Measurement of the medial knee joint space width (JSW1)

Zhu, Jiaan; Li, Bing; Qiu, Li Peng; Liu, Hongmei; Zhang, Mi; Wang, Yuexiang; Wang, Ping; Jiao, Dan; Chen, Tao; Li, Xueling; Cui, Ligang; Shan, Yong; Luo, Baoming; Lin, Ning; Hua, Xing; Hu, Zhenlong; Hu, Yue; Tu, Bin; Zheng, Yuanyi; Chen, Shuqiang; Xu, Sheng; Mao, Jianying; Liu, Weiyong; Xiang, Minghui; Li, Jia; Chen, Jian; Tang, Yuanjiao; Chen, Siming; He, Yanni; Dai, Tao; Zhang, Shumin; Zhang, Yuanyuan; Fang, Mingdi; Hao, Shaoyun; Lin, Xiaoyan; He, Xiuzhen; Bao, Bo; Xi, Zhanguo; Peng, Xiao-Fang; Zhang, Qunxia; Du, Guoqing

doi:10.21037/asvide.2020.131

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, most approaches often focus on analyzing source code in the semantic and syntactic form and then using machine learning or deep learning algorithms to analyze and predict vulnerabilities. However, we believe that the current approaches are facing some of the following problems [3,4,5,6,7,8,9,10,11,53]:…”

Section: Problemmentioning

confidence: 99%

DrCSE: A New Framework For Software Vulnerability Detection base on An Advanced Computing

Cong,

Xuan

2023

Preprint

View full text Add to dashboard Cite

The detection of software vulnerabilities written in C and C++ languages takes a lot of attention and interest today. This paper proposes a new framework called DrCSE to improve software vulnerability detection. It uses an intelligent computation tech-nique based on the combination of two methods: rebalancing data and representation learning to analyze and evaluate the code property graph (CPG) of the source code for detecting abnormal behavior of software vulnerabilities. To do that, DrCSE performs a combination of 3 main processing techniques: i) building the source code feature profiles, ii) rebalancing data, and iii) contrastive learning. In which, the method i) extracts the source code’s features based on the vertices and edges of the CPG. The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset. Finally, contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away. The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabil-ities using the Verum dataset. As a result, the method proposed in the article has brought a pretty good performance in all metrics, especially the Precision and Recall scores of 39.35% and 69.07%, respectively, proving the eﬃciency of the DrCSE Framework. It performs better than other approaches, with a 5% boost in Precision and a 5% boost in Recall. Overall, this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date

show abstract