23 Matching Results

Search Results

A Multi-Time Scale Learning Mechanism for Neuromimic Processing

Description: Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these problems. The adaptrode is shown to emulate the dynamics of real biological synapses and represents a significant departure from the classical weighted input scheme of conventional artificial neural networks. Indeed the adaptrode is shown, by analysis of the deep structure of real synapses, to have a strong structural correspondence with the latter in terms of multi-time scale biophysical processes. Simulations of an adaptrode-based neuron and a small network of neurons are shown to have the same learning capabilities as invertebrate animals in classical conditioning. Classical conditioning is considered a fundamental learning task in animals. Furthermore, it is subject to temporal ordering constraints that fulfill the criteria of causal relations in natural systems. It may offer clues to the learning of causal relations and mechanisms for causal reasoning. The adaptrode is shown to solve an advanced problem in classical conditioning that addresses the problem of real world dynamics. A network is able to learn multiple, contrary associations that separate in time domains, that is a long-term memory can co-exist with a short-term contrary memory without destroying the former. This solves the problem of how to deal with meaningful transients while maintaining long-term memories. Possible applications of adaptrode-based neural networks are explored and suggestions for future research are made.
Date: August 1994
Creator: Mobus, George E. (George Edward)
Partner: UNT Libraries

A Highly Fault-Tolerant Distributed Database System with Replicated Data

Description: Because of the high cost and impracticality of a high connectivity network, most recent research in transaction processing has focused on a distributed replicated database system. In such a system, multiple copies of a data item are created and stored at several sites in the network, so that the system is able to tolerate more crash and communication failures and attain higher data availability. However, the multiple copies also introduce a global inconsistency problem, especially in a partitioned network. In this dissertation a tree quorum algorithm is proposed to solve this problem, imposing a logical tree structure along with dynamic system reconfiguration on all the copies of each data item. The proposed algorithm can be viewed as a dynamic voting technique which, with the help of an appropriate concurrency control algorithm, exhibits the major advantages of quorum-based replica control algorithms and of the available copies algorithm, so that a single copy is read for a read operation and a quorum of copies is written for a write operation. In addition, read and write quorums are computed dynamically and independently. As a result expensive read operations, like those that require several copies of a data item to be read in most quorum schemes, are eliminated. Furthermore, the message costs of read and write operations are reduced by the use of smaller quorum sizes. Quorum sizes can be reduced to a constant in a lightly loaded system, and log n in a failure-free network, as well as [n +1/2] in a partitioned network in a heavily loaded system. On average, our algorithm requires fewer messages than the best known tree quorum algorithm, while still maintaining the same upper bound on quorum size. One-copy serializability is guaranteed with higher data availability and highest degree of fault tolerance (up to n - 1 site ...
Date: December 1994
Creator: Lin, Tsai S. (Tsai Shooumeei)
Partner: UNT Libraries

Recognition of Face Images

Description: The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black ...
Date: December 1994
Creator: Pershits, Edward
Partner: UNT Libraries

The Contributions of Grace Murray Hopper to Computer Science and Computer Education

Description: This study explored the life and work of the late Grace Murray Hopper, Rear Admiral United States Naval Reserve. The study emphasized Hopper's contributions to computer science and computer science education, including her philosophy of teaching and learning, and her pedagogical legacy for today's teachers and scholars of computer science and computer science education.
Date: May 1994
Creator: Mitchell, Carmen L. (Carmen Lois)
Partner: UNT Libraries

A Mechanism for Facilitating Temporal Reasoning in Discrete Event Simulation

Description: This research establishes the feasibility and potential utility of a software mechanism which employs artificial intelligence techniques to enhance the capabilities of standard discrete event simulators. As background, current methods of integrating artificial intelligence with simulation and relevant research are briefly reviewed.
Date: May 1992
Creator: Legge, Gaynor W.
Partner: UNT Libraries

Using Extended Logic Programs to Formalize Commonsense Reasoning

Description: In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose.
Date: May 1992
Creator: Horng, Wen-Bing
Partner: UNT Libraries

Study of Parallel Algorithms Related to Subsequence Problems on the Sequent Multiprocessor System

Description: The primary purpose of this work is to study, implement and analyze the performance of parallel algorithms related to subsequence problems. The problems include string to string correction problem, to determine the longest common subsequence problem and solving the sum-range-product, 1 —D pattern matching, longest non-decreasing (non-increasing) (LNS) and maximum positive subsequence (MPS) problems. The work also includes studying the techniques and issues involved in developing parallel applications. These algorithms are implemented on the Sequent Multiprocessor System. The subsequence problems have been defined, along with performance metrics that are utilized. The sequential and parallel algorithms have been summarized. The implementation issues which arise in the process of developing parallel applications have been identified and studied.
Date: August 1994
Creator: Pothuru, Surendra
Partner: UNT Libraries

A Comparison of File Organization Techniques

Description: This thesis compares the file organization techniques that are implemented on two different types of computer systems, the large-scale and the small-scale. File organizations from representative computers in each class are examined in detail: the IBM System/370 (OS/370) and the Harris 1600 Distributed Processing System with the Extended Communications Operating System (ECOS). In order to establish the basic framework for comparison, an introduction to file organizations is presented. Additionally, the functional requirements for file organizations are described by their characteristics and user demands. Concluding remarks compare file organization techniques and discuss likely future developments of file systems.
Date: August 1977
Creator: Rogers, Roy Lee
Partner: UNT Libraries

Cognitive Level Demands of Test Items in State-Adopted Computer Science Textbooks

Description: Test items supplied with seven textbooks approved for use in Computer Science I and II curricula in Texas public schools were categorized by Bloom's taxonomy of educational objectives. Eating was done by a panel of ten judges selected from a group of participants at a taxonomy workshop. The selection criterion was demonstration of at least 80 percent competency in item classification. Judges received a small stipend for completing the rating task. Of 2020 possible items, 998 were randomly selected for analysis. Equal percentages of items from each text were then randomly assigned to each rater. All statistical analyses were computed using SPSS/PC+ (version 2.1). In both courses, CLD frequencies decreased through the three lower levels. The percentage of questions falling in these levels was approximately 83 percent for both courses. However, the higher-level course contained almost 10 percent more Knowledge level questions than did the lower course. At the higher taxonomic levels, the decline was roughly five percent per level in CS I but erratic in CS II. Analysis by book also revealed wide differences within each course.
Date: August 1988
Creator: Aman, James R.
Partner: UNT Libraries

Modeling and Simulation of the Vector-Borne Dengue Disease and the Effects of Regional Variation of Temperature in the Disease Prevalence in Homogenous and Heterogeneous Human Populations

Description: The history of mitigation programs to contain vector-borne diseases is a story of successes and failures. Due to the complex interplay among multiple factors that determine disease dynamics, the general principles for timely and specific intervention for incidence reduction or eradication of life-threatening diseases has yet to be determined. This research discusses computational methods developed to assist in the understanding of complex relationships affecting vector-borne disease dynamics. A computational framework to assist public health practitioners with exploring the dynamics of vector-borne diseases, such as malaria and dengue in homogenous and heterogeneous populations, has been conceived, designed, and implemented. The framework integrates a stochastic computational model of interactions to simulate horizontal disease transmission. The intent of the computational modeling has been the integration of stochasticity during simulation of the disease progression while reducing the number of necessary interactions to simulate a disease outbreak. While there are improvements in the computational time reducing the number of interactions needed for simulating disease dynamics, the realization of interactions can remain computationally expensive. Using multi-threading technology to improve performance upon the original computational model, multi-threading experimental results have been tested and reported. In addition, to the contact model, the modeling of biological processes specific to the corresponding pathogen-carrier vector to increase the specificity of the vector-borne disease has been integrated. Last, automation for requesting, retrieving, parsing, and storing specific weather data and geospatial information from federal agencies to study the differences between homogenous and heterogeneous populations has been implemented.
Date: August 2016
Creator: Bravo-Salgado, Angel D
Partner: UNT Libraries

Robocamp: Encouraging Young Women to Embrace STEM

Description: This paper describes the efforts and results of a plan for actively recruiting students to undergraduate computer science and engineering programs at the University of North Texas (UNT). Such recruitment of students is critical to the country's efforts to increase the number of engineering professionals, and is a priority for the Computer Science and Engineering (CSE) Department at UNT.
Date: February 2009
Creator: Akl, Robert G. & Keathly, David
Partner: UNT College of Engineering

Retention and Recruitment of Women in Computer Engineering

Description: This presentation discusses strategies and goals for recruiting more women to Computer Science and Engineering degree (CSE) programs at the University of North Texas (UNT). It also describes a series of activities aimed at improving retention rates of women students already in our programs. Such recruitment and retention of women is critical to the country's efforts to increase the number of engineering professionals, and is a priority for the CSE Department at UNT.
Date: July 2006
Creator: Akl, Robert G. & Garlick, Ryan
Partner: UNT College of Engineering

Automatic Speech Recognition Using Finite Inductive Sequences

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 in speech, the training is performed casualty, and a large number of training speakers is used from eight different dialect regions. Hence, a speaker independent recognition system is realized. The matching process compares the incoming speech with each of the templates stored, and a closeness ration is computed. A ratio table is generated anH the matching word that corresponds to the smallest ratio (i.e. indicating that the ruling has removed most of the symbols) is selected. Promising results were obtained for isolated words, and the recognition rates ranged between 50% and 100%.
Date: August 1996
Creator: Cherri, Mona Youssef, 1956-
Partner: UNT Libraries

Inheritance Problems in Object-Oriented Database

Description: This research is concerned with inheritance as used in object-oriented database. More specifically, partial bi-directional inheritance among classes is examined. In partial inheritance, a class can inherit a proper subset of instance variables from another class. Two subclasses of the same superclass do not need to inherit the same proper subset of instance variables from their superclass. Bi-directional partial inheritance allows a class to inherit instance variables from its subclass. The prototype of an object-oriented database that supports both full and partial bi-directional inheritance among classes was developed on top of an existing relational database management system. The prototype was tested with two database applications. One database application needs full and partial inheritance. The second database application required bi-directional inheritance. The result of this testing suggests both advantages and disadvantages of partial bi-directional inheritance. Future areas of research are also suggested.
Date: May 1989
Creator: Auepanwiriyakul, Raweewan
Partner: UNT Libraries

Fast Lane to Python: A Quick, Sensible Route to the Joys of Python Coding

Description: This book aims to enable the reader to quickly acquire a Python foundation. The material particularly feel quite comfortable to anyone with background in an object-oriented programming (OOP) language such as C++ or Java. Even if ones lack this background, they will still be able to read these sections, but will probably need to go through them more slowly than those who do know OOP. Some Linux knowledge would also be helpful, but it certainly is not required. Python is used on Windows and Macintosh platforms too, not just Linux. So, most statements here made for the Linux context will also apply to Macs as well. The author acknowledged that programming is a personal, creative activity, so everyone has his/her own view.
Date: 2014?
Creator: Matloff, Norm
Partner: UNT Libraries

Semaphore Solutions for General Mutual Exclusion Problems

Description: Automatic generation of starvation-free semaphore solutions to general mutual exclusion problems is discussed. A reduction approach is introduced for recognizing edge-solvable problems, together with an O(N^2) algorithm for graph reduction, where N is the number of nodes. An algorithm for the automatic generation of starvation-free edge-solvable solutions is presented. The solutions are proved to be very efficient. For general problems, there are two ways to generate efficient solutions. One associates a semaphore with every node, the other with every edge. They are both better than the standard monitor—like solutions. Besides strong semaphores, solutions using weak semaphores, weaker semaphores and generalized semaphores are also considered. Basic properties of semaphore solutions are also discussed. Tools describing the dynamic behavior of parallel systems, as well as performance criteria for evaluating semaphore solutions are elaborated.
Date: August 1988
Creator: Yue, Kwok B. (Kwok Bun)
Partner: UNT Libraries

Performance study of concurrent search trees and hash algorithms on multiprocessor systems

Description: This study examines the performance of concurrent algorithms for B-trees and linear hashing. B-trees are widely used as an access method for large, single key, database files, stored in lexicographic order on secondary storage devices. Linear hashing is a fast and reliable hash algorithm, suitable for accessing records stored unordered in buckets.
Date: May 1996
Creator: Demuynck, Marie-Anne
Partner: UNT Libraries