Physical Sciences and Mathematics Commons^™

Framework For Bug Inducing Commit Prediction Using Quality Metrics, Alireza Tavakkoli Barzoki

Electronic Thesis and Dissertation Repository

This thesis relates to the topic of software defect prediction within the broader area of continuous software engineering. The approach presented in this thesis is employing source code and process metrics obtained for each commit, and is examining as to whether specific patterns, as the system moves from one commit to another, can predict an impending bug inducing commit. The thesis utilizes the SonarQube Technical Debt open source data which provides source code metrics and process metrics for each commit in 22 medium to large scale open source Apache projects.

Central to this research is the novel utilization of commits …

Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi

Dissertations

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.

First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …

Dealing With Classification Irregularities In Real-World Scenarios., Payel Sadhukhan Dr.

Doctoral Theses

Data processing by the human sensory system comes naturally. This processing, commonly denoted as pattern recognition and analysis are carried out spontaneously by humans. In day to day life, in most cases, decision making by humans come without any conscious effort. From the middle of the past century, humans have shown interest to render their abstraction capabilities (pattern recognition and analysis) to the machine. The abstraction capability of the machine is ’machine intelligence’ or ’machine learning’ [87].The primary goal of machine learning methods is to extract some meaningful information from the ’data’. Data refers to the information or attributes that …

Prediction Of High School Graduation With Decision Trees, Andrea M. Lee

MSU Graduate Theses

While working as an educator for the past fourteen years, we are always looking at data and determining ways to help our students. Graduation status is one area of interest. I wanted to apply statistical methods to try and find early indicators of those students who may drop out, thus being able to provide early intervention to those students. With early intervention, we may be able to lower our dropout rate. While studying different methods of pattern recognition, I found that the decision tree method in machine learning was the best for the data that I had collected. Decision trees …

Methodology To Analyze Tropical Cyclone Intensity From Microwave Imagery, Matthew W. Perkins

Theses and Dissertations

Satellites with microwave remote sensing capabilities can be utilized to study atmospheric phenomena through high-level cloud cover (particularly cirrus), an advantage over visible and infrared bands, which only sense cloud tops. This unique capability makes microwave imagery ideal for studying the cloud structures of tropical cyclones (TCs) in detail, and relating these features to TC intensity. Techniques to estimate the intensity of TCs using infrared imagery, such as the Dvorak technique, have been used in TC forecasting for 40 years. However, due to the inherent temporal limitations of microwave imagery, no such similar technique exists for the microwave spectrum. This …

Multiclass Classification Of Risk Factors For Cervical Cancer Using Artificial Neural Networks, Abdullah Al Mamun

Electronic Theses and Dissertations

World Health Organization statistics show that cervical cancer is the fourth most frequent cancer in women with an estimated 530,000 new cases in 2012. Cervical cancer diagnosis typically involves liquid-based cytology (LBC) followed by a pathologist review. The accuracy of decision is therefore highly influenced by the expert’s skills and experience, resulting in relatively high false positive and/or false negative rates. Moreover, given the fact that the data being analyzed is highly dimensional, same reviewer’s decision is inherently affected by inconsistencies in interpreting the data. In this study, we use an Artificial Neural Network based model that aims to considerably …

Enhancing The Draft Assembly With Minhash, Saju Varghese

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this thesis, we report on the use of minhash techniques to improve the draft assembly of a genome mapping. More specifically, we use minhash to compare the scaffolds of sea urchin and sea cucumber genomes.

One of the main contributions of this thesis is the implementation of minhash with the Message Passing Interface (MPI) utilizing Intel Phi co-processors. It is shown that our implementation significantly reduces the processing time for identification of k-mer similarities.

Building 3d Shape Primitive Based Object Models From Range Images, Vamsikrishna Gopikrishna

Computer Science and Engineering Dissertations

Most pattern recognition approaches to object identification work in the image domain. However this is ignoring potential information that can be provided by depth information. Using range images, we can build a set of geometric depth features. These depth features can be used to identify basic three-dimensional shape primitives. There have been many studies regarding object identification in humans that postulate that at least at a primary level object recognition works by breaking down objects into its component parts. To build a similar Recognition-by-component (RBC) system we need a system to identify these shape primitives. We build a depth feature …

Application Of Cellular Automata To Detection Of Malicious Network Packets, Robert L. Brown

CCE Theses and Dissertations

A problem in computer security is identification of attack signatures in network packets. An attack signature is a pattern of bits that characterizes a particular attack. Because there are many kinds of attacks, there are potentially many attack signatures. Furthermore, attackers may seek to avoid detection by altering the attack mechanism so that the bit pattern presented differs from the known signature. Thus, recognizing attack signatures is a problem in approximate string matching. The time to perform an approximate string match depends upon the length of the string and the number of patterns. For constant string length, the time to …

Pattern Recognition In Earthquake Swarms From The 2009 Eruption Of Redoubt Volcano, Alaska, Catherine Jeanne Carlisle

Boise State University Theses and Dissertations

Earthquake swarms at volcanoes are common indicators of unrest and can be used to predict eruptions. However, not all earthquake swarms lead to an eruption but may die off instead. Variabilities in characteristics of swarms can lead to false predictions of an eruption. During the 2009 eruption of Redoubt Volcano in Alaska, there were five earthquake swarms, three of which preceded explosive eruptions and two that did not. These data were used to explore the variable characteristics that may be diagnostic of whether or not an eruption is imminent.

Data were recorded by the Alaska Volcano Observatory throughout the eruption. …

Real Time Pattern Recognition In Digital Video With Applications To Safety In Construction Sites, Dinesh Bajracharya

UNLV Theses, Dissertations, Professional Papers, and Capstones

In construction sites, various guidelines are provided for the correct use of safety equipment. Many fatalities and injuries occur to people because of the lack of exercise of these guidelines and proper monitoring of the violations. In order to improve these standards and amend the cause, a video based monitoring tool will be created for a construction site.

Based on the real time video obtained from cameras on the site, a classification algorithm will be created which has the intelligence to recognize if any safety rules have been violated. A classification vector will be created based on the different classifiers, …

A Convex Optimization Algorithm For Sparse Representation And Applications In Classification Problems, Reinaldo Sanchez Arias

Open Access Theses & Dissertations

In pattern recognition and machine learning, a classification problem refers to finding an algorithm for assigning a given input data into one of several categories. Many natural signals are sparse or compressible in the sense that they have short representations when expressed in a suitable basis. Motivated by the recent successful development of algorithms for sparse signal recovery, we apply the selective nature of sparse representation to perform classification. Any test sample is represented in an overcomplete dictionary with the training sample as base elements. A given test sample can be expressed as a linear combination of only those training …

Recognizing Patterns In Transmitted Signals For Identification Purposes, Baha' A. Alsaify

Graduate Theses and Dissertations

The ability to identify and authenticate entities in cyberspace such as users, computers, cell phones, smart cards, and radio frequency identification (RFID) tags is usually accomplished by having the entity demonstrate knowledge of a secret key. When the entity is portable and physically accessible, like an RFID tag, it can be difficult to secure given the memory, processing, and economic constraints. This work proposes to use unique patterns in the transmitted signals caused by manufacturing differences to identify and authenticate a wireless device such as an RFID tag. Both manufacturer identification and tag identification are performed on a population of …

Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal

Electrical & Computer Engineering Theses & Dissertations

Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method …

Face Recognition Using Multiple Features In Different Color Spaces, Zhiming Liu

Dissertations

Face recognition as a particular problem of pattern recognition has been attracting substantial attention from researchers in computer vision, pattern recognition, and machine learning. The recent Face Recognition Grand Challenge (FRGC) program reveals that uncontrolled illumination conditions pose grand challenges to face recognition performance. Most of the existing face recognition methods use gray-scale face images, which have been shown insufficient to tackle these challenges. To overcome this challenging problem in face recognition, this dissertation applies multiple features derived from the color images instead of the intensity images only.

First, this dissertation presents two face recognition methods, which operate in different …

A Sparse Representation Technique For Classification Problems, Reinaldo Sanchez Arias

Open Access Theses & Dissertations

In pattern recognition and machine learning, a classification problem refers to finding an algorithm for assigning a given input data into one of several categories. Many natural signals are sparse or compressible in the sense that they have short representations when expressed in a suitable basis. Motivated by the recent successful development of algorithms for sparse signal recovery, we apply the selective nature of sparse representation to perform classification. In order to find such sparse linear representation, we implement an l₁-minimization algorithm. This methodology overcomes the lack of robustness with respect to outliers. In contrast to other classification …

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, …

Minimax And Maximin Fitting Of Geometric Objects To Sets Of Points, Yan B. Mayster

Electronic Theses and Dissertations

This thesis addresses several problems in the facility location sub-area of computational geometry. Let S be a set of n points in the plane. We derive algorithms for approximating S by a step function curve of size k < n, i.e., by an x-monotone orthogonal polyline ℜ with k < n horizontal segments. We use the vertical distance to measure the quality of the approximation, i.e., the maximum distance from a point in S to the horizontal segment directly above or below it. We consider two types of problems: min-ε, where the goal is to minimize the error for a …

Pattern Recognition For Command And Control Data Systems, Jason Schwier

All Dissertations

To analyze real-world events, researchers collect observation data from an underlying process and construct models to represent the observed situation. In this work, we consider issues that affect the construction and usage of a specific type of model. Markov models are commonly used because their combination of discrete states and stochastic transitions is suited to applications with both deterministic and stochastic components. Hidden Markov Models (HMMs) are a class of Markov model commonly used in pattern recognition. We first demonstrate how to construct HMMs using only the observation data, and no a priori information, by extending a previously developed approach …

Learning Semantic Features For Visual Recognition, Jingen Liu

Electronic Theses and Dissertations

Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been widely and successfully explored. The advantages of this representation are: no explicit detection of objects or …

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 …

Detecting Curved Objects Against Cluttered Backgrounds, Jan Prokaj

Electronic Theses and Dissertations

Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting …

Solar Activity Detection And Prediction Using Image Processing And Machine Learning Techniques, Gang Fu

Dissertations

The objective of the research in this dissertation is to develop the methods for automatic detection and prediction of solar activities, including prominence eruptions, emerging flux regions and solar flares. Image processing and machine learning techniques are applied in this study. These methods can be used for automatic observation of solar activities and prediction of space weather that may have great influence on the near earth environment.

The research presented in this dissertation covers the following topics: i) automatic detection of prominence eruptions (PBs), ii) automatic detection of emerging flux regions (EFRs), and iii) automatic prediction of solar flares.

In …

Robot-In-The-Loop Simulation To Support Multi-Robot System Development: A Dynamic Team Formation Example, Ehsan Azarnasab

Computer Science Theses

Modeling and simulation provides a powerful technology for engineers and managers to understand, design, and evaluate a system under development. Traditionally, simulation is only used in early stages of a system design. However, with the advances of hardware and software technology, it is now possible to extend simulation to late stages for supporting a full life cycle simulation-based development. Robot-in-the-loop simulation, where real robots work together with virtual ones, has been developed to support such a development process to bridge the gap between simulation and reality.

Automatic Solar Feature Detection Using Image Processing And Pattern Recognition Techniques, Ming Qu

Dissertations

The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system.

For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In …

Adaptive Intelligent User Interfaces With Emotion Recognition, Fatma Nasoz

Electronic Theses and Dissertations

The focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect physiological data signals from participants experiencing specific emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals …

Handgrip Pattern Recognition, Zong Chen

Dissertations

There are numerous tragic gun deaths each year. Making handguns safer by personalizing them could prevent most such tragedies. Personalized handguns, also called "smart" guns, are handguns that can only be fired by the authorized user. Handgrip pattern recognition holds great promise in the development of the smart gun.

Two algorithms, static analysis algorithm and dynamic analysis algorithm, were developed to find the patterns of a person about how to grasp a handgun. The static analysis algorithm measured 160 subjects' fingertip placements on the replica gun handle. The cluster analysis and discriminant analysis were applied to these fingertip placements, and …

Pattern Recognition For Electric Power System Protection, Yong Sheng

Doctoral Dissertations

The objective of this research is to demonstrate pattern recognition tools such as decision trees (DTs) and neural networks that will improve and automate the design of relay protection functions in electric power systems. Protection functions that will benefit from the research include relay algorithms for high voltage transformer protection (TP) and for high impedance fault (HIF) detection. A methodology, which uses DTs and wavelet analysis to distinguish transformer internal faults from other conditions that are easily mistaken for internal faults, has been developed. Also, a DT based solution is proposed to discriminate HIFs from normal operations that may confuse …

Some Studies On Shape Of Dot Patterns., Anirban Ray Chaudhuri Dr.

Doctoral Theses

The important visual characteristics of an object are shape, size, color, brightness, contrast and texture. Of them, shape is a multidimensional concept that is difficult to define. It takes different meanings in different contexts. We try to explain it in terms of their attributes like elongation, roundness, and symmetry: although these terms do not capture the complete notion of shape.Perhaps Gestalt theory Koffka 351 is the first attempt to study the principles of visual perception in a systematic manner. The central concept of this theory is Gestalt' which means form or configuration. In this theory form is examined from physical. …

Radial Complexity Estimation For Improved Generalization In Artificial Neural Networks, Lemuel R. Myers Jr.

Theses and Dissertations

When training an artificial neural network (ANN) for classification using backpropagation of error, the weights are usually updated by minimizing the sum-squared error on the training set. As training ensues, overtraining may be observed as the network begins to memorize the training data. This occurs because, as the magnitude of the weight vector, W, grows, the decision boundaries become overly complex in much the same way as a too-high order polynomial approximation can overfit a data set in a regression problem. Since w grows during standard backpropagation, it is important to initialize the weights with consideration to the importance of …