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Matrix Lab
Welcome to Matrix Lab! We focus on cutting-edge research in blockchain technology, federated learning, intelligent edge computing, and Web 3.0 ecosystems.
Lab News
Latest Publications in 2025
- IEEE Transactions on Information Forensics and Security paper “Connector: Enhancing the traceability of decentralized bridge applications via automatic cross-chain transaction association” accepted
- IEEE Internet of Things Journal paper “MMFed: A Multimodal Federated Learning Framework for Heterogeneous Devices” accepted
- IEEE Global Blockchain Conference (GBC) paper “Q-DPoS: An Improved DPoS Consensus based on Quadratic Voting Mechanism in Web 3.0” accepted
- IEEE Global Blockchain Conference (GBC) paper “A Semantic Detection Incentive Mechanism For Blockchain Transactions Based on Evolutionary Game Theory in Web3 Ecosystem” accepted
- Neural Networks paper “Enhancing partition distinction: A contrastive policy to recommendation unlearning” accepted
- IEEE Internet of Things Journal paper “Exploring AIoT Blockchain Transaction Semantic Detection and Incentive Mechanism with Evolutionary Game Towards Web 3.0 Ecosystem” accepted
- Science China Information Sciences paper “Agent4Vul: multimodal LLM agents for smart contract vulnerability detection” accepted
- IEEE INFOCOM 2025 paper “ContribChain: A Stress-Balanced Blockchain Sharding Protocol with Node Contribution Awareness” accepted
- IEEE ICDE 2025 paper “Know Your Account: Double Graph Inference-Based Account De-Anonymization on Ethereum” accepted
- arXiv preprint paper “CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain” published
Research Areas
- Blockchain Technology - Consensus mechanisms, cross-chain transactions, smart contract security
- Federated Learning - Multimodal federated learning, cross-domain IoT applications
- Intelligent Edge Computing - Edge intelligence, distributed computing
- Web 3.0 Ecosystem - Decentralized applications, incentive mechanisms
- IoT Applications - AIoT, smart device collaboration