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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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樓主: 可憐
31#
發(fā)表于 2025-3-27 00:13:06 | 只看該作者
1867-8211 Learning and application; Edge Computing and Collaborative working; Blockchain applications; Security and Privacy Protection; Deep Learning and application; Collaborative working; Images processing and recognition..978-3-031-24382-0978-3-031-24383-7Series ISSN 1867-8211 Series E-ISSN 1867-822X
32#
發(fā)表于 2025-3-27 04:44:49 | 只看該作者
33#
發(fā)表于 2025-3-27 05:47:54 | 只看該作者
Collaborative Computing: Networking, Applications and Worksharing18th EAI Internation
34#
發(fā)表于 2025-3-27 09:44:31 | 只看該作者
https://doi.org/10.1007/978-3-658-35721-4strategy through the pooling operation; then, the samples are used in GNN-based recommender systems to obtain more accurate user and service embedding representations; next, calculate the inner product score embedded by the user and the service, and recommend high inner product score services to use
35#
發(fā)表于 2025-3-27 14:45:51 | 只看該作者
36#
發(fā)表于 2025-3-27 19:42:44 | 只看該作者
Sexual Diversity and the Sochi 2014 Olympicsed vote scores respectively. Furthermore, based on the representations of expert and target question, a multi-task learning model is adopted to predict the most suitable expert and his/her potential vote score, which could provide the intuitive explanation that why routes the question to the expert.
37#
發(fā)表于 2025-3-28 00:13:54 | 只看該作者
Russia: Sex, Demographics and LGBT Activism,ements and API services is performed, and the correlation between API services is calculated based on the label and description document of the API services and used as a basis for recommending API services to Mashup requirements. Secondly, edge prediction component is used to extract beneficial fea
38#
發(fā)表于 2025-3-28 04:30:00 | 只看該作者
: Sexual Diversity and New Technologies,o capture the dependence relationship between time series. Using the Attention Mechanism to obtain key features can reduce prediction errors. We propose the context-based prediction model of the number of bike-sharing on the station with LSTM and Attention Mechanism (C-LSTMAM). This model can specif
39#
發(fā)表于 2025-3-28 07:03:16 | 只看該作者
40#
發(fā)表于 2025-3-28 13:29:34 | 只看該作者
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