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FedCSA: Boosting the Convergence Speed of Federated Unlearning under Data Heterogeneity 21

FedCSA: Boosting the Convergence Speed of Federated Unlearni...

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21st IEEE international symposium on Parallel and distributed Processing with applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Zhen Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Cheng, Zeng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

The exponential growth of the Internet and the widespread availability of personal data have raised significant concerns regarding personal privacy and societal security. In response, the 'right to be forgotten' has emerged as a fundamental principle, empowering individuals to request the removal of their personal data. Though relieving the privacy concerns at a certain degree by doing so, Artificial Intelligence (AI) models, trained by personal data, still suffers from privacy leakage risks. Therefore, machine unlearning attracts great attention from both industry and academia, aiming at erasing the risks of privacy leakage of AI models. However, existing machine unlearning methods primarily operate within centralized frame-works, limiting their applicability to decentralized Federated Learning (FL) settings where data is decentralized and not directly accessible. Additionally, current federated unlearning methods only approximate data deletion, leaving a potential risk of data leakage. To address these issues, this paper presents FedCSA, The FedCSA approach adopts a divide-and-conquer strategy by clustering and slicing the users' data. Subsequently, submodels are trained on each individual slice, and their aggregation yields the global model. During the unlearning task, only the corresponding sub-model undergoes retraining, significantly reducing training time while ensuring data privacy and achieving precise data deletion. Extensive experiments are conducted on real-world datasets, including MNIST, Fashion-MNIST, and CIFAR-10, to evaluate the performance of FedCSA. The results demonstrate that FedCSA outperforms state-of-the-art machine learning methods, such as retrain and SISA, in terms of efficiency and global model performance. Our code is publicly available at https://***/ZhenWang9/FedCSA. © 2023 IEEE.

关键词： Data privacy

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Fatigue detection for public transport drivers under the normalization of epidemic prevention 21

Fatigue detection for public transport drivers under the nor...

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21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Yu, Zhe Li, Lei Xu, Lijun Chen, Kansong School of Computer Science and Information Engineering Hubei University Wuhan430062 China

ISBN: (纸本)9781665454629

Several studies have shown that fatigue driving is one of the important causes of public transport safety accidents. With the outbreak of the COVID-19, the wearing of masks by public transport drivers presents new challenges for computer-based visual fatigue detection. In order to achieve the goal of accurately capturing the landmark information of the face even when the face is occluded by a large area, we adopt the DNN-based face detection method which has the highest accuracy and the best occlusion resistance. When the driver's face is blocked, the landmark information of the blocked face can be accurately detected by using our optimized face landmark detector. The accuracy rate of landmark recognition can reach 97.80%. On this basis, we calculate the driver's eye information, mouth information and the driver's head deflection angle information in real time as the judgment indicators of the degree of fatigue to comprehensively evaluate the driver's fatigue state. And use mathematical methods to fuse indicators in real time, classify the driver's fatigue state according to the value of the fusion indicators, and adopt different early warning methods for different levels of fatigue. In addition, in order to further improve the accuracy of the detection results and exclude the influence of other facial behaviors on our fatigue judgment indicators, we propose a kinetic energy calculation formula for facial organs based on the improved optical flow method. According to the different kinetic energy of facial organs in different states, which can accurately distinguish the different behaviors of the same facial organs such as blinking and closing eyes, yawning and speaking, which significantly increases the robustness and generalization ability of the detection program. The final experimental results show that the correct rate of the method for determining the degree of fatigue of the driver and passengers can reach 98.40% and 92.30% respectively when the driver does no

关键词： Face recognition

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Application and Research of Intelligent Security System Based on NFC and Cloud computing Technology 20

Application and Research of Intelligent Security System Base...

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Jiang, Hua Shandong Vocational College of Science and Technology Department of Information Engineering Shandong Province Weifang China

ISBN: (纸本)9781665428897

With the rapid development of urbanization, community security and public security have become an important social issue. As conventional patrol methods can not effectively ensure effective supervision, this paper studies the application of NFC (Near Field Communication) technology in intelligent security system, designs and constructs a set of intelligent security system suitable for public security patrol or security patrol combined with current cloud service technology. The system can not only solve the digital problem of patrol supervision in the current public security, but also greatly improve the efficiency of security and improve the service quality of the industry through the application of intelligent technology. © 2021 IEEE.

关键词： Cloud security

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Campus safety monitoring system based on deep learning 20

Campus safety monitoring system based on deep learning

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Zhang, Qianying Hang, Jin Zhang, Boxuan Wei, Mengzhong Zhu, Quanyin Li, Xiang Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai'an China

ISBN: (纸本)9781665428897

In view of the safety hazards existing on campus, a campus safety detection system based on deep learning behavior recognition, license plate recognition and speed detection is proposed and designed. The design divides the campus safety monitoring system into three functional modules: hazard behavior identification, license plate tracking and vehicle speed measurement, and on this basis, designs the database of storage information and the warning system of speed too fast to realize the monitoring function of campus safety. © 2021 IEEE.

关键词： License plates (automobile)

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Analysis and Decision-making of Regional Economic Vitality 20

Analysis and Decision-making of Regional Economic Vitality

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Zhang, Chunan Gan, Ying Yang, Hao Wang, Bingyun Shao, Ping School of Atmospheric Science Chengdu University of Information Technology Chengdu610225 China

In order to study the impact of different leaders on the U.S. economy, this paper uses a gray correlation model to obtain the more sensitive and prominent factors among all the factors. After that, these factors are u... 详细信息

ISBN: (纸本)9781665428897

关键词： Multiple linear regression

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An Intelligent Identification and Detection Method for Forest Pests Based on YOLOv5s Algorithm 20

An Intelligent Identification and Detection Method for Fores...

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Wang, Zhihong Shi, Zhanhao Wang, Honglei Bu, Fengju Liang, Tian Zhang, Xingbo Shandong Agriculture and Engineering University China Jinan Landscape and Forestry Science Research Institute Jinan China

ISBN: (纸本)9781665428897

Accurate identification of pests is the basis of forestry pests prevention and management. This paper uses the YOLOv5s algorithm to train the pest intelligent identification model by using the cosine annealing learning rate to adjust the learning rate during the training process. The model can identify 15 common forestry pests. When their different physiological forms such as eggs, larvae, pupae and adults are concluded, the model can identify a total of 39 categories accurately. © 2021 IEEE.

关键词： Forestry

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Scaling Knowledge Discovery Services for Efficient Big Data Mining in the Cloud

Scaling Knowledge Discovery Services for Efficient Big Data ...

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2023 international Conference for Technological engineering and its applications in Sustainable Development, ICTEASD 2023

作者： Kamarajapandian, P. Al-Fatlawy, Ramy Riad Malviya, Bhupendra Pant, Ruby Senthilkumar, K.K. Sapaev, I.B. Swathi, Duggi Kommuri Pratap Reddy Institute of Technology Department of Computer Science and Engineering Telangana 501301 India The Islamic University Najaf Iraq IES College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Uttaranchal University Uttaranchal Institute of Technology Department of Mechanical Engineering Dehradun248007 India Padmavathy Engineering College Prince Shri Venkateshwara Chennai127 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

In the present period of information storm, mining significant bits of knowledge from immense datasets is of vital significance. Information disclosure administrations assume a vital part in this undertaking, helping with the extraction of significant examples, patterns, and information from enormous information. Nonetheless, with the consistently expanding size of information, guaranteeing the versatility and proficiency of these administrations is an imposing test. distributed computing has arisen as an extraordinary arrangement, offering a versatile and practical stage for conveying information revelation administrations for enormous information mining. This theoretical gives an extensive outline of the basic parts of scaling information disclosure administrations on mists to work with productive enormous information mining. The volume of information produced and put away is developing dramatically, spreading over different spaces like medical care, finance, web based business, and online entertainment. Separating significant bits of knowledge from these monstrous datasets has turned into an essential basic for associations and scientists. Information disclosure administrations envelop a scope of [10] methods, including information preprocessing, design acknowledgment, and prescient displaying, that empower the change of crude information into noteworthy information. distributed computing has ascended to the front as an optimal stage for conveying information revelation administrations. It offers unrivalled adaptability, permitting associations to saddle figuring assets on-request, making it conceivable to process huge datasets productively. Moreover, the pay-more only as costs arise model of cloud administrations gives savvy arrangements, diminishing the capital venture expected for setting up and keeping up with customary server farms. © 2023 IEEE.

关键词： Security of data

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PERMAVOST '22: Workshop on Performance engineering, Modelling, Analysis, and Visualization Strategy 22

PERMAVOST '22: Workshop on Performance Engineering, Modellin...

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31st international symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Liem, Radita Solorzano, Ana Veroneze Scully-Allison, Connor Rhein Westfal TH Aachen Aachen Germany Northeastern Univ Boston MA USA Univ Arizona Tucson AZ USA

ISBN: (纸本)9781450391993

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. The ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. The fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together.

关键词： Performance analysis and methodology performance engineering

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Political economy analysis of carbon emissions Based on empirical data from the central region 20

Political economy analysis of carbon emissions Based on empi...

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Wen, Lipeng Yin, Tingting Long, Xuemei Zheng, KengCheng School of business Guilin University of Electronic Technology Guilin China School of Finance and Taxation Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9781665428897

In order to correctly understand the influencing factors of carbon emissions in central China and the important role of government governance capacity in achieving the goal of 'carbon neutrality'. In theory, unit carbon emission is explained by Marxist axiology. In the empirical analysis, this paper selects the data from 2004 to 2017 in the central region as samples to verify the theoretical part, and finally puts forward relevant policy suggestions based on the above conclusions, so as to realize the sustainable economic growth in the central region. © 2021 IEEE.

关键词： Carbon dioxide

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Probing the Convergence of Vehicular Edge Metaverse and 6G: Blockchain-enabled Framework 18

Probing the Convergence of Vehicular Edge Metaverse and 6G: ...

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18th IEEE international Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kumar, Shree Chaudhary, Rajat Budhiraja, Ishan Bennett University School of Computer Science and Engineering Technology Greater Noida India

ISBN: (纸本)9798350391725

The convergence of vehicular networks and the Meta-verse, referred to as the Vehicular Edge Metaverse is set to revolutionize both transportation and in-car experiences. This paradigm shift envisions vehicles not only as modes of transport but also as immersive gateways to the Metaverse, offering passengers interactive and engaging experiences while on the move. The advent of 6G, with its ultra-fast speeds and minimal latency is crucial in enabling the real-time data synchronization and low-latency communication required for a seamless Vehicular Edge Metaverse experience. Additionally, blockchain technology can play a key role in ensuring secure and transparent data sharing and transactions within this ecosystem, fostering trust and enabling new business models. This paper explores the potential of this convergence, discussing the key enabling technologies such as 6G, edge computing, and blockchain, and their role in addressing the challenges of real-time synchronization, resource management, and security. Furthermore, we discuss the potential applications like augmented reality-enhanced driving assistance and immersive in-car entertainment, highlighting the transformative impact of the Vehicular Edge Metaverse on the future of transportation. © 2024 IEEE.

关键词： Blockchain

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