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577 results on '"Zhang, Jingqi"'

4. Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report

Author

Qiu, Jielin, Zhu, Jiacheng, Liu, Shiqi, Han, William, Zhang, Jingqi, Duan, Chaojing, Rosenberg, Michael, Liu, Emerson, Weber, Douglas, and Zhao, Ding

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

Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest, most current studies focus solely on classification or regression tasks, which overlook a crucial aspect of clinical cardio-disease diagnosis: the diagnostic report generated by experienced human clinicians. In this paper, we introduce a novel approach to ECG interpretation, leveraging recent breakthroughs in Large Language Models (LLMs) and Vision-Transformer (ViT) models. Rather than treating ECG diagnosis as a classification or regression task, we propose an alternative method of automatically identifying the most similar clinical cases based on the input ECG data. Also, since interpreting ECG as images is more affordable and accessible, we process ECG as encoded images and adopt a vision-language learning paradigm to jointly learn vision-language alignment between encoded ECG images and ECG diagnosis reports. Encoding ECG into images can result in an efficient ECG retrieval system, which will be highly practical and useful in clinical applications. More importantly, our findings could serve as a crucial resource for providing diagnostic services in underdeveloped regions., Comment: Accepted to the ML4H 2023 Proceedings track

Published
2023

5. Physicochemical Remediation of Soil Contamination: From Laboratory to Field

Author

Wang, Hanwen, Zhang, Jingqi, Huang, Hai, Wang, Xingrun, Yang, Xiaojin, Coulon, Frederic, de Boer, Jacob, Editorial Board Member, Barceló, Damià, Series Editor, Garrigues, Philippe, Editorial Board Member, Kostianoy, Andrey G., Series Editor, Gu, Ji-Dong, Editorial Board Member, Hutzinger, Otto, Founding Editor, Jones, Kevin C., Editorial Board Member, Negm, Abdelazim M., Editorial Board Member, Newton, Alice, Editorial Board Member, Nghiem, Duc Long, Editorial Board Member, Garcia-Segura, Sergi, Editorial Board Member, Verlicchi, Paola, Editorial Board Member, Wagner, Stephan, Editorial Board Member, Rocha-Santos, Teresa, Editorial Board Member, Picó, Yolanda, Editorial Board Member, Ortega-Calvo, Jose Julio, editor, and Coulon, Frederic, editor

Published
2024
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6. Measurement and Evaluation of Mental Workload Based on Complex Human–Machine Interaction Tasks

Author

Li, Kuntong, Yang, Chenjie, Li, Jiaying, Zhang, Jingqi, Yang, Meng, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A.M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Chien, Chen-Fu, editor, Dou, Runliang, editor, and Luo, Li, editor

Published
2024
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7. A Survey of Security Vulnerabilities and Detection Methods for Smart Contracts

Author

Zhang, Jingqi, Zhang, Xin, Liu, Zhaojun, Fu, Fa, Nie, Jianyu, Huang, Jianqiang, Dreibholz, Thomas, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Zhang, Yonghong, editor, Qi, Lianyong, editor, Liu, Qi, editor, Yin, Guangqiang, editor, and Liu, Xiaodong, editor

Published
2024
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25. OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints

Author

Han, Qing, Yang, Shusen, Ren, Xuebin, Zhao, Cong, Zhang, Jingqi, and Yang, Xinyu

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Distributed machine learning (ML) at network edge is a promising paradigm that can preserve both network bandwidth and privacy of data providers. However, heterogeneous and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of Edge Learning (i.e. edge-cloud collaborative machine learning). In this article, we propose a novel framework of 'learning to learn' for effective Edge Learning (EL) on heterogeneous edges with resource constraints. We first model the dynamic determination of collaboration strategy (i.e. the allocation of local iterations at edge servers and global aggregations on the Cloud during collaborative learning process) as an online optimization problem to achieve the tradeoff between the performance of EL and the resource consumption of edge servers. Then, we propose an Online Learning for EL (OL4EL) framework based on the budget-limited multi-armed bandit model. OL4EL supports both synchronous and asynchronous learning patterns, and can be used for both supervised and unsupervised learning tasks. To evaluate the performance of OL4EL, we conducted both real-world testbed experiments and extensive simulations based on docker containers, where both Support Vector Machine and K-means were considered as use cases. Experimental results demonstrate that OL4EL significantly outperforms state-of-the-art EL and other collaborative ML approaches in terms of the trade-off between learning performance and resource consumption., Comment: 7 pages, 5 figures, to appear in IEEE Communications Magazine

Published
2020

34. The Influence of Live Commerce on the Impulsive Consumption of Young People

Author

Huang, Zhixuan, Zhang, Jingqi, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Jiang, Yushi, editor, Shvets, Yuriy, editor, and Mallick, Hrushikesh, editor

Published
2022
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35. Problems and Countermeasures of Evacuation in University Canteen in Emergencies

Author

Zhang, Jingqi, Zhang, Meng, Tian, Naixu, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Ali, Ghaffar, editor, Birkök, Mehmet Cüneyt, editor, and Khan, Intakhab Alam, editor

Published
2022
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