An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

Investigating Automatic Static Analysis Results to Identify Quality Problems: an Inductive Study

Background: Automatic static analysis (ASA) tools examine source code to discover "issues", i.e. code patterns that are symptoms of bad programming practices and that can lead to defective behavior. Studies in the literature have shown that these tools find defects earlier than other verification activities, but they produce a substantial number of false positive warnings. For this reason, an alternative approach is to use the set of ASA issues to identify defect prone files and components rather than focusing on the individual issues. Aim: We conducted an exploratory study to investigate whether ASA issues can be used as early indicators of faulty files and components and, for the first time, whether they point to a decay of specific software quality attributes, such as maintainability or functionality. Our aim is to understand the critical parameters and feasibility of such an approach to feed into future research on more specific quality and defect prediction models. Method: We analyzed an industrial C# web application using the Resharper ASA tool and explored if significant correlations exist in such a data set. Results: We found promising results when predicting defect-prone files. A set of specific Resharper categories are better indicators of faulty files than common software metrics or the collection of issues of all issue categories, and these categories correlate to different software quality attributes. Conclusions: Our advice for future research is to perform analysis on file rather component level and to evaluate the generalizability of categories. We also recommend using larger datasets as we learned that data sparseness can lead to challenges in the proposed analysis proces

Air Force Institute of Technology Research Report 2016

This Research Report presents the FY16 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Information Outlook, October 2004

Volume 8, Issue 10https://scholarworks.sjsu.edu/sla_io_2004/1009/thumbnail.jp