Knowledge Science, Engineering and Management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, Proceedings, Part III (Lecture Notes in Artificial Intelligence)

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Knowledge Science, Engineering and Management : 15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, Proceedings, Part III (Lecture Notes in Artificial Intelligence)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 753 p.
  • 言語 ENG
  • 商品コード 9783031109881

Full Description

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)

Contents

​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.