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Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

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computers, Materials & Continua 2024年第4期79卷 1063-1088页

作者： Yiming Tang Jianwei Gao Yifan Huang school of {1. and information Hefei University of TechnologyHefei230601China Department of Electrical and computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group *** robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without ***,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy *** begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual ***,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are *** results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,***,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple *** corresponding solutions and their interval perturbations are *** is found that theα-UTI reasoning chain method is stable in the case of chain *** application examples in affective computing are given to verify the stability of theα-*** summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

关键词： Fuzzy reasoning the CRI method the triple I method fuzzy implication interval robustness

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga school of {1. and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of computer Science University of Science&TechnologyBannu28100Pakistan Department of computer Science University of HertfordshireHatfieldAL109ABUK Department of computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

Online Nonstop Task Management for Storm-Based Distributed Stream Processing Engines

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Journal of computer Science & Technology 2024年第1期39卷 116-138页

作者：张洲金培权谢希科王晓亮刘睿诚万寿红 school of {1. Science and Technology University of Science and Technology of ChinaHefei 230026China Key Laboratory of Electromagnetic Space information Chinese Academy of SciencesHefei 230026China

Most distributed stream processing engines(DSPEs)do not support online task management and cannot adapt to time-varying data ***,some studies have proposed online task deployment algorithms to solve this ***,these approaches do not guarantee the Quality of Service(QoS)when the task deployment changes at runtime,because the task migrations caused by the change of task deployments will impose an exorbitant *** study one of the most popular DSPEs,Apache Storm,and find out that when a task needs to be migrated,Storm has to stop the resource(implemented as a process of Worker in Storm)where the task is *** will lead to the stop and restart of all tasks in the resource,resulting in the poor performance of task *** to solve this problem,in this pa-per,we propose N-Storm(Nonstop Storm),which is a task-resource decoupling DSPE.N-Storm allows tasks allocated to resources to be changed at runtime,which is implemented by a thread-level scheme for task ***,we add a local shared key/value store on each node to make resources aware of the changes in the allocation ***,each resource can manage its tasks at *** on N-Storm,we further propose Online Task Deployment(OTD).Differ-ing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migra-tions caused by a task re-deployment,OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of *** demonstrate that OTD can adapt to different kinds of applications including computation-and communication-intensive *** experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87%of the performance degradation time,compared with Apache Storm and other state-of-the-art *** addition,OTD can increase the average CPU usage by 51.for computation-intensive application

关键词： distributed stream processing engine(DSPE) Apache Storm online task migration online task deployment

Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles

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computers, Materials and Continua 2024年第1期81卷 1581-1593页

作者： Cui, Chi Du, Haiping Jia, Zhijuan He, Yuchu Wang, Lipeng school of {1. Science and Technology Zhengzhou Normal University Zhengzhou 450044 China school of Electrical Computer and Telecommunications Engineering University ofWollongong Wollongong NSW 2500 Australia

The rapid evolution of artificial intelligence (AI) technologies has significantly propelled the advancement of the Internet of Vehicles (IoV). With AI support, represented by machine learning technology, vehicles gain the capability to make intelligent decisions. As a distributed learning paradigm, federated learning (FL) has emerged as a preferred solution in IoV. Compared to traditional centralized machine learning, FL reduces communication overhead and improves privacy *** these benefits, FL still faces some security and privacy concerns, such as poisoning attacks and inference attacks, prompting exploration into blockchain integration to enhance its security posture. This paper introduces a novel blockchain-enabled federated learning (BCFL) scheme with differential privacy (DP) tailored for IoV. In order to meet the performance demanding IoV environment, the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance (PBFT) consensus, which offers superior efficiency over the conventional public blockchains. In addition, the proposed approach utilizes the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the local training process of FL for enhanced privacy protection. Experiment results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning attacks. On the other hand, the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of IoV. Furthermore, by incorporating DP, the proposed approach is shown to have the (Ε-δ) privacy guarantee while maintaining an acceptable level of model accuracy. This enhancement effectively mitigates the threat of inference attacks on private information. © 2024 The Authors. Published by Tech Science Press.

关键词： Federated learning

Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in Engineering & Sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of computer Science Chongqing UniversityChongqing400044China school of {1. and Software Engineering Xihua UniversityChengdu610039China school of {1. and information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China school of {1. Science and Technology Beijing Institute of TechnologyBeijing100083China school of {1. Science and Technology Dongguan University of TechnologyDongguan523808China school of Emergency Management Xihua UniversityChengdu610039China College of computer and {1. Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1. dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

What can we learn from quality assurance badges in open-source software?

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Science China(information Sciences) 2024年第4期67卷 146-163页

作者： Feng LI Yiling LOU Xin TAN Zhenpeng CHEN Jinhao DONG Yang LI Xuanzhi WANG Dan HAO Lu ZHANG MoE Key Lab of HCST School of Computer SciencePeking University Department of computer Science Purdue University school of {1. Science & Engineering Beihang University(BUAA) Department of computer Science University College London(UCL)

In the development of open-source software(OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance(QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly,and aim to produce some interesting findings and provide practical implications. We collect and analyze 1.0000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then,we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics.

关键词： quality assurance badge open-source software code quality empirical study

Scene 3-D Reconstruction System in Scattering Medium

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computers, Materials & Continua 2024年第8期80卷 3405-3420页

作者： Zhuoyifan Zhang Lu Zhang LiangWang Haoming Wu school of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China school of {1. and Communication Engineering Hainan UniversityHaikou570228China school of {1. Science and Technology Hainan UniversityHaikou570228China

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and *** NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also *** underwater 3D reconstruction systems still face challenges such as long training times and low rendering *** paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D ***,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across ***,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction *** pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and *** terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same *** terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 1..40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.

关键词： Underwater scene reconstruction image enhancement NeRF

On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen Bo Yuan Dacheng Tao school of {1. Science University of SydneySydney2008Australia Shenzhen International Graduate school Tsinghua UniversityShenzhen518055China school of {1. Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception

Age-of-information-Aware Federated Learning

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Journal of computer Science & Technology 2024年第3期39卷 637-653页

作者：徐殷肖明军吴晨吴杰周津锐孙贺 Student Member CCF 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China school of Data Science University of Science and Technology of ChinaHefei 230026China Department of computer and {1. Sciences Temple UniversityPhiladelphiaPA 19122U.S.A. CCF IEEE

Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected *** addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

关键词： federated learning Age of information restless multi-armed bandit Whittle’s index