Computer Sciences Commons^™

Development Of A Genotype-By-Sequencing Immunogenetic Assay As Exemplified By Screening For Variation In Red Fox With And Without Endemic Rabies Exposure, Michael E. Donaldson, Yessica Rico, Karsten Hueffer, Halie M. Rando, Anna V. Kukekova, Christopher J. Kyle

Computer Science: Faculty Publications

Pathogens are recognized as major drivers of local adaptation in wildlife systems. By determining which gene variants are favored in local interactions among populations with and without disease, spatially explicit adaptive responses to pathogens can be elucidated. Much of our current understanding of host responses to disease comes from a small number of genes associated with an immune response. High-throughput sequencing (HTS) technologies, such as genotype-by-sequencing (GBS), facilitate expanded explorations of genomic variation among populations. Hybridization-based GBS techniques can be leveraged in systems not well characterized for specific variants associated with disease outcome to “capture” specific genes and regulatory regions …

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

The Use Of Machine Learning To Detect Suckling In Pre-Weaned Calves, Sukumar Katamreddy

Theses

The weaning of cattle is a process which is known to be labour intensive and to have stressful effects on both cow and calf Common methods used in the weaning process include the temporary removal of a mother from the calf and manual observation and intervention. Early and speedy weaning is known to have a number of benefits, including health benefits for both cow and calf, additional weight gains for the calves as well as reduced labour and feed requirements. The process known as Two-Stage Weaning is recognised to be an effective low-stress approach to weaning in which the calf …

A Stochastic Petri Net Reverse Engineering Methodology For Deep Understanding Of Technical Documents, Giorgia Rematska

Browse all Theses and Dissertations

Systems Reverse Engineering has gained great attention over time and is associated with numerous different research areas. The importance of this research derives from several technological necessities. Security analysis and learning purposes are two of them and can greatly benefit from reverse engineering. More specifically, reverse engineering of technical documents for deeper automatic understanding is a research area where reverse engineering can contribute a lot. In this PhD dissertation we develop a novel reverse engineering methodology for deep understanding of architectural description of digital hardware systems that appear in technical documents. Initially, we offer a survey on reverse engineering of …

Building An Abstract-Syntax-Tree-Oriented Symbolic Execution Engine For Php Programs, Jin Huang

Browse all Theses and Dissertations

This thesis presents the design, implementation, and evaluation of an abstract-syntax-tree-oriented symbolic execution engine for the PHP programming language. As a symbolic execution engine, our system emulate the execution of a PHP program by assuming that all inputs are with symbolic rather than concrete values. While our system inherits the basic definition of symbolic execution, it fundamentally differs from existing symbolic execution implementations that mainly leverage intermediate representation (IRs) to operate. Specifically, our system directly takes the abstract syntax tree (AST) of a program as input and subsequently interprets this AST. Performing symbolic execution using AST offers unique advantages. First, …

Slim Embedding Layers For Recurrent Neural Language Models, Zhongliang Li

Browse all Theses and Dissertations

Recurrent neural language (RNN) models are the state-of-the-art method for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of these type of models. We introduce a simple space compression method that stochastically shares the structured parameters at both the input and output embedding layers of RNN models to significantly reduce the size of model parameters, but still compactly represents the original input and the output embedding layers. The method is easy to implement and tune. Experiments on several data sets show that the new method achieves …

Mapping Opioid Mortality Rates Across Treatment Capacity To Identify Need And Access, Garrett K. Wong, Justin R. Chang, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: The opioid and heroin overdose epidemic is a public health emergency in the state of Virginia, resulting in the death of more than 1,100 people in 2016. In order to overcome this epidemic, we need to match the places with the greatest need for services related to substance use disorders with the appropriate healthcare workforce.

Aims: As the data about the overdose outbreak and related socioeconomic factors grow in size and complexity, data scientists have attempted to utilize big data techniques to identify communities and risk factors contributing to addiction.

Methods: Using data obtained from the …

Visualizing The Opioid Overdose With A Dynamic Heat Map To Identify And Predict Vulnerable Communities, Justin R. Chang, Garrett K. Wong, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: Opioid and heroin overdose epidemic is a public health emergency in the state of Virginia. In order to prevent overdose deaths, we need the target expertise in substance use disorders to areas with high rates of overdose. In particular, an area with an acute spike in overdoses might represent an urgent need for intervention.

Aims: The CDC urges the use of near real-time surveillance to effectively identify overdose incidence, and to coordinate community responses in the states affected by the epidemic, including Virginia. However, current opioid overdose datasets for Virginia lack adequate consistency, granularity, and temporality for …

Software Intrusion Detection Evaluation System: A Cost-Based Evaluation Of Intrusion Detection Capability, Agbotiname L. Imoize, Taiwo Oyedare, Michael E. Otuokere, Sachin Shetty

VMASC Publications

In this paper, we consider a cost-based extension of intrusion detection capability (CID). An objective metric motivated by information theory is presented and based on this formulation; a package for computing the intrusion detection capability of intrusion detection system (IDS), given certain input parameters is developed using Java. In order to determine the expected cost at each IDS operating point, the decision tree method of analysis is employed, and plots of expected cost and intrusion detection capability against false positive rate were generated. The point of intersection between the maximum intrusion detection capability and the expected cost is selected as …

A Reliable Data Provenance And Privacy Preservation Architecture For Business-Driven Cyber-Physical Systems Using Blockchain, Xueping Liang, Sachin Shetty, Deepak K. Tosh, Juan Zhao, Danyi Li, Jihong Liu

VMASC Publications

Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate …

Introduction: Metrics, Modeling And Simulation For Cyber Physical Systems, Barry Ezell, Luanne Burns

VMASC Publications

[Introduction] In recent years, there has been a massive increase in cyber crime. Cyber breeches have become ubiquitous. The US government is attempting to adapt through legislation and policy. For instance, business in the defense industrial base must be in compliance of NIST 80-71 by 31 December 2017. Insurance companies as well are beginning to build actuaries on cyber risk. Along with NIST, there are many cyber frameworks that exist, each with their own following. Recently, companies have sought mapping from one framework to another but this is imperfect solution because some frameworks are exclusively technical control measures with very …

Virtual Collaboration With Mobile Social Media In Multiple-Organization Projects, Zhaojun Yang, Jun Sun, Yali Zhang, Ying Wang

Information Systems Faculty Publications

This study investigates the use of mobile social media as emerging collaboration tools by virtual teams. Based on the construal level theory, it develops a research model hypothesizes that collaboration tool effectiveness influence contextual performance and task performance through the mediation of procedure agreeability. In addition, geographic dispersion, team size and project duration serve as moderators as they reflect virtual collaboration complexity. Empirical findings support most hypothesized relationships. Theoretical and practical implications are discussed.

Pricing Of Games As A Service: An Analytical Model For Interactive Digital Services With Hedonic Properties, Tayfun Keskin

Information Systems Faculty Publications

This study explores optimal pricing strategies in games and other interactive digital goods under incomplete information, when bundling is an option. Drawing from research on the pricing of information goods, we propose a pattern of optimal pricing strategies in which hedonic characteristics affect the utility of interactive digital goods and services. This is a new approach to games, to treat them as a service to determine pricing strategies. Findings reveal that there is an optimal pricing solution for firms in the gaming industry. This finding holds both in bundling and non-bundling cases. Utilizing analytical modeling methodology, we propose pricing-inspired business …

Emotion In The Common Model Of Cognition, Othalia Larue, Robert West, Paul Rosenbloom, Christopher L. Dancy, Alexei V. Samsonovich, Dean Petters, Ion Juvina

Faculty Journal Articles

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition.

Generating Classification Rules From Training Samples, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples …

Neutrosophic Operational Research - Vol. 3., Florentin Smarandache, Mohamed Abdel Basset, Victor Chang

Branch Mathematics and Statistics Faculty and Staff Publications

Foreword John R. Edwards This book is an excellent exposition of the use of Data Envelopment Analysis (DEA) to generate data analytic insights to make evidence-based decisions, to improve productivity, and to manage cost-risk and benefitopportunity in public and private sectors. The design and the content of the book make it an up-to-date and timely reference for professionals, academics, students, and employees, in particular those involved in strategic and operational decisionmaking processes to evaluate and prioritize alternatives to boost productivity growth, to optimize the efficiency of resource utilization, and to maximize the effectiveness of outputs and impacts to stakeholders. It …

Fundamentals Of Neutrosophic Logic And Sets And Their Role In Artificial Intelligence (Fundamentos De La Lógica Y Los Conjuntos Neutrosóficos Y Su Papel En La Inteligencia Artificial ), Florentin Smarandache, Maykel Leyva-Vazquez

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophy is a new branch of philosophy which studies the origin, nature and scope of neutralities. This has formed the basis for a series of mathematical theories that generalize the classical and fuzzy theories such as the neutrosophic sets and the neutrosophic logic. In the paper, the fundamental concepts related to neutrosophy and its antecedents are presented. Additionally, fundamental concepts of artificial intelligence will be defined and how neutrosophy has come to strengthen this discipline.

Cyber Security And Criminal Justice Programs In The United States: Exploring The Intersections, Brian K. Payne, Lora Hadzhidimova

Sociology & Criminal Justice Faculty Publications

The study of cyber security is an interdisciplinary pursuit that includes STEM disciplines as well as the social sciences. While research on cyber security appears to be central in STEM disciplines, it is not yet clear how central cyber security and cyber crime is to criminal justice scholarship. In order to examine the connections between cyber security and criminal justice, in this study attention is given to the way that criminal justice scholars have embraced cyber crime research and coursework. Results show that while there are a number of cyber crime courses included in criminal justice majors there are not …

New Dimensions For A Challenging Security Environment: Growing Exposure To Critical Space Infrastructure Disruption Risk, Adrian V. Gheorghe, Alexandru Georgescu, Olga Bucovetchi, Marilena Lazăr, Cezar Scarlat

Engineering Management & Systems Engineering Faculty Publications

Space systems have become a key enabler for a wide variety of applications that are vital to the functioning of advanced societies. The trend is one of quantitative and qualitative increase of this dependence, so much so that space systems have been described as a new example of critical infrastructure. This article argues that the existence of critical space infrastructures implies the emergence of a new category of disasters related to disruption risks. We inventory those risks and make policy recommendations for what is, ultimately, a resilience governance issue.

Advancing Practical Specification Techniques For Modern Software Systems, John Singleton

Electronic Theses and Dissertations

The pervasive nature of software (and the tendency for it to contain errors) has long been a concern of theoretical computer scientists. Many investigators have endeavored to produce theories, tools, and techniques for verifying the behavior of software systems. One of the most promising lines of research is that of formal specification, which is a subset of the larger field of formal methods. In formal specification, one composes a precise mathematical description of a software system and uses tools and techniques to ensure that the software that has been written conforms to this specification. Examples of such systems are Z …

Necessary Conditions For Open-Ended Evolution, Lisa Soros

Electronic Theses and Dissertations

Evolution on Earth is widely considered to be an effectively endless process. Though this phenomenon of open-ended evolution (OEE) has been a topic of interest in the artificial life community since its beginnings, the field still lacks an empirically validated theory of what exactly is necessary to reproduce the phenomenon in general (including in domains quite unlike Earth). This dissertation (1) enumerates a set of conditions hypothesized to be necessary for OEE in addition to (2) introducing an artificial life world called Chromaria that incorporates each of the hypothesized necessary conditions. It then (3) describes a set of experiments with …

Pollinator Power: Supporting Bees Through Ecoregion Specific Planting Guides, Maya Thomas

Scripps Senior Theses

The pollination of flowering crops by bees is an invaluable ecosystem service that supports biodiversity and much of the global agricultural system. Pollinators move pollen between the male structures of a plant to the female structures of a plant of the same species. This fertilizes the female plant, which then produces the next generation. This process also provides the pollinator with the nectar or pollen it needs to survive. While some plants transfer pollen through different means, the majority of plants need help from pollinators to reproduce. Depending on the means of pollination, pollination can be classified as abiotic or …

On The Mixtures Of Weibull And Pareto (Iv) Distribution: An Alternative To Pareto Distribution, I. Ghosh, Gholamhossein G. Hamedani, Naveen K. Bansal, Mehdi Maadooliat

Mathematics, Statistics and Computer Science Faculty Research and Publications

Finite mixture models have provided a reasonable tool to model various types of observed phenomena, specially those which are random in nature. In this article, a finite mixture of Weibull and Pareto (IV) distribution is considered and studied. Some structural properties of the resulting model are discussed including estimation of the model parameters via expectation maximization (EM) algorithm. A real-life data application exhibits the fact that in certain situations, this mixture model might be a better alternative than the rival popular models.

On Characterizations Of Mcllog, Ellogw, Pthl And K-Ge Distributions, Gholamhossein G. Hamedani, Nadeem Shafique Butt

Mathematics, Statistics and Computer Science Faculty Research and Publications

Huang S. and Oluyede (2016), Oluyede et al. (2016), Krishnarani (2016) and Rather and Rather (2017) consider the "McDonald Log-Logistic", the "Exponentiated Log-Logistic Weibull", the "Power Transformation Half-Logistic" and "k-Generalized Exponential" distributions, respectively, and study certain properties and applications of these distributions. The present short note is intended to complete, in some way, the above mentioned works via establishing certain characterizations of these distributions in different directions.

Characterizations And Infinite Divisibility Of Certain Recently Introduced Distributions Iii, Gholamhossein G. Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

Certain characterizations of recently proposed univariate continuous distributions are presented in different directions. This work may be a source of preventing reinventing and duplicating the existing distributions and calling them newly proposed distributions.

A Bayesian Variable Selection Approach Yields Improved Detection Of Brain Activation From Complex-Valued Fmri, Cheng-Han Yu, Raquel Prado, Hernando Ombao, Daniel B. Rowe

Mathematics, Statistics and Computer Science Faculty Research and Publications

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation …

Analysis Of High Performance Scientific Programming Workflows, Withana Kankanamalage Umayanganie Klaassen

Open Access Theses & Dissertations

Substantial time is spent on building, optimizing and maintaining large-scale software that is run on supercomputers. However, little has been done to utilize overall resources efficiently when it comes to including expensive human resources. The community is beginning to acknowledge that optimizing the hardware performance such as speed and memory bottlenecks contributes less to the overall productivity than does the development lifecycle of high-performance scientific applications. Researchers are beginning to look at overall scientific workflows for high performance computing. Scientific programming productivity is measured by time and effort required to develop, configure, and maintain a simulation experiment and its constituent …

Decision Making For Dynamic Systems Under Uncertainty: Predictions And Parameter Recomputations, Leobardo Valera

Open Access Theses & Dissertations

In this Thesis, we are interested in making decision over a model of a dynamic system. We want to know, on one hand, how the corresponding dynamic phenomenon unfolds under different input parameters (simulations). These simulations might help researchers to design devices with a better performance than the actual ones. On the other hand, we are also interested in predicting the behavior of the dynamic system based on knowledge of the phenomenon in order to prevent undesired outcomes. Finally, this Thesis is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior.

Understanding the …

Estimating The Optimal Cutoff Point For Logistic Regression, Zheng Zhang

Open Access Theses & Dissertations

Binary classification is one of the main themes of supervised learning. This research is concerned about determining the optimal cutoff point for the continuous-scaled outcomes (e.g., predicted probabilities) resulting from a classifier such as logistic regression. We make note of the fact that the cutoff point obtained from various methods is a statistic, which can be unstable with substantial variation. Nevertheless, due partly to complexity involved in estimating the cutpoint, there has been no formal study on the variance or standard error of the estimated cutoff point.

In this Thesis, a bootstrap aggregation method is put forward to estimate the …

Forecasting Space Weather Using Deep Learning Techniques, Sumi None Dey

Open Access Theses & Dissertations

Solar activity gives rise to various kinds of space weather among which solar flares have serious detrimental eects on both near-Earth space and our upper atmosphere that will have consequent influence in our lives. For example, solar flares can damage satellite infrastructure, hinder power grids, disrupt Global Positioning Systems (GPS) and disrupt long-distance communication. Airplane pilots, cabin crew and astronauts can be aected by the harmful radiation released from the Sun. As a result, there is a need of a methodology to forecast space weather accurately. In this work, we have developed a deep learning architecture to do the short-range …