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A Framework Based on the DAO and NFT in Blockchain for Electronic Document Sharing

Computer Modeling in engineering & Sciences 2024年第9期140卷 2373-2395页

作者： Lin Chen Jiaming Zhu Yuting Xu Huanqin Zheng Shen Su cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China engineering research center of lntegration and Application of Digital Learning Technology Ministry of EducationBeijing100816China

In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge ***,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative ***,it is crucial to establish an e-document sharing system that enforces copyright ***,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the *** watermark technology is only used as a means of infringement *** paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in *** use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized *** e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document *** mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document ***,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization *** experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in

关键词： Electronic document sharing blockchain DAO NFT evolutionary game

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CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

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Computers, Materials & Continua 2024年第5期79卷 2253-2276页

作者： Di Wang Yuefei Zhu Jinlong Fei Maohua Guo School of cyberspace Security Information Engineering UniversityZhengzhou450000China

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity *** advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial *** a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable ***,these methods suffer from high bandwidth overhead or require access to the target model,which is *** paper proposes CMAES-WFD,a black-box WF defense based on adversarial *** process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization *** are injected into the local parts of the original traffic to control bandwidth *** to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,***,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

关键词： Traffic analysis deep neural network adversarial sample Tor website fingerprinting

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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 *** is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary *** them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most ***,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few ***,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security *** 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 *** on that,we further discuss the current solutions and potential directions ofDeFi *** 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

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Efficient Clustering Network Based on Matrix Factorization

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Computers, Materials & Continua 2024年第7期80卷 281-298页

作者： Jieren Cheng Jimei Li Faqiang Zeng Zhicong Tao and Yue Yang School of Computer Science and Technology Hainan UniversityHaikou570228China School of cyberspace Security Hainan UniversityHaikou570228China Hainan Blockchain Technology engineering research center Haikou570228China

Contrastive learning is a significant research direction in the field of deep ***,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing *** address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the ***,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters *** experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

关键词： Contrastive learning clustering matrix factorization

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MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

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Computers, Materials & Continua 2024年第4期79卷 983-1003页

作者： Xiangyan Tang Chengchun Ruan Xiulai Li Binbin Li Cebin Fu School of Computer Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology engineering research center Hainan UniversityHaikou570228China School of cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion *** enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial *** build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small *** replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small ***,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple *** module enhances the perception of spatial contextual features and the utilizationof multiscale feature *** the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

关键词： Small object detection YOLOv7 multi-scale attention spatial context

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Design of Resilient Sensor Networks Balancing Resilience and Efficiency

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International Journal of Disaster Risk Science 2024年第1期15卷 107-115页

作者： Sergey N.Vecherin Kiril D.Ratmanski Luke Hogewood Igor Linkov Cold Regions research and engineering Laboratory Engineering Research and Development CenterHanoverNH 03755USA Environmental Laboratory Engineering Research and Development CenterVicksburgMS 39180USA

In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk *** a disaster situation,accurate sensing information is the key to efficient recovery *** general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase *** this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with *** application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional *** method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and *** problem formulation,resilience requirements,and application of the optimization algorithm are described in *** of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent *** tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by *** proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

关键词： Binary linear programming Optimal sensor placement Redundant networks Resilience and efciency Resilient sensor networks

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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Computer Modeling in engineering & Sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province cyberspace research center Harbin150001China Information Network engineering and research center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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Fairness in machine learning: definition, testing,debugging, and application

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Science China(Information Sciences) 2024年第9期67卷 41-61页

作者： Xuanqi GAO Chao SHEN Weipeng JIANG Chenhao LIN Qian LI Qian WANG Qi LI Xiaohong GUAN Faculty of Electronic and Information engineering Xi'an Jiaotong University School of Cyber Science and engineering Wuhan University Institute for Network Sciences and cyberspace Tsinghua University

In recent years, artificial intelligence technology has been widely used in many fields, such as computer vision, natural language processing and autonomous driving. Machine learning algorithms, as the core technique of AI, have significantly facilitated people's lives. However, underlying fairness issues in machine learning systems can pose risks to individual fairness and social security. Studying fairness definitions, sources of problems, and testing and debugging methods of fairness can help ensure the fairness of machine learning systems and promote the wide application of artificial intelligence technology in various fields. This paper introduces relevant definitions of machine learning fairness and analyzes the sources of fairness problems. Besides, it provides guidance on fairness testing and debugging methods and summarizes popular datasets. This paper also discusses the technical advancements in machine learning fairness and highlights future challenges in this area.

关键词： artificial intelligence security machine learning security machine learning fairness model testing model debugging

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High adsorption selectivity of activated carbon and carbon molecular sieve boosting CO_(2)/N_(2) and CH_(4)/N_(2) separation

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Chinese Journal of Chemical engineering 2024年第3期67卷 282-297页

作者： Siang Chen Wenling Wu Zhaoyang Niu Deqi Kong Wenbin Li Zhongli Tang Donghui Zhang The research center of Chemical engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China

Flue gas and coal bed methane are two important sources of greenhouse *** swing adsorption process has a wide range of application in the field of gas separation,and the selection of adsorbent is *** this regard,in order to assess the better adsorbent for separating CO_(2) from flue gas and CH_(4) from coal bed methane,adsorption isotherms of CO_(2),CH_(4) and N_(2) on activated carbon and carbon molecular sieve are measured at 303.15,318.15 and 333.15 K,and up to 250 *** experimental data fit better with Langmuir 2 compared to Langmuir 3 and Langmuir-Freundlich models,and Clausius-Clapeyron equation was used to calculate the isosteric *** the order of the adsorbed amount and the adsorption heat on the two adsorbents are CO_(2)>CH_(4)>N_(2).The adsorption kinetics are calculated by the pseudo-first kinetic model,and the order of adsorption rates on activated carbon is N_(2)-CH_(4)>CO_(2),while on carbon molecular sieve,it is CO_(2)-N_(2)>CH_(4).It is shown that relative molecular mass and adsorption heat are the primary effect on kinetics for activated carbon,while kinetic diameter is the main resistance factor for carbon molecular ***,the adsorption selectivity of CH_(4)/N_(2) and CO_(2)/N_(2) were estimated with the ideal adsorption solution theory,and carbon molecular sieve performed best at 318.15 K for both CO_(2) and CH_(4) *** study suggested that activated carbon is a better choice for separating flue gas and carbon molecular sieve can be a strong candidate for separating coal bed methane.

关键词： Activated carbon Carbon molecular sieve Adsorbent evaluation Adsorption equilibrium and kinetics Heat of adsorption Selectivity

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Synthesis of boron nitride nanorod and its performance as a metalfree catalyst for oxidative desulfurization of diesel fuel

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Chinese Journal of Chemical engineering 2024年第4期68卷 120-132页

作者： Tanaz Ghanadi Gholamreza Moradi Alimorad Rashidi Catalyst research center Faculty of Chemical and Petroleum EngineeringRazi UniversityKermanshahIran Nanotechnology research center Research Institute of Petroleum Industry(RIPI)TehranIran

In order to reduce the sulfur compounds in diesel fuel,boron nitride(BN)has been used as a novel metal-free catalyst in the present *** nanocatalyst was synthesized via template-free approach followed by heating treatment at 900℃ in nitrogen atmosphere that the characteristics of the sample were identified by the X-ray diffraction,Fourier-transform infrared spectroscopy,Raman spectroscopy,field emission scanning electron microscopy,transmission electron microscopy,atomic force microscopy,and N2 adsorption-desorption *** results of structural and morphological analysis represented that BN has been successfully *** efficacy of the main operating parameters on the process was studied by using response surface methodology based on the Box-Behnken design *** prepared catalyst showed high efficiency in oxidative desulfurization of diesel fuel with initial sulfur content of 8040 mg·kg^(-1)*** statistical analysis,a significant quadratic model was obtained to predict the sulfur removal as a function of efficient *** maximum efficiency of 72.4%was achieved under optimized conditions at oxidant/sulfur molar ratio of 10.2,temperature of 71℃,reaction time of 113 min,and catalyst dosage of 0.36 ***,the reusability of the BN was studied,and the result showed little reduction in activity of the catalyst after 10 times ***,a plausible mechanism was proposed for oxidation of sulfur compounds on the surface of the *** present study shows that BN materials can be selected as promising metal-free catalysts for desulfurization process.

关键词： Desulfurization Boron nitride(BN)nanostructure Experimental design BoxeBehnken

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