonstrate that the TSI
task is achievable and the TSI architecture can interpret traffic signs from
scenes successfully even if there is a complex semantic logic among signs. The
TSI-CN dataset and the source code of the TSI architecture will be publicly
available after the revision process.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Synthetic Speaking Children -- Why We Need Them and How to Make Them
Abstract: Contemporary Human Computer Interaction (HCI) research relies primarily on
neural network models for machine vision and speech understanding of a system
user. Such models require extensively annotated training datasets for optimal
performance and when building interfaces for users from a vulnerable population
such as young children, GDPR introduces significant complexities in data
collection, management, and processing. Motivated by the training needs of an
Edge AI smart toy platform this research explores the latest advances in
generative neural technologies and provides a working proof of concept of a
controllable data generation pipeline for speech driven facial training data at
scale. In this context, we demonstrate how StyleGAN2 can be finetuned to create
a gender balanced dataset of children's faces. This dataset includes a variety
of controllable factors such as facial expressions, age variations, facial
poses, and even speech-driven animations with realistic lip synchronization. By
combining generative text to speech models for child voice synthesis and a 3D
landmark based talking heads pipeline, we can generate highly realistic,
entirely synthetic, talking child video clips. These video clips can provide
valuable, and controllable, synthetic training data for neural network models,
bridging the gap when real data is scarce or restricted due to privacy
regulations.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: GPT-4V Takes the Wheel: Evaluating Promise and Challenges for Pedestrian Behavior Prediction
Abstract: Existing pedestrian behavior prediction methods rely primarily on deep neural
networks that utilize features extracted from video frame sequences. Although
these vision-based models have shown promising results, they face limitations
in effectively capturing and utilizing the dynamic spatio-temporal interactions
between the target pedestrian and its surrounding traffic elements, crucial for
accurate reasoning. Additionally, training these models requires manually
annotating domain-specific datasets, a process that is expensive,
time-consuming, and difficult to generalize to new environments and scenarios.
The recent emergence of Large Multimodal Models (LMMs) offers potential
solutions to these limitations due to their superior visual understanding and
causal reasoning capabilities, which can be harnessed through semi-supervised
training. GPT-4V(ision), the latest iteration of the state-of-the-art
Large-Language Model GPTs, now incorporates vision input capabilities. This
report provides a comprehensive evaluation of the potential of GPT-4V for
pedestrian behavior prediction in autonomous driving using publicly available
datasets: JAAD, PIE, and WiDEVIEW. Quantitative and qualitative evaluations
demonstrate GPT-4V(ision)'s promise in zero-shot pedestrian behavior prediction
and driving scene understanding ability for autonomous driving. However, it
still falls short of the state-of-the-art traditional domain-specific models.
Challenges include difficulties in handling small pedestrians and vehicles in
motion. These limitations highlight the need for further research and
development in this area.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LLMs-augmented Contextual Bandit
Abstract: Contextual bandits have emerged as a cornerstone in reinforcement learning,
enabling systems to make decisions with partial feedback. However, as contexts
grow in complexity, traditional bandit algorithms can face challenges in
adequately capturing and utilizing such contexts. In this paper, we propose a
novel integration of large language models (LLMs) with the contextual bandit
framework. By leveraging LLMs as an encoder, we enrich the representation of
the context, providing the bandit with a denser and more informative view.
Preliminary results on synthetic datasets demonstrate the potential of this
approach, showing notable improvements in cumulative rewards and reductions in
regret compared to traditional bandit algorithms. This integration not only
showcases the capabilities of LLMs in reinforcement learning but also opens the
door to a new era of contextually-aware decision systems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
Abstract: Communication networks able to withstand hostile environments are critically
important for disaster relief operations. In this paper, we consider a
challenging scenario where drones have been compromised in the supply chain,
during their manufacture, and harbour malicious software capable of
wide-ranging and infectious disruption. We investigate multi-agent deep
reinforcement learning as a tool for learning defensive strategies that
maximise communications bandwidth despite continual adversarial interference.
Using a public challenge for learning network resilience strategies, we propose
a state-of-the-art expert technique and study its superiority over deep
reinforcement learning agents. Correspondingly, we identify three specific
methods for improving the performance of our learning-based agents: (1)
ensuring each observation contains the necessary information, (2) using expert
agents to provide a curriculum for learning, and (3) paying close attention to
reward. We apply our methods and present a new mixed strategy enabling expert
and learning-based agents to work together and improve on all prior results.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Neural Markov Prolog
Abstract: The recent rapid advance of AI has been driven largely by innovations in
neural network architectures. A concomitant concern is how to understand these
resulting systems. In this paper, we propose a tool to assist in both the
design of further innovative architectures and the simple yet precise
communication of their structure. We propose the language Neural Markov Prolog
(NMP), based on both Markov logic and Prolog, as a means to both bridge first
order logic and neural network design and to allow for the easy generation and
presentation of architectures for images, text, relational databases, or other
target data types or their mixtures.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: FD-MIA: Efficient Attacks on Fairness-enhanced Models
Abstract: Previous studies have developed fairness methods for biased models that
exhibit discriminatory behaviors towards specific subgroups. While these models
have shown promise in achieving fair predictions, recent research has
identified their potential vulnerability to score-based membership inference
attacks (MIAs). In these attacks, adversaries can infer whether a particular
data sample was used during training by analyzing the model's prediction
scores. However, our investigations reveal that these score-based MIAs are
ineffective when targeting fairness-enhanced models in binary classifications.
The attack models trained to launch the MIAs degrade into simplistic threshold
models, resulting in lower attack performance. Meanwhile, we observe that
fairness methods often lead to prediction performance degradation for the
majority subgroups of the training data. This raises the barrier to successful
attacks and widens the prediction gaps between member and non-member data.
Building upon these insights, we propose an efficient MIA method against
fairness-enhanced models based on fairness discrepancy results (FD-MIA). It
leverages the difference in the predictions from both the original and
fairness-enhanced models and exploits the observed prediction gaps as attack
clues. We also explore potential strategies for mitigating privacy leakages.
Extensive experiments validate our findings and demonstrate the efficacy of the
proposed method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
Abstract: A popular framework for enforcing safe actions in Reinforcement Learning (RL)
is Constrained RL, where trajectory based constraints on expected cost (or
other cost measures) are employed to enforce safety and more importantly these
constraints are enforced while maximizing expected reward. Most recent
approaches for solving Constrained RL convert the trajectory based cost
constraint into a surrogate problem that can be solved using minor
modifications to RL methods. A key drawback with such approaches is an over or
underestimation of the cost constraint at each state. Therefore, we provide an
approach that does not modify the trajectory based cost constraint and instead
imitates ``good'' trajectories and avoids ``bad'' trajectories generated from
incrementally improving policies. We employ an oracle that utilizes a reward
threshold (which is varied with learning) and the overall cost constraint to
label trajectories as ``good'' or ``bad''. A key advantage of our approach is
that we are able to work from any starting policy or set of trajectories and
improve on it. In an exhaustive set of experiments, we demonstrate that our
approach is able to outperform top benchmark approaches for solving Constrained
RL problems, with respect to expected cost, CVaR cost, or even unknown cost
constraints.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape
Abstract: Neural Architecture Search (NAS) has become a widely used tool for automating
neural network design. While one-shot NAS methods have successfully reduced
computational requirements, they often require extensive training. On the other
hand, zero-shot NAS utilizes training-free proxies to evaluate a candidate
architecture's test performance but has two limitations: (1) inability to use
the information gained as a network improves with training and (2) unreliable
performance, particularly in complex domains like RecSys, due to the
multi-modal data inputs and complex architecture configurations. To synthesize
the benefits of both methods, we introduce a "sub-one-shot" paradigm that
serves as a bridge between zero-shot and one-shot NAS. In sub-one-shot NAS, the
supernet is trained using only a small subset of the training data, a phase we
refer to as "warm-up." Within this framework, we present SiGeo, a proxy founded
on a novel theoretical framework that connects the supernet warm-up with the
efficacy of the proxy. Extensive experiments have shown that SiGeo, with the
benefit of warm-up, consistently outperforms state-of-the-art NAS proxies on
various established NAS benchmarks. When a supernet is warmed up, it can
achieve comparable performance to weight-sharing one-shot NAS methods, but with
a significant reduction ($\sim 60$\%) in computational costs.

Output: Machine Learning