Physical Sciences and Mathematics Commons^™

Discrete Nondeterministic Modeling Of Biochemical Networks, John R. Jack

Doctoral Dissertations

The ideas expressed in this work pertain to biochemical modeling. We explore our technique, the Nondeterministic Waiting Time algorithm, for modeling molecular signaling cascades. The algorithm is presented with pseudocode along with an explanation of its implementation. The entire source code can be found in the Appendices. This algorithm builds on earlier work from the lab of Dr. Andrei Nun, the advisor for this dissertation. We discuss several important extensions including: (i) a heap with special maintenance functions for sorting reaction waiting times, (ii) a nondeterministic component for handling reaction competition, and (iii) a memory enhancement allowing slower reactions to …

Text Summarization Using Concept Hierarchy, Xiaomei Huang

Doctoral Dissertations

This dissertation aims to create new sentences to summarize text documents. In addition to generating new sentences, this project also generates new concepts and extracts key sentences to summarize documents. This project is the first research work that can generate new key concepts and can create new sentences to summarize documents.

Automatic document summarization is the process of creating a condensed version of the document. The condensed version extracts the key contents from the original document. Most related research uses statistical methods that generate a summary based on word distribution in the document. In this dissertation, we create a summary …

Naïve Bayes And Similarity Based Methods For Identifying Computer Users Using Keystroke Patterns, Shrijit S. Joshi

Doctoral Dissertations

In this dissertation, we present two methods for identifying computer users using keystroke patterns. In the first method "Competition between naïve Bayes models for user identification," a naïve Bayes model is created for each user. In the training phase of this method, the model of a user is trained using maximum likelihood estimation on the key press latency values extracted from the texts typed by the user. In the user identification phase of this method, for each user we determine the probabilistic likelihood that the typed text belongs to a user. Finally, the typed text is assigned to the user …