Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part III / / 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 (540 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 III -- Adaptive Graph Attention Hashing for Unsupervised Cross-Modal Retrieval via Multimodal Transformers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation and Problem Definition -- 3.2 Framework Overview -- 3.3 Objective Function -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison Results and Analysis -- 4.4 Ablation Study -- 4.5 Parameter Sensitivity Analysis -- 4.6 Convergence Testing -- 5 Conclusion -- References -- Answering Property Path Queries over Federated RDF Systems -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 The Proposed Method -- 4.1 Query Decomposition and Source Localization -- 4.2 Thompson-Based MinDFA Construction -- 4.3 Query Execution Strategy Base on B-DFS -- 5 Evaluation -- 5.1 Experimental Environment -- 5.2 Performance Comparison of Five Property Path Query Symbols -- 5.3 Performance and Resource Consumption of Different Matching Strategies of MinDFA -- 5.4 Performance Robustness of Five Property Path Query Symbols -- 6 Conclusion -- References -- Distributed Knowledge Graph Query Acceleration Algorithm -- 1 Introduction -- 2 Related Works -- 3 Offline Module for Distributed Construction of Indexes -- 3.1 MapReduce-Based Data Pre-processing -- 3.2 Coding-Oriented Construction of Distributed Hierarchical Clustering -- 4 Online Module for Distributed Parallel Processing of SPARQL Queries -- 4.1 Splitting and Loading of BitSet-Tree -- 4.2 Candidate Solution Acquisition -- 4.3 Shuffle -- 4.4 Merge Splicing of Candidate Vertices -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Results and Discussion -- 6 Conclusions -- References -- Truth Discovery of Source Dependency Perception in Dynamic Scenarios -- 1 Introduction -- 2 Problem Setting -- 2.1 Notation Definition -- 2.2 Task Description. ; 3 Preliminary -- 3.1 Source Dependency Detection Based on Bayesian Model -- 3.2 Truth Discovery Framework Based on Optimization Model -- 4 Methodology -- 4.1 Source Dependency Detection in Dynamic Scenarios -- 4.2 Dynamic Incremental Model Framework -- 4.3 Truth Discovery with Source Dependency Perception -- 5 Experiment -- 5.1 Experimental Setup -- 5.2 The Results on Real-World Datasets -- 5.3 The Results on Synthetic Datasets -- 6 Related Work -- 7 Conclusion -- References -- Truth Discovery Against Disguised Attack Mechanism in Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Disguised Attack Mechanism -- 3.2 Problem Formulation -- 3.3 Truth Discovery -- 4 Methodology -- 4.1 Behavior-Based Truth Discovery -- 4.2 Task Assignment Based on WAM -- 4.3 TD-DA Framework -- 5 Experiments -- 5.1 Experiment Setting -- 5.2 Verification of the Proportion of Malicious Workers -- 5.3 Experiment on Real-World Datasets -- 5.4 Experiment on Synthetic Datasets -- 6 Conclusion -- References -- Continuous Group Nearest Group Search over Streaming Data -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works -- 2.2 Problem Definition -- 3 The Framework KMPT -- 3.1 The Basic Idea -- 3.2 The Initialization Algorithm -- 3.3 The Incremental Maintenance Algorithm -- 4 The Experiment -- 4.1 Experiment Settings -- 4.2 Performance Comparison -- 5 Conclusion -- References -- Approximate Continuous Skyline Queries over Memory Limitation-Based Streaming Data -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works -- 2.2 Problem Definition -- 3 The Self-adaptive-based Framework -SEAK -- 3.1 The -CSS Definition -- 3.2 The Initialization Algorithm -- 3.3 The Incremental Maintenance Algorithm -- 3.4 The Partition-Based Optimization Algorithm -- 4 Performance Evaluation -- 4.1 Experiment Settings -- 4.2 Experimental Evaluation -- 5 Conclusion -- References. ; Identifying Backdoor Attacks in Federated Learning via Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Attacks on Model Faithfulness -- 2.2 Defenses Against Backdoor Attack -- 3 Preliminaries and Attack Formulation -- 3.1 Federated Learning -- 3.2 Threat Model -- 3.3 Choice of Backdoor Triggers -- 4 Methodology -- 4.1 Motivation -- 4.2 Segmenting Local Updates -- 4.3 Identifying Outliers in Fragments -- 4.4 Pruning Backdoored Participants -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Effectiveness of Our Defense -- 5.3 Comparison with Prior Arts -- 5.4 Effectiveness on Advanced Attacks -- 5.5 Ablation Study -- 6 Conclusion -- References -- PaTraS: A Path-Preserving Trajectory Simplification Method for Low-Loss Map Matching -- 1 Introduction -- 2 Related Work -- 2.1 Error-Bounded Line Simplification -- 2.2 Semantic-Preserving Trajectory Simplification -- 2.3 Analysis of Existing Work -- 3 Preliminaries -- 3.1 Basic Definitions -- 3.2 Methodology Analysis -- 4 Path-Preserving Trajectory Simplification -- 4.1 Overview of PaTraS -- 4.2 Preserving Shortest Paths -- 4.3 Candidates Pairing -- 4.4 Similarity Computation -- 4.5 Pairing Optimization -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Oriented to Map-Matching -- 5.3 Parameter Sensitivity Study -- 6 Conclusion -- References -- Coordinate Descent for k-Means with Differential Privacy -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 k-Means -- 3.2 Coordinate Descent for k-Means -- 3.3 A Fast Version of CDKM -- 3.4 Differential Privacy -- 4 Proposed Our Method -- 4.1 Approximate CDKM -- 4.2 Proposed DP-ACDKM -- 4.3 Privacy Analysis -- 5 Experiments -- 5.1 Privacy-Utility Trade-Off -- 5.2 Convergence -- 6 Conclusion -- References -- DADR: A Denoising Approach for Dense Retrieval Model Training -- 1 Introduction -- 2 Related Work -- 3 Method. ; 3.1 Task Formulation -- 3.2 Denoising Approach Based on Dynamical Weight -- 4 Experiment -- 4.1 Dataset and Metrics -- 4.2 Experiment Settings -- 4.3 Experiment Results -- 5 Conclusions -- References -- Multi-pair Contrastive Learning Based on Same-Timestamp Data Augmentation for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 2.1 Self-supervised Learning -- 2.2 Sequential Recommendation -- 3 The Proposed Model -- 3.1 Problem Definition -- 3.2 Model Framework -- 3.3 Data Augmentaion -- 3.4 Masking Operation -- 3.5 Embedding Layer -- 3.6 BERT Encoder -- 3.7 Prediction Layer -- 3.8 Multi-pair Contrastive Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Hyperparameter Experiments -- 4.4 Ablation Study -- 5 Conclusion -- References -- Enhancing Collaborative Features with Knowledge Graph for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 KG Explore Module -- 4.2 Multi-IMP-GCN -- 4.3 Model Prediction -- 4.4 Model Optimization -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Performance Comparison -- 5.3 Ablation Studies -- 5.4 Impacts of Multi-IMP-GCN -- 6 Conclusion and Future Work -- References -- PageCNNs: Convolutional Neural Networks for Multi-label Chinese Webpage Classification with Multi-information Fusion -- 1 Introduction -- 2 Multi-label Chinese Webpage Classification Models -- 3 Experimental Results and Discussions -- 3.1 Multi-label Chinese Webpage Dataset -- 3.2 Implementation Details and Evaluation Metrics -- 3.3 Multi-label Chinese Webpage Classification Results -- 4 Conclusion -- References -- MFF-Trans: Multi-level Feature Fusion Transformer for Fine-Grained Visual Classification -- 1 Introduction -- 2 Related Works -- 2.1 CNN-Based FGVC Methods -- 2.2 ViT-Based FGVC Methods -- 3 Proposed Method -- 3.1 Vision Transformer Encoder. ; 3.2 Important Token Election Module -- 3.3 Semantic Connection Enhancing Module -- 4 Experiments -- 4.1 DataSets and Implement Details -- 4.2 Comparisons with Advanced Methods -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Summarizing Doctor's Diagnoses and Suggestions from Medical Dialogues -- 1 Introduction -- 2 Related Work -- 3 Model -- 3.1 Pointer Generator Network as Backbone -- 3.2 Input Token Enhancement by Speaker-level Embedding -- 3.3 Input Token Enhancement by Utterance-level Embedding -- 4 Experiment -- 4.1 Dataset -- 4.2 Baseline Models -- 4.3 Settings -- 4.4 Evaluation Metrics -- 4.5 Automatic Evaluation -- 4.6 Doctor Evaluation -- 4.7 Case Study -- 5 Conclusion -- References -- HSA: Hyperbolic Self-attention for Sequential Recommendation -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Empirical Analysis of Datasets -- 2.2 Lorentz Model of Hyperbolic Space -- 2.3 Self-attention Mechanism for Sequential Recommendation -- 3 Proposed Approach -- 3.1 Problem Formulation and Approach Overview -- 3.2 Item Embeddings in Hyperbolic Space -- 3.3 Sequence Learning with Self-attention Mechanism -- 3.4 Prediction Layer -- 3.5 Model Training -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Performance as a Plugin on Baselines -- 5 Conclusion -- References -- CFGCon: A Scheme for Accurately Generating Control Flow Graphs of Smart Contracts -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Scheme -- 4.1 Overview of the System Model -- 4.2 Transform Module -- 4.3 Division Module -- 4.4 Connection Module -- 5 Experiment and Performance Evaluation -- 5.1 Dataset -- 5.2 General Test for CFGCon -- 5.3 Performance Comparision with Existing Approaches -- 6 Conclusion -- References -- Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning -- 1 Introduction. ; 2 Related Work.
• 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: 981-9723-87-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996594168203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14333