Digital Commons Network^™

Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo

Electronic Theses and Dissertations

Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how …

On The Resolution Of System Generated User Interruption In Context-Aware Systems, Ashish Godbole

Graduate Theses and Dissertations

No abstract provided.

Development Of A Relevance-Feedback Image Retrieval System Based On Multiple-Instance Learning, Salini Rao Pamidimukkala

Graduate Theses and Dissertations

No abstract provided.

An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong

Electronic Theses and Dissertations

The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by …

Decision Theoretic Agent Design For Personal Rapid Transit Systems, Iheanyi Umez-Eronini, Ferat Sahin

Articles

This paper details a learning decision-theoretic intelligent agent designed to solve the problem of guiding vehicles in the context of Personal Rapid Transit (PRT). The intelligent agents are designed using Bayesian Decision Networks. The agents are designed to utilize the known methods of machine learning with Bayesian Networks (BN): parameter learning and structure learning. In addition, a new method of machine learning with BNs, termed utility learning in this paper, is introduced. BN software for Matlab is used to realize the proposed agent. Additional software is written to simulate the PRT problem using various intelligent agents that utilize one or …

Knowledge-Based Methods For Automatic Extraction Of Domain-Specific Ontologies, Janardhana R. Punuru

LSU Doctoral Dissertations

Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of …

Using Machine Learning Techniques To Create Ai Controlled Players For Video Games, Bhuman Soni

Theses : Honours

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative …

A Decentralized Reinforcement Learning Controller For Collaborative Driving, Luke Ng, Christopher M. Clark, Jan P. Huissoon

Computer Science and Software Engineering

Research in the collaborative driving domain strives to create control systems that coordinate the motion of multiple vehicles in order to navigate traffic both efficiently and safely. In this paper a novel individual vehicle controller based on reinforcement learning is introduced. This controller is capable of both lateral and longitudinal control while driving in a multi-vehicle platoon. The design and development of this controller is discussed in detail and simulation results showing learning progress and performance are presented.

Identification Of Alternatives Strengths And Weaknesses During The Conceptual Design Of Environmental Systems, Xavier Flores Alsina, Manel Poch, Laureano Jiménez, Rene Bañares-Alcántara, Ignasi Rodríguez Roda

International Congress on Environmental Modelling and Software

The objective of this paper is to present a systematic procedure to support environmental engineers during the evaluation of alternative options, identifying both, the strong and weak points of their decisions. The evaluation of alternative options in the design of environmental systems is complex because several objectives must be considered simultaneously (e.g. environmental, technical, economical, legal), i.e. the problem is multicriteria. The systematic procedure presented consists of three steps: 1) multicriteria decision analysis (MCDA), where the best alternative is selected among the most promising options by evaluating the degree of accomplishment of several design objectives; 2) sensitivity analysis, where multidimensional …

Identification Of Alternatives Strengths And Weaknesses During The Conceptual Design Of Environmental Systems, Xavier Flores Alsina, Manel Poch, Laureano Jiménez, Rene Bañares-Alcántara, Ignasi Rodríguez Roda

International Congress on Environmental Modelling and Software

The objective of this paper is to present a systematic procedure to support environmental engineers during the evaluation of alternative options, identifying both, the strong and weak points of their decisions. The evaluation of alternative options in the design of environmental systems is complex because several objectives must be considered simultaneously (e.g. environmental, technical, economical, legal), i.e. the problem is multicriteria. The systematic procedure presented consists of three steps: 1) multicriteria decision analysis (MCDA), where the best alternative is selected among the most promising options by evaluating the degree of accomplishment of several design objectives; 2) sensitivity analysis, where multidimensional …

Learning Real-World Problems By Finding Correlated Basis Functions, Adam C. Drake

Theses and Dissertations

Learning algorithms based on the Fourier transform attempt to learn functions by approximating the largest coefficients of their Fourier representations. Nearly all previous work in Fourier-based learning has been in the theoretical realm, where properties of the transform have made it possible to prove many interesting learnability results. The real-world usefulness of Fourier-based methods, however, has not been thoroughly explored. This thesis explores methods for the practical application of Fourier-based learning. The primary contribution of this thesis is a new search algorithm for finding the largest coefficients of a function's Fourier representation. Although the search space is exponentially large, empirical …

Surface Realization Using A Featurized Syntactic Statistical Language Model, Thomas L. Packer

Theses and Dissertations

An important challenge in natural language surface realization is the generation of grammatical sentences from incomplete sentence plans. Realization can be broken into a two-stage process consisting of an over-generating rule-based module followed by a ranker that outputs the most probable candidate sentence based on a statistical language model. Thus far, an n-gram language model has been evaluated in this context. More sophisticated syntactic knowledge is expected to improve such a ranker. In this thesis, a new language model based on featurized functional dependency syntax was developed and evaluated. Generation accuracies and cross-entropy for the new language model did not …

Ecue: A Spam Filter That Uses Machine Learning To Track Concept Drift, Sarah Jane Delany, Padraig Cunningham, Barry Smyth

Conference papers

While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise to the specifics of the user’s filtering preferences. This classifier can also automatically adjust over time to account for the changing nature of spam (and indeed changes in the profile of legitimate email). A significant software engineering challenge in developing …

K X N Trust-Based Agent Reputation, Christopher Alonzo Parker

Theses and Dissertations

In this research, a multi-agent system called KMAS is presented that models an environment of intelligent, autonomous, rational, and adaptive agents that reason about trust, and adapt trust based on experience. Agents reason and adapt using a modification of the k-Nearest Neighbor algorithm called (k X n) Nearest Neighbor where k neighbors recommend reputation values for trust during each of n interactions. Reputation allows a single agent to receive recommendations about the trustworthiness of others. One goal is to present a recommendation model of trust that outperforms MAS architectures relying solely on direct agent interaction. A second goal is to …

Incremental Learning Of Discrete Hidden Markov Models, German Florez-Larrahondo

Theses and Dissertations

We address the problem of learning discrete hidden Markov models from very long sequences of observations. Incremental versions of the Baum-Welch algorithm that approximate the beta-values used in the backward procedure are commonly used for this problem since their memory complexity is independent of the sequence length. However, traditional approaches have two main disadvantages: the approximation of the beta-values deviates far from the real values, and the learning algorithm requires previous knowledge of the topology of the model. This dissertation describes a new incremental Baum-Welch algorithm with a novel backward procedure that improves the approximation of the â-values based on …

Application Of Neural Network For Selection Of Airport Rigid Pavement Maintenance Strategies, Shu-Meng Huang, Chun-Jung Hsu, Chau Lee, Chung-Chen Chang

Journal of Marine Science and Technology–Taiwan

Maintenance strategy selection has been an important link within the management system for airport rigid pavement. Suitable maintenance strategies will guarantee maintenance success. The maintenance personnel investigating pavement distress usually base their maintenance strategy suggestions on expert knowledge and experience. Nevertheless, in practical situations we often see examples of distress taking place following maintenance, which indicates that the maintenance strategies previously selected can not effectively repair the pavement. Other methods and materials have to be adopted for maintenance so as to ensure pavement performance. This paper uses the machine learning theory of the neural network, conducting a questionnaire survey on …

Dynamically Optimized Context In Recommender Systems, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our …

Evaluating Online Trust Using Machine Learning Methods, Weihua Song

Doctoral Dissertations

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation …

Scaling Up Support Vector Machines With Application To Plankton Recognition, Tong Luo

USF Tampa Graduate Theses and Dissertations

Learning a predictive model for a large scale real-world problem presents several challenges: the choice of a good feature set and a scalable machine learning algorithm with small generalization error. A support vector machine (SVM), based on statistical learning theory, obtains good generalization by restricting the capacity of its hypothesis space. A SVM outperforms classical learning algorithms on many benchmark data sets. Its excellent performance makes it the ideal choice for pattern recognition problems. However, training a SVM involves constrained quadratic programming, which leads to poor scalability. In this dissertation, we propose several methods to improve a SVM's scalability. The …

A Bayesian Technique For Task Localization In Multiple Goal Markov Decision Processes, James Carroll, Kevin Seppi

Faculty Publications

In a reinforcement learning task library system for Multiple Goal Markov Decision Process (MGMDP), localization in the task space allows the agent to determine whether a given task is already in its library in order to exploit previously learned experience. Task localization in MGMDPs can be accomplished through a Bayesian approach, however a trivial approach fails when the rewards are not distributed normally. This can be overcome through our Bayesian Task Localization Technique (BTLT).

Predicting Open-Source Software Quality Using Statistical And Machine Learning Techniques, Amit Ashok Phadke

Theses and Dissertations

Developing high quality software is the goal of every software development organization. Software quality models are commonly used to assess and improve the software quality. These models, based on the past releases of the system, can be used to identify the fault-prone modules for the next release. This information is useful to the open-source software community, including both developers and users. Developers can use this information to clean or rebuild the faulty modules thus enhancing the system. The users of the software system can make informed decisions about the quality of the product. This thesis builds quality models using logistic …

Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke

Faculty Publications

The paired-difference t-test is commonly used in the machine learning community to determine whether one learning algorithm is better than another on a given learning task. This paper suggests the use of the permutation test instead hecause it calculates the exact p-value instead of an estimate. The permutation test is also distribution free and the time complexity is trivial for the commonly used 10-fold cross-validation paired-difference test. Results of experiments on real-world problems suggest it is not uncommon to see the t-test estimate deviate up to 30-50% from the exact p-value.

Knowledge Discovery In Environmental Data Bases Using Gesconda, Karina Gibert, Xavier Flores, Ignasi Rodríguez-Roda, Miquel Sànchez-Marrè

International Congress on Environmental Modelling and Software

In this work, last results of the research project “Development of an Intelligent Data Analysis System for Knowledge Management in Environmental Data Bases (DB)” are presented. The project is focussed on the design and development of a prototype for Knowledge Discovery (KD) and intelligent data analysis, and specially oriented to environmental DB. It is remarkable the high quantity of information and knowledge patterns that are implicit in large DB coming from environmental domains. In this project, several environmental DB such as meteorological phenomena, wastewater treatment plants (WWTP), or environmental emergencies were used for testing. KD is a prior and mandatory …

Solving Large Mdps Quickly With Partitioned Value Iteration, David Wingate

Theses and Dissertations

Value iteration is not typically considered a viable algorithm for solving large-scale MDPs because it converges too slowly. However, its performance can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We present several methods designed to help structure value dependency, and present a systematic study of companion prioritization techniques which focus computation in useful regions of the state space. In order to scale to solve ever larger problems, we evaluate all enhancements and methods in the context of parallelizability. Using the enhancements, we discover that in many instances the limiting …

Symbolic Regression Of Thermo-Physical Model Using Genetic Programming, Ying Zhang

USF Tampa Graduate Theses and Dissertations

The symbolic regression problem is to find a function, in symbolic form, that fits a given data set. Symbolic regression provides a means for function identification. This research describes an adaptive hybrid system for symbolic function identification of thermo-physical model that combines the genetic programming and a modified Marquardt nonlinear regression algorithm.

Genetic Programming (GP) system can extract knowledge from the data in the form of symbolic expressions, i.e. a tree structure, that are used to model and derive equation of state, mixing rules and phase behavior from the experimental data (properties estimation).

During the automatic evolution process of GP, …

Machine Learning Techniques For Characterizing Ieee 802.11b Encrypted Data Streams, Michael J. Henson

Theses and Dissertations

As wireless networks become an increasingly common part of the infrastructure in industrialized nations, the vulnerabilities of this technology need to be evaluated. Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic. These characteristics include packet size, signal strength, channel utilization and others. Using these characteristics, windows of size 11, 31, and 51 packets are collected and machine learning (ML) techniques are trained to classify applications accessing the 802.11b wireless channel. The four applications used for this study included E-Mail, FTP, HTTP, and …

Using Symbolic Knowledge In The Umls To Disambiguate Words In Small Datasets With A Naive Bayes Classifier, Gondy Leroy, Thomas C. Rindflesch

CGU Faculty Publications and Research

Current approaches to word sense disambiguation use and combine various machine-learning techniques. Most refer to characteristics of the ambiguous word and surrounding words and are based on hundreds of examples. Unfortunately, developing large training sets is time-consuming. We investigate the use of symbolic knowledge to augment machine-learning techniques for small datasets. UMLS semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. A naïve Bayes classifier was trained for 15 words with 100 examples for each. The most frequent sense of a word served as the baseline. The effect of increasingly …

Reconstructability Analysis: Theory And Applications [Editorial Introduction], Martin Zwick, Guangfu Shu, Yi Lin

Complex Systems Faculty Publications and Presentations

Reconstructability analysis (RA) dates back to the pioneering work of Ashby in the mid-1960s. In the 1970s and 1980s, RA was the subject of very active research in the systems community. It receded for a time as a focus of activity, but the special issue of the International Journal of General Systems in 1996 on the General Systems Problem Solver and the special IJGS issue in 2000 on Reconstructability Analysis in China marked the renewal of interest in this area. The current volume is part of this resurgence of activity. It collects together papers from the group at Portland State …

Machine Learning Based Cdma Power Control, Chaouki T. Abdallah, Judd A. Rohwer, Christos G. Christodoulou

Electrical & Computer Engineering Faculty Publications

This paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control …

Graph-Theoretic Techniques For Web Content Mining, Adam Schenker

USF Tampa Graduate Theses and Dissertations

In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied …