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9,574 results on '"Lin Hao"'

206. Student Learning Effect Using Flipped Classroom with WPSA Learning Mode -- An Example of Programming Design Course

Author

Kuo, Yu-Chen, Lin, Yu-Hsuan, Wang, Tao-Hua, Lin, Hao-Chiang Koong, Chen, Ju-I, and Huang, Yueh-Min

Abstract

Flipped classroom is one of the important teaching modes among many novel teaching methods in recent years, students watch the video in the pre-class. However, if students cannot focus on the pre-class video learning or have problems with the learning content, the learning effect will be less than expected. Therefore, this research proposes a flipped classroom combined with the WPAQ learning model extended by the WSQ learning model to carry out programming courses, allowing students to perform flipped classroom learning with peers online before class and jointly complete the tasks of flipped classroom learning lists, and share with them a summary of learning content to enhance learning effectiveness. The research results show that the use of flipped classrooms combined with the WPAQ learning model of the learning system can help students improve learning effectiveness, learning motivation, self-efficacy, reflective ability, programming learning attitudes, and reduce students' cognitive load.

Published
2023
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207. Developing an E-Learning Platform Capable of Being Aware of Self-Regulated Learning Behaviors of Role Models

Author

Lin, Jian-Wei, Koong Lin, Hao-Chiang, and Chen, Hong-Ren

Abstract

Conventional e-learning platforms require a high self-regulatory learning (SRL) ability to ensure learning effectiveness. However, because not everyone has high autonomy and a high SRL ability, many students quit during the online learning period. To enhance the SRL ability, many studies have developed e-learning platforms based on Zimmerman's SRL training model. However, these platforms still require learners to complete a series of provided learning tasks independently and autonomously. This article developed an e-learning platform based on Zimmerman's SRL training model by using role-model group awareness tools to visualize the learning context and activities of the group of role models in each SRL training phase for learners' observation, self-reflection, and emulation. To test the model, the learning outcomes of the two groups of students enrolled in the same course were measured experimentally. The experimental class consisted of 36 students using the proposed system, whereas the control class consisted of 35 students using a traditional e-learning platform. The experimental class spent more learning time on the platform and achieved better learning results than did the control class. Moreover, the experimental class had gradually improved assessment scores and SRL-related behaviors. The final section presents explanations, discussion, and implications derived from analytical results.

Published
2022
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208. Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection

Author

Lin, Hao, Luo, Weiqi, Wei, Kangkang, and Liu, Minglin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of the face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification the ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability.

Published
2021

231. Nondestructive technique for identifying nuclides using neutron resonance transmission analysis at CSNS Back-n

Author

Tang, Sheng-Da, Chen, Yong-Hao, Tang, Jing-Yu, Fan, Rui-Rui, Li, Qiang, Li, Gong, Liu, Dong, Jin, Zheng-Yao, Cui, Xing-Zhu, Chen, Tian-Xiang, Yang, Yi-Wei, Liu, Rong, Yi, Han, Li, Yang, Yang, Zhen, An, Qi, Bai, Hao-Fan, Bai, Jiang-Bo, Bao, Jie, Cao, Ping, Chen, Qi-Ping, Chen, Zhen, Cui, Zeng-Qi, Fan, An-Chuan, Feng, Chang-Qing, Feng, Fan-Zhen, Gao, Ke-Qing, Gu, Min-Hao, Han, Chang-Cai, Han, Zi-Jie, He, Guo-Zhu, He, Yong-Cheng, Hong, Yang, Hu, Yi-Wei, Huang, Han-Xiong, Jia, Wei-Hua, Jiang, Hao-yu, Jiang, Wei, Jiang, Zhi-jie, Kang, Ling, Li, Bo, Li, Chao, Li, Jia-Wen, Li, Xiao, Liu, Jie, Liu, Shu-Bin, Luan, Guang-Yuan, Ning, Chang-Jun, Qi, Bin-Bin, Ren, Jie, Ren, Zhi-Zhou, Ruan, Xi-Chao, Song, Zhao-Hui, Sun, Kang, Tan, Zhi-Xin, Wang, Li-Jiao, Wang, Peng-Cheng, Wang, Zhao-Hui, Wen, Zhong-Wei, Wu, Xiao-Guang, Wu, Xuan, Xie, Li-Kun, Yu, Yong-Ji, Zhang, Guo-Hui, Zhang, Lin-Hao, Zhang, Mo-Han, Zhang, Qi-Wei, Zhang, Xian-Peng, Zhang, Yu-Liang, Zhang, Yue, Zhang, Zhi-Yong, Zhao, Mao-Yuan, Zhou, Lu-Ping, Zhou, Zhi-Hao, and Zhu, Ke-Jun

Published
2024
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250. Heisenberg-Limited Waveform Estimation with Solid-State Spins in Diamond

Author

Dong, Yang, Wang, Ze-Hao, Lin, Hao-Bin, Zhang, Shao-Chun, Zheng, Yu, Chen, Xiang-Dong, Zhu, Wei, Wang, Guan-Zhong, Guo, Guang-Can, and Sun, Fang-Wen

Subjects
Quantum Physics
Abstract

The newly established Heisenberg limit in arbitrary waveform estimation is quite different with parameter estimation and shows a unique characteristic of a future quantum version of oscilloscope. However, it is still a non-trivial challenge to generate a large number of exotic quantum entangled states to achieve this quantum limit. Here, by employing the time-domain quantum difference detection method, we demonstrate Heisenberg-limited waveform quantum estimation with diamond spins under ambient condition in the experiment. Periodic dynamical decoupling is applied to enhance both the dynamic range and sensitivity by one order of magnitude. Using this quantum-enhanced estimation scheme, the estimation error of an unknown waveform is reduced by more than $5$ dB below the standard quantum limit with $N\sim{\text{2}} \times {\text{1}}{{\text{0}}^3}$ resources, where more than ${1 \times {\text{1}}{{\text{0}}^5}}$ resources would be required to achieve a similar error level using classical detection. This work provides an essential step towards realizing quantum-enhanced structure recognition in a continuous space and time., Comment: 15 pages, 8 figures

Published
2021

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