Instruction: What field is the article from?

Prompt:
Title: Academic competitions
Abstract: Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Beyond MLE: Convex Learning for Text Generation
Abstract: Maximum likelihood estimation (MLE) is a statistical method used to estimate
the parameters of a probability distribution that best explain the observed
data. In the context of text generation, MLE is often used to train generative
language models, which can then be used to generate new text. However, we argue
that MLE is not always necessary and optimal, especially for closed-ended text
generation tasks like machine translation. In these tasks, the goal of model is
to generate the most appropriate response, which does not necessarily require
it to estimate the entire data distribution with MLE. To this end, we propose a
novel class of training objectives based on convex functions, which enables
text generation models to focus on highly probable outputs without having to
estimate the entire data distribution. We investigate the theoretical
properties of the optimal predicted distribution when applying convex functions
to the loss, demonstrating that convex functions can sharpen the optimal
distribution, thereby enabling the model to better capture outputs with high
probabilities. Experiments on various text generation tasks and models show the
effectiveness of our approach. It enables autoregressive models to bridge the
gap between greedy and beam search, and facilitates the learning of
non-autoregressive models with a maximum improvement of 9+ BLEU points.
Moreover, our approach also exhibits significant impact on large language
models (LLMs), substantially enhancing their generative capability on various
tasks. Source code is available at
\url{https://github.com/ictnlp/Convex-Learning}.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: From Learning Management System to Affective Tutoring system: a preliminary study
Abstract: In this study, we investigate the combination of indicators, including
performance, behavioral engagement, and emotional engagement, to identify
students experiencing difficulties. We analyzed data from two primary sources:
digital traces extracted from th e Learning Management System (LMS) and images
captured by students' webcams. The digital traces provided insights into
students' interactions with the educational content, while the images were
utilized to analyze their emotional expressions during learnin g activities. By
utilizing real data collected from students at a French engineering school,
recorded during the 2022 2023 academic year, we observed a correlation between
positive emotional states and improved academic outcomes. These preliminary
findings support the notion that emotions play a crucial role in
differentiating between high achieving and low achieving students.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: tmn at #SMM4H 2023: Comparing Text Preprocessing Techniques for Detecting Tweets Self-reporting a COVID-19 Diagnosis
Abstract: The paper describes a system developed for Task 1 at SMM4H 2023. The goal of
the task is to automatically distinguish tweets that self-report a COVID-19
diagnosis (for example, a positive test, clinical diagnosis, or
hospitalization) from those that do not. We investigate the use of different
techniques for preprocessing tweets using four transformer-based models. The
ensemble of fine-tuned language models obtained an F1-score of 84.5%, which is
4.1% higher than the average value.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Green Edge AI: A Contemporary Survey
Abstract: Artificial intelligence (AI) technologies have emerged as pivotal enablers
across a multitude of industries, including consumer electronics, healthcare,
and manufacturing, largely due to their resurgence over the past decade. The
transformative power of AI is primarily derived from the utilization of deep
neural networks (DNNs), which require extensive data for training and
substantial computational resources for processing. Consequently, DNN models
are typically trained and deployed on resource-rich cloud servers. However, due
to potential latency issues associated with cloud communications, deep learning
(DL) workflows are increasingly being transitioned to wireless edge networks
near end-user devices (EUDs). This shift is designed to support
latency-sensitive applications and has given rise to a new paradigm of edge AI,
which will play a critical role in upcoming 6G networks to support ubiquitous
AI applications. Despite its potential, edge AI faces substantial challenges,
mostly due to the dichotomy between the resource limitations of wireless edge
networks and the resource-intensive nature of DL. Specifically, the acquisition
of large-scale data, as well as the training and inference processes of DNNs,
can rapidly deplete the battery energy of EUDs. This necessitates an
energy-conscious approach to edge AI to ensure both optimal and sustainable
performance. In this paper, we present a contemporary survey on green edge AI.
We commence by analyzing the principal energy consumption components of edge AI
systems to identify the fundamental design principles of green edge AI. Guided
by these principles, we then explore energy-efficient design methodologies for
the three critical tasks in edge AI systems, including training data
acquisition, edge training, and edge inference. Finally, we underscore
potential future research directions to further enhance the energy efficiency
of edge AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
Abstract: Data sparsity and cold-start problems are persistent challenges in
recommendation systems. Cross-domain recommendation (CDR) is a promising
solution that utilizes knowledge from the source domain to improve the
recommendation performance in the target domain. Previous CDR approaches have
mainly followed the Embedding and Mapping (EMCDR) framework, which involves
learning a mapping function to facilitate knowledge transfer. However, these
approaches necessitate re-engineering and re-training the network structure to
incorporate transferrable knowledge, which can be computationally expensive and
may result in catastrophic forgetting of the original knowledge. In this paper,
we present a scalable and efficient paradigm to address data sparsity and
cold-start issues in CDR, named CDR-Adapter, by decoupling the original
recommendation model from the mapping function, without requiring
re-engineering the network structure. Specifically, CDR-Adapter is a novel
plug-and-play module that employs adapter modules to align feature
representations, allowing for flexible knowledge transfer across different
domains and efficient fine-tuning with minimal training costs. We conducted
extensive experiments on the benchmark dataset, which demonstrated the
effectiveness of our approach over several state-of-the-art CDR approaches.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: A Virtual Reality Training System for Automotive Engines Assembly and Disassembly
Abstract: Automotive engine assembly and disassembly are common and crucial programs in
the automotive industry. Traditional education trains students to learn
automotive engine assembly and disassembly in lecture courses and then to
operate with physical engines, which are generally low effectiveness and high
cost. In this work, we developed a multi-layer structured Virtual Reality (VR)
system to provide students with training in automotive engine (Buick Verano)
assembly and disassembly. We designed the VR training system with The VR
training system is designed to have several major features, including
replaceable engine parts and reusable tools, friendly user interfaces and
guidance, and bottom-up designed multi-layer architecture, which can be
extended to various engine models. The VR system is evaluated with controlled
experiments of two groups of students. The results demonstrate that our VR
training system provides remarkable usability in terms of effectiveness and
efficiency. Currently, our VR system has been demonstrated and employed in the
courses of Chinese colleges to train students in automotive engine assembly and
disassembly. A free-to-use executable file (Microsoft Windows) and open-source
code are available at https://github.com/LadissonLai/SUSTech_VREngine for
facilitating the development of VR systems in the automotive industry. Finally,
a video describing the operations in our VR training system is available at
https://www.youtube.com/watch?v=yZe4YTwwAC4

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
Abstract: Identifying human emotions using AI-based computer vision systems, when
individuals wear face masks, presents a new challenge in the current Covid-19
pandemic. In this study, we propose a facial emotion recognition system capable
of recognizing emotions from individuals wearing different face masks. A novel
data augmentation technique was utilized to improve the performance of our
model using four mask types for each face image. We evaluated the effectiveness
of four convolutional neural networks, Alexnet, Squeezenet, Resnet50 and
VGGFace2 that were trained using transfer learning. The experimental findings
revealed that our model works effectively in multi-mask mode compared to
single-mask mode. The VGGFace2 network achieved the highest accuracy rate, with
97.82% for the person-dependent mode and 74.21% for the person-independent mode
using the JAFFE dataset. However, we evaluated our proposed model using the
UIBVFED dataset. The Resnet50 has demonstrated superior performance, with
accuracies of 73.68% for the person-dependent mode and 59.57% for the
person-independent mode. Moreover, we employed metrics such as precision,
sensitivity, specificity, AUC, F1 score, and confusion matrix to measure our
system's efficiency in detail. Additionally, the LIME algorithm was used to
visualize CNN's decision-making strategy.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
Abstract: Sepsis requires urgent diagnosis, but research is predominantly focused on
Western datasets. In this study, we perform a comparative analysis of two
ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD
dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis
reveals the effectiveness of these methods in addressing healthcare data
imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight
edge in computational efficiency and scalability. The study paves the way for
the broader application of machine learning in critical care, thereby expanding
the reach of predictive analytics in healthcare globally.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
Abstract: This paper explores the image synthesis capabilities of GPT-4, a leading
multi-modal large language model. We establish a benchmark for evaluating the
fidelity of texture features in images generated by GPT-4, comprising manually
painted pictures and their AI-generated counterparts. The contributions of this
study are threefold: First, we provide an in-depth analysis of the fidelity of
image synthesis features based on GPT-4, marking the first such study on this
state-of-the-art model. Second, the quantitative and qualitative experiments
fully reveals the limitations of the GPT-4 model in image synthesis. Third, we
have compiled a unique benchmark of manual drawings and corresponding
GPT-4-generated images, introducing a new task to advance fidelity research in
AI-generated content (AIGC). The dataset will be available after being
accepted: \url{https://github.com/rickwang28574/DeepArt}. We hope this study
will fuel knowledge, scholarship, and innovation, inspiring uses that transform
how we discover and understand the world of art and promote the development of
AIGC while retaining respect for art.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Abstract: Psychiatrists diagnose mental disorders via the linguistic use of patients.
Still, due to data privacy, existing passive mental health monitoring systems
use alternative features such as activity, app usage, and location via mobile
devices. We propose FedTherapist, a mobile mental health monitoring system that
utilizes continuous speech and keyboard input in a privacy-preserving way via
federated learning. We explore multiple model designs by comparing their
performance and overhead for FedTherapist to overcome the complex nature of
on-device language model training on smartphones. We further propose a
Context-Aware Language Learning (CALL) methodology to effectively utilize
smartphones' large and noisy text for mental health signal sensing. Our
IRB-approved evaluation of the prediction of self-reported depression, stress,
anxiety, and mood from 46 participants shows higher accuracy of FedTherapist
compared with the performance with non-language features, achieving 0.15 AUROC
improvement and 8.21% MAE reduction.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Honesty Is the Best Policy: Defining and Mitigating AI Deception
Abstract: Deceptive agents are a challenge for the safety, trustworthiness, and
cooperation of AI systems. We focus on the problem that agents might deceive in
order to achieve their goals (for instance, in our experiments with language
models, the goal of being evaluated as truthful). There are a number of
existing definitions of deception in the literature on game theory and symbolic
AI, but there is no overarching theory of deception for learning agents in
games. We introduce a formal definition of deception in structural causal
games, grounded in the philosophy literature, and applicable to real-world
machine learning systems. Several examples and results illustrate that our
formal definition aligns with the philosophical and commonsense meaning of
deception. Our main technical result is to provide graphical criteria for
deception. We show, experimentally, that these results can be used to mitigate
deception in reinforcement learning agents and language models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
Abstract: This paper studies the problem of weakly open-vocabulary semantic
segmentation (WOVSS), which learns to segment objects of arbitrary classes
using mere image-text pairs. Existing works turn to enhance the vanilla vision
transformer by introducing explicit grouping recognition, i.e., employing
several group tokens/centroids to cluster the image tokens and perform the
group-text alignment. Nevertheless, these methods suffer from a granularity
inconsistency regarding the usage of group tokens, which are aligned in the
all-to-one v.s. one-to-one manners during the training and inference phases,
respectively. We argue that this discrepancy arises from the lack of elaborate
supervision for each group token. To bridge this granularity gap, this paper
explores explicit supervision for the group tokens from the prototypical
knowledge. To this end, this paper proposes the non-learnable prototypical
regularization (NPR) where non-learnable prototypes are estimated from source
features to serve as supervision and enable contrastive matching of the group
tokens. This regularization encourages the group tokens to segment objects with
less redundancy and capture more comprehensive semantic regions, leading to
increased compactness and richness. Based on NPR, we propose the prototypical
guidance segmentation network (PGSeg) that incorporates multi-modal
regularization by leveraging prototypical sources from both images and texts at
different levels, progressively enhancing the segmentation capability with
diverse prototypical patterns. Experimental results show that our proposed
method achieves state-of-the-art performance on several benchmark datasets. The
source code is available at https://github.com/Ferenas/PGSeg.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Towards Accurate Differential Diagnosis with Large Language Models
Abstract: An accurate differential diagnosis (DDx) is a cornerstone of medical care,
often reached through an iterative process of interpretation that combines
clinical history, physical examination, investigations and procedures.
Interactive interfaces powered by Large Language Models (LLMs) present new
opportunities to both assist and automate aspects of this process. In this
study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its
ability to generate a DDx alone or as an aid to clinicians. 20 clinicians
evaluated 302 challenging, real-world medical cases sourced from the New
England Journal of Medicine (NEJM) case reports. Each case report was read by
two clinicians, who were randomized to one of two assistive conditions: either
assistance from search engines and standard medical resources, or LLM
assistance in addition to these tools. All clinicians provided a baseline,
unassisted DDx prior to using the respective assistive tools. Our LLM for DDx
exhibited standalone performance that exceeded that of unassisted clinicians
(top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study
arms, the DDx quality score was higher for clinicians assisted by our LLM
(top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%)
(McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p =
0.03). Further, clinicians assisted by our LLM arrived at more comprehensive
differential lists than those without its assistance. Our study suggests that
our LLM for DDx has potential to improve clinicians' diagnostic reasoning and
accuracy in challenging cases, meriting further real-world evaluation for its
ability to empower physicians and widen patients' access to specialist-level
expertise.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting Self-Stimulatory Behaviours in Children using raw videos
Abstract: Conventionally, evaluation for the diagnosis of Autism spectrum disorder is
done by a trained specialist through questionnaire-based formal assessments and
by observation of behavioral cues under various settings to capture the early
warning signs of autism. These evaluation techniques are highly subjective and
their accuracy relies on the experience of the specialist. In this regard,
machine learning-based methods for automated capturing of early signs of autism
from the recorded videos of the children is a promising alternative. In this
paper, the authors propose a novel pipelined deep learning architecture to
detect certain self-stimulatory behaviors that help in the diagnosis of autism
spectrum disorder (ASD). The authors also supplement their tool with an
augmented version of the Self Stimulatory Behavior Dataset (SSBD) and also
propose a new label in SSBD Action detection: no-class. The deep learning model
with the new dataset is made freely available for easy adoption to the
researchers and developers community. An overall accuracy of around 81% was
achieved from the proposed pipeline model that is targeted for real-time and
hands-free automated diagnosis. All of the source code, data, licenses of use,
and other relevant material is made freely available in
https://github.com/sarl-iiitb/

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Abstract: Generalization remains one of the most important desiderata for robust robot
learning systems. While recently proposed approaches show promise in
generalization to novel objects, semantic concepts, or visual distribution
shifts, generalization to new tasks remains challenging. For example, a
language-conditioned policy trained on pick-and-place tasks will not be able to
generalize to a folding task, even if the arm trajectory of folding is similar
to pick-and-place. Our key insight is that this kind of generalization becomes
feasible if we represent the task through rough trajectory sketches. We propose
a policy conditioning method using such rough trajectory sketches, which we
call RT-Trajectory, that is practical, easy to specify, and allows the policy
to effectively perform new tasks that would otherwise be challenging to
perform. We find that trajectory sketches strike a balance between being
detailed enough to express low-level motion-centric guidance while being coarse
enough to allow the learned policy to interpret the trajectory sketch in the
context of situational visual observations. In addition, we show how trajectory
sketches can provide a useful interface to communicate with robotic policies:
they can be specified through simple human inputs like drawings or videos, or
through automated methods such as modern image-generating or
waypoint-generating methods. We evaluate RT-Trajectory at scale on a variety of
real-world robotic tasks, and find that RT-Trajectory is able to perform a
wider range of tasks compared to language-conditioned and goal-conditioned
policies, when provided the same training data.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: The Transient Nature of Emergent In-Context Learning in Transformers
Abstract: Transformer neural networks can exhibit a surprising capacity for in-context
learning (ICL) despite not being explicitly trained for it. Prior work has
provided a deeper understanding of how ICL emerges in transformers, e.g.
through the lens of mechanistic interpretability, Bayesian inference, or by
examining the distributional properties of training data. However, in each of
these cases, ICL is treated largely as a persistent phenomenon; namely, once
ICL emerges, it is assumed to persist asymptotically. Here, we show that the
emergence of ICL during transformer training is, in fact, often transient. We
train transformers on synthetic data designed so that both ICL and in-weights
learning (IWL) strategies can lead to correct predictions. We find that ICL
first emerges, then disappears and gives way to IWL, all while the training
loss decreases, indicating an asymptotic preference for IWL. The transient
nature of ICL is observed in transformers across a range of model sizes and
datasets, raising the question of how much to "overtrain" transformers when
seeking compact, cheaper-to-run models. We find that L2 regularization may
offer a path to more persistent ICL that removes the need for early stopping
based on ICL-style validation tasks. Finally, we present initial evidence that
ICL transience may be caused by competition between ICL and IWL circuits.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Educating for AI Cybersecurity Work and Research: Ethics, Systems Thinking, and Communication Requirements
Abstract: The present study explored managerial and instructor perceptions of their
freshly employed cybersecurity workers' or students' preparedness to work
effectively in a changing cybersecurity environment that includes AI tools.
Specifically, we related perceptions of technical preparedness to ethical,
systems thinking, and communication skills. We found that managers and
professors perceive preparedness to use AI tools in cybersecurity to be
significantly associated with all three non-technical skill sets. Most
important, ethics is a clear leader in the network of relationships. Contrary
to expectations that ethical concerns are left behind in the rush to adopt the
most advanced AI tools in security, both higher education instructors and
managers appreciate their role and see them closely associated with technical
prowess. Another significant finding is that professors over-estimate students'
preparedness for ethical, system thinking, and communication abilities compared
to IT managers' perceptions of their newly employed IT workers.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: DoGE: Domain Reweighting with Generalization Estimation
Abstract: The coverage and composition of the pretraining data corpus significantly
impacts the generalization ability of large language models. Conventionally,
the pretraining corpus is composed of various source domains (e.g. CommonCrawl,
Wikipedia, Github etc.) according to certain sampling probabilities (domain
weights). However, current methods lack a principled way to optimize domain
weights for ultimate goal for generalization. We propose DOmain reweighting
with Generalization Estimation (DoGE), where we reweigh the sampling
probability from each domain based on its contribution to the final
generalization objective assessed by a gradient-based generalization estimation
function. First, we train a small-scale proxy model with a min-max optimization
to obtain the reweighted domain weights. At each step, the domain weights are
updated to maximize the overall generalization gain by mirror descent. Finally
we use the obtained domain weights to train a larger scale full-size language
model. On SlimPajama-6B dataset, with universal generalization objective, DoGE
achieves better average perplexity and zero-shot reasoning accuracy. On
out-of-domain generalization tasks, DoGE reduces perplexity on the target
domain by a large margin. We further apply a parameter-selection scheme which
improves the efficiency of generalization estimation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Probing LLMs for Joint Encoding of Linguistic Categories
Abstract: Large Language Models (LLMs) exhibit impressive performance on a range of NLP
tasks, due to the general-purpose linguistic knowledge acquired during
pretraining. Existing model interpretability research (Tenney et al., 2019)
suggests that a linguistic hierarchy emerges in the LLM layers, with lower
layers better suited to solving syntactic tasks and higher layers employed for
semantic processing. Yet, little is known about how encodings of different
linguistic phenomena interact within the models and to what extent processing
of linguistically-related categories relies on the same, shared model
representations. In this paper, we propose a framework for testing the joint
encoding of linguistic categories in LLMs. Focusing on syntax, we find evidence
of joint encoding both at the same (related part-of-speech (POS) classes) and
different (POS classes and related syntactic dependency relations) levels of
linguistic hierarchy. Our cross-lingual experiments show that the same patterns
hold across languages in multilingual LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Formulating Discrete Probability Flow Through Optimal Transport
Abstract: Continuous diffusion models are commonly acknowledged to display a
deterministic probability flow, whereas discrete diffusion models do not. In
this paper, we aim to establish the fundamental theory for the probability flow
of discrete diffusion models. Specifically, we first prove that the continuous
probability flow is the Monge optimal transport map under certain conditions,
and also present an equivalent evidence for discrete cases. In view of these
findings, we are then able to define the discrete probability flow in line with
the principles of optimal transport. Finally, drawing upon our newly
established definitions, we propose a novel sampling method that surpasses
previous discrete diffusion models in its ability to generate more certain
outcomes. Extensive experiments on the synthetic toy dataset and the CIFAR-10
dataset have validated the effectiveness of our proposed discrete probability
flow. Code is released at:
https://github.com/PangzeCheung/Discrete-Probability-Flow.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator
Abstract: Large Language Models (LLMs) excel in understanding human instructions,
driving the development of Multimodal LLMs (MLLMs) with instruction tuning.
However, acquiring high-quality multimodal instruction tuning data poses a
significant challenge. Previous approaches relying on GPT-4 for data generation
proved expensive and exhibited unsatisfactory performance for certain tasks. To
solve this, we present Genixer, an innovative data generation pipeline
producing high-quality multimodal instruction tuning data for various tasks.
Genixer collects datasets for ten prevalent multimodal tasks and designs
instruction templates to transform these datasets into instruction-tuning data.
It then trains pretrained MLLMs to generate task-specific instruction data and
proposes an effective data filtering strategy to ensure high quality. To
evaluate Genixer, a base MLLM model, Kakapo, is built and achieves SoTA
performance in image captioning and visual question answering (VQA) tasks
across multiple datasets. Experimental results show that filtered data from
Genixer continually improves Kakapo for image captioning and VQA tasks. For the
SoTA Shikra MLLM model on the image-region-related tasks, e.g., region caption
and detection, Genixer also successfully generates corresponding data and
improves its performance. Genixer opens avenues for generating high-quality
multimodal instruction data for diverse tasks, enabling innovative applications
across domains. The code and models will be released soon.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Clinical Decision Support System for Unani Medicine Practitioners
Abstract: Like other fields of Traditional Medicines, Unani Medicines have been found
as an effective medical practice for ages. It is still widely used in the
subcontinent, particularly in Pakistan and India. However, Unani Medicines
Practitioners are lacking modern IT applications in their everyday clinical
practices. An Online Clinical Decision Support System may address this
challenge to assist apprentice Unani Medicines practitioners in their
diagnostic processes. The proposed system provides a web-based interface to
enter the patient's symptoms, which are then automatically analyzed by our
system to generate a list of probable diseases. The system allows practitioners
to choose the most likely disease and inform patients about the associated
treatment options remotely. The system consists of three modules: an Online
Clinical Decision Support System, an Artificial Intelligence Inference Engine,
and a comprehensive Unani Medicines Database. The system employs advanced AI
techniques such as Decision Trees, Deep Learning, and Natural Language
Processing. For system development, the project team used a technology stack
that includes React, FastAPI, and MySQL. Data and functionality of the
application is exposed using APIs for integration and extension with similar
domain applications. The novelty of the project is that it addresses the
challenge of diagnosing diseases accurately and efficiently in the context of
Unani Medicines principles. By leveraging the power of technology, the proposed
Clinical Decision Support System has the potential to ease access to healthcare
services and information, reduce cost, boost practitioner and patient
satisfaction, improve speed and accuracy of the diagnostic process, and provide
effective treatments remotely. The application will be useful for Unani
Medicines Practitioners, Patients, Government Drug Regulators, Software
Developers, and Medical Researchers.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models
Abstract: Despite recent significant strides achieved by diffusion-based Text-to-Image
(T2I) models, current systems are still less capable of ensuring decent
compositional generation aligned with text prompts, particularly for the
multi-object generation. This work illuminates the fundamental reasons for such
misalignment, pinpointing issues related to low attention activation scores and
mask overlaps. While previous research efforts have individually tackled these
issues, we assert that a holistic approach is paramount. Thus, we propose two
novel objectives, the Separate loss and the Enhance loss, that reduce object
mask overlaps and maximize attention scores, respectively. Our method diverges
from conventional test-time-adaptation techniques, focusing on finetuning
critical parameters, which enhances scalability and generalizability.
Comprehensive evaluations demonstrate the superior performance of our model in
terms of image realism, text-image alignment, and adaptability, notably
outperforming prominent baselines. Ultimately, this research paves the way for
T2I diffusion models with enhanced compositional capacities and broader
applicability. The project webpage is available at
https://zpbao.github.io/projects/SepEn/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Image Clustering Conditioned on Text Criteria
Abstract: Classical clustering methods do not provide users with direct control of the
clustering results, and the clustering results may not be consistent with the
relevant criterion that a user has in mind. In this work, we present a new
methodology for performing image clustering based on user-specified text
criteria by leveraging modern vision-language models and large language models.
We call our method Image Clustering Conditioned on Text Criteria (IC|TC), and
it represents a different paradigm of image clustering. IC|TC requires a
minimal and practical degree of human intervention and grants the user
significant control over the clustering results in return. Our experiments show
that IC|TC can effectively cluster images with various criteria, such as human
action, physical location, or the person's mood, while significantly
outperforming baselines.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
Abstract: Fairness for machine learning predictions is widely required in practice for
legal, ethical, and societal reasons. Existing work typically focuses on
settings without unobserved confounding, even though unobserved confounding can
lead to severe violations of causal fairness and, thus, unfair predictions. In
this work, we analyze the sensitivity of causal fairness to unobserved
confounding. Our contributions are three-fold. First, we derive bounds for
causal fairness metrics under different sources of unobserved confounding. This
enables practitioners to examine the sensitivity of their machine learning
models to unobserved confounding in fairness-critical applications. Second, we
propose a novel neural framework for learning fair predictions, which allows us
to offer worst-case guarantees of the extent to which causal fairness can be
violated due to unobserved confounding. Third, we demonstrate the effectiveness
of our framework in a series of experiments, including a real-world case study
about predicting prison sentences. To the best of our knowledge, ours is the
first work to study causal fairness under unobserved confounding. To this end,
our work is of direct practical value as a refutation strategy to ensure the
fairness of predictions in high-stakes applications.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
Abstract: Neuro-symbolic AI bridges the gap between purely symbolic and neural
approaches to learning. This often requires maximizing the likelihood of a
symbolic constraint w.r.t the neural network's output distribution. Such output
distributions are typically assumed to be fully-factorized. This limits the
applicability of neuro-symbolic learning to the more expressive autoregressive
distributions, e.g., transformers. Under such distributions, computing the
likelihood of even simple constraints is #P-hard. Instead of attempting to
enforce the constraint on the entire output distribution, we propose to do so
on a random, local approximation thereof. More precisely, we optimize the
likelihood of the constraint under a pseudolikelihood-based approximation
centered around a model sample. Our approximation is factorized, allowing the
reuse of solutions to sub-problems, a main tenet for efficiently computing
neuro-symbolic losses. Moreover, it is a local, high-fidelity approximation of
the likelihood, exhibiting low entropy and KL-divergence around the model
sample. We evaluate our approach on Sudoku and shortest-path prediction cast as
autoregressive generation, and observe that we greatly improve upon the base
model's ability to predict logically-consistent outputs. We also evaluate on
the task of detoxifying large language models. Using a simple constraint
disallowing a list of toxic words, we are able to steer the model's outputs
away from toxic generations, achieving SoTA detoxification compared to previous
approaches.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ASI: Accuracy-Stability Index for Evaluating Deep Learning Models
Abstract: In the context of deep learning research, where model introductions
continually occur, the need for effective and efficient evaluation remains
paramount. Existing methods often emphasize accuracy metrics, overlooking
stability. To address this, the paper introduces the Accuracy-Stability Index
(ASI), a quantitative measure incorporating both accuracy and stability for
assessing deep learning models. Experimental results demonstrate the
application of ASI, and a 3D surface model is presented for visualizing ASI,
mean accuracy, and coefficient of variation. This paper addresses the important
issue of quantitative benchmarking metrics for deep learning models, providing
a new approach for accurately evaluating accuracy and stability of deep
learning models. The paper concludes with discussions on potential weaknesses
and outlines future research directions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dense Visual Odometry Using Genetic Algorithm
Abstract: Our work aims to estimate the camera motion mounted on the head of a mobile
robot or a moving object from RGB-D images in a static scene. The problem of
motion estimation is transformed into a nonlinear least squares function.
Methods for solving such problems are iterative. Various classic methods gave
an iterative solution by linearizing this function. We can also use the
metaheuristic optimization method to solve this problem and improve results. In
this paper, a new algorithm is developed for visual odometry using a sequence
of RGB-D images. This algorithm is based on a genetic algorithm. The proposed
iterative genetic algorithm searches using particles to estimate the optimal
motion and then compares it to the traditional methods. To evaluate our method,
we use the root mean square error to compare it with the based energy method
and another metaheuristic method. We prove the efficiency of our innovative
algorithm on a large set of images.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
Abstract: Large-scale video-language pre-training has made remarkable strides in
advancing video-language understanding tasks. However, the heavy computational
burden of video encoding remains a formidable efficiency bottleneck,
particularly for long-form videos. These videos contain massive visual tokens
due to their inherent 3D properties and spatiotemporal redundancy, making it
challenging to capture complex temporal and spatial relationships. To tackle
this issue, we propose an efficient method called TEmporal-Spatial Token
Aggregation (TESTA). TESTA condenses video semantics by adaptively aggregating
similar frames, as well as similar patches within each frame. TESTA can reduce
the number of visual tokens by 75% and thus accelerate video encoding. Building
upon TESTA, we introduce a pre-trained video-language model equipped with a
divided space-time token aggregation module in each video encoder block. We
evaluate our model on five datasets for paragraph-to-video retrieval and
long-form VideoQA tasks. Experimental results show that TESTA improves
computing efficiency by 1.7 times, and achieves significant performance gains
from its scalability in processing longer input frames, e.g., +13.7 R@1 on
QuerYD and +6.5 R@1 on Condensed Movie.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Abstract: Learning analytics research increasingly studies classroom learning with
AI-based systems through rich contextual data from outside these systems,
especially student-teacher interactions. One key challenge in leveraging such
data is generating meaningful insights into effective teacher practices.
Quantitative ethnography bears the potential to close this gap by combining
multimodal data streams into networks of co-occurring behavior that drive
insight into favorable learning conditions. The present study uses transmodal
ordered network analysis to understand effective teacher practices in
relationship to traditional metrics of in-system learning in a mathematics
classroom working with AI tutors. Incorporating teacher practices captured by
position tracking and human observation codes into modeling significantly
improved the inference of how efficiently students improved in the AI tutor
beyond a model with tutor log data features only. Comparing teacher practices
by student learning rates, we find that students with low learning rates
exhibited more hint use after monitoring. However, after an extended visit,
students with low learning rates showed learning behavior similar to their high
learning rate peers, achieving repeated correct attempts in the tutor.
Observation notes suggest conceptual and procedural support differences can
help explain visit effectiveness. Taken together, offering early conceptual
support to students with low learning rates could make classroom practice with
AI tutors more effective. This study advances the scientific understanding of
effective teacher practice in classrooms learning with AI tutors and
methodologies to make such practices visible.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
Abstract: Large Language Models (LLMs) have emerged as dominant tools for various
tasks, particularly when tailored for a specific target by prompt tuning.
Nevertheless, concerns surrounding data privacy present obstacles due to the
tuned prompts' dependency on sensitive private information. A practical
solution is to host a local LLM and optimize a soft prompt privately using
data. Yet, hosting a local model becomes problematic when model ownership is
protected. Alternative methods, like sending data to the model's provider for
training, intensify these privacy issues facing an untrusted provider. In this
paper, we present a novel solution called Differentially-Private Offsite Prompt
Tuning (DP-OPT) to address this challenge. Our approach involves tuning a
discrete prompt on the client side and then applying it to the desired cloud
models. We demonstrate that prompts suggested by LLMs themselves can be
transferred without compromising performance significantly. To ensure that the
prompts do not leak private information, we introduce the first private prompt
generation mechanism, by a differentially-private (DP) ensemble of in-context
learning with private demonstrations. With DP-OPT, generating
privacy-preserving prompts by Vicuna-7b can yield competitive performance
compared to non-private in-context learning on GPT3.5 or local private prompt
tuning. Codes are available at https://github.com/VITA-Group/DP-OPT .

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: InstructPipe: Building Visual Programming Pipelines with Human Instructions
Abstract: Visual programming provides beginner-level programmers with a coding-free
experience to build their customized pipelines. Existing systems require users
to build a pipeline entirely from scratch, implying that novice users need to
set up and link appropriate nodes all by themselves, starting from a blank
workspace. We present InstructPipe, an AI assistant that enables users to start
prototyping machine learning (ML) pipelines with text instructions. We designed
two LLM modules and a code interpreter to execute our solution. LLM modules
generate pseudocode of a target pipeline, and the interpreter renders a
pipeline in the node-graph editor for further human-AI collaboration. Technical
evaluations reveal that InstructPipe reduces user interactions by 81.1%
compared to traditional methods. Our user study (N=16) showed that InstructPipe
empowers novice users to streamline their workflow in creating desired ML
pipelines, reduce their learning curve, and spark innovative ideas with
open-ended commands.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: TD-MPC2: Scalable, Robust World Models for Continuous Control
Abstract: TD-MPC is a model-based reinforcement learning (RL) algorithm that performs
local trajectory optimization in the latent space of a learned implicit
(decoder-free) world model. In this work, we present TD-MPC2: a series of
improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves
significantly over baselines across 104 online RL tasks spanning 4 diverse task
domains, achieving consistently strong results with a single set of
hyperparameters. We further show that agent capabilities increase with model
and data size, and successfully train a single 317M parameter agent to perform
80 tasks across multiple task domains, embodiments, and action spaces. We
conclude with an account of lessons, opportunities, and risks associated with
large TD-MPC2 agents. Explore videos, models, data, code, and more at
https://nicklashansen.github.io/td-mpc2

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Study on the Calibration of In-context Learning
Abstract: Modern auto-regressive language models are trained to minimize log loss on
broad data by predicting the next token so they are expected to get calibrated
answers in next-token prediction tasks. We study this for in-context learning
(ICL), a widely used way to adapt frozen large language models (LLMs) via
crafting prompts, and investigate the trade-offs between performance and
calibration on a wide range of natural language understanding and reasoning
tasks. We conduct extensive experiments to show that such trade-offs may get
worse as we increase model size, incorporate more ICL examples, and fine-tune
models using instruction, dialog, or reinforcement learning from human feedback
(RLHF) on carefully curated datasets. Furthermore, we find that common
recalibration techniques that are widely effective such as temperature scaling
provide limited gains in calibration errors, suggesting that new methods may be
required for settings where models are expected to be reliable.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Real-Time Neural Rasterization for Large Scenes
Abstract: We propose a new method for realistic real-time novel-view synthesis (NVS) of
large scenes. Existing neural rendering methods generate realistic results, but
primarily work for small scale scenes (<50 square meters) and have difficulty
at large scale (>10000 square meters). Traditional graphics-based rasterization
rendering is fast for large scenes but lacks realism and requires expensive
manually created assets. Our approach combines the best of both worlds by
taking a moderate-quality scaffold mesh as input and learning a neural texture
field and shader to model view-dependant effects to enhance realism, while
still using the standard graphics pipeline for real-time rendering. Our method
outperforms existing neural rendering methods, providing at least 30x faster
rendering with comparable or better realism for large self-driving and drone
scenes. Our work is the first to enable real-time rendering of large real-world
scenes.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models
Abstract: The accurate classification of student help requests with respect to the type
of help being sought can enable the tailoring of effective responses.
Automatically classifying such requests is non-trivial, but large language
models (LLMs) appear to offer an accessible, cost-effective solution. This
study evaluates the performance of the GPT-3.5 and GPT-4 models for classifying
help requests from students in an introductory programming class. In zero-shot
trials, GPT-3.5 and GPT-4 exhibited comparable performance on most categories,
while GPT-4 outperformed GPT-3.5 in classifying sub-categories for requests
related to debugging. Fine-tuning the GPT-3.5 model improved its performance to
such an extent that it approximated the accuracy and consistency across
categories observed between two human raters. Overall, this study demonstrates
the feasibility of using LLMs to enhance educational systems through the
automated classification of student needs.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Gaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration
Abstract: Collaborative robots (cobots) are widely used in industrial applications, yet
extensive research is still needed to enhance human-robot collaborations and
operator experience. A potential approach to improve the collaboration
experience involves adapting cobot behavior based on natural cues from the
operator. Inspired by the literature on human-human interactions, we conducted
a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a
trigger for initiating joint activities in collaborative sessions. In this
study, 37 participants engaged in an assembly task while their gaze behavior
was analyzed. We employ a gaze-based attention recognition model to identify
when the participants look at the cobot. Our results indicate that in most
cases (84.88\%), the joint activity is preceded by a gaze towards the cobot.
Furthermore, during the entire assembly cycle, the participants tend to look at
the cobot around the time of the joint activity. To the best of our knowledge,
this is the first study to analyze the natural gaze behavior of participants
working on a joint activity with a robot during a collaborative assembly task.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
Abstract: The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: ChatSOS: LLM-based knowledge Q&A system for safety engineering
Abstract: Recent advancements in large language models (LLMs) have notably propelled
natural language processing (NLP) capabilities, demonstrating significant
potential in safety engineering applications. Despite these advancements, LLMs
face constraints in processing specialized tasks, attributed to factors such as
corpus size, input processing limitations, and privacy concerns. Obtaining
useful information from reliable sources in a limited time is crucial for LLM.
Addressing this, our study introduces an LLM-based Q&A system for safety
engineering, enhancing the comprehension and response accuracy of the model. We
employed prompt engineering to incorporate external knowledge databases, thus
enriching the LLM with up-to-date and reliable information. The system analyzes
historical incident reports through statistical methods, utilizes vector
embedding to construct a vector database, and offers an efficient
similarity-based search functionality. Our findings indicate that the
integration of external knowledge significantly augments the capabilities of
LLM for in-depth problem analysis and autonomous task assignment. It
effectively summarizes accident reports and provides pertinent recommendations.
This integration approach not only expands LLM applications in safety
engineering but also sets a precedent for future developments towards
automation and intelligent systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Towards A Unified View of Answer Calibration for Multi-Step Reasoning
Abstract: Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have
broadened the scope for improving multi-step reasoning capabilities. Usually,
answer calibration strategies such as step-level or path-level calibration play
a vital role in multi-step reasoning. While effective, there remains a
significant gap in our understanding of the key factors that drive their
success. In this paper, we break down the design of recent answer calibration
strategies and present a unified view which establishes connections between
them. We then conduct a thorough evaluation on these strategies from a unified
view, systematically scrutinizing step-level and path-level answer calibration
across multiple paths. Our study holds the potential to illuminate key insights
for optimizing multi-step reasoning with answer calibration.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies
Abstract: Recent digital advances have popularized predictive maintenance (PMx),
offering enhanced efficiency, automation, accuracy, cost savings, and
independence in maintenance. Yet, it continues to face numerous limitations
such as poor explainability, sample inefficiency of data-driven methods,
complexity of physics-based methods, and limited generalizability and
scalability of knowledge-based methods. This paper proposes leveraging Digital
Twins (DTs) to address these challenges and enable automated PMx adoption at
larger scales. While we argue that DTs have this transformative potential, they
have not yet reached the level of maturity needed to bridge these gaps in a
standardized way. Without a standard definition for such evolution, this
transformation lacks a solid foundation upon which to base its development.
This paper provides a requirement-based roadmap supporting standardized PMx
automation using DT technologies. A systematic approach comprising two primary
stages is presented. First, we methodically identify the Informational
Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a
foundation from which any unified framework must emerge. Our approach to
defining and using IRs and FRs to form the backbone of any PMx DT is supported
by the track record of IRs and FRs being successfully used as blueprints in
other areas, such as for product development within the software industry.
Second, we conduct a thorough literature review spanning fields to determine
the ways in which these IRs and FRs are currently being used within DTs,
enabling us to point to the specific areas where further research is warranted
to support the progress and maturation of requirement-based PMx DTs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Two-step dynamic obstacle avoidance
Abstract: Dynamic obstacle avoidance (DOA) is a fundamental challenge for any
autonomous vehicle, independent of whether it operates in sea, air, or land.
This paper proposes a two-step architecture for handling DOA tasks by combining
supervised and reinforcement learning (RL). In the first step, we introduce a
data-driven approach to estimate the collision risk of an obstacle using a
recurrent neural network, which is trained in a supervised fashion and offers
robustness to non-linear obstacle movements. In the second step, we include
these collision risk estimates into the observation space of an RL agent to
increase its situational awareness.~We illustrate the power of our two-step
approach by training different RL agents in a challenging environment that
requires to navigate amid multiple obstacles. The non-linear movements of
obstacles are exemplarily modeled based on stochastic processes and periodic
patterns, although our architecture is suitable for any obstacle dynamics. The
experiments reveal that integrating our collision risk metrics into the
observation space doubles the performance in terms of reward, which is
equivalent to halving the number of collisions in the considered environment.
Furthermore, we show that the architecture's performance improvement is
independent of the applied RL algorithm.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Generative artificial intelligence enhances individual creativity but reduces the collective diversity of novel content
Abstract: Creativity is core to being human. Generative artificial intelligence (GenAI)
holds promise for humans to be more creative by offering new ideas, or less
creative by anchoring on GenAI ideas. We study the causal impact of GenAI ideas
on the production of an unstructured creative output in an online experimental
study where some writers could obtain ideas for a story from a GenAI platform.
We find that access to GenAI ideas causes stories to be evaluated as more
creative, better written and more enjoyable, especially among less creative
writers. However, objective measures of story similarity within each condition
reveal that GenAI-enabled stories are more similar to each other than stories
by humans alone. These results point to an increase in individual creativity,
but at the same time there is a risk of losing collective novelty: this dynamic
resembles a social dilemma where individual writers are better off using GenAI
to improve their own writing, but collectively a narrower scope of novel
content may be produced with GenAI. Our results have implications for
researchers, policy-makers and practitioners interested in bolstering
creativity, but point to potential downstream consequences from over-reliance.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data
Abstract: Federated Learning (FL) is a decentralized machine learning (ML) technique
that allows a number of participants to train an ML model collaboratively
without having to share their private local datasets with others. When
participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would
experience heterogeneity due to the majorly skewed (non-independent and
identically distributed -IID) collected data. In addition, UAVs may demonstrate
unintentional misbehavior in which the latter may fail to send updates to the
FL server due, for instance, to UAVs' disconnectivity from the FL system caused
by high mobility, unavailability, or battery depletion. Such challenges may
significantly affect the convergence of the FL model. A recent way to tackle
these challenges is client selection, based on customized criteria that
consider UAV computing power and energy consumption. However, most existing
client selection schemes neglected the participants' reliability. Indeed, FL
can be targeted by poisoning attacks, in which malicious UAVs upload poisonous
local models to the FL server, by either providing targeted false predictions
for specifically chosen inputs or by compromising the global model's accuracy
through tampering with the local model. Hence, we propose in this paper a novel
client selection scheme that enhances convergence by prioritizing fast UAVs
with high-reliability scores, while eliminating malicious UAVs from training.
Through experiments, we assess the effectiveness of our scheme in resisting
different attack scenarios, in terms of convergence and achieved model
accuracy. Finally, we demonstrate the performance superiority of the proposed
approach compared to baseline methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Stochastic Directly-Follows Process Discovery Using Grammatical Inference
Abstract: Starting with a collection of traces generated by process executions, process
discovery is the task of constructing a simple model that describes the
process, where simplicity is often measured in terms of model size. The
challenge of process discovery is that the process of interest is unknown, and
that while the input traces constitute positive examples of process executions,
no negative examples are available. Many commercial tools discover
Directly-Follows Graphs, in which nodes represent the observable actions of the
process, and directed arcs indicate execution order possibilities over the
actions. We propose a new approach for discovering sound Directly-Follows
Graphs that is grounded in grammatical inference over the input traces. To
promote the discovery of small graphs that also describe the process accurately
we design and evaluate a genetic algorithm that supports the convergence of the
inference parameters to the areas that lead to the discovery of interesting
models. Experiments over real-world datasets confirm that our new approach can
construct smaller models that represent the input traces and their frequencies
more accurately than the state-of-the-art technique. Reasoning over the
frequencies of encoded traces also becomes possible, due to the stochastic
semantics of the action graphs we propose, which, for the first time, are
interpreted as models that describe the stochastic languages of action traces.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: H-GAP: Humanoid Control with a Generalist Planner
Abstract: Humanoid control is an important research challenge offering avenues for
integration into human-centric infrastructures and enabling physics-driven
humanoid animations. The daunting challenges in this field stem from the
difficulty of optimizing in high-dimensional action spaces and the instability
introduced by the bipedal morphology of humanoids. However, the extensive
collection of human motion-captured data and the derived datasets of humanoid
trajectories, such as MoCapAct, paves the way to tackle these challenges. In
this context, we present Humanoid Generalist Autoencoding Planner (H-GAP), a
state-action trajectory generative model trained on humanoid trajectories
derived from human motion-captured data, capable of adeptly handling downstream
control tasks with Model Predictive Control (MPC). For 56 degrees of freedom
humanoid, we empirically demonstrate that H-GAP learns to represent and
generate a wide range of motor behaviours. Further, without any learning from
online interactions, it can also flexibly transfer these behaviors to solve
novel downstream control tasks via planning. Notably, H-GAP excels established
MPC baselines that have access to the ground truth dynamics model, and is
superior or comparable to offline RL methods trained for individual tasks.
Finally, we do a series of empirical studies on the scaling properties of
H-GAP, showing the potential for performance gains via additional data but not
computing. Code and videos are available at
https://ycxuyingchen.github.io/hgap/.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Tuning-less Object Naming with a Foundation Model
Abstract: We implement a real-time object naming system that enables learning a set of
named entities never seen. Our approach employs an existing foundation model
that we consider ready to see anything before starting. It turns seen images
into relatively small feature vectors that we associate with index to a
gradually built vocabulary without any training of fine-tuning of the model.
Our contribution is using the association mechanism known from transformers as
attention. It has features that support generalization from irrelevant
information for distinguishing the entities and potentially enable associating
with much more than indices to vocabulary. As a result, the system can work in
a one-shot manner and correctly name objects named in different contents. We
also outline implementation details of the system modules integrated by a
blackboard architecture. Finally, we investigate the system's quality, mainly
how many objects it can handle in this way.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
Abstract: In this paper, we democratise 3D content creation, enabling precise
generation of 3D shapes from abstract sketches while overcoming limitations
tied to drawing skills. We introduce a novel part-level modelling and alignment
framework that facilitates abstraction modelling and cross-modal
correspondence. Leveraging the same part-level decoder, our approach seamlessly
extends to sketch modelling by establishing correspondence between CLIPasso
edgemaps and projected 3D part regions, eliminating the need for a dataset
pairing human sketches and 3D shapes. Additionally, our method introduces a
seamless in-position editing process as a byproduct of cross-modal part-aligned
modelling. Operating in a low-dimensional implicit space, our approach
significantly reduces computational demands and processing time.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Exploring Geometry of Blind Spots in Vision Models
Abstract: Despite the remarkable success of deep neural networks in a myriad of
settings, several works have demonstrated their overwhelming sensitivity to
near-imperceptible perturbations, known as adversarial attacks. On the other
hand, prior works have also observed that deep networks can be under-sensitive,
wherein large-magnitude perturbations in input space do not induce appreciable
changes to network activations. In this work, we study in detail the phenomenon
of under-sensitivity in vision models such as CNNs and Transformers, and
present techniques to study the geometry and extent of "equi-confidence" level
sets of such networks. We propose a Level Set Traversal algorithm that
iteratively explores regions of high confidence with respect to the input space
using orthogonal components of the local gradients. Given a source image, we
use this algorithm to identify inputs that lie in the same equi-confidence
level set as the source image despite being perceptually similar to arbitrary
images from other classes. We further observe that the source image is linearly
connected by a high-confidence path to these inputs, uncovering a star-like
structure for level sets of deep networks. Furthermore, we attempt to identify
and estimate the extent of these connected higher-dimensional regions over
which the model maintains a high degree of confidence. The code for this
project is publicly available at
https://github.com/SriramB-98/blindspots-neurips-sub

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning
Abstract: Large language models (LLMs) can be used as accessible and intelligent
chatbots by constructing natural language queries and directly inputting the
prompt into the large language model. However, different prompt' constructions
often lead to uncertainty in the answers and thus make it hard to utilize the
specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an
interpretable structure to explain the prompt learning principle in LLMs, which
certificates that the effectiveness of language models is determined by
position changes of the task's related tokens. Therefore, we propose MTPrompt,
a multi-dimensional task prompt learning method consisting based on
task-related object, summary, and task description information. By
automatically building and searching for appropriate prompts, our proposed
MTPrompt achieves the best results on few-shot samples setting and five
different datasets. In addition, we demonstrate the effectiveness and stability
of our method in different experimental settings and ablation experiments. In
interaction with large language models, embedding more task-related information
into prompts will make it easier to stimulate knowledge embedded in large
language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Abstract: Construction of a universal detector poses a crucial question: How can we
most effectively train a model on a large mixture of datasets? The answer lies
in learning dataset-specific features and ensembling their knowledge but do all
this in a single model. Previous methods achieve this by having separate
detection heads on a common backbone but that results in a significant increase
in parameters. In this work, we present Mixture-of-Experts as a solution,
highlighting that MoEs are much more than a scalability tool. We propose
Dataset-Aware Mixture-of-Experts, DAMEX where we train the experts to become an
`expert' of a dataset by learning to route each dataset tokens to its mapped
expert. Experiments on Universal Object-Detection Benchmark show that we
outperform the existing state-of-the-art by average +10.2 AP score and improve
over our non-MoE baseline by average +2.0 AP score. We also observe consistent
gains while mixing datasets with (1) limited availability, (2) disparate
domains and (3) divergent label sets. Further, we qualitatively show that DAMEX
is robust against expert representation collapse.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning
Abstract: In previous works, a mobile application was developed using an unmodified
commercial off-the-shelf smartphone to recognize whole-body exercises. The
working principle was based on the ultrasound Doppler sensing with the device
built-in hardware. Applying such a lab-environment trained model on realistic
application variations causes a significant drop in performance, and thus
decimate its applicability. The reason of the reduced performance can be
manifold. It could be induced by the user, environment, and device variations
in realistic scenarios. Such scenarios are often more complex and diverse,
which can be challenging to anticipate in the initial training data. To study
and overcome this issue, this paper presents a database with controlled and
uncontrolled subsets of fitness exercises. We propose two concepts to utilize
small adaption data to successfully improve model generalization in an
uncontrolled environment, increasing the recognition accuracy by two to six
folds compared to the baseline for different users.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
Abstract: In the context of a voice assistant system, steering refers to the phenomenon
in which a user issues a follow-up command attempting to direct or clarify a
previous turn. We propose STEER, a steering detection model that predicts
whether a follow-up turn is a user's attempt to steer the previous command.
Constructing a training dataset for steering use cases poses challenges due to
the cold-start problem. To overcome this, we developed heuristic rules to
sample opt-in usage data, approximating positive and negative samples without
any annotation. Our experimental results show promising performance in
identifying steering intent, with over 95% accuracy on our sampled data.
Moreover, STEER, in conjunction with our sampling strategy, aligns effectively
with real-world steering scenarios, as evidenced by its strong zero-shot
performance on a human-graded evaluation set. In addition to relying solely on
user transcripts as input, we introduce STEER+, an enhanced version of the
model. STEER+ utilizes a semantic parse tree to provide more context on
out-of-vocabulary words, such as named entities that often occur at the
sentence boundary. This further improves model performance, reducing error rate
in domains where entities frequently appear, such as messaging. Lastly, we
present a data analysis that highlights the improvement in user experience when
voice assistants support steering use cases.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Task Tree Retrieval For Robotic Cooking
Abstract: This paper is based on developing different algorithms, which generate the
task tree planning for the given goal node(recipe). The knowledge
representation of the dishes is called FOON. It contains the different objects
and their between them with respective to the motion node The graphical
representation of FOON is made by noticing the change in the state of an object
with respect to the human manipulators. We will explore how the FOON is created
for different recipes by the robots. Task planning contains difficulties in
exploring unknown problems, as its knowledge is limited to the FOON. To get the
task tree planning for a given recipe, the robot will retrieve the information
of different functional units from the knowledge retrieval process called FOON.
Thus the generated subgraphs will allow the robot to cook the required dish.
Thus the robot can able to cook the given recipe by following the sequence of
instructions.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: TaskWeaver: A Code-First Agent Framework
Abstract: Large Language Models (LLMs) have shown impressive abilities in natural
language understanding and generation, leading to their use in applications
such as chatbots and virtual assistants. However, existing LLM frameworks face
limitations in handling domain-specific data analytics tasks with rich data
structures. Moreover, they struggle with flexibility to meet diverse user
requirements. To address these issues, TaskWeaver is proposed as a code-first
framework for building LLM-powered autonomous agents. It converts user requests
into executable code and treats user-defined plugins as callable functions.
TaskWeaver provides support for rich data structures, flexible plugin usage,
and dynamic plugin selection, and leverages LLM coding capabilities for complex
logic. It also incorporates domain-specific knowledge through examples and
ensures the secure execution of generated code. TaskWeaver offers a powerful
and flexible framework for creating intelligent conversational agents that can
handle complex tasks and adapt to domain-specific scenarios. The code is
open-sourced at https://github.com/microsoft/TaskWeaver/.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Concept Alignment as a Prerequisite for Value Alignment
Abstract: Value alignment is essential for building AI systems that can safely and
reliably interact with people. However, what a person values -- and is even
capable of valuing -- depends on the concepts that they are currently using to
understand and evaluate what happens in the world. The dependence of values on
concepts means that concept alignment is a prerequisite for value alignment --
agents need to align their representation of a situation with that of humans in
order to successfully align their values. Here, we formally analyze the concept
alignment problem in the inverse reinforcement learning setting, show how
neglecting concept alignment can lead to systematic value mis-alignment, and
describe an approach that helps minimize such failure modes by jointly
reasoning about a person's concepts and values. Additionally, we report
experimental results with human participants showing that humans reason about
the concepts used by an agent when acting intentionally, in line with our joint
reasoning model.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning
Abstract: Federated Learning (FL) is a novel privacy-protection distributed machine
learning paradigm that guarantees user privacy and prevents the risk of data
leakage due to the advantage of the client's local training. Researchers have
struggled to design fair FL systems that ensure fairness of results. However,
the interplay between fairness and privacy has been less studied. Increasing
the fairness of FL systems can have an impact on user privacy, while an
increase in user privacy can affect fairness. In this work, on the client side,
we use fairness metrics, such as Demographic Parity (DemP), Equalized Odds
(EOs), and Disparate Impact (DI), to construct the local fair model. To protect
the privacy of the client model, we propose a privacy-protection fairness FL
method. The results show that the accuracy of the fair model with privacy
increases because privacy breaks the constraints of the fairness metrics. In
our experiments, we conclude the relationship between privacy, fairness and
utility, and there is a tradeoff between these.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On The Fairness Impacts of Hardware Selection in Machine Learning
Abstract: In the machine learning ecosystem, hardware selection is often regarded as a
mere utility, overshadowed by the spotlight on algorithms and data. This
oversight is particularly problematic in contexts like ML-as-a-service
platforms, where users often lack control over the hardware used for model
deployment. How does the choice of hardware impact generalization properties?
This paper investigates the influence of hardware on the delicate balance
between model performance and fairness. We demonstrate that hardware choices
can exacerbate existing disparities, attributing these discrepancies to
variations in gradient flows and loss surfaces across different demographic
groups. Through both theoretical and empirical analysis, the paper not only
identifies the underlying factors but also proposes an effective strategy for
mitigating hardware-induced performance imbalances.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Resource-constrained knowledge diffusion processes inspired by human peer learning
Abstract: We consider a setting where a population of artificial learners is given, and
the objective is to optimize aggregate measures of performance, under
constraints on training resources. The problem is motivated by the study of
peer learning in human educational systems. In this context, we study natural
knowledge diffusion processes in networks of interacting artificial learners.
By `natural', we mean processes that reflect human peer learning where the
students' internal state and learning process is mostly opaque, and the main
degree of freedom lies in the formation of peer learning groups by a
coordinator who can potentially evaluate the learners before assigning them to
peer groups. Among else, we empirically show that such processes indeed make
effective use of the training resources, and enable the design of modular
neural models that have the capacity to generalize without being prone to
overfitting noisy labels.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The Limits of Fair Medical Imaging AI In The Wild
Abstract: As artificial intelligence (AI) rapidly approaches human-level performance in
medical imaging, it is crucial that it does not exacerbate or propagate
healthcare disparities. Prior research has established AI's capacity to infer
demographic data from chest X-rays, leading to a key concern: do models using
demographic shortcuts have unfair predictions across subpopulations? In this
study, we conduct a thorough investigation into the extent to which medical AI
utilizes demographic encodings, focusing on potential fairness discrepancies
within both in-distribution training sets and external test sets. Our analysis
covers three key medical imaging disciplines: radiology, dermatology, and
ophthalmology, and incorporates data from six global chest X-ray datasets. We
confirm that medical imaging AI leverages demographic shortcuts in disease
classification. While correcting shortcuts algorithmically effectively
addresses fairness gaps to create "locally optimal" models within the original
data distribution, this optimality is not true in new test settings.
Surprisingly, we find that models with less encoding of demographic attributes
are often most "globally optimal", exhibiting better fairness during model
evaluation in new test environments. Our work establishes best practices for
medical imaging models which maintain their performance and fairness in
deployments beyond their initial training contexts, underscoring critical
considerations for AI clinical deployments across populations and sites.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence
Abstract: We present DARLEI, a framework that combines evolutionary algorithms with
parallelized reinforcement learning for efficiently training and evolving
populations of UNIMAL agents. Our approach utilizes Proximal Policy
Optimization (PPO) for individual agent learning and pairs it with a tournament
selection-based generational learning mechanism to foster morphological
evolution. By building on Nvidia's Isaac Gym, DARLEI leverages GPU accelerated
simulation to achieve over 20x speedup using just a single workstation,
compared to previous work which required large distributed CPU clusters. We
systematically characterize DARLEI's performance under various conditions,
revealing factors impacting diversity of evolved morphologies. For example, by
enabling inter-agent collisions within the simulator, we find that we can
simulate some multi-agent interactions between the same morphology, and see how
it influences individual agent capabilities and long-term evolutionary
adaptation. While current results demonstrate limited diversity across
generations, we hope to extend DARLEI in future work to include interactions
between diverse morphologies in richer environments, and create a platform that
allows for coevolving populations and investigating emergent behaviours in
them. Our source code is also made publicly at
https://saeejithnair.github.io/darlei.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Using Artificial French Data to Understand the Emergence of Gender Bias in Transformer Language Models
Abstract: Numerous studies have demonstrated the ability of neural language models to
learn various linguistic properties without direct supervision. This work takes
an initial step towards exploring the less researched topic of how neural
models discover linguistic properties of words, such as gender, as well as the
rules governing their usage. We propose to use an artificial corpus generated
by a PCFG based on French to precisely control the gender distribution in the
training data and determine under which conditions a model correctly captures
gender information or, on the contrary, appears gender-biased.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Exploring Values in Museum Artifacts in the SPICE project: a Preliminary Study
Abstract: This document describes the rationale, the implementation and a preliminary
evaluation of a semantic reasoning tool developed in the EU H2020 SPICE project
to enhance the diversity of perspectives experienced by museum visitors. The
tool, called DEGARI 2.0 for values, relies on the commonsense reasoning
framework TCL, and exploits an ontological model formalizingthe Haidt's theory
of moral values to associate museum items with combined values and emotions.
Within a museum exhibition, this tool can suggest cultural items that are
associated not only with the values of already experienced or preferred
objects, but also with novel items with different value stances, opening the
visit experience to more inclusive interpretations of cultural content. The
system has been preliminarily tested, in the context of the SPICE project, on
the collection of the Hecht Museum of Haifa.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Causal Disentangled Multi-Granularity Graph Classification Method
Abstract: Graph data widely exists in real life, with large amounts of data and complex
structures. It is necessary to map graph data to low-dimensional embedding.
Graph classification, a critical graph task, mainly relies on identifying the
important substructures within the graph. At present, some graph classification
methods do not combine the multi-granularity characteristics of graph data.
This lack of granularity distinction in modeling leads to a conflation of key
information and false correlations within the model. So, achieving the desired
goal of a credible and interpretable model becomes challenging. This paper
proposes a causal disentangled multi-granularity graph representation learning
method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the
important substructures and bias parts within the graph from a
multi-granularity perspective. The disentanglement of the CDM-GNN model reveals
important and bias parts, forming the foundation for its classification task,
specifically, model interpretations. The CDM-GNN model exhibits strong
classification performance and generates explanatory outcomes aligning with
human cognitive patterns. In order to verify the effectiveness of the model,
this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six
state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are
employed for comparison purposes. Additionally, a qualitative analysis of the
interpretation results is conducted.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: OpinSummEval: Revisiting Automated Evaluation for Opinion Summarization
Abstract: Opinion summarization sets itself apart from other types of summarization
tasks due to its distinctive focus on aspects and sentiments. Although certain
automated evaluation methods like ROUGE have gained popularity, we have found
them to be unreliable measures for assessing the quality of opinion summaries.
In this paper, we present OpinSummEval, a dataset comprising human judgments
and outputs from 14 opinion summarization models. We further explore the
correlation between 24 automatic metrics and human ratings across four
dimensions. Our findings indicate that metrics based on neural networks
generally outperform non-neural ones. However, even metrics built on powerful
backbones, such as BART and GPT-3/3.5, do not consistently correlate well
across all dimensions, highlighting the need for advancements in automated
evaluation methods for opinion summarization. The code and data are publicly
available at https://github.com/A-Chicharito-S/OpinSummEval/tree/main.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generating Valid and Natural Adversarial Examples with Large Language Models
Abstract: Deep learning-based natural language processing (NLP) models, particularly
pre-trained language models (PLMs), have been revealed to be vulnerable to
adversarial attacks. However, the adversarial examples generated by many
mainstream word-level adversarial attack models are neither valid nor natural,
leading to the loss of semantic maintenance, grammaticality, and human
imperceptibility. Based on the exceptional capacity of language understanding
and generation of large language models (LLMs), we propose LLM-Attack, which
aims at generating both valid and natural adversarial examples with LLMs. The
method consists of two stages: word importance ranking (which searches for the
most vulnerable words) and word synonym replacement (which substitutes them
with their synonyms obtained from LLMs). Experimental results on the Movie
Review (MR), IMDB, and Yelp Review Polarity datasets against the baseline
adversarial attack models illustrate the effectiveness of LLM-Attack, and it
outperforms the baselines in human and GPT-4 evaluation by a significant
margin. The model can generate adversarial examples that are typically valid
and natural, with the preservation of semantic meaning, grammaticality, and
human imperceptibility.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: CoheSentia: A Novel Benchmark of Incremental versus Holistic Assessment of Coherence in Generated Texts
Abstract: Coherence is a linguistic term that refers to the relations between small
textual units (sentences, propositions), which make the text logically
consistent and meaningful to the reader. With the advances of generative
foundational models in NLP, there is a pressing need to automatically assess
the human-perceived coherence of automatically generated texts. Up until now,
little work has been done on explicitly assessing the coherence of generated
texts and analyzing the factors contributing to (in)coherence. Previous work on
the topic used other tasks, e.g., sentence reordering, as proxies of coherence,
rather than approaching coherence detection heads on. In this paper, we
introduce {\sc CoheSentia}, a novel benchmark of human-perceived coherence of
automatically generated texts. Our annotation protocol reflects two
perspectives; one is global, assigning a single coherence score, and the other
is incremental, scoring sentence by sentence. The incremental method produces
an (in)coherence score for each text fragment and also pinpoints reasons for
incoherence at that point. Our benchmark contains 500 automatically-generated
and human-annotated paragraphs, each annotated in both methods, by multiple
raters. Our analysis shows that the inter-annotator agreement in the
incremental mode is higher than in the holistic alternative, and our
experiments show that standard LMs fine-tuned for coherence detection show
varied performance on the different factors contributing to (in)coherence. All
in all, these models yield unsatisfactory performance, emphasizing the need for
developing more reliable methods for coherence assessment.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023
Abstract: The Information Retrieval in Software Engineering (IRSE) track aims to
develop solutions for automated evaluation of code comments in a machine
learning framework based on human and large language model generated labels. In
this track, there is a binary classification task to classify comments as
useful and not useful. The dataset consists of 9048 code comments and
surrounding code snippet pairs extracted from open source github C based
projects and an additional dataset generated individually by teams using large
language models. Overall 56 experiments have been submitted by 17 teams from
various universities and software companies. The submissions have been
evaluated quantitatively using the F1-Score and qualitatively based on the type
of features developed, the supervised learning model used and their
corresponding hyper-parameters. The labels generated from large language models
increase the bias in the prediction model but lead to less over-fitted results.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
Abstract: Offline reinforcement learning algorithms often require careful
hyperparameter tuning. Consequently, before deployment, we need to select
amongst a set of candidate policies. As yet, however, there is little
understanding about the fundamental limits of this offline policy selection
(OPS) problem. In this work we aim to provide clarity on when sample efficient
OPS is possible, primarily by connecting OPS to off-policy policy evaluation
(OPE) and Bellman error (BE) estimation. We first show a hardness result, that
in the worst case, OPS is just as hard as OPE, by proving a reduction of OPE to
OPS. As a result, no OPS method can be more sample efficient than OPE in the
worst case. We then propose a BE method for OPS, called Identifiable BE
Selection (IBES), that has a straightforward method for selecting its own
hyperparameters. We highlight that using IBES for OPS generally has more
requirements than OPE methods, but if satisfied, can be more sample efficient.
We conclude with an empirical study comparing OPE and IBES, and by showing the
difficulty of OPS on an offline Atari benchmark dataset.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Data-Driven Risk Modeling for Infrastructure Projects Using Artificial Intelligence Techniques
Abstract: Managing project risk is a key part of the successful implementation of any
large project and is widely recognized as a best practice for public agencies
to deliver infrastructures. The conventional method of identifying and
evaluating project risks involves getting input from subject matter experts at
risk workshops in the early phases of a project. As a project moves through its
life cycle, these identified risks and their assessments evolve. Some risks are
realized to become issues, some are mitigated, and some are retired as no
longer important. Despite the value provided by conventional expert-based
approaches, several challenges remain due to the time-consuming and expensive
processes involved. Moreover, limited is known about how risks evolve from
ex-ante to ex-post over time. How well does the project team identify and
evaluate risks in the initial phase compared to what happens during project
execution? Using historical data and artificial intelligence techniques, this
study addressed these limitations by introducing a data-driven framework to
identify risks automatically and to examine the quality of early risk registers
and risk assessments. Risk registers from more than 70 U.S. major
transportation projects form the input dataset.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples
Abstract: Contrastive learning predicts whether two images belong to the same category
by training a model to make their feature representations as close or as far
away as possible. In this paper, we rethink how to mine samples in contrastive
learning, unlike other methods, our approach is more comprehensive, taking into
account both positive and negative samples, and mining potential samples from
two aspects: First, for positive samples, we consider both the augmented sample
views obtained by data augmentation and the mined sample views through data
mining. Then, we weight and combine them using both soft and hard weighting
strategies. Second, considering the existence of uninformative negative samples
and false negative samples in the negative samples, we analyze the negative
samples from the gradient perspective and finally mine negative samples that
are neither too hard nor too easy as potential negative samples, i.e., those
negative samples that are close to positive samples. The experiments show the
obvious advantages of our method compared with some traditional self-supervised
methods. Our method achieves 88.57%, 61.10%, and 36.69% top-1 accuracy on
CIFAR10, CIFAR100, and TinyImagenet, respectively.

Output: Computer Vision