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Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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書目名稱Web and Big Data
副標題8th International Jo
編輯Wenjie Zhang,Anthony Tung,Hongjie Guo
視頻videohttp://file.papertrans.cn/1022/1021663/1021663.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and
描述.The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024...The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions...The papers are organized in the following topical sections:.Part I:?Natural language processing,?Generative AI and LLM,?Computer Vision and?Recommender System...Part II:?Recommender System,?Knowledge Graph and Spatial and Temporal Data...Part III:?Spatial and Temporal Data,?Graph Neural Network,?Graph Mining and?Database System and Query Optimization...Part IV:?Database System and Query Optimization,?Federated and Privacy-Preserving Learning,?Network, Blockchain and Edge computing,?Anomaly Detection and Security..Part V:?Anomaly Detection and Security,?Information Retrieval,?Machine Learning,?Demonstration Paper and?Industry Paper..
出版日期Conference proceedings 2024
關(guān)鍵詞Machine Learning; Data mining; Graph data, RDF, social networks; Natural language processing; Knowledge
版次1
doihttps://doi.org/10.1007/978-981-97-7238-4
isbn_softcover978-981-97-7237-7
isbn_ebook978-981-97-7238-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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https://doi.org/10.1007/978-981-97-7238-4Machine Learning; Data mining; Graph data, RDF, social networks; Natural language processing; Knowledge
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Temporalformer: A Temporal Decomposition Causal Transformer Network For Wind Power Forecasting Time2Vec instead of the traditional sine and cosine positional encoding to better capture temporal information. Meanwhile, a temporal causal convolutional network (TCN) is added to the multi-head attention mechanism, which helps to enhance the ability of the Transformer network to extract local tem
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Dynamic-Static Fusion for Spatial-Temporal Anomaly Detection and Interpretation in Multivariate Timetegrate them into an effective spatial-temporal conditional constraints through graph convolution operations. During this process, we design a Dual Temporal Graph Attention Module and a Graph Neural Ordinary Differential Equations Module to capture non-stationary temporal features and fully continuo
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