Web and Big Data : 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30-September 1, 2024, Proceedings, Part IV (Lecture Notes in Computer Science) (2024)

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Web and Big Data : 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30-September 1, 2024, Proceedings, Part IV (Lecture Notes in Computer Science) (2024)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 490 p.
  • 言語 ENG
  • 商品コード 9789819772407

Full Description

The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed conference proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024.

The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions.

The papers are organized in the following topical sections:

Volume I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.

Volume II: Recommender System, Knowledge Graph and Spatial and Temporal Data.

Volume III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization.

Volume IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security

Volume V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.

Contents

.- Database System and Query Optimization.

.- SAM: A Spatial-aware Learned Index for Disk-Based Multi-dimensional Search.

.- BIVXDB: A Bottom Information Invert Index to Speed up the Query Performance of LSM-tree.

.- Dual-contrastive multi-view clustering under the guidance of global similarity and pseudo-label.

.- A Powerful Local Search Method for Minimum Steiner Tree Problem.

.- Federated and Privacy-Preserving Learning.

.- FedOCD: A One-Shot Federated Framework for Heterogeneous Cross-Domain Recommendation.

.- Efficient Updateable Private Set Intersection on Outsourced Datasets.

.- Client Evaluation and Revision in Federated Learning: Towards Defending Free-Riders and Promoting Fairness.

.- A Secure Dynamic Incentive Scheme for Federated Learning.

.- A Data Synthesis Approach Based on Local Differential Privacy.

.- Byzantine-Robust Aggregation for Federated Learning with Reinforcement Learning.

.- Differential Privacy with Data Removal for Online Happiness Assessment.

.- EPCQ: Efficient Privacy-preserving Contact Query Processing over Trajectory Data in Cloud.

.- Parallel Secure Inference for Multiple Models based on CKKS.

.- PrivRBFN: Building Privacy-Preserving Radial Basis Function Networks Based on Federated Learning.

.- Robust Federated Learning with Realistic Corruption.

.- Network, Blockchain and Edge computing.

.- BTQoS: A Tenant Relationship-Aware QoS Framework for Multi-Tenant Distributed Storage System.

.- ACMDS: An Anonymous Collaborative Medical Data Sharing Scheme Based on Blockchain.

.- MTEC: A Multi-tier Blockchain Storage Framework using Erasure Coding for IoT Application.

.- Maintaining Data Freshness in Multi-channel Multi-hop Wireless Networks.

.- Proof of Run: A Fair and Sustainable Blockchain Consensus Protocol based on Game Theory in DApps.

.- KTSketch: Finding k-persistent t-spread Flows in High-speed Networks.

.- A Multi-agent Service Migration Algorithm for Mobile Edge Computing with Diversified Services.

.- Dynamic Computation Scheduling for Hybrid Energy Mobile Edge Computing Networks.

.- Anomaly Detection and Security.

.- Malicious Attack Detection Method for Recommendation Systems Based on Meta-pseudo Labels and Dynamic Features.

.- Detecting Camouflaged Social Bots through Multi-level Aggregation and Information Encoding.

.- Deep Sarcasm Detection with Sememe and Syntax Knowledge.

.- Enhancing Few-Shot Multi-Modal Fake News Detection through Adaptive Fusion.

.- AGAE: Unsupervised Anomaly Detection for Encrypted Malicious Traffic.

.- ColBetect: A Contrastive Learning Framework Featuring Dual Negative Samples for Anomaly Behavior Detection.

.- Magnitude-Contrastive Network for Unsupervised Graph Anomaly Detection.

.- Substructure-Guided Graph-level Anomaly with Attention-Aware Aggregation.

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