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Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 18th EAI Internation Honghao Gao,Xinheng Wang,Tasos Dagiuklas Conference

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發(fā)表于 2025-3-21 17:18:07 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Collaborative Computing: Networking, Applications and Worksharing
副標(biāo)題18th EAI Internation
編輯Honghao Gao,Xinheng Wang,Tasos Dagiuklas
視頻videohttp://file.papertrans.cn/230/229415/229415.mp4
叢書名稱Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engi
圖書封面Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 18th EAI Internation Honghao Gao,Xinheng Wang,Tasos Dagiuklas Conference
描述The two-volume set LNICST 460 and 461 constitutes the proceedings of the 18th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2022, held in Hangzhou, China, in October 2022.?.The 57 full papers presented in the proceedings were carefully reviewed and selected from 171 submissions. The papers are organized in the following topical sections: Recommendation System; Federated Learning and application; Edge Computing and Collaborative working; Blockchain applications; Security and Privacy Protection; Deep Learning and application; Collaborative working; Images processing and recognition..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; communication systems; computer hardware; computer networks; computer science; c
版次1
doihttps://doi.org/10.1007/978-3-031-24386-8
isbn_softcover978-3-031-24385-1
isbn_ebook978-3-031-24386-8Series ISSN 1867-8211 Series E-ISSN 1867-822X
issn_series 1867-8211
copyrightICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2022
The information of publication is updating

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Homonegativity within Medicine,r SCADA. We urgently need an effective SCADA risk assessment algorithm to quantify the value at risk. However, traditional algorithms have the shortcomings of excessive parsing variables and inefficient sampling. Existing improved algorithms are far from the optimal distribution of the sampling dens
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https://doi.org/10.1007/b136519 of distributed learning framework, enables data providers to train models locally to protect privacy. It solves the problem of privacy leakage of data by enabling multiple parties, each with their training dataset, to share the model instead of exchanging private data with the server side. However,
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https://doi.org/10.1007/b136519d, the communication privacy is difficult to be hidden. Existing anonymous systems sacrifice anonymity for efficient communication, or vice versa. In this paper, we present ., an efficient messaging system which leverages a two-layer framework to provide tracking-resistance. The first layer is the .
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https://doi.org/10.1007/b136519ting resources on demand. However, security of edge computing service is still a major concern. This paper proposes an edge computing resource allocation mechanism based on dynamic trust. First, security problems due to lack of reliability in the resource allocation process are solved based on the t
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https://doi.org/10.1007/b136519affic. On the contrary, ML-based classifiers introduce adversarial example attacks, which can fool the classifiers into giving wrong outputs with elaborately designed examples. Some adversarial attacks have been proposed to evaluate and improve the robustness of ML-based traffic classifiers. Unfortu
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