Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part I / / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min

• Autore: Song Xiangyu
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (533 pages)
• Disciplina: 005.7
• Soggetto topico: Big data; Data structures (Computer science); Information theory; Application software; Image processing - Digital techniques; Computer vision; Data mining; Big Data; Data Structures and Information Theory; Computer and Information Systems Applications; Computer Imaging, Vision, Pattern Recognition and Graphics; Data Mining and Knowledge Discovery
• Altri autori: FengRuyi ChenYunliang LiJianxin MinGeyong
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part I -- A BERT-Based Semantic Enhanced Model for COVID-19 Fake News Detection -- 1 Introduction -- 2 Related Work -- 2.1 COVID-19 Fake News Collection -- 2.2 COVID-19 Fake News Detection -- 2.3 BERT Model -- 3 Methodology -- 3.1 Dataset -- 3.2 Problem Statement -- 3.3 Text Representation Learning -- 3.4 Topic Generation -- 3.5 Classifier Design -- 4 Experimental Results and Parameter Analysis -- 4.1 Experimental Results -- 4.2 Parameter Analysis -- 5 Conclusion -- References -- Mining Frequent Geo-Subgraphs in a Knowledge Graph -- 1 Introduction -- 2 Problem Definition -- 3 Frequent Geo-Subgraph Mining -- 4 Optimizations -- 4.1 Arc Consistency Based Candidate Generation -- 4.2 Image Vertex Reusage -- 4.3 Geo-Grid Based Vertex Ordering -- 5 Experimental Study -- 5.1 Setup -- 5.2 Performance Evaluations -- 6 Related Work -- 7 Conclusion -- References -- Locality Sensitive Hashing for Data Placement to Optimize Parallel Subgraph Query Evaluation -- 1 Introduction -- 2 Background -- 2.1 Preliminaries -- 2.2 Parallel Execution Model -- 3 Locality Sensitive Hashing for Data Placement -- 3.1 Vertex Similarity -- 3.2 Vertex MinHash -- 4 System Implementation -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Effect of Our Proposed Techniques -- 5.3 Comparison with Other Parallel Subgraph Query Systems -- 5.4 Data Placement Performance -- 6 Related Work -- 7 Conclusion -- References -- DUTD: A Deeper Understanding of Trajectory Data for User Identity Linkage -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Proposed Model -- 4.1 Grid Feature Extractor -- 4.2 Tranformer-Based Encoder -- 4.3 Matcher -- 5 Experiment -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Parameter Setting and Evaluation Metrics -- 5.4 Performance Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References. ; Large-Scale Rank Aggregation from Multiple Data Sources Based D3MOPSO Method -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Formulation -- 4 Proposed Method -- 4.1 Strategy on Encoding Scheme and Multi-directional Search -- 4.2 Particle Swarm Initialization -- 4.3 Definition of Discrete Position and Velocity -- 4.4 Discrete Particle Statue Updating -- 4.5 Framework of the Proposed Algorithm -- 4.6 Complexity Analysis -- 5 Experimental Studies -- 5.1 Comparison Algorithms -- 5.2 Experimental Settings -- 5.3 Evaluation Metrics -- 5.4 The Results -- 6 Conclusion -- References -- Hierarchically Delegatable and Revocable Access Control for Large-Scale IoT Devices with Tradability Based on Blockchain -- 1 Introduction -- 2 Building Blocks -- 2.1 Blockchain and Ethereum -- 2.2 Digital Signature -- 2.3 BIP-32 Standard -- 3 System Assumption and Requirements -- 3.1 System Entities -- 3.2 System Assumption -- 3.3 System Requirements -- 4 The Proposed Framework -- 4.1 High-Level Overview -- 4.2 IoT Device Registration -- 4.3 Ownership Transfer/Trading of IoT Device -- 4.4 (Hierarchical) Delegation of Access Control -- 4.5 Access an IoT Device -- 4.6 Revocation -- 5 Experimental Results -- 6 Security Analysis -- 7 Conclusions -- References -- Distributed Deep Learning for Big Remote Sensing Data Processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study -- 1 Introduction -- 2 Related Works -- 2.1 Distributed Deep Learning's Development Status -- 2.2 DDL-Based Remote Sensing Data Processing -- 3 Distributed Deep Learning Frameworks -- 3.1 MLlib -- 3.2 SparkTorch and TensorflowOnSpark -- 3.3 DeepLearning4Java -- 3.4 BigDL -- 3.5 Horovod -- 4 D-AMSDFNet: Distributed Deep Learning-Based AMSDFNet for Geological Remote Sensing Interpretation -- 4.1 AMSDFNet -- 4.2 Design of Distributed AMSDFNet -- 5 Experiments. ; 5.1 Settings -- 5.2 Analysis of Experimental Results -- 6 Conclusions -- References -- Graph-Enforced Neural Network for Attributed Graph Clustering -- 1 Introduction -- 2 Related Works -- 3 Notations and Problem Formulation -- 4 Degradation Analysis -- 4.1 Intra-cluster Estrangement -- 4.2 Attribute Similarity Neglection -- 4.3 Blurred Cluster Boundaries -- 5 The Proposed Method -- 5.1 Multi-task Learning Framework -- 5.2 High-Order Structural Proximity Enforcement -- 5.3 Attribute Similarity Enforcement -- 5.4 Cluster Boundary Enforcement -- 5.5 Joint Objective Optimization -- 6 Experiments -- 6.1 Experiment Settings -- 6.2 Performance Comparison -- 6.3 Efficiency Comparison -- 6.4 Ablation Study -- 6.5 Hyperparameter Sensitivity Analysis -- 7 Conclusion -- References -- MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation -- 1 Introduction -- 2 Related Work -- 3 MacGAN Overview -- 3.1 GAN -- 3.2 Autoregressive GAN for SMILES Strings -- 3.3 Moment Reward -- 4 Experiment -- 4.1 Dataset -- 4.2 Evaluation Measures -- 4.3 Desired Chemical Properties -- 4.4 Model Setup -- 4.5 Experimental Results -- 5 Conclusion -- References -- Multi-modal Graph Convolutional Network for Knowledge Graph Entity Alignment -- 1 Introduction -- 2 Related Work -- 2.1 Entity Alignment -- 2.2 Multi-modal Knowledge Graph -- 3 Methodology -- 3.1 Definition and Model Overview -- 3.2 Multi-modal Pre-trained Embedding -- 3.3 Multi-modal Enhancement Embedding Mechanism -- 3.4 Objective -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Baselines -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Parameter Analysis -- 5 Conclusion and Future Work -- References -- Subgraph Federated Learning with Global Graph Reconstruction -- 1 Introduction -- 2 Related Work -- 2.1 Subgraph Federated Learning (SFL). ; 2.2 Graph Structure Learning (GSL) -- 2.3 Split Learning -- 3 Problem Setting -- 4 Methodology -- 4.1 Framework Overview -- 4.2 Local Pre-training -- 4.3 The Local Graph Learning Module -- 4.4 The Global Graph Structure Learning Module -- 4.5 Objective and Training Procedure -- 5 Experiment -- 5.1 Experimental Setups -- 5.2 Comparison with State-of-the-art Methods (RQ1) -- 5.3 Ablation Study (RQ2) -- 5.4 Sensitivity Analysis (RQ3) -- 6 Conclusion -- References -- SEGCN: Structural Enhancement Graph Clustering Network -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Notations -- 3.2 Topology Enhancement Module -- 3.3 Improved Attention-Driven Graph Clustering Network with Global Structure Dynamic Fusion Module -- 3.4 Optimization Objective Function -- 4 Experiment -- 4.1 Benchmark Datasets -- 4.2 Experimental Setup and Evaluation -- 4.3 Clustering Results -- 4.4 Ablation Studies -- 4.5 Visualization Results -- 5 Conclusion -- References -- Designing a Knowledge Graph System for Digital Twin to Assess Urban Flood Risk -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 The Proposed UrbanFloodKG System -- 4.1 System Overview -- 4.2 Data Layer -- 4.3 Graph Layer -- 4.4 Algorithm Layer -- 4.5 Digital Twin Layer -- 5 Experiment and Discussion -- 5.1 Dataset and Environment -- 5.2 Link Prediction Analysis -- 5.3 Node Classification Analysis -- 6 Conclusion -- References -- TASML: Two-Stage Adaptive Semi-supervised Meta-learning for Few-Shot Learning -- 1 Introduction -- 2 Related Work -- 2.1 Brain-Inspired Model for Visual Object Recognition -- 2.2 Meta-learning for Few-Shot Learning -- 3 Methodology -- 3.1 Preliminary -- 3.2 The Two-Stage Semi-supervised Meta-learning Framework -- 3.3 Unsupervised Visual Representation Learning -- 3.4 Gradient-Based Meta-learning for Few-Shot Learning -- 3.5 Global Context-Aware Module -- 4 Experiments. ; 4.1 Few-Shot Image Classification -- 4.2 Ablation Study -- 4.3 Visualization -- 5 Conclusion -- References -- An Empirical Study of Attention Networks for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Enrich Contextual Information Based Methods -- 2.2 Reduce Computation Complexity Based Methods -- 3 Experiment -- 3.1 Datasets -- 3.2 Implementation Details -- 4 Analysis -- 5 Conclusions and Future Works -- References -- Epidemic Source Identification Based on Infection Graph Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Description -- 2.2 Propagation Model -- 3 Related Work -- 4 Our Model -- 4.1 Architecture -- 4.2 Input Generation -- 4.3 GCN Layer -- 4.4 Graph Embedding Layer -- 4.5 Output Layer -- 4.6 Loss Function -- 4.7 Model Complexity -- 5 Experiment -- 5.1 Datasets and Baselines -- 5.2 Evaluation Metrics -- 5.3 Experimental Setting -- 5.4 Source Identification Performance -- 5.5 Ablation Study -- 5.6 Impact of Parameters -- 5.7 Model Efficiency -- 6 Conclusion and Future Work -- References -- Joint Training Graph Neural Network for the Bidding Project Title Short Text Classification -- 1 Introduction -- 2 Related Work -- 2.1 Text Classification -- 2.2 Short Text Classification -- 3 Method -- 3.1 Extracting Contextual Information -- 3.2 Graph Structure Construction -- 3.3 Feature Caching and Replacement -- 3.4 Graph Convolution Operation -- 3.5 Classification -- 4 Experiment -- 4.1 Datasets -- 4.2 Data Processing -- 4.3 Baseline Models -- 4.4 Experimental Settings -- 4.5 Results -- 4.6 Parameter Analysis -- 5 Conclusion -- References -- Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Visual Feature Extraction -- 3.2 Regional Channel Screening -- 3.3 Saliency Joint Weighting Method. ; 3.4 Shape Fine Matching Based on Skeleton Context.
• Sommario/riassunto: The 4-volume set LNCS 14331, 14332, 14333, and 14334 constitutes the refereed proceedings of the 7th International Joint Conference, APWeb-WAIM 2023, which took place in Wuhan, China, in October 2023. The total of 138 papers included in the proceedings were carefully reviewed and selected from 434 submissions. They focus on innovative ideas, original research findings, case study results, and experienced insights in the areas of the World Wide Web and big data, covering Web technologies, database systems, information management, software engineering, knowledge graph, recommend system and big data.
• Titolo autorizzato: Web and Big Data
• ISBN: 9789819723034; 9819723035
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910864179303321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14331