Zhang / Tung / Zheng Web and Big Data

8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 – September 1, 2024, Proceedings, Part IV
Erscheinungsjahr 2024
ISBN: 978-981-97-7241-4
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 14964, 512 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Wenjie

Tung, Anthony

Zheng, Zhonglong

Yang, Zhengyi

Wang, Xiaoyang

Guo, Hongjie

Inhaltsverzeichnis

.-
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.