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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Contextualizing Observational Data For Modeling Human Performance, Viet Trinh
Contextualizing Observational Data For Modeling Human Performance, Viet Trinh
Electronic Theses and Dissertations
This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner …
An Architecture For High-Performance Privacy-Preserving And Distributed Data Mining, James Secretan
An Architecture For High-Performance Privacy-Preserving And Distributed Data Mining, James Secretan
Electronic Theses and Dissertations
This dissertation discusses the development of an architecture and associated techniques to support Privacy Preserving and Distributed Data Mining. The field of Distributed Data Mining (DDM) attempts to solve the challenges inherent in coordinating data mining tasks with databases that are geographically distributed, through the application of parallel algorithms and grid computing concepts. The closely related field of Privacy Preserving Data Mining (PPDM) adds the dimension of privacy to the problem, trying to find ways that organizations can collaborate to mine their databases collectively, while at the same time preserving the privacy of their records. Developing data mining algorithms for …
Scalable And Efficient Outlier Detection In Large Distributed Data Sets With Mixed-Type Attributes, Anna Koufakou
Scalable And Efficient Outlier Detection In Large Distributed Data Sets With Mixed-Type Attributes, Anna Koufakou
Electronic Theses and Dissertations
An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding …
Falconet: Force-Feedback Approach For Learning From Coaching And Observation Using Natural And Experiential Training, Gary Stein
Electronic Theses and Dissertations
Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely …
Variable Resolution & Dimensional Mapping For 3d Model Optimization, Joseph Venezia
Variable Resolution & Dimensional Mapping For 3d Model Optimization, Joseph Venezia
Electronic Theses and Dissertations
Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional …