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Privacy-Preserving Supply Chain Payment Solution Based on Blockchain

International Journal of Network Security 2025年第1期27卷 25-34页

作者： Wang, Zhenzhen Liu, Momeng Shao, Mingqiang Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi'an Polytechnic University State Key Laboratory of Integrated Service Networks Xidian University Xi'an China

The development of information technology has gradually automated supply chain management. Although it has greatly improved efficiency, there still exists some deficiencies. Firstly, there may be fund defaults in the financial flow of the supply chain. In addition, complex transactions or settlement operations among participants and manual intervention in processing may lead to low efficiency and high costs. Moreover, the data generated during the transactions contains sensitive information of all parties. If information leakage occurs, it may cause serious damage to enterprises or individuals. Since blockchain has the advantages of tamper-proof, anonymity, and distributed storage, it can be used as a platform for recording transactions. In this paper, we propose a blockchain-based supply chain payment solution. In our scheme, the payment channel is used to provide the atomicity of payment and data privacy, combined with the time-lock to avoid fund defaults. At the same time, we adopt a ring signature so as to ensure the privacy of identity. Through the security analysis and experiments, it shows that our scheme can meet the requirements of value privacy and identity privacy, and the high efficiency in practice. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Differential privacy

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A bytecode-based integrated detection and repair method for reentrancy vulnerabilities in smart contracts

IET Blockchain

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IET Blockchain 2024年第3期4卷 235-251页

作者： Feng, Zijun Feng, Yuming He, Hui Zhang, Weizhe Zhang, Yu School of Cyberspace Science Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of New Networks Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

The reentrancy vulnerability in smart contracts has caused significant losses in the digital currency economy. Existing solutions for detecting and repairing this vulnerability are limited in scope and lack a comprehensive framework. Additionally, there is currently a lack of guidance methods for effectively pinpointing the location of vulnerabilities. The proposed bytecode-level method addresses these challenges by incorporating a detection module, an auxiliary localization module, and a repair module. An opcode classification method is introduced using vulnerability features and a BiLSTM-Attention-based sequence model to enhance detection accuracy. To overcome difficulties in vulnerability localization, an auxiliary localization method based on data flow and control flow analysis is proposed, enabling developers to better locate vulnerabilities. Current reentrancy vulnerability repair methods are analyzed and strategies for three reachable patterns are proposed. The bytecode rewriting strategy utilizes Trampoline technology for repair, while a fuel optimization method reduces bytecode generation length to optimize gas costs. Through extensive experimental validation, the effectiveness and superiority of the proposed methods are confirmed, further validating the feasibility of the entire framework. Experimental results demonstrate that the framework offers enhanced protection against reentrancy vulnerability attacks in smart contracts. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Smart contract

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Low Complexity Convolutional Neural Network Digital Predistortion for Radio Frequency Power Amplifiers 24

Low Complexity Convolutional Neural Network Digital Predisto...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Xie, Qian Wang, Yong Ding, Jianyang Guo, Zihan Niu, Jiajun Xidian University State Key Laboratory of Integrated Services Networks Xi'an710061 China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214000 China

ISBN: (纸本)9798350363760

The predistortion models for radio frequency (RF) power amplifiers (PAs), such as convolutional neural networks (CNNs) and deep neural networks (DNNs), has shown a significant improvement in predistortion performance. However, digital predistortion (DPD) models with neural network structures encounter challenges related to high computational complexity and prolonged training duration. In this study, we propose a DPD model utilizing a low complexity convolutional neural network (LCCNN). The proposed model simplifies the traditional CNN structure, employing an augmented real-valued cross-memorized terms (ARCT) matrix as input layer, and configuring convolutional layer to extract nonlinear features through macro convolutional kernels. Furthermore, migration learning and pruning operations are implemented to expedite model training and parameter reduction, thereby decreasing training duration and floating-point operations (FLOPs). The experiment results based on a 160 MHz Doherty PA indicate that the proposed model effectively reduces computational complexity while maintaining required predistortion performance. © 2024 IEEE.

关键词： Convolutional neural networks

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MIM-HD: Making Smaller Masked Autoencoder Better with Efficient Distillation 27

MIM-HD: Making Smaller Masked Autoencoder Better with Effici...

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27th European Conference on Artificial intelligence, ECAI 2024

作者： Zhang, Zherui Wang, Changwei Xu, Rongtao Xu, Wenhao Xu, Shibiao Guo, Li Zhang, Jiguang Teng, Xiaoqiang Xu, Wenbo Artificial Intelligence Beijing University of Posts and Telecommunications China China Institute of Automation Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

ISBN: (纸本)9781643685489

Self-supervised learning and knowledge distillation intersect to achieve exceptional performance on downstream tasks across diverse network capacities. This paper introduces MIM-HD, which implements enhancements for masked image modeling (MIM) distillation, in two key aspects. First, a vision transformer head-level relation adaptive distillation approach is proposed, allowing the student to dynamically draw multi-source knowledge from the teacher based on its evolving state, compatible with scenarios where teacher-student transformer block head count differs. Second, to address the overemphasis on the encoder and neglect of the decoder role in maintaining representation consistency in previous MIM distillations, a dual-view decoding strategy for latent visual representations is introduced, reusing the teacher's decoder to alleviate MIM burdens on smaller networks. MIM-HD effectiveness is demonstrated through evaluations on ADE20K (mIoU) and ImageNet-1K (Acc), achieving +1.4% and +0.5% improved performance, respectively, compared to state-of-the-art methods, with substantial advantages on smaller pretraining datasets. Moreover, MIM-HD achieves superior efficiency, reducing pre-training epochs from 300 to 100. © 2024 The Authors.

关键词： Decoding

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction 29

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xin Peng, Xueping Guan, Hongjiao Zhao, Long Qiao, Xinxiao Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (纸本)9798350330717

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients' future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient's historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient's next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Bio-Clinical BERT clinical event dynamic hypergraph sequential diagnosis prediction

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Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network 9th

Knowledge Graph Link Prediction Model Based on Attention Gr...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Shi, Junkang Li, Ming Zhao, Jing Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9783031466731

The main purpose of knowledge graph embedding (KGE) is to complete the missing part of triplets. By learning from existing models, we found that adding graph convolutional networks (GCN) to KGE models achieved good performance. These models mainly use the local structural characteristics of data. However, their main goal is to learn an aggregator rather than a feature vector for each node. To address the problems with existing models, we proposed a knowledge graph link prediction model based on attentional relational graph convolutional networks (ARGCN). First, we capture the relationship attribute features between nodes by adding an attention mechanism and extract important node attribute features for visualization. Then, through a relational graph convolutional neural network, we capture the associated attributes between nodes and extract the feature information that can best represent nodes. Finally, we use DistMult as the scoring function. Our experiments show that our proposed is effective on standard FB15k-237 and WN18RR datasets. Compared with other models, it has relative improvements in Hits@1, Hits@3 and Hits@10. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Knowledge graph

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The Immense Impact of Reverse Edges on Large Hierarchical networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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