Tools for Design, Implementation and Verification of Emerging Information Technologies〈1st ed. 2024〉 : 18th EAI International Conference, TRIDENTCOM 2023, Nanjing, China, November 11-13, 2023, Proceedings

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Tools for Design, Implementation and Verification of Emerging Information Technologies〈1st ed. 2024〉 : 18th EAI International Conference, TRIDENTCOM 2023, Nanjing, China, November 11-13, 2023, Proceedings

  • 著者名:Liu, Jianghua (EDT)/Xu, Lei (EDT)/Huang, Xinyi (EDT)
  • 価格 ¥12,141 (本体¥11,038)
  • Springer(2024/01/04発売)
  • 春うらら!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/15)
  • ポイント 3,300pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9783031513985
  • eISBN:9783031513992

ファイル: /

Description

This book constitutes the refereed post-conference proceedings of the 18th EAI International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TridentCom 2023, which was held in Nanjing, China, during November 11-13, 2023.


The 9 full papers were selected from 30 submissions and deal the emerging technologies of big data, cyber-physical systems and computer communications. The papers are grouped in thematical sessions on blockchain and its applications; emerging applications; AI and its security.

Table of Contents

Blockchain and its applications.- A Novel Cross-Chain Relay Method Based on Node Trust Evaluation.- Collateral-Efficient Instant Contingent Payments: The Promise of A Hardware-Driven Off-Chain Payment System.- Emerging Applications.- A Survey on Edge Intelligence for Music Composition: Principles, Applications, and Privacy Implications.- AI-Driven Sentiment Analysis for Music Composition.- Fault Diagnosis Method for Hydro-Power Plant with Bi-LSTM Knowledge Graph aided by Attention Scheme.- AI and its Security.- Zero-Knowledge with Robust Learning: Mitigating Backdoor Attacks in Federated Learning for Enhanced.- Security and Privacy.- PPAPAFL: A Novel Approach to Privacy Protection and Anti-Poisoning Attacks in Federated Learning.- Towards Retentive Proactive Defense Against DeepFakes.- A Fast and Accurate Non-Interactive Privacy-Preserving Neural Network Inference Framework.

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