pwc-archive/papers-with-abstracts huggingface dataset:
SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks
Authors: Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder
Date: 2024-08-07 00:00:00
Tasks: Federated Learning

Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Authors: Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder
Date: 2024-08-06 00:00:00
Tasks: Chatbot, Federated Learning, Information Retrieval, RAG, Retrieval

Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social media data sources, including user posts, multimedia content, and trending news. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on social media trends and user-generated content. The system's innovative architecture enables efficient processing of input files, parsing and enriching text data with metadata, and generating relevant questions and answers using advanced language models. By facilitating interactive access to a wealth of social network information, this personalized chatbot system represents a significant advancement in social media communication and knowledge dissemination.
Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space
Authors: Junbiao Pang, Qingming Huang
Date: 2024-07-26 00:00:00

Organizing a few webpages from social media websites into popular topics is one of the key steps to understand trends on web. Discovering popular topics from web faces a sea of noise webpages which never evolve into popular topics. In this paper, we discover that the similarity values between webpages in a popular topic contain the statistically similar features observed in Levy walks. Consequently, we present a simple, novel, yet very powerful Explore-Exploit (EE) approach to group topics by simulating Levy walks nature in the similarity space. The proposed EE-based topic clustering is an effective and effcient method which is a solid move towards handling a sea of noise webpages. Experiments on two public data sets demonstrate that our approach is not only comparable to the state-of-the-art methods in terms of effectiveness but also significantly outperforms the state-of-the-art methods in terms of efficiency.
IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning
Authors: Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede
Date: 2024-07-25 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems

Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given session (e.g. do they want to watch short videos or a movie or play games; are they shopping for a camping trip), it becomes easier to provide high-quality recommendations. In this paper, we introduce IntentRec, a novel recommendation framework based on hierarchical multi-task neural network architecture that tries to estimate a user's latent intent using their short- and long-term implicit signals as proxies and uses the intent prediction to predict the next item user is likely to engage with. By directly leveraging the intent prediction, we can offer accurate and personalized recommendations to users. Our comprehensive experiments on Netflix user engagement data show that IntentRec outperforms the state-of-the-art next-item and next-intent predictors. We also share several findings and downstream applications of IntentRec.
Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform
Authors: Suchen Xu, Wu Xiao, Chen Yu, Hang Chen  ，Yongzhong Tan
Date: 2023-02-20 00:00:00
Proceeding: Remote Sensing 2023 2
Tasks: Change Detection, Land Cover Classification

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Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform
使用Google Earth Engine平台绘制中国山区耕地撂荒地图
by Suchen Xu 1,Wu Xiao 1,*ORCID,Chen Yu 2,Hang Chen 1ORCID andYongzhong Tan 1
作者：Suchen 1，Wu Xiao 1，* ORCID ，Chen Yu 2，Hang 1 ORCID 和Yongzhong Tan 1
1
Department of Land Management, Zhejiang University, Hangzhou 310058, China
浙江大学土地管理系， 杭州310058， 中国
2
Sichuan Institute of Land Science and Technology, Chengdu 610065, China
四川省土地科学技术研究院， 成都610065， 中国
*
Author to whom correspondence should be addressed.
应向其发送信件的作者。
Remote Sens. 2023, 15(4), 1145; https://doi.org/10.3390/rs15041145
遥感 2023， 15（4）， 1145;https://doi.org/10.3390/rs15041145
Submission received: 22 November 2022 / Revised: 25 January 2023 / Accepted: 16 February 2023 / Published: 20 February 2023
提交截止日期：2022 年 11 月 22 日 / 修订日期：2023 年 1 月 25 日 / 接受日期：2023 年 2 月 16 日 / 发布日期：2023 年 2 月 20 日
(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)
（本文属于《利用Google Earth Engine遥感土地利用与土地变化》专刊）
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Abstract 抽象
Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.
Novel Hardware Implementation of Deduplicating Visually Identical JPEG Image Chunks
Authors: Thang Luong, LUAN DINH, Hung Nguyen, AND LINH TRAN
Date: 2024-05-15 00:00:00
Proceeding: 4 may to 12 may 2024 5
Tasks: CPU

The exponential growth of data, particularly JPEG images spurred by mobile and social media
applications, poses significant storage challenges for data centers. Traditional deduplication methods such
as FSP or CDC rely on binary data, which can differ even when two images are visually identical. PXDedup,
a chunk-based image deduplication strategy, effectively addresses this issue by recognizing and eliminating
visual redundancies. This approach offers substantial improvements in JPEG image deduplication. However,
the software implementation of this method on standard CPUs suffers from low throughput due to the CPUs’
limited processing capabilities. This study introduces an optimized deduplication method that leverages
PXDedup to target visual redundancies in JPEG images. Our research focuses on hardware acceleration
for image dimensions of 512 × 512 pixels. By implementing this approach on the Digilent Genesys
2 board with Xilinx Kintex-7 FPGA, we achieve a performance of 25 MB/s, tenfold faster than single-core
CPU implementation and threefold quicker than quad-core CPU setup. Additionally, our analysis indicates
minimal disparity in the results across all three datasets in software and hardware implementations. This
evaluation underscores the effectiveness of hardware acceleration in enhancing deduplication throughput
while ensuring accuracy and reliability.
Hi-SAM: A high-scalable authentication model for satellite-ground Zero-Trust system using mean field game
Authors: Xuesong Wu, Tianshuai Zheng, Runfang Wu, Jie Ren, Junyan Guo, Ye Du
Date: 2024-08-12 00:00:00
Tasks: Anomaly Detection

As more and more Internet of Thing (IoT) devices are connected to satellite networks, the Zero-Trust Architecture brings dynamic security to the satellite-ground system, while frequent authentication creates challenges for system availability. To make the system's accommodate more IoT devices, this paper proposes a high-scalable authentication model (Hi-SAM). Hi-SAM introduces the Proof-of-Work idea to authentication, which allows device to obtain the network resource based on frequency. To optimize the frequency, mean field game is used for competition among devices, which can reduce the decision space of large-scale population games. And a dynamic time-range message authentication code is designed for security. From the test at large population scales, Hi-SAM is superior in the optimization of authentication workload and the anomaly detection efficiency.
ArtQuest: Countering Hidden Language Biases in ArtVQA
Authors: Tibor Bleidt, Sedigheh Eslami, Gerard de Melo
Date: 2024-01-04 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision 2024 1
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The task of Visual Question Answering (VQA) has been studied extensively on general-domain real-world images. Transferring insights from general domain VQA to the art domain (ArtVQA) is non-trivial, as the latter requires models to identify abstract concepts, details of brushstrokes and styles of paintings in the visual data as well as possess background knowledge about art. This is exacerbated by the lack of high-quality datasets. In this work, we shed light on hidden linguistic biases in the AQUA dataset, which is the only publicly available benchmark dataset for ArtVQA. As a result, the majority of questions can be answered without consulting the visual information, making the “V” in ArtVQA rather insignificant. In order to counter this problem, we create a simple, yet practical dataset, ArtQuest, using structured information from the SemArt collection. Our dataset and the pipeline to reproduce our results are publicly available at [https://github.com/bletib/artquest](https://github.com/bletib/artquest).
CoNECo: A corpus for named entity recognition and normalization of protein complexes
Authors: Katerina Nastou, Mikaela Koutrouli, Sampo Pyysalo, Lars Juhl Jensen
Date: 2024-05-18 00:00:00
Proceeding: bioRxiv 2024 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Motivation: Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Normalization (NEN) of protein-containing complexes. Current resources inadequately address the recognition of protein-containing complex names across different organisms, underscoring the crucial need for a dedicated corpus.

Results: We introduce the Complex Named Entity Corpus (CoNECo), an annotated corpus for NER and NEN of complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, with F1-scores of 73.7% and 61.2%, respectively. Subsequently, we applied the best taggers for comprehensive tagging of the entire openly accessible biomedical literature.

Availability: All resources, including the annotated corpus, training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and GitHub https://zenodo.org/records/10693653.
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic
Authors: Silvan Wehrli, Chisom Ezekannagha, Georges Hattab, Tamara Boender, Bert Arnrich, Christopher Irrgang
Date: 2024-08-01 00:00:00
Proceeding: Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis 2024 8
Tasks: Clustering, Data Visualization, Hierarchical Text Clustering, Misinformation, Sentiment Analysis, Short Text Clustering, Text Clustering, Time Series Analysis

Social media are a critical component of the information ecosystem during public health crises. Understanding the public discourse is essential for effective communication and misinformation mitigation. Computational methods can aid these efforts through online social listening. We combined hierarchical text clustering and sentiment analysis to examine the face mask-wearing discourse in Germany during the COVID-19 pandemic using a dataset of 353,420 German X (formerly Twitter) posts from 2020. For sentiment analysis, we annotated a subsample of the data to train a neural network for classifying the sentiments of posts (neutral, negative, or positive). In combination with clustering, this approach uncovered sentiment patterns of different topics and their subtopics, reflecting the online public response to mask mandates in Germany. We show that our approach can be used to examine long-term narratives and sentiment dynamics and to identify specific topics that explain peaks of interest in the social media discourse.
DTF-AT: Decoupled Time-Frequency Audio Transformer for Event Classification
Authors: Tony Alex, Sara Ahmed, Armin Mustafa, Muhammad Awais, Philip JB Jackson
Date: 2024-03-24 00:00:00
Proceeding: AAAI 2024 3
Tasks: Audio Classification, Information Retrieval

Convolutional neural networks (CNNs) and Transformer-based networks have recently enjoyed significant attention for various audio classification and tagging tasks following their wide adoption in the computer vision domain. Despite the difference in information distribution between audio spectrograms and natural images, there has been limited exploration of effective information retrieval from spectrograms using domain-specific layers tailored for the audio domain. In this paper, we leverage the power of the Multi-Axis Vision Transformer (MaxViT) to create DTF-AT (Decoupled Time-Frequency Audio Transformer) that facilitates interactions across time, frequency, spatial, and channel dimensions. The proposed DTF-AT architecture is rigorously evaluated across diverse audio and speech classification tasks, consistently establishing new benchmarks for state-of-the-art (SOTA) performance. Notably, on the challenging AudioSet 2M classification task, our approach demonstrates a substantial improvement of 4.4% when the model is trained from scratch and 3.2% when the model is initialised from ImageNet-1K pretrained weights. In addition, we present comprehensive ablation studies to investigate the impact and efficacy of our proposed approach. The codebase and pretrained weights are available on https://github.com/ta012/DTFAT.git
Dynamic Pricing of Electric Vehicle Charging Station Alliances Under Information Asymmetry
Authors: Zeyu Liu, Yun Zhou, Donghan Feng, Shaolun Xu, Yin Yi, Hengjie Li, Haojing Wang
Date: 2024-08-13 00:00:00
Tasks: Decision Making

Due to the centralization of charging stations (CSs), CSs are organized as charging station alliances (CSAs) in the commercial competition. Under this situation, this paper studies the profit-oriented dynamic pricing strategy of CSAs. As the practicability basis, a privacy-protected bidirectional real-time information interaction framework is designed, under which the status of EVs is utilized as the reference for pricing, and the prices of CSs are the reference for charging decisions. Based on this framework, the decision-making models of EVs and CSs are established, in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated. To solve the pricing decision model, the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs, the equilibrium of which gives the optimal pricing strategy. Finally, the case study results in a real urban area in Shanghai, China verifies the practicability of the framework and the effectiveness of the dynamic pricing strategy.
Performance Analysis of Distributed Filtering under Mismatched Noise Covariances
Authors: Xiaoxu Lyu, Guanghui Wen, Ling Shi, Peihu Duan, Zhisheng Duan
Date: 2024-08-13 00:00:00

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems,namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations.
Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization
Authors: Xingshi Xu, Hongxing Deng, Yunfei Wang, Shujin Zhang, Huaibo Song
Date: 2024-06-27 00:00:00
Proceeding: Applied Soft Computing 2024 6
Tasks: cow identification, Decision Making, Face Recognition, Management

In Precision Livestock Farming (PLF), automated monitoring and management systems offer farmers and veterinarians reliable information, alerts, and reference decision-making tools, thereby aiding in cost reduction and efficiency enhancement in livestock farming. Achieving individual cattle identification is a fundamental requirement for realizing PLF in cattle farms.
Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology
Authors: Junlin Guo, Siqi Lu, Can Cui, Ruining Deng, Tianyuan Yao, Zhewen Tao, Yizhe Lin, Marilyn Lionts, Quan Liu, Juming Xiong, Yu Wang, Shilin Zhao, Catie Chang, Mitchell Wilkes, Mengmeng Yin, Haichun Yang, Yuankai Huo
Date: 2024-08-09 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation, whole slide images

Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datasets. Recently, the success of foundation models (FMs) has provided a more generalizable solution, potentially enabling the segmentation of any cell type. In this study, we perform a large-scale evaluation of three widely used state-of-the-art (SOTA) cell nuclei foundation models (Cellpose, StarDist, and CellViT). Specifically, we created a highly diverse evaluation dataset consisting of 2,542 kidney whole slide images (WSIs) collected from both human and rodent sources, encompassing various tissue types, sizes, and staining methods. To our knowledge, this is the largest-scale evaluation of its kind to date. Our quantitative analysis of the prediction distribution reveals a persistent performance gap in kidney pathology. Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology. However, none of the foundation models are perfect; a performance gap remains in general nuclei segmentation for kidney pathology.
Leveraging Language Models for Emotion and Behavior Analysis in Education
Authors: Kaito Tanaka, Benjamin Tan, Brian Wong
Date: 2024-08-13 00:00:00
Tasks: Prompt Engineering

The analysis of students' emotions and behaviors is crucial for enhancing learning outcomes and personalizing educational experiences. Traditional methods often rely on intrusive visual and physiological data collection, posing privacy concerns and scalability issues. This paper proposes a novel method leveraging large language models (LLMs) and prompt engineering to analyze textual data from students. Our approach utilizes tailored prompts to guide LLMs in detecting emotional and engagement states, providing a non-intrusive and scalable solution. We conducted experiments using Qwen, ChatGPT, Claude2, and GPT-4, comparing our method against baseline models and chain-of-thought (CoT) prompting. Results demonstrate that our method significantly outperforms the baselines in both accuracy and contextual understanding. This study highlights the potential of LLMs combined with prompt engineering to offer practical and effective tools for educational emotion and behavior analysis.