Automatic Analysis and Reasoning Based on Vulnerability Knowledge Graph

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Download Full-text

On Intelligent Fire Drawings Review Based on Building Information Modeling and Knowledge Graph

Construction Research Congress 2020 ◽

10.1061/9780784482865.086 ◽

2020 ◽

Author(s):

Jia Wang ◽

Lei Mu ◽

Jiansong Zhang ◽

Xiaoping Zhou ◽

Jibao Li

Keyword(s):

Building Information Modeling ◽

Information Modeling ◽

Knowledge Graph ◽

Building Information

Download Full-text

An automatic analysis system for online hotel reviews

Information and Communication Technology for Education ◽

10.2495/icte130071 ◽

2014 ◽

Cited By ~ 1

Author(s):

Phichayasini Kitwatthanathawon ◽

Thara Angskun ◽

Jitimon Angskun

Keyword(s):

Automatic Analysis ◽

Analysis System

Download Full-text

Research on Construction of Knowledge Graph of Intestinal Cells

Journal of Artificial Intelligence for Medical Sciences ◽

10.2991/jaims.d.200902.001 ◽

2020 ◽

Author(s):

Fengfeng He ◽

Ling Zhang ◽

Wei Qu ◽

Chong Teng ◽

Dan Xie

Keyword(s):

Knowledge Graph ◽

Intestinal Cells ◽

Construction Of Knowledge

Download Full-text

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

Journal of Computer Science Research ◽

10.30564/jcsr.v2i2.1765 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Suzanna Schmeelk ◽

Lixin Tao

Keyword(s):

Data Storage ◽

Program Analysis ◽

Web Application ◽

Security Analysis ◽

Knowledge Graph ◽

Healthcare Applications ◽

Sensitive Data ◽

Knowledge Graphs ◽

Mobile Malware Detection ◽

Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate. Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention. This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP). OWASP maintains lists of the top ten security threats to web and mobile applications. We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code. We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.” We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

Download Full-text