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213 results on '"Shen, Liyan"'

1. Survey on Byzantine attacks and defenses in federated learning

Author

ZHAO Xiaojie, SHI Jinqiao, HUANG Mei, KE Zhenhan, and SHEN Liyan

Subjects
federated learning, Byzantine attack, defense method, attack and defense strategy, Telecommunication, TK5101-6720
Abstract

Federated learning as an emerging distributed machine learning, can solve the problem of data islands. However, due to the large-scale, distributed nature and strong autonomy of local clients, federated learning is extremely vulnerable to Byzantine attacks and the attacks are not easy to detect, which seriously damages the integrity and availability of the model. First, taking Byzantine attacks as the research object, a detailed classification and analysis of the attack principles were conducted. Secondly, guided by the classic network security defense model, federated learning defense methods were classified and analyzed from the perspective of defense mechanisms. Finally, the key issues and research challenges that need to be solved in federated learning to resist Byzantine attacks were proposed, providing new references for future relevant researchers.

Published
2024
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2. URM4DMU: an user represention model for darknet markets users

Author

Liu, Hongmeng, Zhao, Jiapeng, Huo, Yixuan, Wang, Yuyan, Liao, Chun, Shen, Liyan, Cui, Shiyao, and Shi, Jinqiao

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, 62 (Primary), 54 (Secondary), I.2.7
Abstract

Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on different markets makes it easy to aggregate user information across different platforms, which helps identify anonymous users. Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts. While recent works mainly use CNN to model the text information of posts, failing to effectively model posts whose length changes frequently in an episode. To address the above problems, we propose a model named URM4DMU(User Representation Model for Darknet Markets Users) which mainly improves the post representation by augmenting convolutional operators and self-attention with an adaptive gate mechanism. It performs much better when combined with the temporal content and the forum interaction of posts. We demonstrate the effectiveness of URM4DMU on four darknet markets. The average improvements on MRR value and Recall@10 are 22.5% and 25.5% over the state-of-the-art method respectively., Comment: 9pages

Published
2023

12. An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference

Author

Shen, Liyan, Chen, Xiaojun, Shi, Jinqiao, Dong, Ye, Fang, Binxing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Liqun, editor, Li, Ninghui, editor, Liang, Kaitai, editor, and Schneider, Steve, editor

Published
2020
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13. Privacy-Preserving Distributed Machine Learning Based on Secret Sharing

Author

Dong, Ye, Chen, Xiaojun, Shen, Liyan, Wang, Dakui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Luo, Xiapu, editor, Shen, Qingni, editor, and Xu, Zhen, editor

Published
2020
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14. The Use of Low-Cost Drone and Multi-Trait Analysis to Identify High Nitrogen Use Lines for Wheat Improvement.

Author

Shen, Liyan, Deakin, Greg, Ding, Guohui, Ali, Mujahid, Dai, Jie, Wen, Zhenjie, Pinheiro, Felipe, Zhou, Ji, and Jackson, Robert

Subjects
*FIELD research, *IMAGE sensors, *NITROGEN analysis, *WHEAT, *FERTILIZERS, *WHEAT breeding
Abstract

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Clinical Uncertainty Influences Antibiotic Prescribing for Upper Respiratory Tract Infections : A Qualitative Study of Township Hospital Physicians and Village Doctors in Rural Shandong Province, China

Author

Shen, Liyan, Wang, Ting, Yin, Jia, Sun, Qiang, Dyar, Oliver J., Shen, Liyan, Wang, Ting, Yin, Jia, Sun, Qiang, and Dyar, Oliver J.

Abstract

Objective: This study aimed to explore how clinical uncertainty influences antibiotic prescribing practices among township hospital physicians and village doctors in rural Shandong Province, China. Methods: Qualitative semi-structured interviews were conducted with 30 township hospital physicians and 6 village doctors from rural Shandong Province, China. A multi-stage random sampling method was used to identify respondents. Conceptual content analysis together with Colaizzi's method were used to generate qualitative codes and identify themes. Results: Three final thematic categories emerged during the data analysis: (1) Incidence and treatment of Upper Respiratory Tract Infections (URTIs) in township hospitals and village clinics; (2) Antibiotic prescribing practices based on the clinical experience of clinicians; (3) Influence of clinical uncertainty on antibiotic prescribing. Respondents from both township hospitals and village clinics reported that URTIs were the most common reason for antibiotic prescriptions at their facilities and that clinical uncertainty appears to be an important driver for the overuse of antibiotics for URTIs. Clinical uncertainty was primarily due to: (1) Diagnostic uncertainty (establishing a relevant diagnosis is hindered by limited diagnostic resources and capacities, as well as limited willingness of patients to pay for investigations), and (2) Insufficient prognostic evidence. As a consequence of the clinical uncertainty caused by both diagnostic and prognostic uncertainty, respondents stated that antibiotics are frequently prescribed for URTIs to prevent both prolonged courses or recurrence of the disease, as well as clinical worsening, hospital admission, or complications. Conclusion: Our study suggests that clinical uncertainty is a key driver for the overuse and misuse of prescribing antibiotics for URTIs in both rural township hospitals and village clinics in Shandong province, China, and that interventions to reduce clinical un

Published
2023
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37. The development of a vision-based phenotypic analysis algorithm for measuring spikelet-related traits in rice

Author

Wang, Dong, Jiawei, Chen, Shen, Liyan, Wang, A-hong, and Zhou, Ji

Subjects
food and beverages
Abstract

Rice (Oryza sativa) and its production is of great importance to China’s food security. To assess its yield potential and grain quality, spikelet-related traits such as the number of spikelets per spike, seed coat color, spikelet length, width and length, and width ratio (W/L ratio) can be employed as key phenotypic evidence. Nevertheless, the present methods for spikelet-related trait analysis are still largely relying on manual counting or various post-harvest grain testing equipment, which is time-consuming, prone to error, and costly. Here, we present an original phenotypic analysis algorithm that can analyze key spikelet-based traits based on images collected by high-definition cameras, photo scanners, or smartphones, under both indoor and outdoor conditions. The vision-based algorithm combines automated image processing and deep learning techniques with domain knowledge in rice spikelet measurement, which does not require any prerequisites such as threshing or post-harvest processing. Besides the number of spikelets per spike, the algorithm can quickly detect and measure complete rice spikelets from the acquired images, quantifying spikelet-related traits such as grain length, grain width, W/L ratio, circularity, and grain coat color. We chose 18 rice varieties to verify the algorithm using images collected from indoor and in-field settings. Based on the trait analysis results, we examined correlations (i.e. the coefficient of determination, R2) between the computational and manual scoring for traits such as the number of spikelets per spike and concluded significant positive correlations: R2 =0.95 (P

Published
2022

40. ABNN 2

Author

Shen, Liyan, primary, Dong, Ye, additional, Fang, Binxing, additional, Shi, Jinqiao, additional, Wang, Xuebin, additional, Pan, Shengli, additional, and Shi, Ruisheng, additional

Published
2022
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49. The Effectiveness of an Educational Intervention on Knowledge, Attitudes and Reported Practices on Antibiotic Use in Humans and Pigs: A Quasi-Experimental Study in Twelve Villages in Shandong Province, China

Author

Shen, Liyan, primary, James Dyar, Oliver, additional, Sun, Qiang, additional, Wei, Xiaolin, additional, Yang, Ding, additional, Sun, Chengtao, additional, Wang, Yang, additional, Li, Hongyu, additional, Liu, Yuqing, additional, Luo, Yanbo, additional, Yin, Jia, additional, and Stålsby Lundborg, Cecilia, additional

Published
2021
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