Search

Your search keyword '"He, Jingyuan"' showing total 127 results
Start Over Author "He, Jingyuan"
127 results on '"He, Jingyuan"'

3. WebCrack: Dynamic Dictionary Adjustment for Web Weak Password Detection based on Blasting Response Event Discrimination

Author

Long, Xiang, Huang, Yan, Liu, Zhendong, Han, Lansheng, Sun, Haili, and He, Jingyuan

Subjects
Computer Science - Cryptography and Security, Computer Science - Data Structures and Algorithms
Abstract

The feature diversity of different web systems in page elements, submission contents and return information makes it difficult to detect weak password automatically. To solve this problem, multi-factor correlation detection method as integrated in the DBKER algorithm is proposed to achieve automatic detection of web weak passwords and universal passwords. It generates password dictionaries based on PCFG algorithm, proposes to judge blasting result via 4 steps with traditional static keyword features and dynamic page feature information. Then the blasting failure events are discriminated and the usernames are blasted based on response time. Thereafter the weak password dictionary is dynamically adjusted according to the hints provided by the response failure page. Based on the algorithm, this paper implements a detection system named WebCrack. Experimental results of two blasting tests on DedeCMS and Discuz! systems as well as a random backend test show that the proposed method can detect weak passwords and universal passwords of various web systems with an average accuracy rate of about 93.75%, providing security advisories for users' password settings with strong practicability., Comment: 22 pages, 6 figures, 4 tables

Published
2022

21. Wear behavior and machining quality of novel high-sharp brazed diamond abrasive core drills during drilling SiCf/SiC composite micro-holes.

Author

He, Jingyuan, Qian, Ning, Su, Honghua, Fu, Yucan, Ding, Wenfeng, and Xu, Jiuhua

Abstract

SiCf/SiC composites have promising applications in aerospace industries. SiCf/SiC composite micro-holes are important components, such as gas film holes on turbine blades. However, micro-hole machining in the SiCf/SiC composite is challenging because of its excellent mechanical and physical properties and poor machinability caused by micro-hole constraints. Therefore, a novel brazed diamond abrasive core drill was designed to achieve high self-sharpness by continuously losing blunt diamond grits and exposing sharp diamond grits. The wear and self-sharpness of the developed drill were investigated and compared with those of an electroplated diamond core drill during ultrasonic vibration-assisted drilling of SiCf/SiC composite micro-holes. The material removal behaviors of SiCf/SiC composites, including drilling forces, exit tearing, hole dimensions, hole wall quality, and the wear of core drills, were also studied. The developed core drill demonstrated reduced drilling force and a significantly prolonged lifetime. Further, it achieved superior drilling performance, drilling accuracy, and micro-hole quality. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

22. The Model 2.0 and Friends: An Interim Report

Author

Cottrell, Garrison W, Gahl, Martha, Kulkarni, Shubham, Venkatramani, Shashank, Shah, Yash, Long, Keyu, Zhi, Xuzhe, Agarwal, Shivaank, Li, Cody, He, Jingyuan, and Fischer, Thomas

Subjects
Biologically-inspired eep learning, Artificial Intelligence and Robotics, Cognitive Science, Face recognition, FFA
Abstract

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation! We look forward to the group’s suggestions for these projects.

Published
2023

28. Integrated Transfer Learning Method for Image Recognition Based on Neural Network.

Author

He, JingYuan, Yang, BaiLong, and Su, Yang

Subjects
*IMAGE recognition (Computer vision), *CONVOLUTIONAL neural networks, *MACHINE learning, *DEEP learning, *SPEECH perception
Abstract

As recognition technology advances in various fields, the problem of requiring a large number of correct image recognition is also accompanied. So as to promote the correctness of image recognition, we adopted two Convolutional Neural Network (CNN) integrated migration learning network models VGG-16 CNN and LeNet-5 CNN. Experimental results prove that the performance of image recognition in various models are improved. Also, the migration learning and convolutional neural network are used to construct a K-selection migration learning algorithm and a migration framework model whose parameters are migrated to the image data set. The use of a label data set with a large sample size, the selection of a pre-trained convolutional neural network model with a deep layer of complex network structure and the selection of an appropriate network fine-tuning depth can optimize the migration adaptation effect of the migration network. The image recognition precision of the algorithm is improved by about 3%, which further improves the migration adaptation effect. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results