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Titlebook: Big Data and Social Computing; 7th China National C Xiaofeng Meng,Qi Xuan,Zi-Ke Zhang Conference proceedings 2022 The Editor(s) (if applica

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樓主: Odious
11#
發(fā)表于 2025-3-23 10:34:30 | 只看該作者
Identifying Spammers by?Completing the?Ratings of?Low-Degree Usersby these spammers do not match the quality of items, confusing the boundaries of good and bad items and seriously endangering the real interests of merchants and normal users. To eliminate the malicious influence caused by these spammers, many effective spamming detection algorithms are proposed in
12#
發(fā)表于 2025-3-23 15:18:12 | 只看該作者
Predicting Upvotes and?Downvotes in?Location-Based Social Networks Using Machine Learningswers or posts, most OSNs design “upvote” or “l(fā)ike” buttons, and some of them provide “downvote” or “dislike” buttons as well. While there are some existing works making predictions related to upvote, downvote prediction has never been systematically explored in OSNs before. However, downvote is jus
13#
發(fā)表于 2025-3-23 20:28:17 | 只看該作者
How Does Participation Experience in Collective Behavior Contribute to Participation Willingness: A participate. Based on a survey of migrant workers from Shenzhen in China, this study constructs a mediated moderating model, focusing on the moderating role of social networks in the relationship and the mediating role of institutional support. The results show that collective behavior participatio
14#
發(fā)表于 2025-3-23 22:42:39 | 只看該作者
15#
發(fā)表于 2025-3-24 03:10:58 | 只看該作者
16#
發(fā)表于 2025-3-24 07:33:50 | 只看該作者
17#
發(fā)表于 2025-3-24 12:45:57 | 只看該作者
18#
發(fā)表于 2025-3-24 15:07:36 | 只看該作者
Research on?Network Invulnerability and?Its Application on?AS-Level Internet Topologyi-attributes. Finally, we conduct vulnerability analysis experiments on five real datasets to verify the validity of our method. Specially, we apply the method to autonomous systems (AS) Internet networks for different countries, which is of great significance to developing network security.
19#
發(fā)表于 2025-3-24 22:31:26 | 只看該作者
FedDFA: Dual-Factor Aggregation for?Federated Driver Distraction Detectionis, FedDFA is introduced, which calculates the aggregation weights based on the number of images and that of drivers on each client for better parameter aggregation during federated learning. Extensive experiments are conducted and experimental results show that FedDFA achieves satisfactory performance.
20#
發(fā)表于 2025-3-25 02:35:26 | 只看該作者
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