Titlebook: Computational Collective Intelligence; 13th International C Ngoc Thanh Nguyen,Lazaros Iliadis,Bogdan Trawiński Conference proceedings 2021

情感

Irritate

圖畫文字

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

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

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

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.

存在主義

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.

訓(xùn)誡

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

異教徒