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检索条件"任意字段=17th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022"

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17th ccf conference on computer supported cooperative work and social computing, chinesecscw 2022

17th CCF Conference on Computer Supported Cooperative Work a...

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17th ccf conference on computer supported cooperative work and social computing, chinesecscw 2022

ISBN: (纸本)9789819923847

the proceedings contain 90 papers. the special focus in this conference is on computer supported cooperative work and social computing. the topics include: Requirements Classification and Identification Approach for E-Collaboration Systems;community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning;university Knowledge Graph Construction Based on Academic social Network;country-Level Collaboration Patterns of social computing Scholars;an Intelligent Mobile System for Monitoring Relapse of Depression;Fine-Grained Sentiment Analysis of Online-Offline Danmaku Based on CNN and Attention;ramp Merging of Connected Vehicle With Virtual Platooning Control;community Detection Based on Enhancing Graph Autoencoder with Node Structural Role;representation of Chinese-Vietnamese Bilingual News Topics Based on Heterogeneous Graph;a Joint Framework for Knowledge Extraction from Flight Training Comments;convolutional Self-attention Network for Sequential Recommendation;towards Using Local Process Mining to Analyse Learning Behavior Pattern;memory-Effective Parallel Mining of Incremental Frequent Itemsets Based on Multi-scale;An AST-Based Collaborative Discussion Tool for the MOOC Environment;DQN-Based Comprehensive Consumption Minimization on Calculation Offloading in Mobile Edge computing;stochastic Task Offloading Problems for Edge computing;Container-Driven Scheduling Strategy for Scientific workflows in Multi-vCPU Environments;A Segmented Path Heuristic Recovery Algorithm for WSNs Based on Mobile Sink;TRindex: Distributed Double-Layer Road Network Trajectory Index;sleep Scheduling for Enhancing the Lifetime of three-Dimensional Heterogeneous Wireless Sensor Networks;scholarRec: A User Recommendation System for Academic social Network;CoSBERT: A Cosine-Based Siamese BERT-Networks Using for Semantic Textual Similarity;towards Heterogeneous Federated Learning;A Graph-Based Efficient Service Composition Method for computer Aided Engineering (CAE);privacy-

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17th ccf conference on computer supported cooperative work and social computing, chinesecscw 2022

17th CCF Conference on Computer Supported Cooperative Work a...

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17th ccf conference on computer supported cooperative work and social computing, chinesecscw 2022

ISBN: (纸本)9789819923557

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16th ccf conference on computer supported cooperative work and social computing, chinesecscw 2021

16th CCF Conference on Computer Supported Cooperative Work a...

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16th ccf conference on computer supported cooperative work and social computing, chinesecscw 2021

ISBN: (纸本)9789811945458

the proceedings contain 87 papers. the special focus in this conference is on computer supported cooperative work and social computing. the topics include: Elastic Container Scheduling for Stochastically Arrived workflows in Cloud and Edge computing;legal Judgement Prediction of Sentence Commutation with Multi-document Information;attention and Multi-granied Feature Learning for Baggage Re-identification;An Improved SSD-Based Gastric Cancer Detection Method;a Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module;research on the Structure and Key Algorithms of Smart Gloves Oriented to Middle School Experimental Scene Perception;a Quantum Evolutionary Algorithm and Its Application to Optimal Dynamic Investment in Market Microstructure Model;CATS: A Cache Time-to-Live Setting Auto Adjustment Strategy for an Air Ticket Query Service;sentiment Analysis of Chinese Complex Long Sentences Based on Reinforcement Learning;an Attention-Based Multiobjective Optimization Evolutionary Algorithm for Community Detection in Attributed Networks;fine-Grained Diagnosis Method for Microservice Faults Based on Hierarchical Correlation Analysis;information Centrality Evaluation Method Based on Cascade Topological Relevance;a Novel Construction Approach for Dehazing Dataset Based on Realistic Rendering Engine;a Novel Traversal Search-Based D2D Collaborative Offloading Approach for workflow Application in Dynamic Edge Environment;intrusion Detection Algorithm of Industrial Control System Based on Improved Bloom Filter;research on Temporal workflow Task Assignment Strategy;decentralized Predictive Enterprise Resource Planning Framework on Private Blockchain Networks Using Neural Networks;optimal Storage Cloud Data Recoverability Audit Method Based on Regenerative Code;modeling Method for Function Trees Guided by the Symmetric Quintuple Implicational Controller;Merge Multiscale Attention Mechanism MSGAN-ACNN-BiLSTM Bearing Fault Diagnosis Model;a Survey on Learning

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16th ccf conference on computer supported cooperative work and social computing, chinesecscw 2021

16th CCF Conference on Computer Supported Cooperative Work a...

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16th ccf conference on computer supported cooperative work and social computing, chinesecscw 2021

ISBN: (纸本)9789811945489

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An Energy-Aware IoT Functions Offloading Strategy in Solar-Powered Edge Environment for Smart Agriculture 19th

An Energy-Aware IoT Functions Offloading Strategy in Solar-...

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

作者： Cao, Han Chen, Long Zhang, Jinquan Wang, Shuang Zhu, Xia School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9789819623723

Edge intelligence is a new trend for monitoring and management in remote animal husbandry. However, the deployment of IoT applications in remote Edge Servers (ESs) faces significant challenges, such as limited computing and battery resources of servers and the mobility of sensors. In this paper, we consider the problem of IoT functions offloading in solar-powered edge environments. the computational, communication, and energy consumption models were established first. A novel approach Energy and Container State aware Offloading Algorithm (ECSOA) was proposed to schedule functions among ESs. ECSOA uses the NSGA-II algorithm to determine optimal parameters, ensuring efficient energy usage and service delivery. Our real-time simulation experiments demonstrate that ECSOA significantly reduces system energy consumption and the number of function rejections, presenting a promising solution for sustainable and reliable serverless edge computing in IoT-enabled animal husbandry. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Adaptive DeepWalk and Prior-Enhanced Graph Neural Network for Scholar Influence Maximization in social Networks 19th

Adaptive DeepWalk and Prior-Enhanced Graph Neural Network f...

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

作者： Wang, Yijia Lin, Junjie Tang, Yong Yuan, Chengzhe Zhang, Luming School of Computer Science South China Normal University Guangzhou China School of Electronic and Information Guangdong Polytechnic Normal University Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9789819623723

With the rapid development of academic social media, the problem of node influence diffusion in scholar social networks has increasingly received extensive attention from the field of influence maximization (IM). Existing learning-based methods for solving the IM problem usually rely solely on network topology or individual node activities, lacking comprehensive consideration of both network topology and important information of nodes, leading to poor model performance. By comprehensively considering the network topology as well as the global information and importance of nodes, we propose a deep reinforcement learning (DRL) framework, named APGD-IM, which is based on an adaptive DeepWalk algorithm and a prior-enhanced graph neural network (GNN), aiming to optimize the performance degradation caused by the above issue. Specifically, we propose an adaptive DeepWalk algorithm DRA based on attention mechanism and node importance information, along with a prior-enhanced GNN module PGNN, for generating node embeddings. these embeddings are then used to learn parameters by combining double deep Q-network to address scholar influence maximization problem in social networks. Experimental results on four real-world social networks demonstrate that our proposed model outperforms other baseline methods and maintains stable performance advantages across different diffusion models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network topology

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UGCM-LU: A Unified Stream and Batch Graph computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

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

作者： Li, Hong Wu, Ling 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

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. this paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. the experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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

作者： Li, Xinying Wu, Ling 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

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Visual Analyze the Impact of Structural Features and Node Attributes on Collaborative Relationships 19th

Visual Analyze the Impact of Structural Features and Node...

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

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). the first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. the second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. the third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4Vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4Vis visual analysis system through specific case studies and structured interviews with users. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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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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