Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative.

Abstract: PURPOSEBecause of expanding interoperability requirements, structured patient data are increasingly available in electronic health records. Many oncology data elements (eg, staging, biomarkers, doc...

Manipulating recovered software architecture views

Abstract: Automatically recovered (reverse engineered) architectural views of legacy software are valuable for carrying out many software engineering tasks. However, while directly recovered views provide some relevant information, they often are either too fragmented or complex to be really useful in practice. This paper describes methods for making views more useful. We automatically combine and/or simplify views to produce hierarchies, hybrids, and abstractions. The methods we use take advantage of the source code fragments underlying the views to cross-reference between parts from different views.

Humans and bots in internet chat: measurement, analysis, and automated classification

Abstract: The abuse of chat services by automated programs, known as chat bots, poses a serious threat to Internet users. Chat bots target popular chat networks to distribute spam and malware. In this paper, we first conduct a series of measurements on a large0 commercial chat network. Our measurements capture a total of 16 different types of chat bots ranging from simple to advanced. Moreover, we observe that human behavior is more complex than bot behavior. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from human users. The proposed classification system consists of two components: 1) an entropy-based classifier; and 2) a Bayesian-based classifier. The two classifiers complement each other in chat bot detection. The entropy-based classifier is more accurate to detect unknown chat bots, whereas the Bayesian-based classifier is faster to detect known chat bots. Our experimental evaluation shows that the proposed classification system is highly effective in differentiating bots from humans.