Instruction: What field is the article from?

Prompt:
Title: A Framework to Assess (Dis)agreement Among Diverse Rater Groups
Abstract: Recent advancements in conversational AI have created an urgent need for
safety guardrails that prevent users from being exposed to offensive and
dangerous content. Much of this work relies on human ratings and feedback, but
does not account for the fact that perceptions of offense and safety are
inherently subjective and that there may be systematic disagreements between
raters that align with their socio-demographic identities. Instead, current
machine learning approaches largely ignore rater subjectivity and use gold
standards that obscure disagreements (e.g., through majority voting). In order
to better understand the socio-cultural leanings of such tasks, we propose a
comprehensive disagreement analysis framework to measure systematic diversity
in perspectives among different rater subgroups. We then demonstrate its
utility by applying this framework to a dataset of human-chatbot conversations
rated by a demographically diverse pool of raters. Our analysis reveals
specific rater groups that have more diverse perspectives than the rest, and
informs demographic axes that are crucial to consider for safety annotations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Data-driven building energy efficiency prediction based on envelope heat losses using physics-informed neural networks
Abstract: The analytical prediction of building energy performance in residential
buildings based on the heat losses of its individual envelope components is a
challenging task. It is worth noting that this field is still in its infancy,
with relatively limited research conducted in this specific area to date,
especially when it comes for data-driven approaches. In this paper we introduce
a novel physics-informed neural network model for addressing this problem.
Through the employment of unexposed datasets that encompass general building
information, audited characteristics, and heating energy consumption, we feed
the deep learning model with general building information, while the model's
output consists of the structural components and several thermal properties
that are in fact the basic elements of an energy performance certificate (EPC).
On top of this neural network, a function, based on physics equations,
calculates the energy consumption of the building based on heat losses and
enhances the loss function of the deep learning model. This methodology is
tested on a real case study for 256 buildings located in Riga, Latvia. Our
investigation comes up with promising results in terms of prediction accuracy,
paving the way for automated, and data-driven energy efficiency performance
prediction based on basic properties of the building, contrary to exhaustive
energy efficiency audits led by humans, which are the current status quo.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Synthetic Data as Validation
Abstract: This study leverages synthetic data as a validation set to reduce overfitting
and ease the selection of the best model in AI development. While synthetic
data have been used for augmenting the training set, we find that synthetic
data can also significantly diversify the validation set, offering marked
advantages in domains like healthcare, where data are typically limited,
sensitive, and from out-domain sources (i.e., hospitals). In this study, we
illustrate the effectiveness of synthetic data for early cancer detection in
computed tomography (CT) volumes, where synthetic tumors are generated and
superimposed onto healthy organs, thereby creating an extensive dataset for
rigorous validation. Using synthetic data as validation can improve AI
robustness in both in-domain and out-domain test sets. Furthermore, we
establish a new continual learning framework that continuously trains AI models
on a stream of out-domain data with synthetic tumors. The AI model trained and
validated in dynamically expanding synthetic data can consistently outperform
models trained and validated exclusively on real-world data. Specifically, the
DSC score for liver tumor segmentation improves from 26.7% (95% CI:
22.6%-30.9%) to 34.5% (30.8%-38.2%) when evaluated on an in-domain dataset and
from 31.1% (26.0%-36.2%) to 35.4% (32.1%-38.7%) on an out-domain dataset.
Importantly, the performance gain is particularly significant in identifying
very tiny liver tumors (radius < 5mm) in CT volumes, with Sensitivity improving
from 33.1% to 55.4% on an in-domain dataset and 33.9% to 52.3% on an out-domain
dataset, justifying the efficacy in early detection of cancer. The application
of synthetic data, from both training and validation perspectives, underlines a
promising avenue to enhance AI robustness when dealing with data from varying
domains.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Challenges of Large Language Models for Mental Health Counseling
Abstract: The global mental health crisis is looming with a rapid increase in mental
disorders, limited resources, and the social stigma of seeking treatment. As
the field of artificial intelligence (AI) has witnessed significant
advancements in recent years, large language models (LLMs) capable of
understanding and generating human-like text may be used in supporting or
providing psychological counseling. However, the application of LLMs in the
mental health domain raises concerns regarding the accuracy, effectiveness, and
reliability of the information provided. This paper investigates the major
challenges associated with the development of LLMs for psychological
counseling, including model hallucination, interpretability, bias, privacy, and
clinical effectiveness. We explore potential solutions to these challenges that
are practical and applicable to the current paradigm of AI. From our experience
in developing and deploying LLMs for mental health, AI holds a great promise
for improving mental health care, if we can carefully navigate and overcome
pitfalls of LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A method for recovery of multidimensional time series based on the detection of behavioral patterns and the use of autoencoders
Abstract: This article presents a method for recovering missing values in
multidimensional time series. The method combines neural network technologies
and an algorithm for searching snippets (behavioral patterns of a time series).
It includes the stages of data preprocessing, recognition and reconstruction,
using convolutional and recurrent neural networks. Experiments have shown high
accuracy of recovery and the advantage of the method over SOTA methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
Abstract: Proactive dialogues serve as a practical yet challenging dialogue problem in
the era of large language models (LLMs), where the dialogue policy planning is
the key to improving the proactivity of LLMs. Most existing studies enable the
dialogue policy planning of LLMs using various prompting schemes or iteratively
enhance this capability in handling the given case with verbal AI feedback.
However, these approaches are either bounded by the policy planning capability
of the frozen LLMs or hard to be transferred to new cases. In this work, we
introduce a new dialogue policy planning paradigm to strategize LLMs for
proactive dialogue problems with a tunable language model plug-in as a
plug-and-play dialogue policy planner, named PPDPP. Specifically, we develop a
novel training framework to facilitate supervised fine-tuning over available
human-annotated data as well as reinforcement learning from goal-oriented AI
feedback with dynamic interaction data collected by the LLM-based self-play
simulation. In this manner, the LLM-powered dialogue agent can not only be
generalized to different cases after the training, but also be applicable to
different applications by just substituting the learned plug-in. In addition,
we propose to evaluate the policy planning capability of dialogue systems under
the interactive setting. Experimental results demonstrate that PPDPP
consistently and substantially outperforms existing approaches on three
different proactive dialogue applications, including negotiation, emotional
support, and tutoring dialogues.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: How to Configure Good In-Context Sequence for Visual Question Answering
Abstract: Inspired by the success of Large Language Models in dealing with new tasks
via In-Context Learning (ICL) in NLP, researchers have also developed Large
Vision-Language Models (LVLMs) with ICL capabilities. However, when
implementing ICL using these LVLMs, researchers usually resort to the simplest
way like random sampling to configure the in-context sequence, thus leading to
sub-optimal results. To enhance the ICL performance, in this study, we use
Visual Question Answering (VQA) as case study to explore diverse in-context
configurations to find the powerful ones. Additionally, through observing the
changes of the LVLM outputs by altering the in-context sequence, we gain
insights into the inner properties of LVLMs, improving our understanding of
them. Specifically, to explore in-context configurations, we design diverse
retrieval methods and employ different strategies to manipulate the retrieved
demonstrations. Through exhaustive experiments on three VQA datasets: VQAv2,
VizWiz, and OK-VQA, we uncover three important inner properties of the applied
LVLM and demonstrate which strategies can consistently improve the ICL VQA
performance. Our code is provided in:
https://github.com/GaryJiajia/OFv2_ICL_VQA.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
Abstract: With the advent of large language models and large-scale robotic datasets,
there has been tremendous progress in high-level decision-making for object
manipulation. These generic models are able to interpret complex tasks using
language commands, but they often have difficulties generalizing to
out-of-distribution objects due to the inability of low-level action
primitives. In contrast, existing task-specific models excel in low-level
manipulation of unknown objects, but only work for a single type of action. To
bridge this gap, we present M2T2, a single model that supplies different types
of low-level actions that work robustly on arbitrary objects in cluttered
scenes. M2T2 is a transformer model which reasons about contact points and
predicts valid gripper poses for different action modes given a raw point cloud
of the scene. Trained on a large-scale synthetic dataset with 128K scenes, M2T2
achieves zero-shot sim2real transfer on the real robot, outperforming the
baseline system with state-of-the-art task-specific models by about 19% in
overall performance and 37.5% in challenging scenes where the object needs to
be re-oriented for collision-free placement. M2T2 also achieves
state-of-the-art results on a subset of language conditioned tasks in RLBench.
Videos of robot experiments on unseen objects in both real world and simulation
are available on our project website https://m2-t2.github.io.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning
Abstract: In urban planning, land use readjustment plays a pivotal role in aligning
land use configurations with the current demands for sustainable urban
development. However, present-day urban planning practices face two main
issues. Firstly, land use decisions are predominantly dependent on human
experts. Besides, while resident engagement in urban planning can promote urban
sustainability and livability, it is challenging to reconcile the diverse
interests of stakeholders. To address these challenges, we introduce a
Consensus-based Multi-Agent Reinforcement Learning framework for real-world
land use readjustment. This framework serves participatory urban planning,
allowing diverse intelligent agents as stakeholder representatives to vote for
preferred land use types. Within this framework, we propose a novel consensus
mechanism in reward design to optimize land utilization through collective
decision making. To abstract the structure of the complex urban system, the
geographic information of cities is transformed into a spatial graph structure
and then processed by graph neural networks. Comprehensive experiments on both
traditional top-down planning and participatory planning methods from
real-world communities indicate that our computational framework enhances
global benefits and accommodates diverse interests, leading to improved
satisfaction across different demographic groups. By integrating Multi-Agent
Reinforcement Learning, our framework ensures that participatory urban planning
decisions are more dynamic and adaptive to evolving community needs and
provides a robust platform for automating complex real-world urban planning
processes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Data Acquisition: A New Frontier in Data-centric AI
Abstract: As Machine Learning (ML) systems continue to grow, the demand for relevant
and comprehensive datasets becomes imperative. There is limited study on the
challenges of data acquisition due to ad-hoc processes and lack of consistent
methodologies. We first present an investigation of current data marketplaces,
revealing lack of platforms offering detailed information about datasets,
transparent pricing, standardized data formats. With the objective of inciting
participation from the data-centric AI community, we then introduce the DAM
challenge, a benchmark to model the interaction between the data providers and
acquirers. The benchmark was released as a part of DataPerf. Our evaluation of
the submitted strategies underlines the need for effective data acquisition
strategies in ML.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: SENetV2: Aggregated dense layer for channelwise and global representations
Abstract: Convolutional Neural Networks (CNNs) have revolutionized image classification
by extracting spatial features and enabling state-of-the-art accuracy in
vision-based tasks. The squeeze and excitation network proposed module gathers
channelwise representations of the input. Multilayer perceptrons (MLP) learn
global representation from the data and in most image classification models
used to learn extracted features of the image. In this paper, we introduce a
novel aggregated multilayer perceptron, a multi-branch dense layer, within the
Squeeze excitation residual module designed to surpass the performance of
existing architectures. Our approach leverages a combination of squeeze
excitation network module with dense layers. This fusion enhances the network's
ability to capture channel-wise patterns and have global knowledge, leading to
a better feature representation. This proposed model has a negligible increase
in parameters when compared to SENet. We conduct extensive experiments on
benchmark datasets to validate the model and compare them with established
architectures. Experimental results demonstrate a remarkable increase in the
classification accuracy of the proposed model.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems
Abstract: Industrial Cyber-Physical Systems (ICPS) integrate the disciplines of
computer science, communication technology, and engineering, and have emerged
as integral components of contemporary manufacturing and industries. However,
ICPS encounters various challenges in long-term operation, including equipment
failures, performance degradation, and security threats. To achieve efficient
maintenance and management, prognostics and health management (PHM) finds
widespread application in ICPS for critical tasks, including failure
prediction, health monitoring, and maintenance decision-making. The emergence
of large-scale foundation models (LFMs) like BERT and GPT signifies a
significant advancement in AI technology, and ChatGPT stands as a remarkable
accomplishment within this research paradigm, harboring potential for General
Artificial Intelligence. Considering the ongoing enhancement in data
acquisition technology and data processing capability, LFMs are anticipated to
assume a crucial role in the PHM domain of ICPS. However, at present, a
consensus is lacking regarding the application of LFMs to PHM in ICPS,
necessitating systematic reviews and roadmaps to elucidate future directions.
To bridge this gap, this paper elucidates the key components and recent
advances in the underlying model.A comprehensive examination and comprehension
of the latest advances in grand modeling for PHM in ICPS can offer valuable
references for decision makers and researchers in the industrial field while
facilitating further enhancements in the reliability, availability, and safety
of ICPS.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Abstract: Identifying and resolving logic errors can be one of the most frustrating
challenges for novices programmers. Unlike syntax errors, for which a compiler
or interpreter can issue a message, logic errors can be subtle. In certain
conditions, buggy code may even exhibit correct behavior -- in other cases, the
issue might be about how a problem statement has been interpreted. Such errors
can be hard to spot when reading the code, and they can also at times be missed
by automated tests. There is great educational potential in automatically
detecting logic errors, especially when paired with suitable feedback for
novices. Large language models (LLMs) have recently demonstrated surprising
performance for a range of computing tasks, including generating and explaining
code. These capabilities are closely linked to code syntax, which aligns with
the next token prediction behavior of LLMs. On the other hand, logic errors
relate to the runtime performance of code and thus may not be as well suited to
analysis by LLMs. To explore this, we investigate the performance of two
popular LLMs, GPT-3 and GPT-4, for detecting and providing a novice-friendly
explanation of logic errors. We compare LLM performance with a large cohort of
introductory computing students $(n=964)$ solving the same error detection
task. Through a mixed-methods analysis of student and model responses, we
observe significant improvement in logic error identification between the
previous and current generation of LLMs, and find that both LLM generations
significantly outperform students. We outline how such models could be
integrated into computing education tools, and discuss their potential for
supporting students when learning programming.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Abstract: Reinforcement learning (RL) with dense rewards and imitation learning (IL)
with human-generated trajectories are the most widely used approaches for
training modern embodied agents. RL requires extensive reward shaping and
auxiliary losses and is often too slow and ineffective for long-horizon tasks.
While IL with human supervision is effective, collecting human trajectories at
scale is extremely expensive. In this work, we show that imitating
shortest-path planners in simulation produces agents that, given a language
instruction, can proficiently navigate, explore, and manipulate objects in both
simulation and in the real world using only RGB sensors (no depth map or GPS
coordinates). This surprising result is enabled by our end-to-end,
transformer-based, SPOC architecture, powerful visual encoders paired with
extensive image augmentation, and the dramatic scale and diversity of our
training data: millions of frames of shortest-path-expert trajectories
collected inside approximately 200,000 procedurally generated houses containing
40,000 unique 3D assets. Our models, data, training code, and newly proposed
10-task benchmarking suite CHORES will be open-sourced.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Guarding Barlow Twins Against Overfitting with Mixed Samples
Abstract: Self-supervised Learning (SSL) aims to learn transferable feature
representations for downstream applications without relying on labeled data.
The Barlow Twins algorithm, renowned for its widespread adoption and
straightforward implementation compared to its counterparts like contrastive
learning methods, minimizes feature redundancy while maximizing invariance to
common corruptions. Optimizing for the above objective forces the network to
learn useful representations, while avoiding noisy or constant features,
resulting in improved downstream task performance with limited adaptation.
Despite Barlow Twins' proven effectiveness in pre-training, the underlying SSL
objective can inadvertently cause feature overfitting due to the lack of strong
interaction between the samples unlike the contrastive learning approaches.
From our experiments, we observe that optimizing for the Barlow Twins objective
doesn't necessarily guarantee sustained improvements in representation quality
beyond a certain pre-training phase, and can potentially degrade downstream
performance on some datasets. To address this challenge, we introduce Mixed
Barlow Twins, which aims to improve sample interaction during Barlow Twins
training via linearly interpolated samples. This results in an additional
regularization term to the original Barlow Twins objective, assuming linear
interpolation in the input space translates to linearly interpolated features
in the feature space. Pre-training with this regularization effectively
mitigates feature overfitting and further enhances the downstream performance
on CIFAR-10, CIFAR-100, TinyImageNet, STL-10, and ImageNet datasets. The code
and checkpoints are available at: https://github.com/wgcban/mix-bt.git

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series
Abstract: Multi-modal biomedical time series (MBTS) data offers a holistic view of the
physiological state, holding significant importance in various bio-medical
applications. Owing to inherent noise and distribution gaps across different
modalities, MBTS can be complex to model. Various deep learning models have
been developed to learn representations of MBTS but still fall short in
robustness due to the ignorance of modal-to-modal variations. This paper
presents a multi-scale and multi-modal biomedical time series representation
learning (MBSL) network with contrastive learning to migrate these variations.
Firstly, MBTS is grouped based on inter-modal distances, then each group with
minimum intra-modal variations can be effectively modeled by individual
encoders. Besides, to enhance the multi-scale feature extraction (encoder),
various patch lengths and mask ratios are designed to generate tokens with
semantic information at different scales and diverse contextual perspectives
respectively. Finally, cross-modal contrastive learning is proposed to maximize
consistency among inter-modal groups, maintaining useful information and
eliminating noises. Experiments against four bio-medical applications show that
MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in
respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in
human activity recognition, and by 1.14% F1-score in obstructive sleep
apnea-hypopnea syndrome.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Abstract: LLaVA-Interactive is a research prototype for multimodal human-AI
interaction. The system can have multi-turn dialogues with human users by
taking multimodal user inputs and generating multimodal responses. Importantly,
LLaVA-Interactive goes beyond language prompt, where visual prompt is enabled
to align human intents in the interaction. The development of LLaVA-Interactive
is extremely cost-efficient as the system combines three multimodal skills of
pre-built AI models without additional model training: visual chat of LLaVA,
image segmentation from SEEM, as well as image generation and editing from
GLIGEN. A diverse set of application scenarios is presented to demonstrate the
promises of LLaVA-Interactive and to inspire future research in multimodal
interactive systems.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Identifying Spurious Correlations using Counterfactual Alignment
Abstract: Models driven by spurious correlations often yield poor generalization
performance. We propose the counterfactual alignment method to detect and
explore spurious correlations of black box classifiers. Counterfactual images
generated with respect to one classifier can be input into other classifiers to
see if they also induce changes in the outputs of these classifiers. The
relationship between these responses can be quantified and used to identify
specific instances where a spurious correlation exists as well as compute
aggregate statistics over a dataset. Our work demonstrates the ability to
detect spurious correlations in face attribute classifiers. This is validated
by observing intuitive trends in a face attribute classifier as well as
fabricating spurious correlations and detecting their presence, both visually
and quantitatively. Further, utilizing the CF alignment method, we demonstrate
that we can rectify spurious correlations identified in classifiers.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: FOCAL: A Cost-Aware Video Dataset for Active Learning
Abstract: In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active
Learning) dataset which enables the study of the impact of annotation-cost
within a video active learning setting. Annotation-cost refers to the time it
takes an annotator to label and quality-assure a given video sequence. A
practical motivation for active learning research is to minimize
annotation-cost by selectively labeling informative samples that will maximize
performance within a given budget constraint. However, previous work in video
active learning lacks real-time annotation labels for accurately assessing cost
minimization and instead operates under the assumption that annotation-cost
scales linearly with the amount of data to annotate. This assumption does not
take into account a variety of real-world confounding factors that contribute
to a nonlinear cost such as the effect of an assistive labeling tool and the
variety of interactions within a scene such as occluded objects, weather, and
motion of objects. FOCAL addresses this discrepancy by providing real
annotation-cost labels for 126 video sequences across 69 unique city scenes
with a variety of weather, lighting, and seasonal conditions. We also introduce
a set of conformal active learning algorithms that take advantage of the
sequential structure of video data in order to achieve a better trade-off
between annotation-cost and performance while also reducing floating point
operations (FLOPS) overhead by at least 77.67%. We show how these approaches
better reflect how annotations on videos are done in practice through a
sequence selection framework. We further demonstrate the advantage of these
approaches by introducing two performance-cost metrics and show that the best
conformal active learning method is cheaper than the best traditional active
learning method by 113 hours.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Conformal Prediction in Multi-User Settings: An Evaluation
Abstract: Typically, machine learning models are trained and evaluated without making
any distinction between users (e.g, using traditional hold-out and
cross-validation). However, this produces inaccurate performance metrics
estimates in multi-user settings. That is, situations where the data were
collected by multiple users with different characteristics (e.g., age, gender,
height, etc.) which is very common in user computer interaction and medical
applications. For these types of scenarios model evaluation strategies that
provide better performance estimates have been proposed such as mixed,
user-independent, user-dependent, and user-adaptive models. Although those
strategies are better suited for multi-user systems, they are typically
assessed with respect to performance metrics that capture the overall behavior
of the models and do not provide any performance guarantees for individual
predictions nor they provide any feedback about the predictions' uncertainty.
In order to overcome those limitations, in this work we evaluated the conformal
prediction framework in several multi-user settings. Conformal prediction is a
model agnostic method that provides confidence guarantees on the predictions,
thus, increasing the trustworthiness and robustness of the models. We conducted
extensive experiments using different evaluation strategies and found
significant differences in terms of conformal performance measures. We also
proposed several visualizations based on matrices, graphs, and charts that
capture different aspects of the resulting prediction sets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights
Abstract: In this study, we investigate the potential of Large Language Models to
complement biomedical knowledge graphs in the training of semantic models for
the biomedical and clinical domains. Drawing on the wealth of the UMLS
knowledge graph and harnessing cutting-edge Large Language Models, we propose a
new state-of-the-art approach for obtaining high-fidelity representations of
biomedical concepts and sentences, consisting of three steps: an improved
contrastive learning phase, a novel self-distillation phase, and a weight
averaging phase. Through rigorous evaluations via the extensive BioLORD testing
suite and diverse downstream tasks, we demonstrate consistent and substantial
performance improvements over the previous state of the art (e.g. +2pts on
MedSTS, +2.5pts on MedNLI-S, +6.1pts on EHR-Rel-B). Besides our new
state-of-the-art biomedical model for English, we also distill and release a
multilingual model compatible with 50+ languages and finetuned on 7 European
languages. Many clinical pipelines can benefit from our latest models. Our new
multilingual model enables a range of languages to benefit from our
advancements in biomedical semantic representation learning, opening a new
avenue for bioinformatics researchers around the world. As a result, we hope to
see BioLORD-2023 becoming a precious tool for future biomedical applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in
various tasks. However, their suitability for domain-specific tasks, is limited
due to their immense scale at deployment, susceptibility to misinformation, and
more importantly, high data annotation costs. We propose a novel Interactive
Multi-Fidelity Learning (IMFL) framework for the cost-effective development of
small domain-specific LMs under limited annotation budgets. Our approach
formulates the domain-specific fine-tuning process as a multi-fidelity learning
problem, focusing on identifying the optimal acquisition strategy that balances
between low-fidelity automatic LLM annotations and high-fidelity human
annotations to maximize model performance. We further propose an
exploration-exploitation query strategy that enhances annotation diversity and
informativeness, incorporating two innovative designs: 1) prompt retrieval that
selects in-context examples from human-annotated samples to improve LLM
annotation, and 2) variable batch size that controls the order for choosing
each fidelity to facilitate knowledge distillation, ultimately enhancing
annotation quality. Extensive experiments on financial and medical tasks
demonstrate that IMFL achieves superior performance compared with single
fidelity annotations. Given a limited budget of human annotation, IMFL
significantly outperforms the human annotation baselines in all four tasks and
achieves very close performance as human annotations on two of the tasks. These
promising results suggest that the high human annotation costs in
domain-specific tasks can be significantly reduced by employing IMFL, which
utilizes fewer human annotations, supplemented with cheaper and faster LLM
(e.g., GPT-3.5) annotations to achieve comparable performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
Abstract: Climate downscaling is a crucial technique within climate research, serving
to project low-resolution (LR) climate data to higher resolutions (HR).
Previous research has demonstrated the effectiveness of deep learning for
downscaling tasks. However, most deep learning models for climate downscaling
may not perform optimally for high scaling factors (i.e., 4x, 8x) due to their
limited ability to capture the intricate details required for generating HR
climate data. Furthermore, climate data behaves differently from image data,
necessitating a nuanced approach when employing deep generative models. In
response to these challenges, this paper presents a deep generative model for
downscaling climate data, specifically precipitation on a regional scale. We
employ a denoising diffusion probabilistic model (DDPM) conditioned on multiple
LR climate variables. The proposed model is evaluated using precipitation data
from the Community Earth System Model (CESM) v1.2.2 simulation. Our results
demonstrate significant improvements over existing baselines, underscoring the
effectiveness of the conditional diffusion model in downscaling climate data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: HADES: Fast Singularity Detection with Local Measure Comparison
Abstract: We introduce Hades, an unsupervised algorithm to detect singularities in
data. This algorithm employs a kernel goodness-of-fit test, and as a
consequence it is much faster and far more scaleable than the existing
topology-based alternatives. Using tools from differential geometry and optimal
transport theory, we prove that Hades correctly detects singularities with high
probability when the data sample lives on a transverse intersection of
equidimensional manifolds. In computational experiments, Hades recovers
singularities in synthetically generated data, branching points in road network
data, intersection rings in molecular conformation space, and anomalies in
image data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: TrackDiffusion: Multi-object Tracking Data Generation via Diffusion Models
Abstract: Diffusion models have gained prominence in generating data for perception
tasks such as image classification and object detection. However, the potential
in generating high-quality tracking sequences, a crucial aspect in the field of
video perception, has not been fully investigated. To address this gap, we
propose TrackDiffusion, a novel architecture designed to generate continuous
video sequences from the tracklets. TrackDiffusion represents a significant
departure from the traditional layout-to-image (L2I) generation and copy-paste
synthesis focusing on static image elements like bounding boxes by empowering
image diffusion models to encompass dynamic and continuous tracking
trajectories, thereby capturing complex motion nuances and ensuring instance
consistency among video frames. For the first time, we demonstrate that the
generated video sequences can be utilized for training multi-object tracking
(MOT) systems, leading to significant improvement in tracker performance.
Experimental results show that our model significantly enhances instance
consistency in generated video sequences, leading to improved perceptual
metrics. Our approach achieves an improvement of 8.7 in TrackAP and 11.8 in
TrackAP$_{50}$ on the YTVIS dataset, underscoring its potential to redefine the
standards of video data generation for MOT tasks and beyond.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Improving Minority Stress Detection with Emotions
Abstract: Psychological stress detection is an important task for mental healthcare
research, but there has been little prior work investigating the effectiveness
of psychological stress models on minority individuals, who are especially
vulnerable to poor mental health outcomes. In this work, we use the related
task of minority stress detection to evaluate the ability of psychological
stress models to understand the language of sexual and gender minorities. We
find that traditional psychological stress models underperform on minority
stress detection, and we propose using emotion-infused models to reduce that
performance disparity. We further demonstrate that multi-task psychological
stress models outperform the current state-of-the-art for minority stress
detection without directly training on minority stress data. We provide
explanatory analysis showing that minority communities have different
distributions of emotions than the general population and that emotion-infused
models improve the performance of stress models on underrepresented groups
because of their effectiveness in low-data environments, and we propose that
integrating emotions may benefit underrepresented groups in other mental health
detection tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Abstract: This paper considers optimal traffic signal control in smart cities, which
has been taken as a complex networked system control problem. Given the
interacting dynamics among traffic lights and road networks, attaining
controller adaptivity and scalability stands out as a primary challenge.
Capturing the spatial-temporal correlation among traffic lights under the
framework of Multi-Agent Reinforcement Learning (MARL) is a promising solution.
Nevertheless, existing MARL algorithms ignore effective information aggregation
which is fundamental for improving the learning capacity of decentralized
agents. In this paper, we design a new decentralized control architecture with
improved environmental observability to capture the spatial-temporal
correlation. Specifically, we first develop a topology-aware information
aggregation strategy to extract correlation-related information from
unstructured data gathered in the road network. Particularly, we transfer the
road network topology into a graph shift operator by forming a diffusion
process on the topology, which subsequently facilitates the construction of
graph signals. A diffusion convolution module is developed, forming a new MARL
algorithm, which endows agents with the capabilities of graph learning.
Extensive experiments based on both synthetic and real-world datasets verify
that our proposal outperforms existing decentralized algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Abstract: Large language models with instruction-following capabilities open the door
to a wider group of users. However, when it comes to information extraction - a
classic task in natural language processing - most task-specific systems cannot
align well with long-tail ad hoc extraction use cases for non-expert users. To
address this, we propose a novel paradigm, termed On-Demand Information
Extraction, to fulfill the personalized demands of real-world users. Our task
aims to follow the instructions to extract the desired content from the
associated text and present it in a structured tabular format. The table
headers can either be user-specified or inferred contextually by the model. To
facilitate research in this emerging area, we present a benchmark named
InstructIE, inclusive of both automatically generated training data, as well as
the human-annotated test set. Building on InstructIE, we further develop an
On-Demand Information Extractor, ODIE. Comprehensive evaluations on our
benchmark reveal that ODIE substantially outperforms the existing open-source
models of similar size. Our code and dataset are released on
https://github.com/yzjiao/On-Demand-IE.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering
Abstract: We address the task of evidence retrieval for long document question
answering, which involves locating relevant paragraphs within a document to
answer a question. We aim to assess the applicability of large language models
(LLMs) in the task of zero-shot long document evidence retrieval, owing to
their unprecedented performance across various NLP tasks. However, currently
the LLMs can consume limited context lengths as input, thus providing document
chunks as inputs might overlook the global context while missing out on
capturing the inter-segment dependencies. Moreover, directly feeding the large
input sets can incur significant computational costs, particularly when
processing the entire document (and potentially incurring monetary expenses
with enterprise APIs like OpenAI's GPT variants). To address these challenges,
we propose a suite of techniques that exploit the discourse structure commonly
found in documents. By utilizing this structure, we create a condensed
representation of the document, enabling a more comprehensive understanding and
analysis of relationships between different parts. We retain $99.6\%$ of the
best zero-shot approach's performance, while processing only $26\%$ of the
total tokens used by the best approach in the information seeking evidence
retrieval setup. We also show how our approach can be combined with
\textit{self-ask} reasoning agent to achieve best zero-shot performance in
complex multi-hop question answering, just $\approx 4\%$ short of zero-shot
performance using gold evidence.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets
Abstract: The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI)
methods to their underlying models is a challenging task, primarily due to the
absence of a ground truth for explanations. However, assessing fidelity is a
necessary step for ensuring a correct XAI methodology. In this study, we
conduct a fair and objective comparison of the current state-of-the-art XAI
methods by introducing three novel image datasets with reliable ground truth
for explanations. The primary objective of this comparison is to identify
methods with low fidelity and eliminate them from further research, thereby
promoting the development of more trustworthy and effective XAI techniques. Our
results demonstrate that XAI methods based on the backpropagation of output
information to input yield higher accuracy and reliability compared to methods
relying on sensitivity analysis or Class Activation Maps (CAM). However, the
backpropagation method tends to generate more noisy saliency maps. These
findings have significant implications for the advancement of XAI methods,
enabling the elimination of erroneous explanations and fostering the
development of more robust and reliable XAI.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: The logic of NTQR evaluations of noisy AI agents: Complete postulates and logically consistent error correlations
Abstract: In his "ship of state" allegory (\textit{Republic}, Book VI, 488) Plato poses
a question -- how can a crew of sailors presumed to know little about the art
of navigation recognize the true pilot among them? The allegory argues that a
simple majority voting procedure cannot safely determine who is most qualified
to pilot a ship when the voting members are ignorant or biased. We formalize
Plato's concerns by considering the problem in AI safety of monitoring noisy AI
agents in unsupervised settings. An algorithm evaluating AI agents using
unlabeled data would be subject to the evaluation dilemma - how would we know
the evaluation algorithm was correct itself? This endless validation chain can
be avoided by considering purely algebraic functions of the observed responses.
We can construct complete postulates than can prove or disprove the logical
consistency of any grading algorithm. A complete set of postulates exists
whenever we are evaluating $N$ experts that took $T$ tests with $Q$ questions
with $R$ responses each. We discuss evaluating binary classifiers that have
taken a single test - the $(N,T=1,Q,R=2)$ tests. We show how some of the
postulates have been previously identified in the ML literature but not
recognized as such - the \textbf{agreement equations} of Platanios. The
complete postulates for pair correlated binary classifiers are considered and
we show how it allows for error correlations to be quickly calculated. An
algebraic evaluator based on the assumption that the ensemble is error
independent is compared with grading by majority voting on evaluations using
the \uciadult and and \texttt{two-norm} datasets. Throughout, we demonstrate
how the formalism of logical consistency via algebraic postulates of evaluation
can help increase the safety of machines using AI algorithms.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Abstract: We introduce Llama Guard, an LLM-based input-output safeguard model geared
towards Human-AI conversation use cases. Our model incorporates a safety risk
taxonomy, a valuable tool for categorizing a specific set of safety risks found
in LLM prompts (i.e., prompt classification). This taxonomy is also
instrumental in classifying the responses generated by LLMs to these prompts, a
process we refer to as response classification. For the purpose of both prompt
and response classification, we have meticulously gathered a dataset of high
quality. Llama Guard, a Llama2-7b model that is instruction-tuned on our
collected dataset, albeit low in volume, demonstrates strong performance on
existing benchmarks such as the OpenAI Moderation Evaluation dataset and
ToxicChat, where its performance matches or exceeds that of currently available
content moderation tools. Llama Guard functions as a language model, carrying
out multi-class classification and generating binary decision scores.
Furthermore, the instruction fine-tuning of Llama Guard allows for the
customization of tasks and the adaptation of output formats. This feature
enhances the model's capabilities, such as enabling the adjustment of taxonomy
categories to align with specific use cases, and facilitating zero-shot or
few-shot prompting with diverse taxonomies at the input. We are making Llama
Guard model weights available and we encourage researchers to further develop
and adapt them to meet the evolving needs of the community for AI safety.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization
Abstract: Achieving human-level dexterity in robotic grasping remains a challenging
endeavor. Robotic hands frequently encounter slippage and deformation during
object manipulation, issues rarely encountered by humans due to their sensory
receptors, experiential learning, and motor memory. The emulation of the human
grasping reflex within robotic hands is referred to as the ``bionic reflex".
Past endeavors in the realm of bionic reflex control predominantly relied on
model-based and supervised learning approaches, necessitating human
intervention during thresholding and labeling tasks. In this study, we
introduce an innovative bionic reflex control pipeline, leveraging
reinforcement learning (RL); thereby eliminating the need for human
intervention during control design. Our proposed bionic reflex controller has
been designed and tested on an anthropomorphic hand, manipulating deformable
objects in the PyBullet physics simulator, incorporating domain randomization
(DR) for enhanced Sim2Real transferability. Our findings underscore the promise
of RL as a potent tool for advancing bionic reflex control within
anthropomorphic robotic hands. We anticipate that this autonomous, RL-based
bionic reflex controller will catalyze the development of dependable and highly
efficient robotic and prosthetic hands, revolutionizing human-robot interaction
and assistive technologies.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
Abstract: Pathological captioning of Whole Slide Images (WSIs), though is essential in
computer-aided pathological diagnosis, has rarely been studied due to the
limitations in datasets and model training efficacy. In this paper, we propose
a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological
captioning based on Transformers, which treats a WSI as a sequence of sparse
patches and generates an overall caption sentence from the sequence. An
accompanying subtype prediction is introduced into SGMT to guide the training
process and enhance the captioning accuracy. We also present an Asymmetric
Masked Mechansim approach to tackle the large size constraint of pathological
image captioning, where the numbers of sequencing patches in SGMT are sampled
differently in the training and inferring phases, respectively. Experiments on
the PatchGastricADC22 dataset demonstrate that our approach effectively adapts
to the task with a transformer-based model and achieves superior performance
than traditional RNN-based methods. Our codes are to be made available for
further research and development.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Advancing State of the Art in Language Modeling
Abstract: Generalization is arguably the most important goal of statistical language
modeling research. Publicly available benchmarks and papers published with an
open-source code have been critical to advancing the field. However, it is
often very difficult, and sometimes even impossible, to reproduce the results
fully as reported in publications. In this paper, we propose a simple framework
that should help advance the state of the art in language modeling in terms of
generalization. We propose to publish not just the code, but also probabilities
on dev and test sets with future publications so that one can easily add the
new model into an ensemble. This has crucial advantages: it is much easier to
determine whether a newly proposed model is actually complementary to the
current baseline. Therefore, instead of inventing new names for the old tricks,
the scientific community can advance faster. Finally, this approach promotes
diversity of ideas: one does not need to create an individual model that is the
new state of the art to attract attention; it will be sufficient to develop a
new model that learns patterns which other models do not. Thus, even a
suboptimal model can be found to have value. Remarkably, our approach has
yielded new state-of-the-art results across various language modeling
benchmarks up to 10%.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Applying Large Language Models to Power Systems: Potential Security Threats
Abstract: Applying large language models (LLMs) to power systems presents a promising
avenue for enhancing decision-making and operational efficiency. However, this
action may also incur potential security threats, which have not been fully
recognized so far. To this end, this letter analyzes potential threats incurred
by applying LLMs to power systems, emphasizing the need for urgent research and
development of countermeasures.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
Abstract: Understanding how the 3D scene evolves is vital for making decisions in
autonomous driving. Most existing methods achieve this by predicting the
movements of object boxes, which cannot capture more fine-grained scene
information. In this paper, we explore a new framework of learning a world
model, OccWorld, in the 3D Occupancy space to simultaneously predict the
movement of the ego car and the evolution of the surrounding scenes. We propose
to learn a world model based on 3D occupancy rather than 3D bounding boxes and
segmentation maps for three reasons: 1) expressiveness. 3D occupancy can
describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D
occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3)
versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the
modeling of the world evolution, we learn a reconstruction-based scene
tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the
surrounding scenes. We then adopt a GPT-like spatial-temporal generative
transformer to generate subsequent scene and ego tokens to decode the future
occupancy and ego trajectory. Extensive experiments on the widely used nuScenes
benchmark demonstrate the ability of OccWorld to effectively model the
evolution of the driving scenes. OccWorld also produces competitive planning
results without using instance and map supervision. Code:
https://github.com/wzzheng/OccWorld.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities
Abstract: Large-Language Models (LLMs) have shifted the paradigm of natural language
data processing. However, their black-boxed and probabilistic characteristics
can lead to potential risks in the quality of outputs in diverse LLM
applications. Recent studies have tested Quality Attributes (QAs), such as
robustness or fairness, of LLMs by generating adversarial input texts. However,
existing studies have limited their coverage of QAs and tasks in LLMs and are
difficult to extend. Additionally, these studies have only used one evaluation
metric, Attack Success Rate (ASR), to assess the effectiveness of their
approaches. We propose a MEtamorphic Testing for Analyzing LLMs (METAL)
framework to address these issues by applying Metamorphic Testing (MT)
techniques. This approach facilitates the systematic testing of LLM qualities
by defining Metamorphic Relations (MRs), which serve as modularized evaluation
metrics. The METAL framework can automatically generate hundreds of MRs from
templates that cover various QAs and tasks. In addition, we introduced novel
metrics that integrate the ASR method into the semantic qualities of text to
assess the effectiveness of MRs accurately. Through the experiments conducted
with three prominent LLMs, we have confirmed that the METAL framework
effectively evaluates essential QAs on primary LLM tasks and reveals the
quality risks in LLMs. Moreover, the newly proposed metrics can guide the
optimal MRs for testing each task and suggest the most effective method for
generating MRs.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
Abstract: Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an
effective and flexible solution, attracting extensive research. Deep neural
networks can learn rich representations from vast amounts of representative
labeled data for various applications. In IFD, they achieve high classification
performance from signals in an end-to-end manner, without requiring extensive
domain knowledge. However, deep learning models usually only perform well on
the data distribution they have been trained on. When applied to a different
distribution, they may experience performance drops. This is also observed in
IFD, where assets are often operated in working conditions different from those
in which labeled data have been collected. Unsupervised domain adaptation (UDA)
deals with the scenario where labeled data are available in a source domain,
and only unlabeled data are available in a target domain, where domains may
correspond to operating conditions. Recent methods rely on training with
confident pseudo-labels for target samples. However, the confidence-based
selection of pseudo-labels is hindered by poorly calibrated confidence
estimates in the target domain, primarily due to over-confident predictions,
which limits the quality of pseudo-labels and leads to error accumulation. In
this paper, we propose a novel UDA method called Calibrated Adaptive Teacher
(CAT), where we propose to calibrate the predictions of the teacher network
throughout the self-training process, leveraging post-hoc calibration
techniques. We evaluate CAT on domain-adaptive IFD and perform extensive
experiments on the Paderborn benchmark for bearing fault diagnosis under
varying operating conditions. Our proposed method achieves state-of-the-art
performance on most transfer tasks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Exploring Popularity Bias in Session-based Recommendation
Abstract: Existing work has revealed that large-scale offline evaluation of recommender
systems for user-item interactions is prone to bias caused by the deployed
system itself, as a form of closed loop feedback. Many adopt the
\textit{propensity} concept to analyze or mitigate this empirical issue. In
this work, we extend the analysis to session-based setup and adapted propensity
calculation to the unique characteristics of session-based recommendation
tasks. Our experiments incorporate neural models and KNN-based models, and
cover both the music and the e-commerce domain. We study the distributions of
propensity and different stratification techniques on different datasets and
find that propensity-related traits are actually dataset-specific. We then
leverage the effect of stratification and achieve promising results compared to
the original models.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation
Abstract: The advent of large language models (LLMs) has opened up new opportunities in
the field of mobile task automation. Their superior language understanding and
reasoning capabilities allow users to automate complex and repetitive tasks.
However, due to the inherent unreliability and high operational cost of LLMs,
their practical applicability is quite limited. To address these issues, this
paper introduces MemoDroid, an innovative LLM-based mobile task automator
enhanced with a unique app memory. MemoDroid emulates the cognitive process of
humans interacting with a mobile app -- explore, select, derive, and recall.
This approach allows for a more precise and efficient learning of a task's
procedure by breaking it down into smaller, modular components that can be
re-used, re-arranged, and adapted for various objectives. We implement
MemoDroid using online LLMs services (GPT-3.5 and GPT-4) and evaluate its
performance on 50 unique mobile tasks across 5 widely used mobile apps. The
results indicate that MemoDroid can adapt learned tasks to varying contexts
with 100% accuracy and reduces their latency and cost by 69.22% and 77.36%
compared to a GPT-4 powered baseline.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: DiffusionSat: A Generative Foundation Model for Satellite Imagery
Abstract: Diffusion models have achieved state-of-the-art results on many modalities
including images, speech, and video. However, existing models are not tailored
to support remote sensing data, which is widely used in important applications
including environmental monitoring and crop-yield prediction. Satellite images
are significantly different from natural images -- they can be multi-spectral,
irregularly sampled across time -- and existing diffusion models trained on
images from the Web do not support them. Furthermore, remote sensing data is
inherently spatio-temporal, requiring conditional generation tasks not
supported by traditional methods based on captions or images. In this paper, we
present DiffusionSat, to date the largest generative foundation model trained
on a collection of publicly available large, high-resolution remote sensing
datasets. As text-based captions are sparsely available for satellite images,
we incorporate the associated metadata such as geolocation as conditioning
information. Our method produces realistic samples and can be used to solve
multiple generative tasks including temporal generation, superresolution given
multi-spectral inputs and in-painting. Our method outperforms previous
state-of-the-art methods for satellite image generation and is the first
large-scale $\textit{generative}$ foundation model for satellite imagery.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Like an Open Book? Read Neural Network Architecture with Simple Power Analysis on 32-bit Microcontrollers
Abstract: Model extraction is a growing concern for the security of AI systems. For
deep neural network models, the architecture is the most important information
an adversary aims to recover. Being a sequence of repeated computation blocks,
neural network models deployed on edge-devices will generate distinctive
side-channel leakages. The latter can be exploited to extract critical
information when targeted platforms are physically accessible. By combining
theoretical knowledge about deep learning practices and analysis of a
widespread implementation library (ARM CMSIS-NN), our purpose is to answer this
critical question: how far can we extract architecture information by simply
examining an EM side-channel trace? For the first time, we propose an
extraction methodology for traditional MLP and CNN models running on a high-end
32-bit microcontroller (Cortex-M7) that relies only on simple pattern
recognition analysis. Despite few challenging cases, we claim that, contrary to
parameters extraction, the complexity of the attack is relatively low and we
highlight the urgent need for practicable protections that could fit the strong
memory and latency requirements of such platforms.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation
Abstract: Vision Transformer (ViT) has demonstrated promising performance in computer
vision tasks, comparable to state-of-the-art neural networks. Yet, this new
type of deep neural network architecture is vulnerable to adversarial attacks
limiting its capabilities in terms of robustness. This article presents a novel
contribution aimed at further improving the accuracy and robustness of ViT,
particularly in the face of adversarial attacks. We propose an augmentation
technique called `Dynamic Scanning Augmentation' that leverages dynamic input
sequences to adaptively focus on different patches, thereby maintaining
performance and robustness. Our detailed investigations reveal that this
adaptability to the input sequence induces significant changes in the attention
mechanism of ViT, even for the same image. We introduce four variations of
Dynamic Scanning Augmentation, outperforming ViT in terms of both robustness to
adversarial attacks and accuracy against natural images, with one variant
showing comparable results. By integrating our augmentation technique, we
observe a substantial increase in ViT's robustness, improving it from $17\%$ to
$92\%$ measured across different types of adversarial attacks. These findings,
together with other comprehensive tests, indicate that Dynamic Scanning
Augmentation enhances accuracy and robustness by promoting a more adaptive type
of attention. In conclusion, this work contributes to the ongoing research on
Vision Transformers by introducing Dynamic Scanning Augmentation as a technique
for improving the accuracy and robustness of ViT. The observed results
highlight the potential of this approach in advancing computer vision tasks and
merit further exploration in future studies.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Vignat: Vulnerability identification by learning code semantics via graph attention networks
Abstract: Vulnerability identification is crucial to protect software systems from
attacks for cyber-security. However, huge projects have more than millions of
lines of code, and the complex dependencies make it hard to carry out
traditional static and dynamic methods. Furthermore, the semantic structure of
various types of vulnerabilities differs greatly and may occur simultaneously,
making general rule-based methods difficult to extend. In this paper, we
propose \textit{Vignat}, a novel attention-based framework for identifying
vulnerabilities by learning graph-level semantic representations of code. We
represent codes with code property graphs (CPGs) in fine grain and use graph
attention networks (GATs) for vulnerability detection. The results show that
Vignat is able to achieve $57.38\%$ accuracy on reliable datasets derived from
popular C libraries. Furthermore, the interpretability of our GATs provides
valuable insights into vulnerability patterns.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills
Abstract: The fast adoption of new technologies forces companies to continuously adapt
their operations making it harder to predict workforce requirements. Several
recent studies have attempted to predict the emergence of new roles and skills
in the labour market from online job ads. This paper aims to present a novel
ontology linking business transformation initiatives to occupations and an
approach to automatically populating it by leveraging embeddings extracted from
job ads and Wikipedia pages on business transformation and emerging
technologies topics. To our knowledge, no previous research explicitly links
business transformation initiatives, like the adoption of new technologies or
the entry into new markets, to the roles needed. Our approach successfully
matches occupations to transformation initiatives under ten different
scenarios, five linked to technology adoption and five related to business.
This framework presents an innovative approach to guide enterprises and
educational institutions on the workforce requirements for specific business
transformation initiatives.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A trainable manifold for accurate approximation with ReLU Networks
Abstract: We present a novel technique for exercising greater control of the weights of
ReLU activated neural networks to produce more accurate function
approximations. Many theoretical works encode complex operations into ReLU
networks using smaller base components. In these works, a common base component
is a constant width approximation to x^2, which has exponentially decaying
error with respect to depth. We extend this block to represent a greater range
of convex one-dimensional functions. We derive a manifold of weights such that
the output of these new networks utilizes exponentially many piecewise-linear
segments. This manifold guides their training process to overcome drawbacks
associated with random initialization and unassisted gradient descent. We train
these networks to approximate functions which do not necessarily lie on the
manifold, showing a significant reduction of error values over conventional
approaches.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Development of a Legal Document AI-Chatbot
Abstract: With the exponential growth of digital data and the increasing complexity of
legal documentation, there is a pressing need for efficient and intelligent
tools to streamline the handling of legal documents.With the recent
developments in the AI field, especially in chatbots, it cannot be ignored as a
very compelling solution to this problem.An insight into the process of
creating a Legal Documentation AI Chatbot with as many relevant features as
possible within the given time frame is presented.The development of each
component of the chatbot is presented in detail.Each component's workings and
functionality has been discussed.Starting from the build of the Android app and
the Langchain query processing code till the integration of both through a
Flask backend and REST API methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: NPCL: Neural Processes for Uncertainty-Aware Continual Learning
Abstract: Continual learning (CL) aims to train deep neural networks efficiently on
streaming data while limiting the forgetting caused by new tasks. However,
learning transferable knowledge with less interference between tasks is
difficult, and real-world deployment of CL models is limited by their inability
to measure predictive uncertainties. To address these issues, we propose
handling CL tasks with neural processes (NPs), a class of meta-learners that
encode different tasks into probabilistic distributions over functions all
while providing reliable uncertainty estimates. Specifically, we propose an
NP-based CL approach (NPCL) with task-specific modules arranged in a
hierarchical latent variable model. We tailor regularizers on the learned
latent distributions to alleviate forgetting. The uncertainty estimation
capabilities of the NPCL can also be used to handle the task head/module
inference challenge in CL. Our experiments show that the NPCL outperforms
previous CL approaches. We validate the effectiveness of uncertainty estimation
in the NPCL for identifying novel data and evaluating instance-level model
confidence. Code is available at \url{https://github.com/srvCodes/NPCL}.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dense Retrieval as Indirect Supervision for Large-space Decision Making
Abstract: Many discriminative natural language understanding (NLU) tasks have large
label spaces. Learning such a process of large-space decision making is
particularly challenging due to the lack of training instances per label and
the difficulty of selection among many fine-grained labels. Inspired by dense
retrieval methods for passage finding in open-domain QA, we propose a
reformulation of large-space discriminative NLU tasks as a learning-to-retrieve
task, leading to a novel solution named Dense Decision Retrieval (DDR ).
Instead of predicting fine-grained decisions as logits, DDR adopts a
dual-encoder architecture that learns to predict by retrieving from a decision
thesaurus. This approach not only leverages rich indirect supervision signals
from easy-to-consume learning resources for dense retrieval, it also leads to
enhanced prediction generalizability with a semantically meaningful
representation of the large decision space. When evaluated on tasks with
decision spaces ranging from hundreds to hundred-thousand scales, DDR
outperforms strong baselines greatly by 27.54% in P@1 on two extreme
multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing,
and 1.26% in accuracy on three few-shot intent classification tasks on average.
Code and resources are available at https://github.com/luka-group/DDR

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Input Reconstruction Attack against Vertical Federated Large Language Models
Abstract: Recently, large language models (LLMs) have drawn extensive attention from
academia and the public, due to the advent of the ChatGPT. While LLMs show
their astonishing ability in text generation for various tasks, privacy
concerns limit their usage in real-life businesses. More specifically, either
the user's inputs (the user sends the query to the model-hosting server) or the
model (the user downloads the complete model) itself will be revealed during
the usage. Vertical federated learning (VFL) is a promising solution to this
kind of problem. It protects both the user's input and the knowledge of the
model by splitting the model into a bottom part and a top part, which is
maintained by the user and the model provider, respectively. However, in this
paper, we demonstrate that in LLMs, VFL fails to protect the user input since
it is simple and cheap to reconstruct the input from the intermediate
embeddings. Experiments show that even with a commercial GPU, the input
sentence can be reconstructed in only one second. We also discuss several
possible solutions to enhance the privacy of vertical federated LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: GlitchBench: Can large multimodal models detect video game glitches?
Abstract: Large multimodal models (LMMs) have evolved from large language models (LLMs)
to integrate multiple input modalities, such as visual inputs. This integration
augments the capacity of LLMs for tasks requiring visual comprehension and
reasoning. However, the extent and limitations of their enhanced abilities are
not fully understood, especially when it comes to real-world tasks. To address
this gap, we introduce GlitchBench, a novel benchmark derived from video game
quality assurance tasks, to test and evaluate the reasoning capabilities of
LMMs. Our benchmark is curated from a variety of unusual and glitched scenarios
from video games and aims to challenge both the visual and linguistic reasoning
powers of LMMs in detecting and interpreting out-of-the-ordinary events. We
evaluate multiple state-of-the-art LMMs, and we show that GlitchBench presents
a new challenge for these models. Code and data are available at:
https://glitchbench.github.io/

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Game Solving with Online Fine-Tuning
Abstract: Game solving is a similar, yet more difficult task than mastering a game.
Solving a game typically means to find the game-theoretic value (outcome given
optimal play), and optionally a full strategy to follow in order to achieve
that outcome. The AlphaZero algorithm has demonstrated super-human level play,
and its powerful policy and value predictions have also served as heuristics in
game solving. However, to solve a game and obtain a full strategy, a winning
response must be found for all possible moves by the losing player. This
includes very poor lines of play from the losing side, for which the AlphaZero
self-play process will not encounter. AlphaZero-based heuristics can be highly
inaccurate when evaluating these out-of-distribution positions, which occur
throughout the entire search. To address this issue, this paper investigates
applying online fine-tuning while searching and proposes two methods to learn
tailor-designed heuristics for game solving. Our experiments show that using
online fine-tuning can solve a series of challenging 7x7 Killall-Go problems,
using only 23.54% of computation time compared to the baseline without online
fine-tuning. Results suggest that the savings scale with problem size. Our
method can further be extended to any tree search algorithm for problem
solving. Our code is available at
https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Self-Evaluation Improves Selective Generation in Large Language Models
Abstract: Safe deployment of large language models (LLMs) may benefit from a reliable
method for assessing their generated content to determine when to abstain or to
selectively generate. While likelihood-based metrics such as perplexity are
widely employed, recent research has demonstrated the limitations of using
sequence-level probability estimates given by LLMs as reliable indicators of
generation quality. Conversely, LLMs have demonstrated strong calibration at
the token level, particularly when it comes to choosing correct answers in
multiple-choice questions or evaluating true/false statements. In this work, we
reformulate open-ended generation tasks into token-level prediction tasks, and
leverage LLMs' superior calibration at the token level. We instruct an LLM to
self-evaluate its answers, employing either a multi-way comparison or a
point-wise evaluation approach, with the option to include a ``None of the
above'' option to express the model's uncertainty explicitly. We benchmark a
range of scoring methods based on self-evaluation and evaluate their
performance in selective generation using TruthfulQA and TL;DR. Through
experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based
scores not only improve accuracy, but also correlate better with the overall
quality of generated content.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Abstract: Sampling from diffusion models can be treated as solving the corresponding
ordinary differential equations (ODEs), with the aim of obtaining an accurate
solution with as few number of function evaluations (NFE) as possible.
Recently, various fast samplers utilizing higher-order ODE solvers have emerged
and achieved better performance than the initial first-order one. However,
these numerical methods inherently result in certain approximation errors,
which significantly degrades sample quality with extremely small NFE (e.g.,
around 5). In contrast, based on the geometric observation that each sampling
trajectory almost lies in a two-dimensional subspace embedded in the ambient
space, we propose Approximate MEan-Direction Solver (AMED-Solver) that
eliminates truncation errors by directly learning the mean direction for fast
diffusion sampling. Besides, our method can be easily used as a plugin to
further improve existing ODE-based samplers. Extensive experiments on image
synthesis with the resolution ranging from 32 to 256 demonstrate the
effectiveness of our method. With only 5 NFE, we achieve 7.14 FID on CIFAR-10,
13.75 FID on ImageNet 64$\times$64, and 12.79 FID on LSUN Bedroom. Our code is
available at https://github.com/zhyzhouu/amed-solver.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
Abstract: The potential of digital twin technology is yet to be fully realized due to
its diversity and untapped potential. Digital twins enable systems' analysis,
design, optimization, and evolution to be performed digitally or in conjunction
with a cyber-physical approach to improve speed, accuracy, and efficiency over
traditional engineering methods. Industry 4.0, factories of the future, and
digital twins continue to benefit from the technology and provide enhanced
efficiency within existing systems. Due to the lack of information and security
standards associated with the transition to cyber digitization, cybercriminals
have been able to take advantage of the situation. Access to a digital twin of
a product or service is equivalent to threatening the entire collection. There
is a robust interaction between digital twins and artificial intelligence
tools, which leads to strong interaction between these technologies, so it can
be used to improve the cybersecurity of these digital platforms based on their
integration with these technologies. This study aims to investigate the role of
artificial intelligence in providing cybersecurity for digital twin versions of
various industries, as well as the risks associated with these versions. In
addition, this research serves as a road map for researchers and others
interested in cybersecurity and digital security.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions
Abstract: In this paper, we extend an available neural network verification technique
to support a wider class of piece-wise linear activation functions.
Furthermore, we extend the algorithms, which provide in their original form
exact respectively over-approximative results for bounded input sets
represented as start sets, to allow also unbounded input set. We implemented
our algorithms and demonstrated their effectiveness in some case studies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models
Abstract: In the digital age, it is crucial to make sure that financial transactions
are as secure and reliable as possible. This abstract offers a ground-breaking
method that combines smart contracts, blockchain technology, FaceNet512 for
improved face recognition, and Gaussian Mixture Models (GMM) for speech
authentication to create a system for video and audio verification that is
unmatched. Smart contracts and the immutable ledger of the blockchain are
combined to offer a safe and open environment for financial transactions.
FaceNet512 and GMM offer multi-factor biometric authentication simultaneously,
enhancing security to new heights. By combining cutting-edge technology, this
system offers a strong defense against identity theft and illegal access,
establishing a new benchmark for safe financial transactions.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
Abstract: In production applications of recommender systems, a continuous data flow is
employed to update models in real-time. Many recommender models often require
complete retraining to adapt to new data. In this work, we introduce a novel
collaborative filtering model for sequential problems known as Tucker
Integrator Recommender - TIRecA. TIRecA efficiently updates its parameters
using only the new data segment, allowing incremental addition of new users and
items to the recommender system. To demonstrate the effectiveness of the
proposed model, we conducted experiments on four publicly available datasets:
MovieLens 20M, Amazon Beauty, Amazon Toys and Games, and Steam. Our comparison
with general matrix and tensor-based baselines in terms of prediction quality
and computational time reveals that TIRecA achieves comparable quality to the
baseline methods, while being 10-20 times faster in training time.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Personality of AI
Abstract: This research paper delves into the evolving landscape of fine-tuning large
language models (LLMs) to align with human users, extending beyond basic
alignment to propose "personality alignment" for language models in
organizational settings. Acknowledging the impact of training methods on the
formation of undefined personality traits in AI models, the study draws
parallels with human fitting processes using personality tests. Through an
original case study, we demonstrate the necessity of personality fine-tuning
for AIs and raise intriguing questions about applying human-designed tests to
AIs, engineering specialized AI personality tests, and shaping AI personalities
to suit organizational roles. The paper serves as a starting point for
discussions and developments in the burgeoning field of AI personality
alignment, offering a foundational anchor for future exploration in
human-machine teaming and co-existence.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Ontology Revision based on Pre-trained Language Models
Abstract: Ontology revision aims to seamlessly incorporate new information into an
existing ontology and plays a crucial role in tasks such as ontology evolution,
ontology maintenance, and ontology alignment. Similar to repair single
ontologies, resolving logical incoherence in the task of ontology revision is
also important and meaningful since incoherence is a main potential factor to
cause inconsistency and reasoning with an inconsistent ontology will obtain
meaningless answers. To deal with this problem, various ontology revision
methods have been proposed to define revision operators and design ranking
strategies for axioms in an ontology. However, they rarely consider axiom
semantics which provides important information to differentiate axioms. On the
other hand, pre-trained models can be utilized to encode axiom semantics, and
have been widely applied in many natural language processing tasks and
ontology-related ones in recent years. Therefore, in this paper, we define four
scoring functions to rank axioms based on a pre-trained model by considering
various information from a rebuttal ontology and its corresponding reliable
ontology. Based on such a scoring function, we propose an ontology revision
algorithm to deal with unsatisfiable concepts at once. If it is hard to resolve
all unsatisfiable concepts in a rebuttal ontology together, an adapted revision
algorithm is designed to deal with them group by group. We conduct experiments
over 19 ontology pairs and compare our algorithms and scoring functions with
existing ones. According to the experiments, it shows that our algorithms could
achieve promising performance. The adapted revision algorithm could improve the
efficiency largely, and at most 96% time could be saved for some ontology
pairs. Some of our scoring functions help a revision algorithm obtain better
results in many cases, especially for the challenging pairs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Towards Adaptive RF Fingerprint-based Authentication of IIoT devices
Abstract: As IoT technologies mature, they are increasingly finding their way into more
sensitive domains, such as Medical and Industrial IoT, in which safety and
cyber-security are of great importance. While the number of deployed IoT
devices continues to increase exponentially, they still present severe
cyber-security vulnerabilities. Effective authentication is paramount to
support trustworthy IIoT communications, however, current solutions focus on
upper-layer identity verification or key-based cryptography which are often
inadequate to the heterogeneous IIoT environment. In this work, we present a
first step towards achieving powerful and flexible IIoT device authentication,
by leveraging AI adaptive Radio Frequency Fingerprinting technique selection
and tuning, at the PHY layer for highly accurate device authentication over
challenging RF environments.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now
Abstract: Generative models can produce impressively realistic images. This paper
demonstrates that generated images have geometric features different from those
of real images. We build a set of collections of generated images, prequalified
to fool simple, signal-based classifiers into believing they are real. We then
show that prequalified generated images can be identified reliably by
classifiers that only look at geometric properties. We use three such
classifiers. All three classifiers are denied access to image pixels, and look
only at derived geometric features. The first classifier looks at the
perspective field of the image, the second looks at lines detected in the
image, and the third looks at relations between detected objects and shadows.
Our procedure detects generated images more reliably than SOTA local signal
based detectors, for images from a number of distinct generators. Saliency maps
suggest that the classifiers can identify geometric problems reliably. We
conclude that current generators cannot reliably reproduce geometric properties
of real images.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Pragmatic Radiology Report Generation
Abstract: When pneumonia is not found on a chest X-ray, should the report describe this
negative observation or omit it? We argue that this question cannot be answered
from the X-ray alone and requires a pragmatic perspective, which captures the
communicative goal that radiology reports serve between radiologists and
patients. However, the standard image-to-text formulation for radiology report
generation fails to incorporate such pragmatic intents. Following this
pragmatic perspective, we demonstrate that the indication, which describes why
a patient comes for an X-ray, drives the mentions of negative observations and
introduce indications as additional input to report generation. With respect to
the output, we develop a framework to identify uninferable information from the
image as a source of model hallucinations, and limit them by cleaning
groundtruth reports. Finally, we use indications and cleaned groundtruth
reports to develop pragmatic models, and show that they outperform existing
methods not only in new pragmatics-inspired metrics (+4.3 Negative F1) but also
in standard metrics (+6.3 Positive F1 and +11.0 BLEU-2).

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: State-Wise Safe Reinforcement Learning With Pixel Observations
Abstract: In the context of safe exploration, Reinforcement Learning (RL) has long
grappled with the challenges of balancing the tradeoff between maximizing
rewards and minimizing safety violations, particularly in complex environments
with contact-rich or non-smooth dynamics, and when dealing with
high-dimensional pixel observations. Furthermore, incorporating state-wise
safety constraints in the exploration and learning process, where the agent
must avoid unsafe regions without prior knowledge, adds another layer of
complexity. In this paper, we propose a novel pixel-observation safe RL
algorithm that efficiently encodes state-wise safety constraints with unknown
hazard regions through a newly introduced latent barrier-like function learning
mechanism. As a joint learning framework, our approach begins by constructing a
latent dynamics model with low-dimensional latent spaces derived from pixel
observations. We then build and learn a latent barrier-like function on top of
the latent dynamics and conduct policy optimization simultaneously, thereby
improving both safety and the total expected return. Experimental evaluations
on the safety-gym benchmark suite demonstrate that our proposed method
significantly reduces safety violations throughout the training process, and
demonstrates faster safety convergence compared to existing methods while
achieving competitive results in reward return.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation
Abstract: Current Visual-Language Pre-training (VLP) models are vulnerable to
adversarial examples. These adversarial examples present substantial security
risks to VLP models, as they can leverage inherent weaknesses in the models,
resulting in incorrect predictions. In contrast to white-box adversarial
attacks, transfer attacks (where the adversary crafts adversarial examples on a
white-box model to fool another black-box model) are more reflective of
real-world scenarios, thus making them more meaningful for research. By
summarizing and analyzing existing research, we identified two factors that can
influence the efficacy of transfer attacks on VLP models: inter-modal
interaction and data diversity. Based on these insights, we propose a
self-augment-based transfer attack method, termed SA-Attack. Specifically,
during the generation of adversarial images and adversarial texts, we apply
different data augmentation methods to the image modality and text modality,
respectively, with the aim of improving the adversarial transferability of the
generated adversarial images and texts. Experiments conducted on the FLickr30K
and COCO datasets have validated the effectiveness of our method. Our code will
be available after this paper is accepted.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications
Abstract: Can generative AI help us speed up the authoring of tools to help
self-represented litigants?
  In this paper, we describe 3 approaches to automating the completion of court
forms: a generative AI approach that uses GPT-3 to iteratively prompt the user
to answer questions, a constrained template-driven approach that uses
GPT-4-turbo to generate a draft of questions that are subject to human review,
and a hybrid method. We use the open source Docassemble platform in all 3
experiments, together with a tool created at Suffolk University Law School
called the Assembly Line Weaver. We conclude that the hybrid model of
constrained automated drafting with human review is best suited to the task of
authoring guided interviews.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Abstract: Large Language Models (LLMs) have demonstrated superior performance in
language understanding benchmarks. CALM, a popular approach, leverages
linguistic priors of LLMs -- GPT-2 -- for action candidate recommendations to
improve the performance in text games in Jericho without environment-provided
actions. However, CALM adapts GPT-2 with annotated human gameplays and keeps
the LLM fixed during the learning of the text based games. In this work, we
explore and evaluate updating LLM used for candidate recommendation during the
learning of the text based game as well to mitigate the reliance on the human
annotated gameplays, which are costly to acquire. We observe that by updating
the LLM during learning using carefully selected in-game transitions, we can
reduce the dependency on using human annotated game plays for fine-tuning the
LLMs. We conducted further analysis to study the transferability of the updated
LLMs and observed that transferring in-game trained models to other games did
not result in a consistent transfer.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Abstract: Knowledge tracing (KT) plays a crucial role in computer-aided education and
intelligent tutoring systems, aiming to assess students' knowledge proficiency
by predicting their future performance on new questions based on their past
response records. While existing deep learning knowledge tracing (DLKT) methods
have significantly improved prediction accuracy and achieved state-of-the-art
results, they often suffer from a lack of interpretability. To address this
limitation, current approaches have explored incorporating psychological
influences to achieve more explainable predictions, but they tend to overlook
the potential influences of historical responses. In fact, understanding how
models make predictions based on response influences can enhance the
transparency and trustworthiness of the knowledge tracing process, presenting
an opportunity for a new paradigm of interpretable KT. However, measuring
unobservable response influences is challenging. In this paper, we resort to
counterfactual reasoning that intervenes in each response to answer
\textit{what if a student had answered a question incorrectly that he/she
actually answered correctly, and vice versa}. Based on this, we propose RCKT, a
novel response influence-based counterfactual knowledge tracing framework. RCKT
generates response influences by comparing prediction outcomes from factual
sequences and constructed counterfactual sequences after interventions.
Additionally, we introduce maximization and inference techniques to leverage
accumulated influences from different past responses, further improving the
model's performance and credibility. Extensive experimental results demonstrate
that our RCKT method outperforms state-of-the-art knowledge tracing methods on
four datasets against six baselines, and provides credible interpretations of
response influences.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Generalizable Imitation Learning Through Pre-Trained Representations
Abstract: In this paper we leverage self-supervised vision transformer models and their
emergent semantic abilities to improve the generalization abilities of
imitation learning policies. We introduce BC-ViT, an imitation learning
algorithm that leverages rich DINO pre-trained Visual Transformer (ViT)
patch-level embeddings to obtain better generalization when learning through
demonstrations. Our learner sees the world by clustering appearance features
into semantic concepts, forming stable keypoints that generalize across a wide
range of appearance variations and object types. We show that this
representation enables generalized behaviour by evaluating imitation learning
across a diverse dataset of object manipulation tasks. Our method, data and
evaluation approach are made available to facilitate further study of
generalization in Imitation Learners.

Output: Robotics