Tools for Design, Implementation and Verification of Emerging Information Technologies: 18th EAI International Conference, TRIDENTCOM 2023, Nanjing, ... and Telecommunications Engineering)

Preface
Organization
Contents
Blockchain and Its Applications
A Novel Cross-Chain Relay Method Based on Node Trust Evaluation
	1 Introduction
	2 Related Works
	3 System Model
		3.1 System Architecture
		3.2 Parameters
	4 The Proposed Cross-Chain Relay Method
		4.1 Cross-Chain Node Trust Model
		4.2 Weighted Randomized Relay Node Election Algorithm
		4.3 Cross-Chain Transactions
		4.4 Countermeasures for Some Special Cases
	5 Experiments
		5.1 Trust Model Testing
		5.2 Algorithm Testing
		5.3 Cross-Chain Transaction Testing
	6 Conclusion
	References
Collateral-Efficient Instant Contingent Payments: The Promise of a Hardware-Driven Off-Chain Payment System
	1 Introduction
	2 Preliminaries
	3 Problem Statement
		3.1 System Model
		3.2 Design Goals
	4 Design
		4.1 Normal-Case Operation
		4.2 Force Exit
		4.3 Analysis
	5 Evaluation
		5.1 Off-Chain Throughput
		5.2 On-Chain Throughput and Transaction Fee
	6 Related Work
	7 Conclusion
	References
Emerging Applications
A Survey on Edge Intelligence for Music Composition: Principles, Applications, and Privacy Implications
	1 Introduction
	2 Background and Related Work
		2.1 Traditional Music Composition Techniques and Challenges
		2.2 Discuss the Emergence of Artificial Intelligence (AI) in Music Composition
		2.3 Review Existing Literature on AI-Based Music Composition Techniques
		2.4 Highlight the Limitations of Cloud-Based Approaches and the Need for Edge Intelligence
	3 Edge Intelligence in Music Composition
		3.1 Define the Concept of Edge Intelligence in the Context of Music Composition
		3.2 Explain the Advantages and Capabilities of Edge Computing for Music Composition
		3.3 Discuss the Potential of Real-Time and On-Device Music Generation Using Edge Intelligence
		3.4 Explore the Possibilities of Enhancing the Creative Process and Expanding Accessibility
	4 Applications of Edge Intelligence in Music Composition
		4.1 Explore Various Aspects of Music Composition that Can Benefit from Edge Intelligence
		4.2 Discuss the Potential of Edge Intelligence in Melody Creation, Harmonization, Rhythm Generation, Arrangement and Orchestration, and Lyric Writing
		4.3 Provide Examples and Case Studies Showcasing the Application of Edge Intelligence in These Areas
	5 AI-Based Music Composition Tools and Platforms
		5.1 Review Existing AI-Based Music Composition Tools and Platforms
		5.2 Highlight Examples that Utilize Edge Intelligence in Their Design and Implementation
		5.3 Discuss the Features, Capabilities, and User Experiences of These Tools
	6 Challenges and Limitations
		6.1 Identify and Discuss the Challenges and Limitations of Incorporating Edge Intelligence in Music Composition
		6.2 Address Issues Such as Limited Computational Resources, Latency Concerns, and Model Complexity
		6.3 Discuss the Impact of Privacy and Data Security Considerations in Edge Intelligence Systems
	7 Future Directions and Research Opportunities
		7.1 Discuss Potential Future Developments and Advancements in Edge Intelligence for Music Composition
		7.2 Identify Areas that Require Further Research and Exploration
		7.3 Propose Novel Approaches and Methodologies to Address Current Limitations
	8 Summary and Conclusion
	References
AI-Driven Sentiment Analysis for Music Composition
	1 Introduction
	2 Background and Literature Review
		2.1 Evolution of Sentiment Analysis
		2.2 AI in Music Composition
		2.3 Sentiment in Traditional Music Composition
		2.4 Previous Research on AI-Driven Sentiment Analysis in Music
	3 Methodology
		3.1 Data Collection and Preprocessing
		3.2 Supervised Machine Learning: K-Nearest Neighbors (KNN)
		3.3 Unsupervised Machine Learning: Multi-layer Perceptron (MLP)
		3.4 Feature Extraction and Sentiment Analysis
		3.5 Model Validation and Performance Metrics
	4 Performance Evaluation
		4.1 Data Preprocessing Outcomes
		4.2 K-Nearest Neighbors (KNN) Performance
		4.3 Multi-layer Perceptron (MLP) Performance
		4.4 Comparative Analysis
		4.5 Key Insights
	5 Further Discussion
		5.1 Interpretation of Results
		5.2 Implications for AI in Music Composition
		5.3 Broader Applications
		5.4 Limitations and Areas for Future Research
	6 Summary and Future Work
	References
Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants
	1 Introduction
	2 Construction of Knowledge Graph
	3 Ontologies Construction
		3.1 BERT-BiLSTM-CRF Network
		3.2 Entity Recognize
	4 Relation Exaction
		4.1 Word Attention with BERT
	5 Experimental Result
		5.1 Entity Recognize Experiment
		5.2 Relation Recognize Experiment
		5.3 Fault Diagnosis for Different Types
	6 Conclusions
	References
AI and Its Security
Zero-Knowledge with Robust Learning: Mitigating Backdoor Attacks in Federated Learning for Enhanced Security and Privacy
	1 Introduction
	2 Preliminaries
		2.1 Notation
		2.2 Federated Learning
		2.3 Zero-Knowledge Proof
		2.4 Backdoor Attacks and Defense Solutions
	3 Problem Overview
		3.1 Security Goals
		3.2 Threat Model
		3.3 Solution Overview
	4 ZKRL Design
		4.1 Secure Aggregation with Commitments
		4.2 Zero-Knowledge Range Proof
		4.3 ZKRL Workflow
	5 Security Analysis
	6 Experimental Evaluation
		6.1 Experimental Setup
		6.2 ZKRL Defense Performance Analysis
	7 Conclusion
	References
PPAPAFL: A Novel Approach to Privacy Protection and Anti-poisoning Attacks in Federated Learning
	1 Introduction
	2 Preliminaries
		2.1 Federal Learning
		2.2 Homomomorphic Encryption
		2.3 Poisoning Attacks
		2.4 Inference Attacks
	3 Problem Statement
		3.1 System Model
		3.2 Threat Model
		3.3 Design Goals
	4 Our PPAPAFL
		4.1 Overview
		4.2 PPAPAFL Construction
	5 Experimental Evaluation
		5.1 Experimental Settings
		5.2 Experimental Results
	6 Conclusion
	References
Towards Retentive Proactive Defense Against DeepFakes
	1 Introduction
	2 Related Work
		2.1 DeepFake Generation
		2.2 DeepFake Defense
	3 Method
		3.1 Adversarial Attacks Against DeepFake
		3.2 Perturbation Generator
	4 Experiments
		4.1 Architectures and Datasets
		4.2 Evaluation Metrics
		4.3 Attack Performance Evaluation
		4.4 Comparison with Other Methods
		4.5 Ablation Study
	5 Conclusion
	References
A Fast and Accurate Non-interactive Privacy-Preserving Neural Network Inference Framework
	1 Introduction
		1.1 Related Work
		1.2 Contribution
	2 Preliminaries
		2.1 Basic Notations
		2.2 Homomorphic Encryption
		2.3 System Model
		2.4 Threat Model
	3 System Description
		3.1 Linear Layers
		3.2 Non-linear Layers
		3.3 Noise Management
		3.4 Security Analysis
	4 Experiments
	5 Conclusion
	References
Author Index