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Identity-based searchable attribute signcryption in lattice for a blockchain-based medical system

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 461-471页

作者： Huifang YU Xiaoping BAI School of cyberspace security Xi'an University of Posts&TelecommunicationsXi'an 710121China School of Information Engineering Qinghai Communications Technical CollegeXining 810003China

Electronic healthcare systems can offer convenience but face the risk of data forgery and information leakage.To solve these issues,we propose an identity-based searchable attribute signcryption in lattice for a blockchain-based medical system(BCMS-LIDSASC).BCMS-LIDSASC achieves decentralization and anti-quantum security in the blockchain environment,and provides fine-grained access control and searchability.Furthermore,smart contracts are used to replace traditional trusted third parties,and the interplanetary file system(IPFS)is used for ciphertext storage to alleviate storage pressure on the blockchain.Compared to other schemes,BCMS-LIDSASC requires smaller key size and less storage,and has lower computation cost.It contributes to secure and efficient management of medical data and can protect patient privacy and ensure the integrity of electronic healthcare systems.

关键词： Blockchain Identity-based searchable attribute signcryption Distributed storage NTRU lattice

Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

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Science China(Technological Sciences) 2024年第5期67卷 1612-1627页

作者： YAO Yu SONG YongHua YE ChengJin DING Yi ZHAO YuMing College of Electrical Engineering Zhejiang UniversityHangzhou310027China State key laboratory of Internet of Things for smart city University of MacaoMacao SAR519000China Shenzhen Power Supply Bureau Co. Ltd.Shenzhen518051China

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency response.Consequently,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency.Firstly,a performance curve is developed to describe the cost,capacity,and response speed of DFRs.Moreover,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients.Then,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and solved.Furthermore,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security.Finally,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial solver.The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

关键词： demand-side heterogenous flexible resources risk management of power system frequency performance curve aggregation method mixed integer second-order cone programming

Bond-Slip Behavior of Steel Bar and Recycled Steel Fibre-Reinforced Concrete

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Journal of Renewable Materials 2024年第1期12卷 167-186页

作者： Ismail Shah Jing Li Nauman Khan Hamad R.Almujibah Muhammad Mudassar Rehman Ali Raza Yun Peng School of Architecture and Civil Engineering Yunnan Agricultural UniversityKunming650000China Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu610041China yunnan International Joint R&D Center of smart Agriculture and Water security Yunnan Agricultural UniversityKunming650201China School of Water Conservancy Yunnan Agricultural UniversityKunming650201China School of Civil Engineering Guizhou UniversityGuiyang550025China Department of Civil Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif City21974Saudi Arabia School of Civil Engineering Zhengzhou UniversityZhengzhou450001China

Recycled steel fiber reinforced concrete is an innovative construction material that offers exceptional mechanical properties and durability.It is considered a sustainable material due to its low carbon footprint and environmental friendly characteristics.This study examines the key influencing factors that affect the behavior of this material,such as the steel fiber volume ratio,recycled aggregate replacement rate,concrete strength grade,anchorage length,and stirrup constraint.The study investigates the bond failure morphology,bond-slip,and bond strength constitutive relationship of steel fiber recycled concrete.The results show that the addition of steel fibers at 0.5%,1.0%,and 1.5%volume ratios can improve the ultimate bond strength of pull-out specimens by 9.05%,6.94%,and 5.52%,respectively.The replacement rate of recycled aggregate has minimal effect on the typical bond strength of pull-out specimens.However,the ultimate bond strengths of pull-out specimens with concrete strength grades C45 and C60 have improved compared to those with C30 grade.The specimens with longer anchorage lengths exhibit lower ultimate bond strength,with a reduction of 33.19%and 46.37%for anchorage lengths of 5D and 7D,respectively,compared to those without stirrups.Stirrup restraint of 1φ8 and 2φ8 improves the ultimate bond strength by 5.29%and 6.90%,respectively.Steel fibers have a significant effect on the behavior of concrete after it cracks,especially during the stable expansion stage,crack instability expansion stage,and failure stage.

关键词： Recycled coarse aggregates(RCA) steel fiber bonding performance bond-slip environmental challenges

A General Authentication and key Agreement Framework for Industrial Control System

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Chinese Journal of Electronics 2024年第4期33卷 1046-1062页

作者： Shan GAO Junjie CHEN Bingsheng ZHANG Kui REN Xiaohua YE Yongsheng SHEN School of Cyber Science and Technology Zhejiang University Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Zhejiang Provincial key laboratory of Blockchain and cyberspace Governance Hangzhou city Brain Co. Ltd

In modern industrial control systems(ICSs), when user retrieving the data stored in field device like smart sensor, there exists two main problems: one is lack of the verification for identification of user and field device;the other is that user and field device need exchange a key to encrypt sensitive data transmitted over the network.We propose a comprehensive authentication and key agreement framework that enables all connected devices in an ICS to mutually authenticate each other and establish a peer-to-peer session key. The framework combines two types of protocols for authentication and session key agreement: The first one is an asymmetric cryptographic key agreement protocol based on transport layer security handshake protocol used for Internet access, while the second one is a newly designed lightweight symmetric cryptographic key agreement protocol specifically for field devices. This proposed lightweight protocol imposes very light computational load and merely employs simple operations like one-way hash function and exclusive-or(XOR) operation. In comparison to other lightweight protocols, our protocol requires the field device to perform fewer computational operations during the authentication phase. The simulation results obtained using Open SSL demonstrates that each authentication and key agreement process in the lightweight protocol requires only 0.005 ms. Our lightweight key agreement protocol satisfies several essential security features, including session key secrecy, identity anonymity, untraceability, integrity, forward secrecy, and mutual authentication. It is capable of resisting impersonation, man-in-the-middle, and replay attacks. We have employed the Gong-NeedhamYahalom(GNY) logic and automated validation of Internet security protocols and application tool to verify the security of our symmetric cryptographic key agreement protocol.

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Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

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Computers, Materials & Continua 2024年第5期79卷 3355-3372页

作者： Jianfeng He Haowei Ye Jie Ning Hui Zhou Bo She Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan Key Laboratory of Artificial IntelligenceKunming650500China School of Physics and Electronic Engineering Yuxi Normal UniversityYuxi653100China PET/CT Center Affiliated Hospital of Kunming University of Science and TechnologyFirst People’s Hospital of Yunnan ProvinceKunming650031China

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://***/yehaowei/temt.

关键词： PET lung scans respiratory motion correction triple equivariant motion transformer lie group motion decomposition

Deep knowledge tracking algorithm based on forgetting law

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 17-27页

作者： Guo Xiangbo Wang Jian Huang Mengjie Wang Minghui Yang Jian Yu Yongtao School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China yunnan key laboratory of smart city in cyberspace security Yuxi Normal UniversityYuxi 653100China

Knowledge tracking(KT)algorithm,which can model the cognitive level of learners,is a fundamental artificial intelligence approach to solve the personalized learning problem in the field of education.The recently presented separated self-attentive neural knowledge tracing(SAINT)algorithm has got a great improvement on predictingthe accuracy of students’answers in comparison with the present other methods.However there is still potential to enhance its performance for it fails to effectively utilize temporal features.In this paper,an optimization algorithm for SAINT based on Ebbinghaus’law of forgetting was proposed which took temporal features into account.The proposed algorithm used forgetting law-based data binning to discretize the time information sequences,so as to obtain the temporal featuresin accordance with people’s forgetting pattern.Then the temporal features were used as input in the decoder of SAINT model to improve its accuracy.Ablation experiments and comparison experiments were performed on the EdNet dataset in order to verify the effectiveness of the proposed model.Seen in the experimental results,it achieved higher area under curve(AUC)values than the other present representative knowledge tracing algorithms.It demonstrates that temporal featuresare necessary for KT algorithms if it can be properly dealt with.

关键词： knowledge tracking neural network attention mechanism personalized learning Ebbinghaus' law of forgetting

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4703-4724页

作者： Peng Xiao Qi Zhong Jingxue Chen Dongyuan Wu Zhen Qin Erqiang Zhou Network and Data security key laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China

In the intelligent medical diagnosis area,Artificial Intelligence(AI)’s trustworthiness,reliability,and interpretability are critical,especially in cancer diagnosis.Traditional neural networks,while excellent at processing natural images,often lack interpretability and adaptability when processing high-resolution digital pathological images.This limitation is particularly evident in pathological diagnosis,which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease.Therefore,the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and reliability.In this paper,we introduce an innovative Multi-Scale Multi-Branch Feature Encoder(MSBE)and present the design of the CrossLinkNet Framework.The MSBE enhances the network’s capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and modules.The CrossLinkNet Framework,serving as a versatile image segmentation network architecture,employs cross-layer encoder-decoder connections for multi-level feature fusion,thereby enhancing feature integration and segmentation accuracy.Comprehensive quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet,equipped with the MSBE encoder,not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature encoders.Crucially,CrossLinkNet emphasizes the interpretability of the AI model,a crucial aspect for medical professionals,providing an in-depth understanding of the model’s decisions and thereby enhancing trust and reliability in AI-assisted diagnostics.

关键词： Explainable AI security trustworthy CrossLinkNet whole slide images

A Review on the security of the Ethereum-Based DeFi Ecosystem

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 69-101页

作者： Yue Xue Dunqiu Fan Shen Su Jialu Fu Ning Hu Wenmao Liu Zhihong Tian cyberspace Institute of Advanced Technology Guangzhou UniversityGuanzhou510000China Innovation Center NSFOCUS Inc.Beijing100089China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing100816China

Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

关键词： Blockchain smart contract decentralized finance DeFi security

Adaptive learning path recommendation model for examination-oriented education

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The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 77-88页

作者： Wang Jian Qiao Kuoyuan Yuan Yanlei Liu Xiaole Yang Jian School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Research and Development Department Tongfang Knowledge Network Technology Company LimitedBeijing 100192China The Teaching Affaires Office Zhengzhou Hi-Tech Zone Langyuehui Foreign Language Middle SchoolZhengzhou 450001China yunnan key laboratory of smart city in cyberspace security Yuxi Normal UniversityYuxi 653100China

Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

关键词： adaptive learning learning path recommendation Dempster-Shaferevidence theory knowledge model learner model