Instruction: What field is the article from?

Prompt:
Title: Scattering Vision Transformer: Spectral Mixing Matters
Abstract: Vision transformers have gained significant attention and achieved
state-of-the-art performance in various computer vision tasks, including image
classification, instance segmentation, and object detection. However,
challenges remain in addressing attention complexity and effectively capturing
fine-grained information within images. Existing solutions often resort to
down-sampling operations, such as pooling, to reduce computational cost.
Unfortunately, such operations are non-invertible and can result in information
loss. In this paper, we present a novel approach called Scattering Vision
Transformer (SVT) to tackle these challenges. SVT incorporates a spectrally
scattering network that enables the capture of intricate image details. SVT
overcomes the invertibility issue associated with down-sampling operations by
separating low-frequency and high-frequency components. Furthermore, SVT
introduces a unique spectral gating network utilizing Einstein multiplication
for token and channel mixing, effectively reducing complexity. We show that SVT
achieves state-of-the-art performance on the ImageNet dataset with a
significant reduction in a number of parameters and FLOPS. SVT shows 2\%
improvement over LiTv2 and iFormer. SVT-H-S reaches 84.2\% top-1 accuracy,
while SVT-H-B reaches 85.2\% (state-of-art for base versions) and SVT-H-L
reaches 85.7\% (again state-of-art for large versions). SVT also shows
comparable results in other vision tasks such as instance segmentation. SVT
also outperforms other transformers in transfer learning on standard datasets
such as CIFAR10, CIFAR100, Oxford Flower, and Stanford Car datasets. The
project page is available on this
webpage.\url{https://badripatro.github.io/svt/}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Zero-Shot Question Answering over Financial Documents using Large Language Models
Abstract: We introduce a large language model (LLM) based approach to answer complex
questions requiring multi-hop numerical reasoning over financial reports. While
LLMs have exhibited remarkable performance on various natural language and
reasoning tasks, complex reasoning problems often rely on few-shot prompts that
require carefully crafted examples. In contrast, our approach uses novel
zero-shot prompts that guide the LLM to encode the required reasoning into a
Python program or a domain specific language. The generated program is then
executed by a program interpreter, thus mitigating the limitations of LLM in
performing accurate arithmetic calculations.
  We evaluate the proposed approach on three financial datasets using some of
the recently developed generative pretrained transformer (GPT) models and
perform comparisons with various zero-shot baselines. The experimental results
demonstrate that our approach significantly improves the accuracy for all the
LLMs over their respective baselines. We provide a detailed analysis of the
results, generating insights to support our findings. The success of our
approach demonstrates the enormous potential to extract complex domain specific
numerical reasoning by designing zero-shot prompts to effectively exploit the
knowledge embedded in LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor
Abstract: In this paper, we present RESIN-EDITOR, an interactive event graph visualizer
and editor designed for analyzing complex events. Our RESIN-EDITOR system
allows users to render and freely edit hierarchical event graphs extracted from
multimedia and multi-document news clusters with guidance from human-curated
event schemas. RESIN-EDITOR's unique features include hierarchical graph
visualization, comprehensive source tracing, and interactive user editing,
which is more powerful and versatile than existing Information Extraction (IE)
visualization tools. In our evaluation of RESIN-EDITOR, we demonstrate ways in
which our tool is effective in understanding complex events and enhancing
system performance. The source code, a video demonstration, and a live website
for RESIN-EDITOR have been made publicly available.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
Abstract: Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research
focused on learning-based code intelligence, such as automated bug repair, and
test case generation. Despite their great potential, language models for code
intelligence (LM4Code) are susceptible to potential pitfalls, which hinder
realistic performance and further impact their reliability and applicability in
real-world deployment. Such challenges drive the need for a comprehensive
understanding - not just identifying these issues but delving into their
possible implications and existing solutions to build more reliable language
models tailored to code intelligence. Based on a well-defined systematic
research approach, we conducted an extensive literature review to uncover the
pitfalls inherent in LM4Code. Finally, 67 primary studies from top-tier venues
have been identified. After carefully examining these studies, we designed a
taxonomy of pitfalls in LM4Code research and conducted a systematic study to
summarize the issues, implications, current solutions, and challenges of
different pitfalls for LM4Code systems. We developed a comprehensive
classification scheme that dissects pitfalls across four crucial aspects: data
collection and labeling, system design and learning, performance evaluation,
and deployment and maintenance. Through this study, we aim to provide a roadmap
for researchers and practitioners, facilitating their understanding and
utilization of LM4Code in reliable and trustworthy ways.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Vamos: Versatile Action Models for Video Understanding
Abstract: What makes good video representations for video understanding, such as
anticipating future activities, or answering video-conditioned questions? While
earlier approaches focus on end-to-end learning directly from video pixels, we
propose to revisit text-based representations, such as discrete action labels,
or free-form video captions, which are interpretable and can be directly
consumed by large language models (LLMs). Intuitively, different video
understanding tasks may require representations that are complementary and at
different granularities. To this end, we propose versatile action models
(Vamos), a learning framework powered by a large language model as the
"reasoner", and can flexibly leverage visual embeddings, action labels, and
free-form descriptions extracted from videos as its input. We evaluate Vamos on
four complementary video understanding benchmarks, Ego4D, Next-QA, IntentQA,
and EgoSchema, on its capability to model temporal dynamics, encode visual
history, and perform reasoning. Surprisingly, we observe that text-based
representations consistently achieve competitive performance on all benchmarks,
and that visual embeddings provide marginal or no performance improvement,
demonstrating the effectiveness of text-based video representation in the LLM
era. We perform extensive ablation study and qualitative analysis to support
our observations, and achieve state-of-the-art performance on three benchmarks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Evaluating Agents using Social Choice Theory
Abstract: We argue that many general evaluation problems can be viewed through the lens
of voting theory. Each task is interpreted as a separate voter, which requires
only ordinal rankings or pairwise comparisons of agents to produce an overall
evaluation. By viewing the aggregator as a social welfare function, we are able
to leverage centuries of research in social choice theory to derive principled
evaluation frameworks with axiomatic foundations. These evaluations are
interpretable and flexible, while avoiding many of the problems currently
facing cross-task evaluation. We apply this Voting-as-Evaluation (VasE)
framework across multiple settings, including reinforcement learning, large
language models, and humans. In practice, we observe that VasE can be more
robust than popular evaluation frameworks (Elo and Nash averaging), discovers
properties in the evaluation data not evident from scores alone, and can
predict outcomes better than Elo in a complex seven-player game. We identify
one particular approach, maximal lotteries, that satisfies important
consistency properties relevant to evaluation, is computationally efficient
(polynomial in the size of the evaluation data), and identifies game-theoretic
cycles.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A ripple in time: a discontinuity in American history
Abstract: In this note we use the State of the Union Address dataset from Kaggle to
make some surprising (and some not so surprising) observations pertaining to
the general timeline of American history, and the character and nature of the
addresses themselves. Our main approach is using vector embeddings, such as
BERT (DistilBERT) and GPT-2. While it is widely believed that BERT (and its
variations) is most suitable for NLP classification tasks, we find out that
GPT-2 in conjunction with nonlinear dimension reduction methods such as UMAP
provide better separation and stronger clustering. This makes GPT-2 + UMAP an
interesting alternative. In our case, no model fine-tuning is required, and the
pre-trained out-of-the-box GPT-2 model is enough. We also used a fine-tuned
DistilBERT model for classification (detecting which president delivered which
address), with very good results (accuracy 93% - 95% depending on the run). All
computations can be replicated by using the accompanying code on GitHub.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
Abstract: With the ever-increasing popularity of pretrained Video-Language Models
(VidLMs), there is a pressing need to develop robust evaluation methodologies
that delve deeper into their visio-linguistic capabilities. To address this
challenge, we present ViLMA (Video Language Model Assessment), a task-agnostic
benchmark that places the assessment of fine-grained capabilities of these
models on a firm footing. Task-based evaluations, while valuable, fail to
capture the complexities and specific temporal aspects of moving images that
VidLMs need to process. Through carefully curated counterfactuals, ViLMA offers
a controlled evaluation suite that sheds light on the true potential of these
models, as well as their performance gaps compared to human-level
understanding. ViLMA also includes proficiency tests, which assess basic
capabilities deemed essential to solving the main counterfactual tests. We show
that current VidLMs' grounding abilities are no better than those of
vision-language models which use static images. This is especially striking
once the performance on proficiency tests is factored in. Our benchmark serves
as a catalyst for future research on VidLMs, helping to highlight areas that
still need to be explored.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: RO-LLaMA: Generalist LLM for Radiation Oncology via Noise Augmentation and Consistency Regularization
Abstract: Recent advancements in Artificial Intelligence (AI) have profoundly
influenced medical fields, by providing tools to reduce clinical workloads.
However, most AI models are constrained to execute uni-modal tasks, in stark
contrast to the comprehensive approaches utilized by medical professionals. To
address this, here we present RO-LLaMA, a versatile generalist large language
model (LLM) tailored for the field of radiation oncology. This model seamlessly
covers a wide range of the workflow of radiation oncologists, adept at various
tasks such as clinical report summarization, radiation therapy plan suggestion,
and plan-guided therapy target volume segmentation. In particular, to maximize
the end-to-end performance, we further present a novel Consistency Embedding
Fine-Tuning (CEFTune) technique, which boosts LLM's robustness to additional
errors at the intermediates while preserving the capability of handling clean
inputs, and creatively transform this concept into LLM-driven segmentation
framework as Consistency Embedding Segmentation (CESEG). Experimental results
on multi-centre cohort sets demonstrate our proposed RO-LLaMA's promising
performance for diverse tasks with generalization capabilities.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Learning From Scenarios for Stochastic Repairable Scheduling
Abstract: When optimizing problems with uncertain parameter values in a linear
objective, decision-focused learning enables end-to-end learning of these
values. We are interested in a stochastic scheduling problem, in which
processing times are uncertain, which brings uncertain values in the
constraints, and thus repair of an initial schedule may be needed. Historical
realizations of the stochastic processing times are available. We show how
existing decision-focused learning techniques based on stochastic smoothing can
be adapted to this scheduling problem. We include an extensive experimental
evaluation to investigate in which situations decision-focused learning
outperforms the state of the art for such situations: scenario-based stochastic
optimization.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
Abstract: The interactive use of large language models (LLMs) in AI assistants (at
work, home, etc.) introduces a new set of inference-time privacy risks: LLMs
are fed different types of information from multiple sources in their inputs
and are expected to reason about what to share in their outputs, for what
purpose and with whom, within a given context. In this work, we draw attention
to the highly critical yet overlooked notion of contextual privacy by proposing
ConfAIde, a benchmark designed to identify critical weaknesses in the privacy
reasoning capabilities of instruction-tuned LLMs. Our experiments show that
even the most capable models such as GPT-4 and ChatGPT reveal private
information in contexts that humans would not, 39% and 57% of the time,
respectively. This leakage persists even when we employ privacy-inducing
prompts or chain-of-thought reasoning. Our work underscores the immediate need
to explore novel inference-time privacy-preserving approaches, based on
reasoning and theory of mind.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models
Abstract: The assessment of advanced generative large language models (LLMs) poses a
significant challenge, given their heightened complexity in recent
developments. Furthermore, evaluating the performance of LLM-based applications
in various industries, as indicated by Key Performance Indicators (KPIs), is a
complex undertaking. This task necessitates a profound understanding of
industry use cases and the anticipated system behavior. Within the context of
the automotive industry, existing evaluation metrics prove inadequate for
assessing in-car conversational question answering (ConvQA) systems. The unique
demands of these systems, where answers may relate to driver or car safety and
are confined within the car domain, highlight the limitations of current
metrics. To address these challenges, this paper introduces a set of KPIs
tailored for evaluating the performance of in-car ConvQA systems, along with
datasets specifically designed for these KPIs. A preliminary and comprehensive
empirical evaluation substantiates the efficacy of our proposed approach.
Furthermore, we investigate the impact of employing varied personas in prompts
and found that it enhances the model's capacity to simulate diverse viewpoints
in assessments, mirroring how individuals with different backgrounds perceive a
topic.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Overcoming Pathology Image Data Deficiency: Generating Images from Pathological Transformation Process
Abstract: Histopathology serves as the gold standard for medical diagnosis but faces
application limitations due to the shortage of medical resources. Leveraging
deep learning, computer-aided diagnosis has the potential to alleviate the
pathologist scarcity and provide timely clinical analysis. However, developing
a reliable model generally necessitates substantial data for training, which is
challenging in pathological field. In response, we propose an adaptive
depth-controlled bidirectional diffusion (ADBD) network for image data
generation. The domain migration approach can work with small trainset and
overcome the diffusion overfitting by source information guidance.
Specifically, we developed a hybrid attention strategy to blend global and
local attention priorities, which guides the bidirectional diffusion and
ensures the migration success. In addition, we developed the adaptive
depth-controlled strategy to simulate physiological transformations, capable of
yielding unlimited cross-domain intermediate images with corresponding soft
labels. ADBD is effective for overcoming pathological image data deficiency and
supportable for further pathology-related research.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Generic NLI approach for Classification of Sentiment Associated with Therapies
Abstract: This paper describes our system for addressing SMM4H 2023 Shared Task 2 on
"Classification of sentiment associated with therapies (aspect-oriented)". In
our work, we adopt an approach based on Natural language inference (NLI) to
formulate this task as a sentence pair classification problem, and train
transformer models to predict sentiment associated with a therapy on a given
text. Our best model achieved 75.22\% F1-score which was 11\% (4\%) more than
the mean (median) score of all teams' submissions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data
Abstract: Code datasets, often collected from diverse and uncontrolled sources such as
GitHub, potentially suffer from quality issues, thereby affecting the
performance and training efficiency of Large Language Models (LLMs) optimized
for code generation. Previous studies demonstrated the benefit of using
embedding spaces for data pruning, but they mainly focused on duplicate removal
or increasing variety, and in other modalities, such as images. Our work
focuses on using embeddings to identify and remove "low-quality" code data.
First, we explore features of "low-quality" code in embedding space, through
the use of synthetic corruptions. Armed with this knowledge, we devise novel
pruning metrics that operate in embedding space to identify and remove
low-quality entries in the Stack dataset. We demonstrate the benefits of this
synthetic corruption informed pruning (SCIP) approach on the well-established
HumanEval and MBPP benchmarks, outperforming existing embedding-based methods.
Importantly, we achieve up to a 3% performance improvement over no pruning,
thereby showing the promise of insights from synthetic corruptions for data
pruning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Matching of Descriptive Labels to Glossary Descriptions
Abstract: Semantic text similarity plays an important role in software engineering
tasks in which engineers are requested to clarify the semantics of descriptive
labels (e.g., business terms, table column names) that are often consists of
too short or too generic words and appears in their IT systems. We formulate
this type of problem as a task of matching descriptive labels to glossary
descriptions. We then propose a framework to leverage an existing semantic text
similarity measurement (STS) and augment it using semantic label enrichment and
set-based collective contextualization where the former is a method to retrieve
sentences relevant to a given label and the latter is a method to compute
similarity between two contexts each of which is derived from a set of texts
(e.g., column names in the same table). We performed an experiment on two
datasets derived from publicly available data sources. The result indicated
that the proposed methods helped the underlying STS correctly match more
descriptive labels with the descriptions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Leveraging LLMs in Scholarly Knowledge Graph Question Answering
Abstract: This paper presents a scholarly Knowledge Graph Question Answering (KGQA)
that answers bibliographic natural language questions by leveraging a large
language model (LLM) in a few-shot manner. The model initially identifies the
top-n similar training questions related to a given test question via a
BERT-based sentence encoder and retrieves their corresponding SPARQL. Using the
top-n similar question-SPARQL pairs as an example and the test question creates
a prompt. Then pass the prompt to the LLM and generate a SPARQL. Finally, runs
the SPARQL against the underlying KG - ORKG (Open Research KG) endpoint and
returns an answer. Our system achieves an F1 score of 99.0%, on SciQA - one of
the Scholarly-QALD-23 challenge benchmarks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Bridging The Gaps Between Token Pruning and Full Pre-training via Masked Fine-tuning
Abstract: Despite the success of transformers on various computer vision tasks, they
suffer from excessive memory and computational cost. Some works present dynamic
vision transformers to accelerate inference by pruning redundant tokens. A key
to improving token pruning is using well-trained models as initialization for
faster convergence and better performance. However, current base models usually
adopt full image training, i.e., using full images as inputs and keeping the
whole feature maps through the forward process, which causes inconsistencies
with dynamic models that gradually reduce tokens, including calculation
pattern, information amount and token selection strategy inconsistencies.
Inspired by MAE which performs masking and reconstruction self-supervised task,
we devise masked fine-tuning to bridge the gaps between pre-trained base models
used for initialization and token pruning based dynamic vision transformers, by
masking image patches and predicting the image class label based on left
unmasked patches. Extensive experiments on ImageNet demonstrate that base
models via masked fine-tuning gain strong occlusion robustness and ability
against information loss. With this better initialization, Dynamic ViT achieves
higher accuracies, especially under large token pruning ratios (e.g., 81.9% vs.
81.3%, and 62.3% vs. 58.9% for DeiT based Dynamic ViT/0.8 and Dynamic ViT/0.3).
Moreover, we apply our method into different token pruning based dynamic vision
transformers, different pre-trained models and randomly initialized models to
demonstrate the generalization ability.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs
Abstract: Imitation learning (IL) can train computationally-efficient sensorimotor
policies from a resource-intensive Model Predictive Controller (MPC), but it
often requires many samples, leading to long training times or limited
robustness. To address these issues, we combine IL with a variant of robust MPC
that accounts for process and sensing uncertainties, and we design a data
augmentation (DA) strategy that enables efficient learning of vision-based
policies. The proposed DA method, named Tube-NeRF, leverages Neural Radiance
Fields (NeRFs) to generate novel synthetic images, and uses properties of the
robust MPC (the tube) to select relevant views and to efficiently compute the
corresponding actions. We tailor our approach to the task of localization and
trajectory tracking on a multirotor, by learning a visuomotor policy that
generates control actions using images from the onboard camera as only source
of horizontal position. Our evaluations numerically demonstrate learning of a
robust visuomotor policy with an 80-fold increase in demonstration efficiency
and a 50% reduction in training time over current IL methods. Additionally, our
policies successfully transfer to a real multirotor, achieving accurate
localization and low tracking errors despite large disturbances, with an
onboard inference time of only 1.5 ms.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Bayesian Metaplasticity from Synaptic Uncertainty
Abstract: Catastrophic forgetting remains a challenge for neural networks, especially
in lifelong learning scenarios. In this study, we introduce MEtaplasticity from
Synaptic Uncertainty (MESU), inspired by metaplasticity and Bayesian inference
principles. MESU harnesses synaptic uncertainty to retain information over
time, with its update rule closely approximating the diagonal Newton's method
for synaptic updates. Through continual learning experiments on permuted MNIST
tasks, we demonstrate MESU's remarkable capability to maintain learning
performance across 100 tasks without the need of explicit task boundaries.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Abstract: Recently, artificial intelligence for drug discovery has raised increasing
interest in both machine learning and chemistry domains. The fundamental
building block for drug discovery is molecule geometry and thus, the molecule's
geometrical representation is the main bottleneck to better utilize machine
learning techniques for drug discovery. In this work, we propose a pretraining
method for molecule joint auto-encoding (MoleculeJAE). MoleculeJAE can learn
both the 2D bond (topology) and 3D conformation (geometry) information, and a
diffusion process model is applied to mimic the augmented trajectories of such
two modalities, based on which, MoleculeJAE will learn the inherent chemical
structure in a self-supervised manner. Thus, the pretrained geometrical
representation in MoleculeJAE is expected to benefit downstream
geometry-related tasks. Empirically, MoleculeJAE proves its effectiveness by
reaching state-of-the-art performance on 15 out of 20 tasks by comparing it
with 12 competitive baselines.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
Abstract: Offline imitation from observations aims to solve MDPs where only
task-specific expert states and task-agnostic non-expert state-action pairs are
available. Offline imitation is useful in real-world scenarios where arbitrary
interactions are costly and expert actions are unavailable. The
state-of-the-art "DIstribution Correction Estimation" (DICE) methods minimize
divergence of state occupancy between expert and learner policies and retrieve
a policy with weighted behavior cloning; however, their results are unstable
when learning from incomplete trajectories, due to a non-robust optimization in
the dual domain. To address the issue, in this paper, we propose
Trajectory-Aware Imitation Learning from Observations (TAILO). TAILO uses a
discounted sum along the future trajectory as the weight for weighted behavior
cloning. The terms for the sum are scaled by the output of a discriminator,
which aims to identify expert states. Despite simplicity, TAILO works well if
there exist trajectories or segments of expert behavior in the task-agnostic
data, a common assumption in prior work. In experiments across multiple
testbeds, we find TAILO to be more robust and effective, particularly with
incomplete trajectories.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On the Interplay Between Stepsize Tuning and Progressive Sharpening
Abstract: Recent empirical work has revealed an intriguing property of deep learning
models by which the sharpness (largest eigenvalue of the Hessian) increases
throughout optimization until it stabilizes around a critical value at which
the optimizer operates at the edge of stability, given a fixed stepsize (Cohen
et al, 2022). We investigate empirically how the sharpness evolves when using
stepsize-tuners, the Armijo linesearch and Polyak stepsizes, that adapt the
stepsize along the iterations to local quantities such as, implicitly, the
sharpness itself. We find that the surprisingly poor performance of a classical
Armijo linesearch may be well explained by its tendency to ever-increase the
sharpness of the objective in the full or large batch regimes. On the other
hand, we observe that Polyak stepsizes operate generally at the edge of
stability or even slightly beyond, while outperforming its Armijo and constant
stepsizes counterparts. We conclude with an analysis that suggests unlocking
stepsize tuners requires an understanding of the joint dynamics of the step
size and the sharpness.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models
Abstract: Designing high-quality educational questions is a challenging and
time-consuming task. In this work, we propose a novel approach that utilizes
prompt-based techniques to generate descriptive and reasoning-based questions.
However, current question-answering (QA) datasets are inadequate for conducting
our experiments on prompt-based question generation (QG) in an educational
setting. Therefore, we curate a new QG dataset called EduProbe for school-level
subjects, by leveraging the rich content of NCERT textbooks. We carefully
annotate this dataset as quadruples of 1) Context: a segment upon which the
question is formed; 2) Long Prompt: a long textual cue for the question (i.e.,
a longer sequence of words or phrases, covering the main theme of the context);
3) Short Prompt: a short textual cue for the question (i.e., a condensed
representation of the key information or focus of the context); 4) Question: a
deep question that aligns with the context and is coherent with the prompts. We
investigate several prompt-based QG methods by fine-tuning pre-trained
transformer-based large language models (LLMs), namely PEGASUS, T5, MBART, and
BART. Moreover, we explore the performance of two general-purpose pre-trained
LLMs such as Text-Davinci-003 and GPT-3.5-Turbo without any further training.
By performing automatic evaluation, we show that T5 (with long prompt)
outperforms all other models, but still falls short of the human baseline.
Under human evaluation criteria, TextDavinci-003 usually shows better results
than other models under various prompt settings. Even in the case of human
evaluation criteria, QG models mostly fall short of the human baseline. Our
code and dataset are available at: https://github.com/my625/PromptQG

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information
Abstract: As the IT industry advances, system log data becomes increasingly crucial.
Many computer systems rely on log texts for management due to restricted access
to source code. The need for log anomaly detection is growing, especially in
real-world applications, but identifying anomalies in rapidly accumulating logs
remains a challenging task. Traditional deep learning-based anomaly detection
models require dataset-specific training, leading to corresponding delays.
Notably, most methods only focus on sequence-level log information, which makes
the detection of subtle anomalies harder, and often involve inference processes
that are difficult to utilize in real-time. We introduce RAPID, a model that
capitalizes on the inherent features of log data to enable anomaly detection
without training delays, ensuring real-time capability. RAPID treats logs as
natural language, extracting representations using pre-trained language models.
Given that logs can be categorized based on system context, we implement a
retrieval-based technique to contrast test logs with the most similar normal
logs. This strategy not only obviates the need for log-specific training but
also adeptly incorporates token-level information, ensuring refined and robust
detection, particularly for unseen logs. We also propose the core set
technique, which can reduce the computational cost needed for comparison.
Experimental results show that even without training on log data, RAPID
demonstrates competitive performance compared to prior models and achieves the
best performance on certain datasets. Through various research questions, we
verified its capability for real-time detection without delay.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning
Abstract: Learning to collaborate has witnessed significant progress in multi-agent
reinforcement learning (MARL). However, promoting coordination among agents and
enhancing exploration capabilities remain challenges. In multi-agent
environments, interactions between agents are limited in specific situations.
Effective collaboration between agents thus requires a nuanced understanding of
when and how agents' actions influence others. To this end, in this paper, we
propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based
Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a
novel Intrinsic reward mechanism based on a new cooperation criterion measured
by situation-dependent causal influence among agents. Our approach aims to
detect inter-agent causal influences in specific situations based on the
criterion using causal intervention and conditional mutual information. This
effectively assists agents in exploring states that can positively impact other
agents, thus promoting cooperation between agents. The resulting update links
coordinated exploration and intrinsic reward distribution, which enhance
overall collaboration and performance. Experimental results on various MARL
benchmarks demonstrate the superiority of our method compared to
state-of-the-art approaches.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Abstract: Most current methods for multi-hop question answering (QA) over knowledge
graphs (KGs) only provide final conclusive answers without explanations, such
as a set of KG entities that is difficult for normal users to review and
comprehend. This issue severely limits the application of KG-based QA in
real-world scenarios. However, it is non-trivial to solve due to two
challenges: First, annotations of reasoning chains of multi-hop questions,
which could serve as supervision for explanation generation, are usually
lacking. Second, it is difficult to maintain high efficiency when explicit KG
triples need to be retrieved to generate explanations. In this paper, we
propose a novel Graph Neural Network-based Two-Step Reasoning model (GNN2R) to
solve this issue. GNN2R can provide both final answers and reasoning subgraphs
as a rationale behind final answers efficiently with only weak supervision that
is available through question-final answer pairs. We extensively evaluated
GNN2R with detailed analyses in experiments. The results demonstrate that, in
terms of effectiveness, efficiency, and quality of generated explanations,
GNN2R outperforms existing state-of-the-art methods that are applicable to this
task. Our code and pre-trained models are available at
https://github.com/ruijie-wang-uzh/GNN2R.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning active tactile perception through belief-space control
Abstract: Robots operating in an open world will encounter novel objects with unknown
physical properties, such as mass, friction, or size. These robots will need to
sense these properties through interaction prior to performing downstream tasks
with the objects. We propose a method that autonomously learns tactile
exploration policies by developing a generative world model that is leveraged
to 1) estimate the object's physical parameters using a differentiable Bayesian
filtering algorithm and 2) develop an exploration policy using an
information-gathering model predictive controller. We evaluate our method on
three simulated tasks where the goal is to estimate a desired object property
(mass, height or toppling height) through physical interaction. We find that
our method is able to discover policies that efficiently gather information
about the desired property in an intuitive manner. Finally, we validate our
method on a real robot system for the height estimation task, where our method
is able to successfully learn and execute an information-gathering policy from
scratch.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Embedding in Recommender Systems: A Survey
Abstract: Recommender systems have become an essential component of many online
platforms, providing personalized recommendations to users. A crucial aspect is
embedding techniques that coverts the high-dimensional discrete features, such
as user and item IDs, into low-dimensional continuous vectors and can enhance
the recommendation performance. Applying embedding techniques captures complex
entity relationships and has spurred substantial research. In this survey, we
provide an overview of the recent literature on embedding techniques in
recommender systems. This survey covers embedding methods like collaborative
filtering, self-supervised learning, and graph-based techniques. Collaborative
filtering generates embeddings capturing user-item preferences, excelling in
sparse data. Self-supervised methods leverage contrastive or generative
learning for various tasks. Graph-based techniques like node2vec exploit
complex relationships in network-rich environments. Addressing the scalability
challenges inherent to embedding methods, our survey delves into innovative
directions within the field of recommendation systems. These directions aim to
enhance performance and reduce computational complexity, paving the way for
improved recommender systems. Among these innovative approaches, we will
introduce Auto Machine Learning (AutoML), hash techniques, and quantization
techniques in this survey. We discuss various architectures and techniques and
highlight the challenges and future directions in these aspects. This survey
aims to provide a comprehensive overview of the state-of-the-art in this
rapidly evolving field and serve as a useful resource for researchers and
practitioners working in the area of recommender systems.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer
Abstract: With the development of Internet of Things (IoT) systems, precise long-term
forecasting method is requisite for decision makers to evaluate current
statuses and formulate future policies. Currently, Transformer and MLP are two
paradigms for deep time-series forecasting and the former one is more
prevailing in virtue of its exquisite attention mechanism and encoder-decoder
architecture. However, data scientists seem to be more willing to dive into the
research of encoder, leaving decoder unconcerned. Some researchers even adopt
linear projections in lieu of the decoder to reduce the complexity. We argue
that both extracting the features of input sequence and seeking the relations
of input and prediction sequence, which are respective functions of encoder and
decoder, are of paramount significance. Motivated from the success of FPN in CV
field, we propose FPPformer to utilize bottom-up and top-down architectures
respectively in encoder and decoder to build the full and rational hierarchy.
The cutting-edge patch-wise attention is exploited and further developed with
the combination, whose format is also different in encoder and decoder, of
revamped element-wise attention in this work. Extensive experiments with six
state-of-the-art baselines on twelve benchmarks verify the promising
performances of FPPformer and the importance of elaborately devising decoder in
time-series forecasting Transformer. The source code is released in
https://github.com/OrigamiSL/FPPformer.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation
Abstract: This paper presents a comprehensive evaluation of the code generation
capabilities of ChatGPT, a prominent large language model, compared to human
programmers. A novel dataset of 131 code-generation prompts across 5 categories
was curated to enable robust analysis. Code solutions were generated by both
ChatGPT and humans for all prompts, resulting in 262 code samples. A meticulous
manual assessment methodology prioritized evaluating correctness,
comprehensibility, and security using 14 established code quality metrics. The
key findings reveal ChatGPT's strengths in crafting concise, efficient code
with advanced constructs, showcasing strengths in data analysis tasks (93.1%
accuracy) but limitations in visual-graphical challenges. Comparative analysis
with human code highlights ChatGPT's inclination towards modular design and
superior error handling. Additionally, machine learning models effectively
distinguished ChatGPT from human code with up to 88% accuracy, suggesting
detectable coding style disparities. By providing profound insights into
ChatGPT's code generation capabilities and limitations through quantitative
metrics and qualitative analysis, this study makes valuable contributions
toward advancing AI-based programming assistants. The curated dataset and
methodology offer a robust foundation for future research in this nascent
domain. All data and codes are available on
https://github.com/DSAatUSU/ChatGPT-promises-and-pitfalls.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Talent-Interview: Web-Client Cheating Detection for Online Exams
Abstract: Online exams are more attractive after the Covid-19 pandemic. Furthermore,
during recruitment, online exams are used. However, there are more cheating
possibilities for online exams. Assigning a proctor for each exam increases
cost. At this point, automatic proctor systems detect possible cheating status.
This article proposes an end-to-end system and submodules to get better results
for online proctoring. Object detection, face recognition, human voice
detection, and segmentation are used in our system. Furthermore, our proposed
model works on the PCs of users, meaning a client-based system. So, server cost
is eliminated. As far as we know, it is the first time the client-based online
proctoring system has been used for recruitment. Online exams are more
attractive after the Covid-19 pandemic. Furthermore, during recruitment, online
exams are used. However, there are more cheating possibilities for online
exams. Assigning a proctor for each exam increases cost. At this point,
automatic proctor systems detect possible cheating status. This article
proposes an end-to-end system and submodules to get better results for online
proctoring. Object detection, face recognition, human voice detection, and
segmentation are used in our system. Furthermore, our proposed model works on
the PCs of users, meaning a client-based system. So, server cost is eliminated.
As far as we know, it is the first time the client-based online proctoring
system has been used for recruitment. Furthermore, this cheating system works
at https://www.talent-interview.com/tr/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: After-Stroke Arm Paresis Detection using Kinematic Data
Abstract: This paper presents an approach for detecting unilateral arm
paralysis/weakness using kinematic data. Our method employs temporal
convolution networks and recurrent neural networks, guided by knowledge
distillation, where we use inertial measurement units attached to the body to
capture kinematic information such as acceleration, rotation, and flexion of
body joints during an action. This information is then analyzed to recognize
body actions and patterns. Our proposed network achieves a high paretic
detection accuracy of 97.99\%, with an action classification accuracy of
77.69\%, through knowledge sharing. Furthermore, by incorporating causal
reasoning, we can gain additional insights into the patient's condition, such
as their Fugl-Meyer assessment score or impairment level based on the machine
learning result. Overall, our approach demonstrates the potential of using
kinematic data and machine learning for detecting arm paralysis/weakness. The
results suggest that our method could be a useful tool for clinicians and
healthcare professionals working with patients with this condition.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales
Abstract: Deep Reinforcement Learning (DRL) has gained prominence as an effective
approach for control systems. However, its practical deployment is impeded by
state perturbations that can severely impact system performance. Addressing
this critical challenge requires robustness verification about system
performance, which involves tackling two quantitative questions: (i) how to
establish guaranteed bounds for expected cumulative rewards, and (ii) how to
determine tail bounds for cumulative rewards. In this work, we present the
first approach for robustness verification of DRL-based control systems by
introducing reward martingales, which offer a rigorous mathematical foundation
to characterize the impact of state perturbations on system performance in
terms of cumulative rewards. Our verified results provide provably quantitative
certificates for the two questions. We then show that reward martingales can be
implemented and trained via neural networks, against different types of control
policies. Experimental results demonstrate that our certified bounds tightly
enclose simulation outcomes on various DRL-based control systems, indicating
the effectiveness and generality of the proposed approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications
Abstract: Sentiment analysis (SA) is an emerging field in text mining. It is the
process of computationally identifying and categorizing opinions expressed in a
piece of text over different social media platforms. Social media plays an
essential role in knowing the customer mindset towards a product, services, and
the latest market trends. Most organizations depend on the customer's response
and feedback to upgrade their offered products and services. SA or opinion
mining seems to be a promising research area for various domains. It plays a
vital role in analyzing big data generated daily in structured and unstructured
formats over the internet. This survey paper defines sentiment and its recent
research and development in different domains, including voice, images, videos,
and text. The challenges and opportunities of sentiment analysis are also
discussed in the paper.
  \keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep
Learning, Natural Language Processing}

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
Abstract: Most reinforcement learning methods rely heavily on dense, well-normalized
environment rewards. DreamerV3 recently introduced a model-based method with a
number of tricks that mitigate these limitations, achieving state-of-the-art on
a wide range of benchmarks with a single set of hyperparameters. This result
sparked discussion about the generality of the tricks, since they appear to be
applicable to other reinforcement learning algorithms. Our work applies
DreamerV3's tricks to PPO and is the first such empirical study outside of the
original work. Surprisingly, we find that the tricks presented do not transfer
as general improvements to PPO. We use a high quality PPO reference
implementation and present extensive ablation studies totaling over 10,000 A100
hours on the Arcade Learning Environment and the DeepMind Control Suite. Though
our experiments demonstrate that these tricks do not generally outperform PPO,
we identify cases where they succeed and offer insight into the relationship
between the implementation tricks. In particular, PPO with these tricks
performs comparably to PPO on Atari games with reward clipping and
significantly outperforms PPO without reward clipping.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Abstract: We present RoboGen, a generative robotic agent that automatically learns
diverse robotic skills at scale via generative simulation. RoboGen leverages
the latest advancements in foundation and generative models. Instead of
directly using or adapting these models to produce policies or low-level
actions, we advocate for a generative scheme, which uses these models to
automatically generate diversified tasks, scenes, and training supervisions,
thereby scaling up robotic skill learning with minimal human supervision. Our
approach equips a robotic agent with a self-guided propose-generate-learn
cycle: the agent first proposes interesting tasks and skills to develop, and
then generates corresponding simulation environments by populating pertinent
objects and assets with proper spatial configurations. Afterwards, the agent
decomposes the proposed high-level task into sub-tasks, selects the optimal
learning approach (reinforcement learning, motion planning, or trajectory
optimization), generates required training supervision, and then learns
policies to acquire the proposed skill. Our work attempts to extract the
extensive and versatile knowledge embedded in large-scale models and transfer
them to the field of robotics. Our fully generative pipeline can be queried
repeatedly, producing an endless stream of skill demonstrations associated with
diverse tasks and environments.

Output: Robotics