Memmi / Yang / Kong Knowledge Science, Engineering and Management

15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part III
Erscheinungsjahr 2022
ISBN: 978-3-031-10989-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 753 Seiten

Reihe: Lecture Notes in Computer Science

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6–8, 2022. 
The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections:
Volume I:Knowledge Science with Learning and AI (KSLA)
Volume II:Knowledge Engineering Research and Applications (KERA)
Volume III:Knowledge Management with Optimization and Security (KMOS)

Zielgruppe

Research

Autoren/Hrsg.

Memmi, Gerard

Yang, Baijian

Kong, Linghe

Zhang, Tianwei

Qiu, Meikang

Inhaltsverzeichnis

Knowledge Management with Optimization and Security (KMOS). - Study on Chinese Named Entity Recognition Based on  Dynamic Fusion and Adversarial Training.- Spatial Semantic Learning for Travel Time Estimation.- A Fine-Grained Approach for Vulnerabilities Discovery using Augmented Vulnerability Signatures.- PPBR-FL: a Privacy-preserving and Byzantine-robust Federated Learning System.- GAN-Based Fusion Adversarial Training.- MAST-NER: A Low-Resource Named Entity Recognition Method based on Trigger Pool.- Fuzzy information measures feature selection using descriptive statistics data.- Prompt-Based Self-Training Framework for Few-Shot Named Entity Recognition.- Learning Advisor-Advisee Relationship from Multiplex Network Structure.- CorefDRE: Coref-aware Document-level Relation Extraction.- Single Pollutant Prediction Approach by Fusing MLSTM and CNN.- A Multi-objective Evolutionary Algorithm Based on Multi-layer Network Reduction for Community Detection.- Detection DDoS of attacks based on federated learning with Digital Twin Network.- A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy.