Archipelago 发表于 2025-3-23 10:34:30
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 infebrile 发表于 2025-3-23 15:18:12
Predicting Upvotes and Downvotes in Location-Based Social Networks Using Machine Learningswers or posts, most OSNs design “upvote” or “like” 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小样他闲聊 发表于 2025-3-23 20:28:17
How Does Participation Experience in Collective Behavior Contribute to Participation Willingness: Aparticipate. 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伦理学 发表于 2025-3-23 22:42:39
http://reply.papertrans.cn/19/1857/185686/185686_14.png多节 发表于 2025-3-24 03:10:58
http://reply.papertrans.cn/19/1857/185686/185686_15.pngFriction 发表于 2025-3-24 07:33:50
http://reply.papertrans.cn/19/1857/185686/185686_16.png共同生活 发表于 2025-3-24 12:45:57
http://reply.papertrans.cn/19/1857/185686/185686_17.pngAlveolar-Bone 发表于 2025-3-24 15:07:36
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.pineal-gland 发表于 2025-3-24 22:31:26
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.幼稚 发表于 2025-3-25 02:35:26
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