Zobrazeno 1 - 10
of 317
pro vyhledávání: '"Kim, Dohee"'
Autor:
Kim, Dohee, Lee, Unggi, Lee, Sookbun, Bae, Jiyeong, Ahn, Taekyung, Park, Jaekwon, Lee, Gunho, Kim, Hyeoncheol
This paper introduces ES-KT-24, a novel multimodal Knowledge Tracing (KT) dataset for intelligent tutoring systems in educational game contexts. Although KT is crucial in adaptive learning, existing datasets often lack game-based and multimodal eleme
Externí odkaz:
http://arxiv.org/abs/2409.10244
Autor:
Park, Jaekwon, Bae, Jiyoung, Lee, Unggi, Ahn, Taekyung, Lee, Sookbun, Kim, Dohee, Choi, Aram, Jeong, Yeil, Moon, Jewoong, Kim, Hyeoncheol
This study investigates the design, development, and evaluation of a Large Language Model (LLM)-based chatbot for teaching English conversations in an English as a Foreign Language (EFL) context. Employing the Design and Development Research (DDR), w
Externí odkaz:
http://arxiv.org/abs/2409.04987
Autor:
Lee, Unggi, Bae, Jiyeong, Jung, Yeonji, Kang, Minji, Byun, Gyuri, Lee, Yeonseo, Kim, Dohee, Lee, Sookbun, Park, Jaekwon, Ahn, Taekyung, Lee, Gunho, Kim, Hyeoncheol
Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovati
Externí odkaz:
http://arxiv.org/abs/2409.00323
The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly
Externí odkaz:
http://arxiv.org/abs/2409.10519
Most real-world variables are multivariate time series influenced by past values and explanatory factors. Consequently, predicting these time series data using artificial intelligence is ongoing. In particular, in fields such as healthcare and financ
Externí odkaz:
http://arxiv.org/abs/2408.16896
Autor:
Lee, Unggi, Bae, Jiyeong, Kim, Dohee, Lee, Sookbun, Park, Jaekwon, Ahn, Taekyung, Lee, Gunho, Stratton, Damji, Kim, Hyeoncheol
Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences of numbers as data, most existing approaches fail to leverage the ric
Externí odkaz:
http://arxiv.org/abs/2406.02893
This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric veh
Externí odkaz:
http://arxiv.org/abs/2403.11104
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 2022, 1201-1209
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced
Externí odkaz:
http://arxiv.org/abs/2306.16661
Autor:
Kim, Dohee1 (AUTHOR), Hwang, Jinsu1 (AUTHOR), Yoo, Jin2 (AUTHOR), Choi, Jiyun1 (AUTHOR), Ramalingam, Mahesh1 (AUTHOR), Kim, Seongryul1 (AUTHOR), Cho, Hyong-Ho3 (AUTHOR), Kim, Byeong C.4 (AUTHOR), Jeong, Han-Seong1 (AUTHOR) jhsjeong@hanmail.net, Jang, Sujeong1 (AUTHOR) sujeong.jjang@gmail.com
Publikováno v:
PLoS ONE. 9/6/2024, Vol. 19 Issue 9, p1-18. 18p.
The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the
Externí odkaz:
http://arxiv.org/abs/2211.16653