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  Partner: UNT Libraries
 Department: Department of Computer Science
 Degree Level: Doctoral
Using Normal Deduction Graphs in Common Sense Reasoning

Using Normal Deduction Graphs in Common Sense Reasoning

Date: May 1992
Creator: Munoz, Ricardo A. (Ricardo Alberto)
Description: This investigation proposes a powerful formalization of common sense knowledge based on function-free normal deduction graphs (NDGs) which form a powerful tool for deriving Horn and non-Horn clauses without functions. Such formalization allows common sense reasoning since it has the ability to handle not only negative but also incomplete information.
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Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design

Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design

Date: December 1993
Creator: Halverson, Ranette Hudson
Description: The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching.
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Multiresolutional/Fractal Compression of Still and Moving Pictures

Multiresolutional/Fractal Compression of Still and Moving Pictures

Date: December 1993
Creator: Kiselyov, Oleg E.
Description: The scope of the present dissertation is a deep lossy compression of still and moving grayscale pictures while maintaining their fidelity, with a specific goal of creating a working prototype of a software system for use in low bandwidth transmission of still satellite imagery and weather briefings with the best preservation of features considered important by the end user.
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A Theoretical Network Model and the Incremental Hypercube-Based Networks

A Theoretical Network Model and the Incremental Hypercube-Based Networks

Date: May 1995
Creator: Mao, Ai-sheng
Description: The study of multicomputer interconnection networks is an important area of research in parallel processing. We introduce vertex-symmetric Hamming-group graphs as a model to design a wide variety of network topologies including the hypercube network.
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Practical Cursive Script Recognition

Practical Cursive Script Recognition

Date: August 1995
Creator: Carroll, Johnny Glen, 1953-
Description: This research focused on the off-line cursive script recognition application. The problem is very large and difficult and there is much room for improvement in every aspect of the problem. Many different aspects of this problem were explored in pursuit of solutions to create a more practical and usable off-line cursive script recognizer than is currently available.
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Convexity-Preserving Scattered Data Interpolation

Convexity-Preserving Scattered Data Interpolation

Date: December 1995
Creator: Leung, Nim Keung
Description: Surface fitting methods play an important role in many scientific fields as well as in computer aided geometric design. The problem treated here is that of constructing a smooth surface that interpolates data values associated with scattered nodes in the plane. The data is said to be convex if there exists a convex interpolant. The problem of convexity-preserving interpolation is to determine if the data is convex, and construct a convex interpolant if it exists.
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A Machine Learning Method Suitable for Dynamic Domains

A Machine Learning Method Suitable for Dynamic Domains

Date: July 1996
Creator: Rowe, Michael C. (Michael Charles)
Description: The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. ...
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Automatic Speech Recognition Using Finite Inductive Sequences

Automatic Speech Recognition Using Finite Inductive Sequences

Date: August 1996
Creator: Cherri, Mona Youssef, 1956-
Description: This dissertation addresses the general problem of recognition of acoustic signals which may be derived from speech, sonar, or acoustic phenomena. The specific problem of recognizing speech is the main focus of this research. The intention is to design a recognition system for a definite number of discrete words. For this purpose specifically, eight isolated words from the T1MIT database are selected. Four medium length words "greasy," "dark," "wash," and "water" are used. In addition, four short words are considered "she," "had," "in," and "all." The recognition system addresses the following issues: filtering or preprocessing, training, and decision-making. The preprocessing phase uses linear predictive coding of order 12. Following the filtering process, a vector quantization method is used to further reduce the input data and generate a finite inductive sequence of symbols representative of each input signal. The sequences generated by the vector quantization process of the same word are factored, and a single ruling or reference template is generated and stored in a codebook. This system introduces a new modeling technique which relies heavily on the basic concept that all finite sequences are finitely inductive. This technique is used in the training stage. In order to accommodate the variabilities ...
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A Unifying Version Model for Objects and Schema in Object-Oriented Database System

A Unifying Version Model for Objects and Schema in Object-Oriented Database System

Date: August 1997
Creator: Shin, Dongil
Description: There have been a number of different versioning models proposed. The research in this area can be divided into two categories: object versioning and schema versioning. In this dissertation, both problem domains are considered as a single unit. This dissertation describes a unifying version model (UVM) for maintaining changes to both objects and schema. UVM handles schema versioning operations by using object versioning techniques. The result is that the UVM allows the OODBMS to be much smaller than previous systems. Also, programmers need know only one set of versioning operations; thus, reducing the learning time by half. This dissertation shows that UVM is a simple but semantically sound and powerful version model for both objects and schema.
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Exon/Intron Discrimination Using the Finite Induction Pattern Matching Technique

Exon/Intron Discrimination Using the Finite Induction Pattern Matching Technique

Date: December 1997
Creator: Taylor, Pamela A., 1941-
Description: DNA sequence analysis involves precise discrimination of two of the sequence's most important components: exons and introns. Exons encode the proteins that are responsible for almost all the functions in a living organism. Introns interrupt the sequence coding for a protein and must be removed from primary RNA transcripts before translation to protein can occur. A pattern recognition technique called Finite Induction (FI) is utilized to study the language of exons and introns. FI is especially suited for analyzing and classifying large amounts of data representing sequences of interest. It requires no biological information and employs no statistical functions. Finite Induction is applied to the exon and intron components of DNA by building a collection of rules based upon what it finds in the sequences it examines. It then attempts to match the known rule patterns with new rules formed as a result of analyzing a new sequence. A high number of matches predict a probable close relationship between the two sequences; a low number of matches signifies a large amount of difference between the two. This research demonstrates FI to be a viable tool for measurement when known patterns are available for the formation of rule sets.
Contributing Partner: UNT Libraries
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