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检索条件"任意字段=19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024"

共 176 条记录，以下是21-30 订阅

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A Multi-agent Collaboration Approach for Identifying Developer-Fixed Issues in Github Projects 19th

A Multi-agent Collaboration Approach for Identifying Develo...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Han, Zhengru Jiang, Bo Xue, Weihao Dai, Chaoqun Huang, Qiao Wang, Ye School of Computer Science and Technology Zhejiang Gongshang University Zhejiang China

ISBN: (纸本)9789819623754

Github is one of the most popular collaborative development tools used by many institutions and enterprises. the Github issue community serves as the primary tool for project developers to gather program bug information, with Github users posting issues based on encountered program bugs. However, a considerable portion of these issues ultimately remain unfixed by developers. We term the issues fixed by developers as ‘dev-fixed’ issues, and conversely, we call them ‘wontfix’ issues. through manual analysis of 2500 bug-related issues collected from five Github projects, we summarize the unresolved issues from four aspects and propose a multi-agent approach based on large language model (LLM) to identify dev-fixed issues. We design the previously summarized four aspects into different agents, independently analyzed by the LLM, and ultimately parallelly provide information to a summary agent for the final determination. Compared to the baseline, our approach shows the best performance in the metric of F1 score, recall rate and precision rate, with the value of 0.852, 0.862 and 0.857 respectively. Ablation experiments demonstrate the critical importance of each agent to our approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Project management

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A Scholar Recommendation System Based on Academic social Network and Word Cloud 19th

A Scholar Recommendation System Based on Academic Social Net...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Zhang, Luming Tang, Feiyi Tang, Yong Lin, Ronghua Wang, Yijia Lan, Yangbo South China Normal University Guangzhou510631 China Guangzhou Panyu Polytechnic Guangzhou511483 China

ISBN: (纸本)9789819623754

With the rapid development of information age, recommendation systems have played an important role in many fields. However, traditional recommendation systems still face challenges in information overload, cold-start problem and inaccurate recommendations. To address the issue of inaccurate recommendations, this paper presents a scholar recommendation system based on the think tank, an academic information retrieval platform. First, we use the SCHOLAT data to establish the think tank social network, and established the think tank scholar word cloud by using the HanLP model and the TF-IDF algorithm. Based on this, we constructed a think tank dataset. Subsequently, we use a modified Variational Graph Normalized Auto-Encoders(VGNAE) model to perform the scholar recommendation task on the think tank dataset and propose an efficient scholar update method. Finally, we deposit the recommendation results in the cache to improve the recommendation efficiency. the experimental results show that compared with other baseline models, the improved VGNAE model performs well on the think tank dataset, and achieves the best results on both AUC and AP, verifying the effectiveness and reliability of our proposed think tank recommendation system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph representation learning Recommendation system Scholar think tank SCHOLAT

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A Domain-Adapted Brain-computer Interface for Implicit Emotion Tagging of Multimedia Content 19th

A Domain-Adapted Brain-Computer Interface for Implicit Emot...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Cui, Yanmeng Liang, Wenbin Yang, Yikun Hou, Kechen Shi, Yuhan Zhao, Qiqi Zhang, Xiaowei Lanzhou University Lanzhou730000 China

ISBN: (纸本)9789819623723

Tagging multimedia content in terms of emotions can improve the efficiency of user recommendations. Using a Brain-computer Interface of emotion based on Electroencephalogram (EEG) is a reliable method to annotate the emotions evoked by content. However, the subject-independent models’ generalization and re-usability often degrade due to individual differences in EEG data. Hence, in this paper, we proposed a novel unsupervised domain adaptation method incorporating a variant of MMD (Maximum Mean Discrepancy) called Categorical MMD (CMMD) with a curriculum pseudo-labeling module to tackle the above mentioned issue. Unlike the original MMD, CMMD considers the relationship between data belonging to the same category from the source and target domain so that CMMD can align the distribution more than fine-grained. Besides, to mitigate the influence of false pseudo labels on CMMD and final classification, we proposed a curriculum pseudo labeling module that leverages reliable pseudo-target samples and labeled source samples to train the classifier. the experiment was conducted on the SEED dataset to verify our method. the experimental results showed that the proposed method achieves significant performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain-computer Interface Domain Adaptation Electroencephalogram Emotion Tagging

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Blockchain-Based Traffic Accident Evidence Management Scheme 19th

Blockchain-Based Traffic Accident Evidence Management Scheme

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： An, Qi Zhao, Yi Yu, Meiju Sa, Rula Gao, Qiaomei Zhang, Jin College of Computer Science-College of Software Inner Mongolia University Hohhot China

ISBN: (纸本)9789819623723

the management of evidence for traffic accidents under surveillance video faces numerous challenges. Currently, traffic accidents evidence heavily relies on manual video review, and manually stored in third-party databases. this method of evidence storage cannot ensure the reliable source and trustworthy storage of evidence. therefore, we propose a framework that combines deep learning with blockchain technology for traffic accident evidence management. It focuses on securely storing accident evidence and efficiently extracting accident information, providing a practical solution for traffic accident evidence management. In this framework, blockchain is combined with IPFS to overcome the drawbacks of centralized evidence storage, ensuring the security of evidence while alleviating the problem of limited blockchain storage capacity. Furthermore, a traffic accident evidence forensic model (YOLO-MBC) is constructed, which uses artificial intelligence to replace manual evidence collection. this not only ensures the reliability of evidence sources but also enhances the efficiency of evidence collection. Finally, experimental results demonstrate that the framework achieves secure evidence storage and the accuracy of the accident evidence forensic model (YOLO-MBC) reaches 92.4%, meeting the practical requirements of traffic accident evidence management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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UAV Swarm Network Coverage with Rechargeable Deployments on Buildings 19th

UAV Swarm Network Coverage with Rechargeable Deployments on...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Hu, Yibo Zhou, Yu Zhao, Yingchao Zhang, Xiao College of Computer Science South-Central Minzu University Wuhan430074 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Sciences Caritas Institute of Higher Education Hong Kong China

ISBN: (纸本)9789819623754

Due to their exceptional mobility, simplified deployment process, and excellent line-of-sight communication links, Unmanned Aerial Vehicles (UAVs) are capable of delivering highly dependable and cost-effective communication services to ground users in emergency regions via their miniaturized base stations. When it comes to energy-efficient deployment and long-lasting service, this paper evaluates the possibility of placing rechargeable devices atop high-rise buildings in urban environments as potential sites for UAV deployment. For vast urban areas, characterized by an extensive number of buildings, this paper endeavors to minimize the energy consumption of the UAV swarm system by discretizing the transmission power of the UAVs. the optimal locations for deploying UAV base stations are determined by the Dijkstra Algorithm on the constructed directed weighted graphs (DWG-DA), ensuring uninterrupted network coverage in linear areas like urban streets or roads. the superiority of the DWG-DA algorithm is demonstrated through simulation experiments and algorithm comparison tests, taking into account various distribution patterns of UAVs and buildings. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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How Effective is the Shorts Transformation of Traditional Media? An Analysis from the Perspective of User-Generated Content 19th

How Effective is the Shorts Transformation of Traditional Me...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Zhang, Pu Xu, Corey Kewei Guangzhou511453 China

ISBN: (纸本)9789819623723

the digital transformation of traditional media outlets has become a critical imperative in the evolving media landscape. As traditional media grapple with the challenges of adapting to the rise of digital and social media platforms, understanding the factors that shape their performance and user engagement on these emerging channels is of paramount importance. this study examines the comparative performance of a traditional media account with a government background (Hubei Daily) and a specialized new media account (Cover News) on Douyin platform, with a focus on the distribution of emotional sentiment and thematic diversity in user-generated content. We collected 19,343 comments from "Hubei Daily," with 18,792 valid (97.15%), and 36,737 comments from "Cover News," with 28,910 valid (78.69%). Our research used a BERT fine-tuned model for sentiment analysis, TF-IDF for keyword extraction, keyword co-occurrence analysis, and a large language model for topic modeling. this approach allowed us to capture a wide range of discussion topics and understand the thematic focus. Findings show that the new media account covered broader topics, whereas the traditional media account had a more concentrated thematic focus. Typically, such events should generate negative emotions and discussions around accountability. However, the study indicates that users are more cautious in their comments on official media, suggesting ineffective digital transformation by traditional media. this underscores the need for traditional media to better understand and adapt to content preferences and engagement patterns of shorts platform users, highlighting the complexities of transitioning to emerging platforms and the importance of effective content strategies and meaningful user engagement. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Media Transformation Online Public Opinion social Media Analytics

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Stable Attribution with Local Surrogate Model 19th

Stable Attribution with Local Surrogate Model

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Pan, Changfan Wang, Qing Zhu, Jia Cao, Xinran Guo, Hanghui Huang, Changqin School of Computer Science and Technology Zhejiang Normal University Jinhua China College of Education Zhejiang Normal University Jinhua China

ISBN: (纸本)9789819623754

In many critical machine learning applications, the lack of transparency and interpretability of black box models raises concerns about their trustworthiness. this issue highlights the importance of understanding the causality behind predictions, which is related to endogeneity, causal inference, and transfer learning. Accurately estimating the true coefficients and determining the important feature set is necessary for accurate attribution. To address this challenge, we propose a new explainable model called Stable Local Interpretable Surrogate (SLIS), which learns the causal structure and provides trustworthy local interpretation. SLIS exploits the conditional expectation invariance structure and environmental heterogeneity to determine causal relationships. It can select truly important features without requiring additional domain knowledge. We provide the theoretical basis for this technique and confirm its effectiveness on synthetic and real-world datasets. Compared to existing methods, SLIS provides robust and stable estimates of the local surrogate model, a simple and interpretable model that approximates the behavior of a black-box model. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. this design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Knowledge Graph-Enhanced Session-Based Recommendation with Two-Stage Feature Filtering 19th

Knowledge Graph-Enhanced Session-Based Recommendation with ...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Liu, Chang Li, Ruolin Lin, Ronghua Li, Dingding Chen, Jiemin Tang, Yong School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9789819623723

Session-based recommendation has demonstrated notable advantages, since it dynamically adjusts recommendations based on user’s behavior and context within the current session. Recently, researchers have extensively investigated the integration of knowledge graphs (KGs) into recommendation systems, with Graph Neural Network (GNN) achieving impressive results in deeply exploring and learning the relationships between users. However, despite these encouraging results, previous studies have overlooked the semantic information of items within sessions and the contextual environment outside sessions, making it challenging to mine the complex associations between items. Simultaneously, traditional algorithms encounter issues with high computational costs and excessive memory usage. To address the above challenges, in this paper, we propose a KG-enhanced session-based recommendation with two-stage feature filtering, named KGFF-SR. We introduce KG as an external knowledge base and utilize the link information between entities in the graph to enrich the session graph. Furthermore, we propose a two-stage item feature filtering mechanism to capture potential relationships between users and items while reducing computational and storage costs effectively. We conduct extensive experiments on three real-world public sub-datasets, and the results show that our proposed method significantly outperforms powerful baseline models in performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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RGB+Skeleton: Cross-Modal Fusion Training for Human Action Recognition 19th

RGB+Skeleton: Cross-Modal Fusion Training for Human Action ...

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19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024

作者： Lin, Ming-Xuan Zhang, Wei-Huang Qiu, Hong-Ming Zhang, Hong-Bo Zhang, Miao-Hui Department of Computer Science and Technology Huaqiao University Xiamen China Institute of Energy Research Jiangxi Academy of Sciences Nanchang China

ISBN: (纸本)9789819623723

Single-modality Human Action Recognition (HAR) approaches often fall short in achieving satisfactory performance in recognizing specific actions, attributable to inherent data features. therefore, we propose a multimodal fusion approach for HAR, rooted in the fusion of skeleton and RGB video. this framework entails training the model separately using both skeleton and RGB video to extract action features intrinsic to each modality. Subsequently, the fusion of skeleton and RGB video is pursued from two key perspectives: classification results and action features. In terms of classification results, we explore and discuss four methods: the confidence-based optimal selection method, the bimodal weighted sum method, and two variations of models reliant on the fusion of classification probability. Regarding action features, we propose a cross-modal fusion training based on action features, operating on both skeleton and RGB video models. this strategy utilizes features from one modality to augment the training of the other, facilitating multi-modality fusion at the feature level. the proposed multimodal fusion strategy is compared to existing methods on two large datasets: NTU RGB+D and NTU RGB+D 120. Experimental results underscore the effectiveness of the propsoed approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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