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Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 17th EAI Internation Honghao Gao,Xinheng Wang Conference proceedings 202

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書目名稱Collaborative Computing: Networking, Applications and Worksharing
副標(biāo)題17th EAI Internation
編輯Honghao Gao,Xinheng Wang
視頻videohttp://file.papertrans.cn/230/229417/229417.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; 17th EAI Internation Honghao Gao,Xinheng Wang Conference proceedings 202
描述This two-volume set constitutes the refereed proceedings of the 17th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually..The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; communication channels (information theory); communication systems; computer n
版次1
doihttps://doi.org/10.1007/978-3-030-92635-9
isbn_softcover978-3-030-92634-2
isbn_ebook978-3-030-92635-9Series ISSN 1867-8211 Series E-ISSN 1867-822X
issn_series 1867-8211
copyrightICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021
The information of publication is updating

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Failure” Service Pattern Mining for Exploratory Service Compositionining algorithm (FSPMA) for exploratory service composition, which extends the gSpan algorithm, and can mine “failure” service patterns from service composition processes for further reuse. Meanwhile, the exploratory service composition model and the service pattern model are explained for the FSPMA
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Attention Based Spatial-Temporal Graph Convolutional Networks for?RSU Communication Load ForecastingU. Finally, according to the forecast of the future communication load, a RSU working mode alteration scheme is proposed with respect to the safety range amongst vehicles in order to control the corresponding area communication load. Compared with other models, our model has better accuracy and perf
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