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636 results on '"Cao, Xiaohua"'

1. ECG Signal Delineation Based on Multi-scale Channel Attention Convolutional Neural Network

Author

Liu, Mingqi, Zhao, Siyu, Zhang, Zeqing, Zhang, Jieshuo, Du, Haiman, Cao, Xiaohua, Xiong, Peng, 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, Tan, Kay Chen, Series Editor, You, Peng, editor, Liu, Shuaiqi, editor, and Wang, Jun, editor

Published
2024
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14. Adaptive selection method of desensitization algorithm based on privacy risk assessment

Author

Lijun ZU, Yalin CAO, Xiaohua MEN, Zhihui LYU, Jiawei YE, Hongyi LI, Liang ZHANG

Subjects
automatic data desensitization, privacy risk assessment, artificial intelligence, financial sensitive data, Electronic computers. Computer science, QA75.5-76.95
Abstract

The financial industry deals with a vast amount of sensitive data in its business operations.However, the conventional approach of binding financial data for desensitization and using desensitization algorithms is becoming inefficient due to the fast-paced growth of financial businesses and the proliferation of data types.Additionally, manual verification and assessment of desensitized data by security experts are time-consuming and may carry potential privacy risks due to the improper selection of desensitization algorithms.While prior research has emphasized desensitization methods and privacy-preserving technologies, limited work has been conducted on desensitization algorithms from the perspective of automation.To address this issue, an adaptive recommendation framework was propose for selecting desensitization strategies that consider various factors, such as existing privacy protection technologies, data quality requirements of business scenarios, security risk requirements of financial institutions, and data attributes.Specifically, a dual-objective evaluation function was established for privacy risk and data quality to optimize the selection of desensitization algorithm parameters for different algorithms.Furthermore, the desensitization algorithm and parameters were adaptively selected by considering the data attributes through a multi-decision factor system and desensitization effect evaluation system.Compared to traditional approaches, the proposed framework effectively tackle issues of reduced data usability and inadequate personal data privacy protection that derive from manual intervention.Testing on a dataset with multiple financial institution types, the experiments show that the proposed method achieves a recommendation accuracy exceeding 95%, while the desensitized privacy risk level differed by less than 10% from the expected level.Additionally, the recommendation efficiency is 100 times faster than expert manual processing.

Published
2023
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35. Category specificity of N170 response recovery speeds for faces and Chinese characters

Author

Ma, Xiaoli, Zhu, Cuiyin, Li, Chenglin, and Cao, Xiaohua

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Neural selectivity of N170 responses is an important phenomenon in perceptual processing; however, the recovery times of neural selective responses remain unclear. In the present study, we used an adaptation paradigm to test the recovery speeds of N170 responses to faces and Chinese characters. The results showed that recovery of N170 responses elicited by faces occurred between 1400 and 1800 ms after stimuli onset, whereas recovery of N170 responses elicited by Chinese characters occurred between 600 and 800 ms after stimuli onset. These results demonstrate category-specific recovery speeds of N170 responses involved in the processing of faces and Chinese characters.

Published
2016

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