pwc-archive/papers-with-abstracts huggingface dataset:
CalliRewrite: Recovering Handwriting Behaviors from Calligraphy Images without Supervision
Authors: Yuxuan Luo, Zekun Wu, Zhouhui Lian
Date: 2024-03-20 00:00:00

Human-like planning skills and dexterous manipulation have long posed challenges in the fields of robotics and artificial intelligence (AI). The task of reinterpreting calligraphy presents a formidable challenge, as it involves the decomposition of strokes and dexterous utensil control. Previous efforts have primarily focused on supervised learning of a single instrument, limiting the performance of robots in the realm of cross-domain text replication. To address these challenges, we propose CalliRewrite: a coarse-to-fine approach for robot arms to discover and recover plausible writing orders from diverse calligraphy images without requiring labeled demonstrations. Our model achieves fine-grained control of various writing utensils. Specifically, an unsupervised image-to-sequence model decomposes a given calligraphy glyph to obtain a coarse stroke sequence. Using an RL algorithm, a simulated brush is fine-tuned to generate stylized trajectories for robotic arm control. Evaluation in simulation and physical robot scenarios reveals that our method successfully replicates unseen fonts and styles while achieving integrity in unknown characters.
Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions
Authors: Chan hur, Jeong-hun Hong, Dong-hun Lee, Dabin Kang, Semin Myeong, Sang-hyo Park, Hyeyoung Park
Date: 2025-03-07 00:00:00
Proceeding: CVPR 2025 1
Tasks: Retrieval, Video Retrieval, Video Similarity

In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.
PromptPex: Automatic Test Generation for Language Model Prompts
Authors: Reshabh K Sharma, Jonathan De Halleux, Shraddha Barke, Benjamin Zorn
Date: 2025-03-07 00:00:00
Tasks: Language Modeling, Language Modelling

Large language models (LLMs) are being used in many applications and prompts for these models are integrated into software applications as code-like artifacts. These prompts behave much like traditional software in that they take inputs, generate outputs, and perform some specific function. However, prompts differ from traditional code in many ways and require new approaches to ensure that they are robust. For example, unlike traditional software the output of a prompt depends on the AI model that interprets it. Also, while natural language prompts are easy to modify, the impact of updates is harder to predict. New approaches to testing, debugging, and modifying prompts with respect to the model running them are required. To address some of these issues, we developed PromptPex, an LLM-based tool to automatically generate and evaluate unit tests for a given prompt. PromptPex extracts input and output specifications from a prompt and uses them to generate diverse, targeted, and valid unit tests. These tests are instrumental in identifying regressions when a prompt is changed and also serve as a tool to understand how prompts are interpreted by different models. We use PromptPex to generate tests for eight benchmark prompts and evaluate the quality of the generated tests by seeing if they can cause each of four diverse models to produce invalid output. PromptPex consistently creates tests that result in more invalid model outputs than a carefully constructed baseline LLM-based test generator. Furthermore, by extracting concrete specifications from the input prompt, PromptPex allows prompt writers to clearly understand and test specific aspects of their prompts. The source code of PromptPex is available at https://github.com/microsoft/promptpex.
Invisible Walls in Cities: Leveraging Large Language Models to Predict Urban Segregation Experience with Social Media Content
Authors: Bingbing Fan, Lin Chen, Songwei Li, Jian Yuan, Fengli Xu, Pan Hui, Yong Li
Date: 2025-02-17 00:00:00
Tasks: Prediction

Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on social media encapsulates nuanced perceptions and feelings associated with different places, offering rich insights into segregation. However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose using Large Language Models (LLMs) to automate online review mining for segregation prediction. We design a Reflective LLM Coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement. Guided by the codebook, LLMs can generate both informative review summaries and ratings for segregation prediction. Moreover, we design a REasoning-and-EMbedding (RE'EM) framework, which combines the reasoning and embedding capabilities of language models to integrate multi-channel features for segregation prediction. Experiments on real-world data demonstrate that our framework greatly improves prediction accuracy, with a 22.79% elevation in R2 and a 9.33% reduction in MSE. The derived codebook is generalizable across three different cities, consistently improving prediction accuracy. Moreover, our user study confirms that the codebook-guided summaries provide cognitive gains for human participants in perceiving POIs' social inclusiveness. Our study marks an important step toward understanding implicit social barriers and inequalities, demonstrating the great potential of promoting social inclusiveness with AI.
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems
Authors: Mahfuz Ahmed Anik, Abdur Rahman, Azmine Toushik Wasi, Md Manjurul Ahsan
Date: 2025-03-05 00:00:00
Tasks: Diversity, Translation

Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations, a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly available at: https://github.com/ciol-researchlab/Context-Aware_Translation_MAS
Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline
Authors: Qi Jia, Baoyu Fan, Cong Xu, Lu Liu, Liang Jin, Guoguang Du, Zhenhua Guo, YaQian Zhao, Xuanjing Huang, RenGang Li
Date: 2024-05-15 00:00:00
Tasks: Sentiment Analysis

Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. Induced sentiment of viewers is essential for inferring the public response to videos, has broad application in analyzing public societal sentiment, effectiveness of advertising and other areas. The micro videos and the related comments provide a rich application scenario for viewers induced sentiment analysis. In light of this, we introduces a novel research task, Multi-modal Sentiment Analysis for Comment Response of Video Induced(MSA-CRVI), aims to inferring opinions and emotions according to the comments response to micro video. Meanwhile, we manually annotate a dataset named Comment Sentiment toward to Micro Video (CSMV) to support this research. It is the largest video multi-modal sentiment dataset in terms of scale and video duration to our knowledge, containing 107,267 comments and 8,210 micro videos with a video duration of 68.83 hours. To infer the induced sentiment of comment should leverage the video content, so we propose the Video Content-aware Comment Sentiment Analysis (VC-CSA) method as baseline to address the challenges inherent in this new task. Extensive experiments demonstrate that our method is showing significant improvements over other established baselines.
Chat-GPT: An AI Based Educational Revolution
Authors: Sasa Maric, Sonja Maric, Lana Maric
Date: 2025-02-10 00:00:00

The AI revolution is gathering momentum at an unprecedented rate. Over the past decade, we have witnessed a seemingly inevitable integration of AI in every facet of our lives. Much has been written about the potential revolutionary impact of AI in education. AI has the potential to completely revolutionise the educational landscape as we could see entire courses and degrees developed by programs such as ChatGPT. AI has the potential to develop courses, set assignments, grade and provide feedback to students much faster than a team of teachers. In addition, because of its dynamic nature, it has the potential to continuously improve its content. In certain fields such as computer science, where technology is continuously evolving, AI based applications can provide dynamically changing, relevant material to students. AI has the potential to replace entire degrees and may challenge the concept of higher education institutions. We could also see entire new disciplines emerge as a consequence of AI. This paper examines the practical impact of ChatGPT and why it is believed that its implementation is a critical step towards a new era of education. We investigate the impact that ChatGPT will have on learning, problem solving skills and cognitive ability of students. We examine the positives, negatives and many other aspects of AI and its applications throughout this paper.
ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces
Authors: Anudeep Vurity, Emanuela Marasco, Raghavendra Ramachandra, Jongwoo Park
Date: 2025-03-07 00:00:00

Finger photo Presentation Attack Detection (PAD) can significantly strengthen smartphone device security. However, these algorithms are trained to detect certain types of attacks. Furthermore, they are designed to operate on images acquired by specific capture devices, leading to poor generalization and a lack of robustness in handling the evolving nature of mobile hardware. The proposed investigation is the first to systematically analyze the performance degradation of existing deep learning PAD systems, convolutional and transformers, in cross-capture device settings. In this paper, we introduce the ColFigPhotoAttnNet architecture designed based on window attention on color channels, followed by the nested residual network as the predictor to achieve a reliable PAD. Extensive experiments using various capture devices, including iPhone13 Pro, GooglePixel 3, Nokia C5, and OnePlusOne, were carried out to evaluate the performance of proposed and existing methods on three publicly available databases. The findings underscore the effectiveness of our approach.
Routing for Large ML Models
Authors: Ofir Cohen, Jose Yallouz Michael Schapira, Shahar Belkar, Tal Mizrahi
Date: 2025-03-07 00:00:00

Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for \textit{quantifying} network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically \textit{optimizing} routing with respect to this global metric.
Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research
Authors: Jingcheng Du1, Ekin Soysal1, 3, Dong Wang2, Long He1, Bin Lin1, Jingqi Wang1, Frank J. Manion1, Yeran Li2, Elise Wu2  and Lixia Yao2
Date: 2024-05-09 00:00:00
Proceeding: journal 2024 5
Tasks: Articles, Deep Learning, Systematic Literature Review, text-classification, Text Classification

Abstract
 Objective Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection 
and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two 
disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for 
pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to 
facilitate automation of the SLR abstract screening.
 Methods This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained 
citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and 
F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and 
MeSH terms.
 Results and conclusions The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. 
The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features 
beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% 
for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed 
conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models 
for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
 Keywords Machine learning, Deep learning, Text classification, Article screening, Systematic literature review
Cloud Object Detector Adaptation by Integrating Different Source Knowledge
Authors: Shuaifeng Li, Mao Ye, Lihua Zhou, Nianxin Li, Siying Xiao, Song Tang, Xiatian Zhu
Date: 2024-12-10 00:00:00
Proceeding: Advances in Neural Information Processing Systems 37 (NeurIPS 2024) 2024 12
Tasks: Domain Adaptation, Knowledge Distillation, Object Detection

We propose to explore an interesting and promising problem, Cloud Object Detector Adaptation (CODA), where the target domain leverages detections provided by a large cloud model to build a target detector. Despite with powerful generalization capability, the cloud model still cannot achieve error-free detection in a specific target domain. In this work, we present a novel Cloud Object detector adaptation method by Integrating different source kNowledge (COIN). The key idea is to incorporate a public vision-language model (CLIP) to distill positive knowledge while refining negative knowledge for adaptation by self-promotion gradient direction alignment. To that end, knowledge dissemination, separation, and distillation are carried out successively. Knowledge dissemination combines knowledge from cloud detector and CLIP model to initialize a target detector and a CLIP detector in target domain. By matching CLIP detector with the cloud detector, knowledge separation categorizes detections into three parts: consistent, inconsistent and private detections such that divide-and-conquer strategy can be used for knowledge distillation. Consistent and private detections are directly used to train target detector; while inconsistent detections are fused based on a consistent knowledge generation network, which is trained by aligning the gradient direction of inconsistent detections to that of consistent detections, because it provides a direction toward an optimal target detector. Experiment results demonstrate that the proposed COIN method achieves the state-of-the-art performance.
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification
Authors: Dingzhao Li, Student Member, Xiaowei Chen, Shaohua Hong, Member, Jie Qi, and Haixin Sun, Senior Member, IEEE
Date: 2025-03-03 00:00:00
Proceeding: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 74, 2025 2025 3
Tasks: class-incremental learning, Class Incremental Learning, Contrastive Learning, Few-Shot Class-Incremental Learning, Incremental Learning

—Specific emitter identification (SEI) is a
non-password authentication method that adds an extra
layer of security to wireless devices. However, existing SEI
methods are unable to continuously learn new classes from
a limited number of training examples due to data scarcity,
which is more challenging than the catastrophic forgetting
and overfitting problems associated with the widely studied
class-incremental learning (CIL). In this article, we propose a
novel few-shot class-incremental specific emitter identification
(FSCIL-SEI) framework to address the challenge of catastrophic
forgetting and overfitting in CIL. Specifically, to ensure interclass
discriminability during the incremental process, we first employ
prototype learning training methods in the base task and
introduce a self-supervised contrastive learning (SSCL) that
increases interclass distances and reduces intraclass distances
in the feature space. Second, we propose a separation of
class weights (SCWs) to isolate old and new class weights in
the classification layer, which effectively mitigates the issue
of catastrophic forgetting. Finally, to alleviate the problem
of overfitting due to insufficient samples during incremental
training, we introduce a three-stage course learning (CL)
approach that advances from simple to complex tasks, which not
only mitigates overfitting but also improves the generalization
ability of the model. Experimental results demonstrate that
our method outperforms other FSCIL methods in terms of
both performance degradation (PD) and incremental accuracy
when evaluated on automatic identification system (AIS) and
automatic dependent surveillance–broadcast (ADS-B) datasets
Towards Data-Efficient Language Models: A Child-Inspired Approach to Language Learning
Authors: Mohammad Amin Ghanizadeh, Mohammad Javad Dousti
Date: 2025-03-06 00:00:00
Tasks: Language Acquisition

In this work, we explain our approach employed in the BabyLM Challenge, which uses various methods of training language models (LMs) with significantly less data compared to traditional large language models (LLMs) and are inspired by how human children learn. While a human child is exposed to far less linguistic input than an LLM, they still achieve remarkable language understanding and generation abilities. To this end, we develop a model trained on a curated dataset consisting of 10 million words, primarily sourced from child-directed transcripts. The 2024 BabyLM Challenge initial dataset of 10M words is filtered to 8.5M. Next, it is supplemented with a randomly selected subset of TVR dataset consisting of 1.5M words of television dialogues. The latter dataset ensures that similar to children, the model is also exposed to language through media. Furthermore, we reduce the vocabulary size to 32,000 tokens, aligning it with the limited vocabulary of children in the early stages of language acquisition. We use curriculum learning and is able to match the baseline on certain benchmarks while surpassing the baseline on others. Additionally, incorporating common LLM training datasets, such as MADLAD-400, degrades performance. These findings underscore the importance of dataset selection, vocabulary scaling, and curriculum learning in creating more data-efficient language models that better mimic human learning processes.
Ensemble classifier fostered detection of arrhythmia using ECG data
Authors: M. Ramkumar, Manjunathan Alagarsamy, A. Balakumar & S. Pradeep
Date: 2023-05-05 00:00:00
Proceeding: Medical & Biological Engineering & Computing 2023 5
Tasks: Arrhythmia Detection, Ensemble Learning, Rhythm

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).