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  Partner: UNT Libraries
 Language: English
 Degree Discipline: Computer Science
 Collection: UNT Theses and Dissertations
An Annotated Bibliography of Mobile Agents in Networks

An Annotated Bibliography of Mobile Agents in Networks

Date: December 2002
Creator: Sriraman, Sandhya
Description: The purpose of this thesis is to present a comprehensive colligation of applications of mobile agents in networks, and provide a baseline association of these systems. This work has been motivated by the fact that mobile agent systems have been deemed proficuous alternatives in system applications. Several mobile agent systems have been developed to provide scalable and cogent solutions in network-centric applications. This thesis examines some existing mobile agent systems in core networking areas, in particular, those of network and resource management, routing, and the provision of fault tolerance and security. The inherent features of these systems are discussed with respect to their specific functionalities. The applicability and efficacy of mobile agents are further considered in the specific areas mentioned above. Although an initial foray into a collation of this nature, the goal of this annotated bibliography is to provide a generic referential view of mobile agent systems in network applications.
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An Approach Towards Self-Supervised Classification Using Cyc

An Approach Towards Self-Supervised Classification Using Cyc

Date: December 2006
Creator: Coursey, Kino High
Description: Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.
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Arithmetic Computations and Memory Management Using a Binary Tree Encoding af Natural Numbers

Arithmetic Computations and Memory Management Using a Binary Tree Encoding af Natural Numbers

Date: December 2011
Creator: Haraburda, David
Description: Two applications of a binary tree data type based on a simple pairing function (a bijection between natural numbers and pairs of natural numbers) are explored. First, the tree is used to encode natural numbers, and algorithms that perform basic arithmetic computations are presented along with formal proofs of their correctness. Second, using this "canonical" representation as a base type, algorithms for encoding and decoding additional isomorphic data types of other mathematical constructs (sets, sequences, etc.) are also developed. An experimental application to a memory management system is constructed and explored using these isomorphic types. A practical analysis of this system's runtime complexity and space savings are provided, along with a proof of concept framework for both applications of the binary tree type, in the Java programming language.
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Automated Classification of Emotions Using Song Lyrics

Automated Classification of Emotions Using Song Lyrics

Date: December 2012
Creator: Schellenberg, Rajitha
Description: This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.
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Automated Defense Against Worm Propagation.

Automated Defense Against Worm Propagation.

Access: Use of this item is restricted to the UNT Community.
Date: December 2005
Creator: Patwardhan, Sudeep
Description: Worms have caused significant destruction over the last few years. Network security elements such as firewalls, IDS, etc have been ineffective against worms. Some worms are so fast that a manual intervention is not possible. This brings in the need for a stronger security architecture which can automatically react to stop worm propagation. The method has to be signature independent so that it can stop new worms. In this thesis, an automated defense system (ADS) is developed to automate defense against worms and contain the worm to a level where manual intervention is possible. This is accomplished with a two level architecture with feedback at each level. The inner loop is based on control system theory and uses the properties of PID (proportional, integral and differential controller). The outer loop works at the network level and stops the worm to reach its spread saturation point. In our lab setup, we verified that with only inner loop active the worm was delayed, and with both loops active we were able to restrict the propagation to 10% of the targeted hosts. One concern for deployment of a worm containment mechanism was degradation of throughput for legitimate traffic. We found that with proper ...
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Automated Syndromic Surveillance using Intelligent Mobile Agents

Automated Syndromic Surveillance using Intelligent Mobile Agents

Date: December 2007
Creator: Miller, Paul
Description: Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates.
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Automatic Software Test Data Generation

Automatic Software Test Data Generation

Access: Use of this item is restricted to the UNT Community.
Date: December 2002
Creator: Munugala, Ajay Kumar
Description: In software testing, it is often desirable to find test inputs that exercise specific program features. Finding these inputs manually, is extremely time consuming, especially, when the software being tested is complex. Therefore, there have been numerous attempts automate this process. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired software features. Often the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. In contrast, combinatorial optimization techniques, such as those using genetic algorithms, are meant to solve difficult problems involving simultaneous satisfaction of many constraints.
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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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Automatic Tagging of Communication Data

Automatic Tagging of Communication Data

Date: August 2012
Creator: Hoyt, Matthew Ray
Description: Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
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Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems

Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems

Date: May 2014
Creator: Guan, Qiang
Description: The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the ...
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