Big data and social computing : 7th China National Conference, BDSC 2022, Hangzhou, China, August 11-13, 2022, revised selected papers / / edited by Xiaofeng Meng, [and four others]

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (398 pages)
• Disciplina: 170
• Soggetto topico: Big data
• Persona (resp. second.): MengXiaofeng
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Urban Computing and Social Governance -- Resilience-Based Epidemic Strategy Evaluation Method Under Post-Covid-19 -- 1 Introduction -- 2 Theoretical Basis -- 2.1 Urban Subsystems Applied to Resilience -- 2.2 Resilience Concept Applied to Urban System -- 2.3 Strategy Evaluation Method Applied to Urban Resilience -- 3 Strategy Evaluation Framework -- 4 Case Analysis and Discussion -- 4.1 Identifying Evaluable Strategies -- 4.2 Identifying Evaluable Strategies -- 4.3 Resilience Capacity Index System of Urban Social System Under Epidemic -- 4.4 Accumulation -- 4.5 Discussion -- 5 Conclusion -- References -- The Effects of Intervention Strategies for COVID-19 Transmission Control on Campus Activity -- 1 Introduction -- 2 Methodology -- 2.1 Model of Infection Risk -- 2.2 Case Design -- 3 Results -- 3.1 The Transmission of COVID-19 Caused by the Alumni Group for the Baseline Case -- 3.2 The Effect of Ventilation, Social Distancing and Wearing Mask on COVID-19 Transmission -- 3.3 The Impact of Combined Intervention Measures on the COVID-19 Transmission -- 4 Discussion -- 4.1 The Transmission Risk Brought by Staff During the Anniversary -- 4.2 The Comparison of Cases with Different Initial Infector Proportions -- 4.3 The Limitations of This Study -- 5 Conclusions -- References -- Social Resilience Assessment for Urban System: A Case Study of COVID-19 Epidemic -- 1 Introduction -- 2 Gap Analysis-Based Assessment Method -- 3 Case Analysis of COVID-19 Epidemic -- 3.1 Materials and Methods -- 3.2 Data Analysis Results -- 3.3 Resilience Analysis -- 4 Discussion -- 5 Conclusion -- References -- Prediction of Female Fertility Structure and Population Change in China by Modified SIR Model -- 1 Introduction -- 2 SIR Fertility Structure and Population Prediction Model -- 2.1 Model Front -- 2.2 Basic Prediction Model. ; 2.3 Parameters of the Model -- 3 Data Source and Parameter Setting -- 3.1 Data Sources -- 3.2 Parameter Setting -- 4 Analysis of Empirical Results -- 4.1 SIR Population Model Prediction Analysis -- 4.2 Model Prediction Error Validation Analysis -- 4.3 Model Prediction of Future Population Scenarios -- 5 Conclusion -- References -- Generative Adversarial Network for Imputation of Road Network Traffic State Data -- 1 Introduction -- 2 Methodology -- 2.1 Data Preprocessing -- 2.2 The Feature Abstraction of Road Network Based on GAE -- 2.3 The Design of Generative Adversarial Network for Spatio-Temporal Feature Based on LSTM -- 2.4 The Imputation of Road Network Data Based on GAE-GAN -- 3 Experiment -- 3.1 Experimental Design -- 3.2 Parameter Setting and Model Index -- 4 Results and Discussion -- 4.1 Effectiveness of GAE -- 4.2 Effectiveness of Internal Structure of Generator (LSTM) -- 4.3 Comparison with Other Models -- 5 Conclusions -- References -- Artificial Intelligence and Cognitive Science -- Improving Events Classification with Latent Space Clustering-Based Similarities -- 1 Introduction -- 2 Related Work -- 2.1 Keyword Extraction -- 2.2 Text Clustering -- 3 Method -- 3.1 Data Preprocessing -- 3.2 Initialize Clustering of the Training Data -- 3.3 Calculate the Classification Similarities of Event Categories -- 3.4 Process New Monitoring Event -- 3.5 Update Event Knowledge Database -- 3.6 Process Accumulated Noise Data -- 3.7 Optimal Learning of the Model -- 4 Experimental Results and Discussion -- 4.1 Experimental Datasets -- 4.2 Baseline Models and Parameter Settings -- 4.3 Overall Performance -- 5 Conclusion -- References -- SubGraph Networks Based Entity Alignment for Cross-Lingual Knowledge Graph -- 1 Introduction -- 2 Related Work -- 2.1 GCN -- 2.2 SGN -- 2.3 Knowledge Graph Alignment Based on Embedding -- 3 Methodology. ; 3.1 Problem Definition -- 3.2 Enhanced Structure Embedding Based on Subgraph Feature -- 3.3 GCN-Based Entity Embedding -- 3.4 Model Training -- 3.5 Knowledge Graph Entity Alignment Prediction -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 DataSet -- 4.3 Result -- 5 Conclusion -- References -- A Secured Deep Reinforcement Learning Model Based on Vertical Federated Learning -- 1 Introduction -- 2 Related Works -- 2.1 Deep Reinforcement Learning -- 2.2 Defense for Deep Reinforcement Learning -- 3 VF-DRL -- 3.1 Overview -- 3.2 Global Model -- 3.3 Building VF-DRL Model Framework -- 3.4 Model Training and Implementation -- 4 Experiment and Analysis -- 4.1 Experiment Setup -- 4.2 Environment of Experiment -- 4.3 Training Model -- 4.4 Robustness Verification of VF-DRL Models -- 5 Conclusion -- References -- An Improved K-means Algorithm Based on the Bayesian Inference -- 1 Introduction -- 2 Bayes-K-means Clustering Algorithm -- 2.1 Improvement of K-means Algorithm -- 2.2 Algorithm Steps -- 2.3 Algorithm Convergence -- 3 Experimental Results and Analysis -- 3.1 Data Acquisition and Processing -- 3.2 Apply Bayes-K-means Algorithm to Cluster Dataset -- 3.3 Experimental Results and Analysis on the Self-made Dataset -- 3.4 Experimental Results and Analysis on the UCI Datasets -- 4 Conclusion -- References -- Social Network and Group Behavior -- Inductive Matrix Completion Based on Graph Attention -- 1 Introduction -- 2 Related Works -- 3 Inductive Graph-Attention Based Matrix Completion -- 3.1 One-Hop Enclosing Subgraph Extraction and Node Labeling -- 3.2 Graph Neural Network Architecture -- 3.3 Model Training -- 4 Experiment and Result -- 5 Conclusion -- References -- Identifying Spammers by Completing the Ratings of Low-Degree Users -- 1 Introduction -- 2 Preliminaries -- 2.1 Network -- 2.2 Rating Network -- 3 Methodology. ; 3.1 Iterative Optimization Ranking (IOR) -- 3.2 Completing the Ratings of Low-degree Users on IOR (IOR_LU) -- 4 Data and Metric -- 4.1 Rating Data Set -- 4.2 Generating Artificial Spammers -- 4.3 Evaluation Metric -- 5 Experimental Results -- 5.1 Effectiveness -- 5.2 Robustness -- 6 Conclusion and Discussion -- References -- Predicting Upvotes and Downvotes in Location-Based Social Networks Using Machine Learning -- 1 Introduction -- 2 Data Collection and Conventional Feature Analysis -- 2.1 Dataset -- 2.2 Analysis of Conventional Features -- 3 System Design and Implementation -- 3.1 Overview -- 3.2 Entropy -- 3.3 Effective Size -- 3.4 Model Construction -- 4 Evaluation of Prediction Performance -- 4.1 Performance Evaluation -- 4.2 Feature Importance Analysis -- 5 Related Work -- 6 Conclusion -- References -- How Does Participation Experience in Collective Behavior Contribute to Participation Willingness: A Survey of Migrant Workers in China -- 1 Introduction -- 2 Literature Review and Research Hypotheses -- 2.1 Planned Behavior and Resource Mobilization -- 2.2 Research Hypotheses -- 3 Methodology -- 3.1 Data -- 3.2 Variables -- 4 Results -- 4.1 Common Method Deviation Biases -- 4.2 Descriptive Statistical Analysis -- 4.3 Hypothesis Testing -- 4.4 Robustness Test -- 5 Conclusion -- References -- Research on Network Invulnerability and Its Application on AS-Level Internet Topology -- 1 Introduction -- 2 Theory -- 2.1 Node Importance Indicators -- 2.2 Neighbor Influence-based Node Ranking -- 2.3 Multi-attribute Fusion-Based Node Ranking -- 3 Evaluation Metrics -- 4 Experiment -- 4.1 Datasets -- 4.2 Structure-Based Evaluation Experiments -- 4.3 Multi-attribute-Based Evaluation Experiments -- 5 Conclusion -- References -- Digital Society and Public Security -- FedDFA: Dual-Factor Aggregation for Federated Driver Distraction Detection -- 1 Introduction. ; 2 Related Work -- 2.1 Vision-Based Driver Distraction Detection -- 2.2 Federated Learning -- 3 Pre-experiments -- 4 Method -- 4.1 Problem Definition -- 4.2 Overview -- 4.3 Federated Dual-Factor Aggregation -- 5 Experiments -- 5.1 Datasets -- 5.2 Comparison Method -- 5.3 Implementation Details -- 5.4 Experimental Analysis -- 6 Discussion and Conclusion -- References -- Defense of Signal Modulation Classification Attack Based on GAN -- 1 Introduction -- 2 Related Work -- 3 Our Work -- 3.1 Model -- 3.2 Training -- 3.3 Reconstruction -- 4 Experiment -- 4.1 Data Selection -- 4.2 Experiment Setup -- 4.3 Result -- 5 Conclusion -- References -- Dual-Channel Early Warning Framework for Ethereum Ponzi Schemes -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Micro Transaction Graph -- 3.3 Temporal Evolution Augmentation of Transaction Graph -- 3.4 Dual-Channel Early Warning Model -- 4 Experiments -- 4.1 Data Setting -- 4.2 Baselines -- 4.3 Experiment Setting -- 4.4 Evaluation on Ponzi Detection (RQ1) -- 4.5 Single Channel Analysis (RQ2) -- 4.6 Threshold of Reporting Ponzi Schemes (RQ3) -- 4.7 Ablation Study -- 5 Conclusion -- References -- Rumor Detection Based on the Temporal Sentiment -- 1 Introduction -- 2 Related Work -- 2.1 Single-Modal Rumor Detection -- 2.2 Multi-modal Rumor Detection -- 2.3 Rumor Detection Based on Sentiment Analyze -- 3 Method -- 3.1 Problem Statement -- 3.2 Microblog Representation -- 3.3 Comprehensive Representation -- 3.4 Rumor Classifier -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Models -- 4.3 Experimental Settings -- 4.4 Evaluation Metrics -- 4.5 Experimental Results -- 4.6 Discussions -- 5 Conclusion -- References -- Research on Users' Trust in Customer Service Chatbots Based on Human-Computer Interaction -- 1 Introduction -- 2 Literature Review. ; 3 Interview Analysis Based on Value Focus Thinking.
• Titolo autorizzato: Big data and social computing
• ISBN: 981-19-7532-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910634045503321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science