Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The

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书目名称Complex Networks & Their Applications XII影响因子(影响力)




书目名称Complex Networks & Their Applications XII影响因子(影响力)学科排名




书目名称Complex Networks & Their Applications XII网络公开度




书目名称Complex Networks & Their Applications XII网络公开度学科排名




书目名称Complex Networks & Their Applications XII被引频次




书目名称Complex Networks & Their Applications XII被引频次学科排名




书目名称Complex Networks & Their Applications XII年度引用




书目名称Complex Networks & Their Applications XII年度引用学科排名




书目名称Complex Networks & Their Applications XII读者反馈




书目名称Complex Networks & Their Applications XII读者反馈学科排名

AWL

Too Overloaded to Use: An Adaptive Network Model of Information Overload During Smartphone App Usagegic use of attention resources. Furthermore, external factors, such as context and influence of the environment to use the apps, are included to model the usage decision of the agent. Simulations in two scenarios illustrate that the model captures expected dynamics of the phenomenon.

缺陷

凶残

Conference proceedings 2024te on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as networ

filial

https://doi.org/10.1007/978-3-322-85109-3). The obtained insights offer an understanding of group interaction dynamics. Future research may consider larger groups and more variations of synchrony detection functions to widen the obtained findings.

Heterodoxy

https://doi.org/10.1007/978-3-662-13030-8gic use of attention resources. Furthermore, external factors, such as context and influence of the environment to use the apps, are included to model the usage decision of the agent. Simulations in two scenarios illustrate that the model captures expected dynamics of the phenomenon.

Heterodoxy

纪念

VAN

minaret

Multilingual Hate Speech Detection Using Semi-supervised Generative Adversarial NetworkT. We managed to detect hate speech in Indo-European languages (in English, German, and Hindi) using only 20% labeled data from the HASOC2019 dataset. Our approach excelled in multilingual, zero-shot cross-lingual, and monolingual paradigms, achieving, on average, a 9.23% F1 score boost and 5.75% accuracy increase over baseline mBERT model.