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[null,null,"Titolo",null,"Computer supported cooperative work and social computing : 16th CCF conference, ChineseCSCW 2021, Xiangtan, China, November 26-28, 2021 : revised selected papers. Part II / / edited by Yuqing Sun [and six others]"]
[null,null,"Pubbl/distr/stampa",null,"Gateway East, Singapore : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"981-19-4549-7"]
[null,null,"Descrizione fisica",null,"1 online resource (548 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science ; ; v.1492"]
[null,null,"Disciplina",null,"658.302"]
[null,null,"Soggetti",null,"Teams in the workplace - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- Locally Linear Embedding Discriminant Feature Learning Model -- 1 Introduction -- 2 Related Work -- 3 UD-LLE Model -- 3.1 Construction of UD-LLE -- 3.2 Algorithm Based on UD-LLE Model -- 4 Experimental Results -- 4.1 Evaluation Measures -- 4.2 Experimental Settings -- 4.3 Comparison Results -- 5 Conclusion -- References -- Cache Optimization Based on Linear Regression and Directed Acyclic Task Graph -- 1 Introduction -- 2 Prearrangement Knowledge -- 2.1 Multiple Linear Regression Algorithm -- 2.2 DAG Task Graph -- 3 Method -- 3.1 Problem Definition -- 3.2 Feature Processing -- 3.3 Model Training -- 3.4 Component Design -- 4 Experimental Analysis -- 4.1 Evaluation Index -- 4.2 Experimental Results -- 5 Summary -- References -- A University Portrait System Incorporating Academic Social Network -- 1 Introduction -- 2 Preliminary -- 2.1 Text Mining Technique -- 2.2 Scraping Techniques -- 2.3 SCHOLAT Data -- 3 System Design -- 3.1 Data Collection and Data Cleaning -- 3.2 University Tags Extraction -- 4 Implementation -- 4.1 Data Collection -- 4.2 University Tagging -- 4.3 Portrait Construction and Updating -- 5 Evaluation -- 5.1 Evaluation of University Portrait -- 5.2 Comparison -- 5.3 Discussion -- 6 Conclusion -- References -- Multi-objective Optimization of Ticket Assignment Problem in Large Data Centers -- 1 Introduction -- 2 Related Work -- 2.1 Ticket Routing -- 2.2 Ticket Scheduling -- 3 Problem Description -- 4 Proposed GAMOA* -- 4.1 "]
[{"$nulls":4},"Transforming Ticket Scheduling to Routing -- 4.2 Multi-objective A* for Scheduling Ordered Tickets -- 4.3 Genetic Algorithm for Ticket Ordering -- 5 Performance Evaluation -- 5.1 Experimental Results -- 6 Conclusion -- References."]
[{"$nulls":4},"Joint Embedding Multiple Feature and Rule for Paper Recommendation -- 1 Introduction -- 2 Related Work -- 2.1 Recommendations Based on Content and Rating Features -- 2.2 Recommendations Based on Structural Features -- 3 Joint Multi-feature and Rules Paper Embedding Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Rule-Based Sample Selection Strategy -- 3.4 Joint Embedding Based Academic Paper Recommendation -- 4 Experiments -- 4.1 Dataset -- 4.2 Baselines and Experiment Setup -- 4.3 Results -- 5 Conclusion -- References -- Predicting Drug-Target Interactions Binding Affinity by Using Dual Updating Multi-task Learning -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Datasets -- 3.2 Model Architecture -- 3.3 Training Procedure -- 3.4 Adaptation Strategy -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Comparative Study -- 4.3 Results -- 5 Conclusions and Discussion -- References -- GRE: A GAT-Based Relation Embedding Model of Knowledge Graph for Recommendation -- 1 Introduction -- 2 Related Work -- 2.1 Graph Attention Networks -- 2.2 Knowledge Graph Aware Recommender Systems -- 3 The Proposed Model -- 3.1 Problem Formulation -- 3.2 Triple Set and Triple Group Extraction -- 3.3 GAT-Based Entity Aggregation and Relation Embedding -- 3.4 Click-Through-Rate Prediction -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experiment Setup -- 4.4 Experiment Results -- 5 Conclusion -- References -- Locating Hidden Sources in Evolutionary Games Based on Fuzzy Cognitive Map -- 1 Introduction -- 2 Background -- 2.1 Fuzzy Cognitive Maps -- 2.2 EG Model -- 3 Locating Hidden Agents in FCMs -- 3.1 Overall Design Process -- 3.2 Learning FCMs from Time Series -- 3.3 Model EG Using FCM -- 3.4 Measuring Anomaly -- 4 Experiment -- 4.1 Performance Measures -- 4.2 Case 1: Brazilian Amazon Example."]
[{"$nulls":4},"4.3 Case 2: Supervisory Control Systems -- 4.4 Case 3: Mobile Payment System Project -- 4.5 Effect of NMon LHN -- 5 Conclusions -- References -- Deep Bug Triage Model Based on Multi-head Self-attention Mechanism -- 1 Introduction -- 2 Related Work -- 3 MSDBT Model -- 3.1 Input Layer -- 3.2 Feature Extraction Layer -- 3.3 Multi-head Self-attention Layer -- 3.4 Output Layer -- 4 Experimental -- 4.1 Data Set -- 4.2 Metrics -- 4.3 Comparative Models -- 4.4 Experimental Result -- 5 Conclusion -- References -- Taxi Pick-Up Area Recommendation via Integrating Spatio-Temporal Contexts into XDeepFM -- 1 Introduction -- 2 Related Work -- 3 The Framework of Taxi Pick-Up Area Recommendation -- 3.1 Related Definition -- 3.2 Recommendation Framework -- 4 Methodology Overview -- 4.1 XDeepFM -- 4.2 Construction of Spatio-Temporal Contexts Matrix -- 4.3 Pick-Up Area Recommendation -- 5 Experiments -- 5.1 Data Set -- 5.2 Performance Comparison with Other Recommendation Methods -- 5.3 Performance Evaluation of Spatio-Temporal Contexts -- 6 Conclusion -- References -- Learning When to Communicate Among Actors with the Centralized Critic for the Multi-agent System -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Experiments -- 4.1 Setup -- 4.2 Results and Analysis -- 5 Conclusions -- References -- Social Media and Online Communities -- Academic Article Classification Algorithm Based on Pre-trained Model and Keyword Extraction -- 1 Introduction -- 2 Background and Related Work -- 3 Our Model -- 3.1 Problem Formulation -- 3.2 The Design of Neural Network -- 3.3 Fine-Tuning Technique Based on Keyword Extraction -- "]
[{"$nulls":4},"4 Experimental Setup -- 4.1 Dataset Description -- 4.2 Dataset Preparation -- 4.3 Evaluation Metrics -- 4.4 Pre-experiments -- 4.5 Experiments -- 5 Results and Discussion -- 5.1 Results -- 5.2 Discussion -- 5.3 Future Trend -- 6 Conclusion and Future Work."]
[{"$nulls":4},"References -- ResConvE: Deeper Convolution-Based Knowledge Graph Embeddings -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 ResConvE -- 4.1 Overview -- 4.2 Detail -- 5 Experiments -- 5.1 Knowledge Graph Datasets -- 5.2 Evaluation Protocol -- 5.3 Experimental Setup -- 6 Result -- 6.1 Comparison of Performance -- 7 Conclusion and Future Work -- References -- Extractive-Abstractive: A Two-Stage Model for Long Text Summarization -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Extractive Model -- 3.2 Abstractive Model -- 4 Experimental Setup -- 4.1 Experimental Dataset -- 4.2 Evaluation Metrics -- 4.3 Model Settings -- 5 Results and Analysis -- 5.1 Results -- 5.2 Analysis -- 5.3 Application Case -- 6 Conclusion and Future Work -- References -- A Random-Walk-Based Heterogeneous Attention Network for Community Detection -- 1 Introduction -- 2 Related Work -- 2.1 NRL Algorithms on Homogeneous Networks -- 2.2 NRL Algorithms on Heterogeneous Networks -- 3 Preliminaries -- 4 RHAN -- 4.1 Random Walk with Classification -- 4.2 Intra-type Attention Computation -- 4.3 Inter-type Attention Computation -- 4.4 Loss Function for Training -- 4.5 Time Complexity Analysis -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Algorithms -- 5.4 Parameter Settings -- 5.5 Parameter Experiment -- 5.6 Ablation Experiment -- 5.7 Accuracy Experiment -- 6 Conclusions -- References -- Attributed Network Embedding Based on Attributed-Subgraph-Based Random Walk for Community Detection -- 1 Introduction -- 2 Related Work -- 3 ANE_ASRW Algorithm -- 3.1 Structure Information Extraction -- 3.2 Attribute Information Extraction -- 3.3 Embedding Vector Generation -- 3.4 Embedding Vector Enhancement -- 3.5 Time Complexity Analysis -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Algorithms -- 4.3 Parameter Settings -- 4.4 Evaluation Metric."]
[{"$nulls":4},"4.5 Parameter Experiment -- 4.6 Accuracy Experiment -- 4.7 Ablation Experiment -- 4.8 Embedding Vectors Visualization -- 5 Conclusions -- References -- Adaptive Seed Expansion Based on Composite Similarity for Community Detection in Attributed Networks -- 1 Introduction -- 2 Related Work -- 2.1 Community Detection Based on Seed Expansion -- 2.2 Community Detection in Attributed Networks -- 2.3 Community Detection Based on High-Order Structure -- 3 Preliminaries -- 4 ASECS -- 4.1 Weighted KNN Graph Generation -- 4.2 Community Detection by Seed Expansion -- 4.3 Complexity Analysis -- 5 Experiments -- 5.1 Datasets Description -- 5.2 Experimental Settings -- 5.3 Parameter Experiments -- 5.4 Accuracy Experiments -- 6 Conclusions -- References -- MDN: Meta-transfer Learning Method for Fake News Detection -- 1 Introduction -- 2 Related Work -- 2.1 Fake New Detection -- 2.2 Few-Shot Learning -- 3 Methodology -- 3.1 Problem Statement -- 3.2 The MDN Framework -- 3.3 Training of MDN -- 4 Experiments -- 4.1 Datasets -- 4.2 Model Training -- 4.3 Detection Results -- 5 Conclusion -- References -- Local Community Detection Algorithm Based on Core Area Expansion -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Concepts and Definitions -- 3.2 The Proposed Algorithm -- 3.3 Complexity -- 4 Experiments -- 4.1 Datesets -- 4.2 Comparison Algorithms and Evaluation Metrics -- 4.3 Parameter Experiments -- 4.4 Accuracy Experiments -- 4.5 Visualization Experiments -- 5 Conclusion -- References -- Federated Clique Percolation for Overlapping Community Detection on Attributed Networks -- 1 Introduction -- 2 Related Work -- 2.1 Clique Percolation "]
[{"$nulls":4},"Algorithms -- 2.2 Privacy-Preserving Community Detection -- 2.3 Federated Learning on Graphs -- 3 Preliminaries -- 3.1 Problem Definition -- 3.2 Network Privacy -- 4 Proposed Method -- 4.1 SCPAN -- 4.2 FCPAN."]
[{"$nulls":4},"4.3 Complexity Analysis."]