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Titlebook: Computer Supported Cooperative Work and Social Computing; 17th CCF Conference, Yuqing Sun,Tun Lu,Bowen Du Conference proceedings 2023 The E

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11#
發(fā)表于 2025-3-23 13:18:59 | 只看該作者
12#
發(fā)表于 2025-3-23 16:44:17 | 只看該作者
Community Evolution Tracking Based on?Core Node Extension and?Edge Variation Discerningf dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matchin
13#
發(fā)表于 2025-3-23 18:40:18 | 只看該作者
14#
發(fā)表于 2025-3-23 23:07:06 | 只看該作者
15#
發(fā)表于 2025-3-24 03:55:20 | 只看該作者
An Intelligent Mobile System for Monitoring Relapse of Depressionpse patients fail to receive timely treatment. Aiming to provide a self-monitoring means in home environment and daily life, this paper studied the machine learning and natural language processing technologies for extracting the patient’s acoustic features and semantic features from the designed spe
16#
發(fā)表于 2025-3-24 08:20:57 | 只看該作者
17#
發(fā)表于 2025-3-24 13:04:37 | 只看該作者
Ramp Merging of Connected Vehicle With Virtual Platooning Control merge area is prone to stop and congestion, which is the bottleneck area affecting the road capacity. Intelligent connected vehicles with strong detection and perception and network interaction ability can effectively cooperate with traffic participants, to achieve effective regulation of traffic f
18#
發(fā)表于 2025-3-24 18:41:10 | 只看該作者
Community Detection Based on?Enhancing Graph Autoencoder with?Node Structural Rolency matrix is the most common form of network representation, but it only represents the direct connection relationship of network nodes and lacks more useful topological information. Existing approaches, such as jaccard coefficient for topology extraction, are still limited to neighborhoods, and th
19#
發(fā)表于 2025-3-24 19:30:20 | 只看該作者
https://doi.org/10.1007/978-3-030-55004-2Firstly, a set of phase space features are obtained from PM2.5 historical data based on PSR technique and combined with numerical weather prediction (NWP) data to construct a pool of candidate features. Secondly, the optimal feature subset is selected and input to the multi-step ahead prediction mod
20#
發(fā)表于 2025-3-25 01:39:56 | 只看該作者
https://doi.org/10.1007/978-3-030-55004-2ledge from the flight training comments can help improve the flight training level. To this end, a joint extraction framework is proposed in this paper. For the model input, we use an end-to-end grid tagging scheme as the decoding algorithm to extract aspect terms, opinion terms and opinion pairs si
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