情感
发表于 2025-3-26 23:38:06
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Irritate
发表于 2025-3-27 04:34:34
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图画文字
发表于 2025-3-27 05:52:24
0302-9743 ; natural language processing; Internet of Things: technologies and applications; Internet of Things and computational technologies for collective intelligence; computational intelligence for multimedia understanding..978-3-030-88080-4978-3-030-88081-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
cloture
发表于 2025-3-27 12:13:15
https://doi.org/10.1007/978-3-642-29618-5l information to the learning process. In addition, we also optimized the loss function by using the unlimited loss function. Experiments reflect the suggestions increasing the accuracy of the original model on the standard datasets.
幻想
发表于 2025-3-27 13:54:41
https://doi.org/10.1007/978-3-642-29621-5sness exemplified by conspiracy theories. We review the global discussion of the COVID-19 pandemic to illustrate healthy and unhealthy forms of noospheric consciousness. We then argue for the need to promote the healthy form via the modelling of the dynamics of idea propagation and the dissemination of narratives promoting open conversation.
傻瓜
发表于 2025-3-27 18:33:41
RotatHS: Rotation Embedding on the Hyperplane with Soft Constraints for Link Prediction on Knowledgel information to the learning process. In addition, we also optimized the loss function by using the unlimited loss function. Experiments reflect the suggestions increasing the accuracy of the original model on the standard datasets.
存在主义
发表于 2025-3-28 01:35:41
Collective Consciousness Supported by the Web: Healthy or Toxic?sness exemplified by conspiracy theories. We review the global discussion of the COVID-19 pandemic to illustrate healthy and unhealthy forms of noospheric consciousness. We then argue for the need to promote the healthy form via the modelling of the dynamics of idea propagation and the dissemination of narratives promoting open conversation.
训诫
发表于 2025-3-28 02:11:36
Negative Sampling for Knowledge Graph Completion Based on Generative Adversarial Networkor positive sample. In this paper, we apply the generative adversarial network to the ConvKB method to generate negative samples, thereby producing a better graph embedding. Experiments show that our approach has quality improvement compared to the original method on well-known datasets.
透明
发表于 2025-3-28 07:58:24
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异教徒
发表于 2025-3-28 11:32:15
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