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2,592 results on '"Zhang, XiangYang"'

5. The UNESCO OER Recommendation: Some Observations from the ICDE OER Advocacy Committee

Author

Ossiannilsson, Ebba, Cazarez, Rosa Leonor Ulloa, de Gusmão, Cristine Martins Gomes, Zhang, Xiangyang, Blomgren, Constance, Chaplin-Cheyne, Trish, and Burgos, Daniel

Abstract

In this article, ambassadors of the International Council for Open and Distance Education (ICDE) Open Educational Resources (OER) Advocacy Committee (OERAC) provide a snapshot of regional and global Open Educational Resources (OER)initiatives. This committee has been active since 2017 with membership renewed biannually. The ambassadors work to further OER awareness and understanding, to increase global recognition of OER, and provide policy support for the acceptance and application of OER. This overview highlights national and regional initiatives associated with the UNESCO OER recommendation and the five action areas that include: building capacity and leveraging OER; developing supporting policies; ensuring equity and effectiveness; encouraging sustainable OER model development; and, promoting and facilitating international collaboration. In addition, monitoring and evaluation of the action areas are suggested to be prioritized. This overview is not exhaustive, and much work remains to implement the OER Recommendation at scale, maximize its implementation, connect these recommendations to the United Nation's Sustainable Development Goals (SDGs), along with the futures of education with a new social contract for education, individuals, and the planet.

Published
2023

6. Security Analysis of CMAC in the Multi-user Model

Author

Zhang, Xiangyang, Shen, Yaobin, Wang, Lei, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mouha, Nicky, editor, and Nikiforakis, Nick, editor

Published
2025
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13. Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma

Author

Fang, Zijie, Liu, Yihan, Wang, Yifeng, Zhang, Xiangyang, Chen, Yang, Cai, Changjing, Lin, Yiyang, Han, Ying, Wang, Zhi, Zeng, Shan, Shen, Hong, Tan, Jun, and Zhang, Yongbing

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, a Multi-Biomarker Histomorphology Discoverer (Multi-Beholder) model based on the multiple instance learning (MIL) framework, to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images and slide-level biomarker status labels. Specifically, by incorporating the one-class classification into the MIL framework, accurate instance pseudo-labeling is realized for instance-level supervision, which greatly complements the slide-level labels and improves the biomarker prediction performance. Multi-Beholder demonstrates superior prediction performance and generalizability for five LGG biomarkers (AUROC=0.6469-0.9735) in two cohorts (n=607) with diverse races and scanning protocols. Moreover, the excellent interpretability of Multi-Beholder allows for discovering the quantitative and qualitative correlations between biomarker status and histomorphology characteristics. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression., Comment: 47 pages, 6 figures

Published
2023

15. Integrating Multi-modal Features for Evaluating and Predicting Sleep Status

Author

Huang, Xujie, Zhou, Huixia, Zhang, Xiangyang, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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18. Cognitive enhancing effect of rTMS combined with tDCS in patients with major depressive disorder: a double-blind, randomized, sham-controlled study

Author

Li, Xingxing, Liu, Junyao, Wei, Shuochi, Yu, Chang, Wang, Dongmei, Li, Yuchen, Li, Jiaxin, Zhuang, Wenhao, Luo, Rui-Chen-Xi, Li, Yanli, Liu, Zhiwang, Su, Yuqiu, Liu, Jimeng, Xu, Yongming, Fan, Jialin, Zhu, Guidong, Xu, Weiqian, Tang, Yiping, Yan, Hui, Cho, Raymond Y., Kosten, Thomas R., Zhou, Dongsheng, and Zhang, Xiangyang

Published
2024
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29. Peer-Review Statements

Author

Kuang, Yunshan, Zhu, Lixin, Zhang, Xiangyang, Khan, Intakhab Alam, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kuang, Yunshan, editor, Zhu, Lixin, editor, Zhang, Xiangyang, editor, and Khan, Intakhab Alam, editor

Published
2024
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31. Evidence of a large current of transcranial alternating current stimulation directly to deep brain regions

Author

Shan, Yongzhi, Wang, Hongxing, Yang, Yanfeng, Wang, Jiahao, Zhao, Wenfeng, Huang, Yuda, Wang, Huang, Han, Bing, Pan, Na, Jin, Xiukun, Fan, Xiaotong, Liu, Yunyun, Wang, Jun, Wang, Changming, Zhang, Huaqiang, Chen, Sichang, Liu, Ting, Yan, Tianyi, Si, Tianmei, Yin, Lu, Li, Xinmin, Cosci, Fiammetta, Zhang, Xiangyang, Zhang, Guanghao, Gao, Keming, and Zhao, Guoguang

Published
2023
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33. L2 Learner Cognitive Psychological Factors about Artificial Intelligence Writing Corrective Feedback

Author

Wu, LiQin, Wu, Yong, and Zhang, XiangYang

Abstract

Although the study of artificial intelligence (AI) used in language teaching and learning is increasingly prevailing, research on language two (L2) learner cognitive psychological factors about AI writing corrective feedback (WCF) is scarce. This paper explores L2 learner cognitive psychology of pigai, an AI evaluating system for English writings in China, from perspectives of perception, noticing, uptake, initiative, retention and emotion. It investigates the consistency between learner cognitive psychology about AI WCF and the expected one and probes into the correlation of learner cognitive psychological factors about AI WCF, aiming at bridging the gap between the research of AI WCF and that of L2 learner cognitive psychology. After a 5-point Likert anonymous questionnaire survey of 1952 undergraduate L2 learners in Anhui University of Finance and Economics (AUFE), the statistical data of Pearson correlation coefficient indicate that learner perception, noticing, uptake, initiative, retention and emotion are positively related in the context of AI WCF, which conforms to the early research of learner cognitive psychology about WCF. But one sample t-test reveals that learner cognitive psychology of AI WCF only occasionally or sometimes consists with the expected one. The subsequent random interviews with 15 respondents suggest that pigai WCF is beneficial to L2 writing, yet there is still much room for it to improve to be deeply integrated with human WCF.

Published
2021

41. Pseudo-siamese network image tampering localization model based on reinforced samples

Author

Jinwei WANG, Zihe ZHANG, Xiangyang LUO, Bin MA

Subjects
enhanced sample, tampering positioning, pseudo-siamese network, data augmentation, tampering image, Electronic computers. Computer science, QA75.5-76.95
Abstract

With the continuous development of the internet, an increasing number of images have been tampered with on the network, accompanied by a growing range of techniques to cover up tampering traces.However, most current detection models neglect the impact of image post-processing on tamper detection algorithms, limiting their real-life applications.To address these issues, a general image tampering location model based on enhanced samples and the pseudo-twin network was proposed.The pseudo-twin network enabled the model to learn tampering features in real images.On one hand, by applying convolution constraints, the image content was suppressed, allowing the model to focus more on residual trace information of tampering.The two-branch structure of the network facilitated the comprehensive utilization of image feature information.By utilizing enhanced samples, the model could dynamically generate the most crucial pictures for learning tamper types, enabling targeted training of the model.This approach ensured that the model converged in all directions, ultimately obtaining the global optimal model.The idea of data enhancement was employed to automatically generate abundant tampered images and corresponding masks, effectively resolving the limited tampering dataset issue.Extensive experiments were conducted on four datasets, demonstrating the feasibility and effectiveness of the proposed model in pixel-level tamper detection.Particularly on the Columbia dataset, the algorithm achieves a 33.5% increase in F1 score and a 23.3% increase in MCC score.These results indicate that the proposed model harnesses the advantages of deep learning models and significantly improves the effectiveness of tamper location detection.

Published
2024
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45. EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT

Author

Wang, Jinke, Zhang, Xiangyang, Lv, Peiqing, Zhou, Lubiao, and Wang, Haiying

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Purpose: This paper proposes a new network framework called EAR-U-Net, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. Methods: The proposed method is based on the U-Net framework. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. Results: We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, Attention U-Net, and Attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. Moreover, we also participated in the MICCIA-LiTS17 challenge, and the Dice per case score was 0.952. Conclusion: The proposed method's qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation., Comment: 26 pages

Published
2021

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