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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Qian, Haodong Li, De Li, Qiyu Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Guangxi Normal University School of Computer Science and Engineering Guilin541004 China

ISBN: (纸本)9798331509712

Graph Neural Networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect the application of graph neural networks in the real world. Hence, we introduce Robust Graph Neural Networks with Noisy label Learning (RLGNN) to tackle the challenges posed by label noise and sparsity in graph-structured data. Its key components include unsupervised contrastive learning for deriving robust node representations, a two-stage clean sample selection mechanism, and a semi-supervised loss function to harness information from unlabeled nodes. RLGNN ingeniously merges contrastive learning with the prediction space, refining the label selection process and boosting the model's robustness and generalizability. Comprehensive experiments indicate that RLGNN significantly surpasses existing models in managing label noise in semi-supervised node classification scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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Connecting Large Language Models with blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence

arXiv

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arXiv 2024年

作者： Xian, Youquan Zeng, Xueying Xuan, Duancheng Yang, Danping Li, Chunpei Fan, Peng Liu, Peng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education China School of Computer Science and Engineering Guangxi Normal University China

blockchain smart contracts have catalyzed the development of decentralized applications across various domains, including decentralized finance. However, due to constraints in computational resources and the prevalence of data silos, current smart contracts face significant challenges in fully leveraging the powerful capabilities of Large Language Models (LLMs) for tasks such as intelligent analysis and reasoning. To address this gap, this paper proposes and implements a universal framework for integrating LLMs with blockchain data, C-LLM, effectively overcoming the interoperability barriers between blockchain and LLMs. By combining semantic relatedness with truth discovery methods, we introduce an innovative data aggregation approach, SenteTruth, which significantly enhances the accuracy and trustworthiness of data generated by LLMs. To validate the framework’s effectiveness, we construct a dataset consisting of three types of questions, capturing Q&A interactions between 10 oracle nodes and 5 LLM models. Experimental results demonstrate that, even with 40% malicious nodes, the proposed solution improves data accuracy by an average of 17.74% compared to the optimal baseline. This research not only provides an innovative solution for the intelligent enhancement of smart contracts but also highlights the potential for deep integration between LLMs and blockchain technology, paving the way for more intelligent and complex applications of smart contracts in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

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Dynamic Social Recommendation with High-Matching Inhomogeneous Relations

Dynamic Social Recommendation with High-Matching Inhomogeneo...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Shenghan Wang, Jinyan Zeng, Huachang Li, Jun Wei, Yuecen Li, Xianxian Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining Security Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Computer and Information Technology Anhui Vocational and Technical College Hefei China

ISBN: (纸本)9798350337020

Social recommendation utilizing social relations as auxiliary information can alleviate data sparsity issues of collaborative filtering methods. However, existing social recommendation methods usually focus on the shallow relations of user-item interactions and social networks to mine potential user preferences, which fail to provide a fine-grained consideration of the inhomogeneous influence and the changes in user interest or item attractiveness over time in high-order interactions of multiple relation types. To address these issues, we propose a novel method named Dynamic Social Recommendation with High-matching Inhomogeneous Relations (DSRHIR) to reveal the influence of inhomogeneity in dynamic interactions on social recommendation from multiple perspectives. Specifically, we first model the short-term dynamic and long-term static representations of both users with social relations and similar relations, and items with correlations. Then, we combine their dynamic and static representations during the interaction and the information from the high-order neighbors of these three relations to obtain a graph-augmented representation. Finally, the graph-augmented representation is used to modulate high-matching user interest and item attractiveness. Extensive experiments on two real-world datasets demonstrate the effectiveness of DSRHIR. © 2023 IEEE.

关键词： graph neural network inhomogeneous relations Recommender systems social networks

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A Novel Optimized Asynchronous Federated Learning Framework 23

A Novel Optimized Asynchronous Federated Learning Framework

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhou, Zhicheng Chen, Hailong Li, Kunhua Hu, Fei Yan, Bingjie Cheng, Jieren Wei, Xuyan Liu, Bernie Li, Xiulai Chen, Fuwen Sui, Yongji Hainan University School of Computer Science and Technology Haikou China Haikou China RobAI-Lab Hainan University China Hainan Blockchain Technology Engineering Research Center Haikou China

ISBN: (纸本)9781665494571

Federated Learning (FL) since proposed has been applied in many fields, such as credit assessment, medical, etc. Because of the difference in the network or computing resource, the clients may not update their gradients at the same time that may take a lot of time to wait or idle. That's why Asynchronous Federated Learning (AFL) method is needed. The main bottleneck in AFL is communication. How to find a balance between the model performance and the communication cost is a challenge in AFL. This paper proposed a novel AFL framework VAFL. And we verified the performance of the algorithm through sufficient experiments. The experiments show that VAFL can reduce the communication times about 51.02% with 48.23% average communication compression rate and allow the model to be converged faster. The code is available at https://***/RobAI-lab/NAFL © 2021 IEEE.

关键词： Training Costs Smart cities Focusing Collaborative work Servers Object recognition

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FedGAC: Graph Federated Learning with Gradients Aggregation Calibration for Non-IID and Long-Tailed Data 22

FedGAC: Graph Federated Learning with Gradients Aggregation ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Jie Wang, Jinyan Deng, Rongbin Yan, Dongqi Li, Qiyu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Guangxi Normal University School of Computer Science and Engineering Guilin541004 China

ISBN: (纸本)9798331509712

Federated graph learning (FGL) combines the advantages of graph neural networks and federated learning, extracting valuable information from decentralized graph data while preserving data privacy. Existing FGL methods typically consider mitigating the effect of non-IID under the class balanced assumption. However, in many real-world scenarios, the data class distribution often exhibits a long-tailed distribution, which can lead to the model being severely biased towards the head classes, thus affecting the overall performance of the global model. To address the challenges posed by non-IID and long-tailed distribution data in FGL, this paper proposes a method called FedGAC to train a globally balanced model. In the local stage, FedGAC employs feature-structure decoupled graph attention network to train client models, introduces class-importance balanced strategy to mitigate class imbalance issues, and leverages knowledge relearning strategy to enhance model generalization. In the global stage, FedGAC employs an adaptive gradients aggregation strategy that dynamically adjusts balancing factors based on the degree of class imbalance in each client's data, thereby optimizing the aggregation process. Additionally, it utilizes global class gradients to calibrate learnable features. Experimental results demonstrate that, compared to the state-of-the-art federated learning methods, FedGAC achieves superior performance on highly non-IID and long-tailed distribution datasets. © 2024 IEEE.

关键词： Federated learning

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Bc²FL: Double-Layer blockchain-Driven Federated Learning Framework for Agricultural IoT

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IEEE Internet of Things Journal 2024年第4期12卷 4362-4374页

作者： Qingyang Ding Xiaofei Yue Qinnan Zhang Zehui Xiong Jinping Chang Hongwei Zheng School of Management Beijing Union University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Tampines Singapore Blockchain and Privacy Computing Technology R&D Department Beijing Academy of Blockchain and Edge Computing Beijing China

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

关键词： blockchains Internet of Things Privacy Computational modeling Federated learning Data models Biological system modeling Adaptation models Accuracy Servers

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Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System 42

Roland: Robust In-band Parallel Communication for Magnetic M...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhou, Wangqiu Zhou, Hao Cui, Xiang Wang, Xinyu Wang, Xiaoyan Liu, Zhi Linke Lab School of Computer Science and Technology University of Science and Technology of China China University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications China Deqing Alpha Innovation Institute Huzhou China Ibaraki University Graduate School of Science and Engineering Ibaraki Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350334142

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn't be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%. © 2023 IEEE.

关键词： MIMO systems

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FasterCReW: Performance or Efficiency? A Lightweight Conditional Residual DNN-based Watermarking Based on FasterNet

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Baowei Zhang, Jianbo Wu, Yufeng Cui, Qi Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education The School of Computer Science The Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology The Jiangsu Yuchi Blockchain Technology Research Institute Nanjing210044 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Deep neural networks (DNNs) based watermarking algorithms have made significant strides in recent years. However, existing methods either demand substantial resources for image feature extraction during watermark embedding, sacrificing efficiency, or completely neglect image texture information, resulting in suboptimal performance. Moreover, current algorithms struggle with real-time watermark extraction. To address these limitations, we propose a lightweight conditional residual watermarking (CReW) architecture. Specifically, CReW employs a Conditional Generative Adversarial Network (CGAN) framework to generate an adaptive residual image guided by the structure of the cover image, which is decoupled from the network to reduce computational complexity. This design enables CReW to achieve an optimal balance between performance and efficiency. Additionally, by directly optimizing the residual image to capture variations in watermark behavior under distortion, CReW significantly enhances robustness. Furthermore, we design redundancy coding blocks to increase the mutual information of the watermark, along with a patch-level discriminator to improve local patch discrimination, thereby further enhancing image quality. Finally, by reducing channel redundancy and leveraging FasterNet, we developed a low-complexity network architecture, Faster-CReW, which facilitates real-time watermark embedding and extraction. Extensive experimental results demonstrate that, despite having 36× fewer network parameters and 30× fewer floating point operations (FLOPs) than Adaptor, Faster-CReW exhibits excellent robustness against distortions such as cropout, JPEG compression, and Gaussian noise. Furthermore, Faster-CReW significantly outperforms other existing DNN-based watermarking algorithms in terms of running speed, achieving an 8× speed increase over UDH and a 28× increase over Adaptor on an Intel Core i7-8750H CPU. (Figure presented) © 2025 IEEE. All rights reserved.

关键词： Redundancy

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DecTest: A Decentralised Testing Architecture for Improving Data Accuracy of blockchain Oracle

DecTest: A Decentralised Testing Architecture for Improving ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xueying Zeng Youquan Xian Chunpei Li Zhengdong Hu Aoxiang Zhou Peng Liu Key Lab of Education Blockchain and Intelligent Technology Ministry of Education School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

blockchain technology ensures secure and trust-worthy data flow between multiple participants on the chain, but interoperability of on-chain and off-chain data has always been a difficult problem that needs to be solved. To solve the problem that blockchain systems cannot access off-chain data, oracle is introduced. However, existing research mainly focuses on the consistency and integrity of data, but ignores the problem that oracle nodes may be externally attacked or provide false data for selfish motives, resulting in the unresolved problem of data accuracy. In this paper, we introduce a new Decentralized Testing architecture (DecTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes by introducing a dynamic anonymized question-verification committee. Based on this, a comprehensive evaluation incentive mechanism is designed to incentivize honest work performance by evaluating nodes based on their reputation scores. The simulation results show that we successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4 %.

关键词： Accuracy Publishing Simulation Entropy blockchains Feeds Monitoring Interoperability Cybernetics Testing

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A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

HighTech and Innovation Journal

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HighTech and innovation Journal 2023年第2期4卷 349-363页

作者： Puttarattanamanee, Maneerat Boongasame, Laor Thammarak, Karanrat KMITL-Digital Analytics and Intelligence Center Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand Department of Mathematics Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand Business Innovation and Investment Laboratory: B2I-Lab School of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand Department of Computer Engineering and Electronics School of Engineering and Technology Walailak University Nakhon Si Thammarat80160 Thailand

The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on *** with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research. © 2023, Ital Publication. All rights reserved..

关键词： Fake detection

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