Physical Sciences and Mathematics Commons^™

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Automating Software Changes Via Recommendation Systems, Xiaoyu Liu

Computer Science and Engineering Theses and Dissertations

As the complexity of software systems is growing tremendously, it came with increasingly sophisticated data provided during development. The systematic and large-scale accumulation of software engineering data opened up new opportunities that infer information appropriately can be helpful to software development in a given context. This type of intelligent software development tools came to be known as recommendation systems.

Recommendation Systems in Software Change (RSSCs) share commonalities with conventional recommendation systems: mainly in their usage model, the usual reliance on data mining, and in the predictive nature of their functionality. So a major challenge for designing RSSCs is to automatically …

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

University of New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …

A Domain Specific Language For Digital Forensics And Incident Response Analysis, Christopher D. Stelly

University of New Orleans Theses and Dissertations

One of the longstanding conceptual problems in digital forensics is the dichotomy between the need for verifiable and reproducible forensic investigations, and the lack of practical mechanisms to accomplish them. With nearly four decades of professional digital forensic practice, investigator notes are still the primary source of reproducibility information, and much of it is tied to the functions of specific, often proprietary, tools.

The lack of a formal means of specification for digital forensic operations results in three major problems. Specifically, there is a critical lack of:

a) standardized and automated means to scientifically verify accuracy of digital forensic tools; …

The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, Jerod F A Dunbar

University of New Orleans Theses and Dissertations

This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before …

A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis

Electronic Theses and Dissertations

Regions and lines are common geographic abstractions for geographic objects. Collections of regions, lines, and other representations of spatial objects form a spatial scene, along with their relations. For instance, the states of Maine and New Hampshire can be represented by a pair of regions and related based on their topological properties. These two states are adjacent (i.e., they meet along their shared boundary), whereas Maine and Florida are not adjacent (i.e., they are disjoint).

A detailed model for qualitatively describing spatial scenes should capture the essential properties of a configuration such that a description of the represented objects …

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Ordinal Hyperplane Loss, Bob Vanderheyden

Doctor of Data Science and Analytics Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …

Towards Interpretable Machine Learning With Applications To Clinical Decision Support, Zhicheng Cui

McKelvey School of Engineering Theses & Dissertations

Machine learning models have achieved impressive predictive performance in various applications such as image classification and object recognition. However, understanding how machine learning models make decisions is essential when deploying those models in critical areas such as clinical prediction and market analysis, where prediction accuracy is not the only concern. For example, in the clinical prediction of ICU transfers, in addition to accurate predictions, doctors need to know the contributing factors that triggered the alert, which factors can be quickly altered to prevent the ICU transfer. While interpretable machine learning has been extensively studied for years, challenges remain as among …

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin

Master of Science in Computer Science Theses

This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

On The Human Factors Impact Of Polyglot Programming On Programmer Productivity, Phillip Merlin Uesbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Polyglot programming is a common practice in modern software development. This practice is often considered useful to create software by allowing developers to use whichever language they consider most well suited for the different parts of their software. Despite this ubiquity of polyglot programming there is no empirical research into how this practice affects software developers and their productivity. In this dissertation, after reviewing the state of the art in programming language and linguistic research pertaining to the topic, this matter is investigated by way of two empirical studies with 109 and 171 participants solving programming tasks. Based on the …

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi

Master's Projects

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different …

Image-Based Localization Of User-Interfaces, Riti Gupta

Master's Projects

Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks …

A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma

Master's Projects

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all …

3d Shape Prediction On Convolutional Deep Belief Networks, Gregory Y. Enriquez

Master's Projects

The field of image recognition software has grown immensely in recent years with the emergence of new deep learning techniques. Deep belief networks inspired by Hinton [11] were one of the earliest methodologies of deep learning in the late 2000s. More recently, convolutional neural networks have been used in deep learning techniques, architecture, and software to identify patterns in imagery in order to make predictions such as classification, image segmentation, etc. Traditional two-dimensional, or 2D, images stored as picture files, typically contain red, green, and blue color data for each individual pixel in the picture. However, more recent commercial 2.5D …

Music Retrieval System Using Query-By-Humming, Parth Patel

Master's Projects

Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is …

Expediting Learning With Interactive Demonstrations, Brandon Rozek

Student Research Submissions

Deep Reinforcement Learning has shown great progress in domains such as the Atari Arcade Learning Environment. The problem, however, is that the agent playing the game requires many interactions before it starts to show good performance. This is okay for some domains such as video games, but as we start to look towards integrating deep reinforcement learning into real world applications, we need to minimize the number of interactions required. Interactive demonstrations help expedite the agent’s learning process by providing a shared environment between the agent and the demonstrator to take turns in. This helps the agent learn directly from …

A Desire Fulfillment Theory Of Digital Game Enjoyment, Owen M. Schaffer

College of Computing and Digital Media Dissertations

Empirical research on what makes digital games enjoyable is critical for practitioners who want to design for enjoyment, including for Game Design, Gamification, and Serious Games. But existing theories of what leads to digital game enjoyment have been incomplete or lacking in empirical support showing their impact on enjoyment.

Desire Fulfillment Theory is proposed as a new theory of what leads to digital game enjoyment and tested through research with people who have recently played a digital game. This theory builds on three established theories: Expectancy Disconfirmation Theory, Theory of Basic Human Desires, and Flow Theory. These three theories are …

A New Method To Solve Same-Different Problems With Few-Shot Learning, Yuanyuan Han

Electronic Thesis and Dissertation Repository

Visual learning of highly abstract concepts is often simple for humans but very challenging for machines. Same-different (SD) problems are a visual reasoning task with highly abstract concepts. Previous work has shown that SD problems are difficult to solve with standard deep learning algorithms, especially in the few-shot case, despite the ability of such algorithms to learn abstract features. In this thesis, we propose a new method to solve SD problems with few training samples, in which same-different visual concepts can be recognized by examining similarities between Regions of Interest by using a same-different twins network. Our method achieves state-of-the-art …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …