Web and Big Data

8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 ¿ September 1, 2024, Proceedings, Part IV
Herausgegeben:Zhang, Wenjie; Tung, Anthony; Zheng, Zhonglong; Yang, Zhengyi; Wang, Xiaoyang; Guo, Hongjie

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 14964
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-97-7240-7
• 2024
• Seitenzahl: 532
• Erscheinungstermin: 28. August 2024
• Englisch
• Abmessung: 235mm x 155mm x 29mm
• Gewicht: 797g
• ISBN-13: 9789819772407
• ISBN-10: 9819772400
• Artikelnr.: 71261066

Inhaltsangabe

.- 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.