Zobrazeno 1 - 10
of 977
pro vyhledávání: '"I Ta"'
Publikováno v:
SAGE Open, Vol 14 (2024)
This systematic review examines the efficacy of the Suzuki music teaching method in cultivating instrumental skills through experimental research. The research indicates that the Suzuki music teaching method accommodates various instruments for stude
Externí odkaz:
https://doaj.org/article/17c545b4bb0c46c5a041c4353fe4c846
From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on th
Externí odkaz:
http://arxiv.org/abs/2410.03151
Autor:
Wang, Yu Guo, Wang, I Ta
Publikováno v:
Education + Training, 2024, Vol. 66, Issue 1, pp. 35-53.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ET-05-2023-0171
Autor:
Tseng, Chien-Fu, Lee, I-Ta, Wu, Sheng-Han, Chen, Hsin-Ming, Mine, Yuichi, Peng, Tzu-Yu, Kok, Sang-Heng
Publikováno v:
In Journal of Dental Sciences October 2024 19(4):2018-2026
Publikováno v:
In Solid State Ionics August 2024 411
Publikováno v:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Understanding narrative text requires capturing characters' motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal-states of characters in a story. We explicitly model entities, their in
Externí odkaz:
http://arxiv.org/abs/2104.07079
Autor:
Liu, Sheng-Yuan, Su, Yu-Nung, Zinchenko, Igor, Wang, Kuo-Song, Meyer, Dominique M. -A., Wang, Yuan, Hsieh, I-Ta
The massive young stellar object S255IR NIRS3 embedded in the star forming core SMA1 has been recently observed with a luminosity burst, which is conjectured as a disc-mediated variable accretion event. In this context, it is imperative to characteri
Externí odkaz:
http://arxiv.org/abs/2010.09199
While applications of machine learning in cyber-security have grown rapidly, most models use manually constructed features. This manual approach is error-prone and requires domain expertise. In this paper, we design a self-supervised sequence-to-sequ
Externí odkaz:
http://arxiv.org/abs/2003.10639
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