Computer Sciences Commons^™

Predicting Changes To Source Code, Justin James Roll

Master's Theses

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement …

Real-Time Netnography: Rejecting The Passive Shift, Leesa Costello, Marie-Louise Mcdermott

Research outputs 2014 to 2021

Although netnography emerged in the 1990s, it is a term unfamiliar to many ethnographers and is still touted as a new methodology. Once explained, ethnographers often understand it in terms of online ethnography. While this is helpful, netnography, however, offers a set of steps and analytic approaches that can be applied across a spectrum of involvement online. Its focus is on gaining entree to an online community, distinguishing between participant observation and nonparticipant observation.

Exploration Of Web Technologies: A Real World Application, Andrew Ballard, James Francis, Sam Jentsch

Undergraduate Research Conference

Our team created a web application for a photography studio. In addition to a portfolio for the studio, the application required the ability to manage photographer schedules, handle and organize orders and provide secure user accounts with different access levels for the site.

Ensemble Forecasts: Probabilistic Seasonal Forecasts Based On A Model Ensemble, Hannah Aizenman, Michael D. Grossberg, Nir Y. Krakauer, Irina Gladkova

Publications and Research

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 …

Masthead

Journal of Digital Forensics, Security and Law

No abstract provided.

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Back Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2016, Ven Basco

Libraries' Newsletters

No abstract provided.

Deep Learning For Population Genetic Inference, Sara Sheehan, Yun S. Song

Computer Science: Faculty Publications

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statis- tics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on …

Examining The Longitudinal Nature Of Information Privacy Perceptions And Behaviors

Journal of Undergraduate Research

The purpose of this project was to develop and execute improved research methodology for studying how consumer information privacy perceptions and behaviors change over time. This project is unique because most of the behavioral research regarding information privacy (and with mobile devices in particular) had previously been based entirely on surveys and laboratory experiments with low external validity. Therefore, to accomplish our objective, several mobile applications were developed or improved with built-in capabilities for experimental manipulations which were tested in real-life field studies. We found several interesting new findings which have resulted in published conference paper proceedings with student authors …

Modeling And Survival Analysis Of Breast Cancer: A Statistical, Artificial Neural Network, And Decision Tree Approach, Venkateswara Rao Mudunuru

USF Tampa Graduate Theses and Dissertations

Survival analysis today is widely implemented in the fields of medical and biological sciences, social sciences, econometrics, and engineering. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. Events or outcomes are defined by a transition from one discrete state to another at an instantaneous moment in time. In the recent years, research …

Whitelisting System State In Windows Forensic Memory Visualizations, Joshua A. Lapso

Theses and Dissertations

Examiners in the field of digital forensics regularly encounter enormous amounts of data and must identify the few artifacts of evidentiary value. The most pressing challenge these examiners face is manual reconstruction of complex datasets with both hierarchical and associative relationships. The complexity of this data requires significant knowledge, training, and experience to correctly and efficiently examine. Current methods provide primarily text-based representations or low-level visualizations, but levee the task of maintaining global context of system state on the examiner. This research presents a visualization tool that improves analysis methods through simultaneous representation of the hierarchical and associative relationships and …

Cyberspace And Organizational Structure: An Analysis Of The Critical Infrastructure Environment, Michael D. Quigg Ii

Theses and Dissertations

Now more than ever, organizations are being created to protect the cyberspace environment. The capability of cyber organizations tasked to defend critical infrastructure has been called into question by numerous cybersecurity experts. Organizational theory states that organizations should be constructed to fit their operating environment properly. Little research in this area links existing organizational theory to cyber organizational structure. Because of the cyberspace connection to critical infrastructure assets, the factors that influence the structure of cyber organizations designed to protect these assets warrant analysis to identify opportunities for improvement.

This thesis analyzes the cyber‐connected critical infrastructure environment using the dominant …

Key Detection Rate Modeling And Analysis For Satellite-Based Quantum Key Distribution, Jonathan C. Denton

Theses and Dissertations

A satellite QKD model was developed and validated, that allows a user to determine the optimum wavelength for use in a satellite-based QKD link considering the location of ground sites, selected orbit and hardware performance. This thesis explains how the model was developed, validated and presents results from a simulated year-long study of satellite-based quantum key distribution. It was found that diffractive losses and atmospheric losses define a fundamental trade space that drives both orbit and wavelength selection. The optimal orbit is one which generates the highest detection rates while providing equal pass elevation angles and durations to multiple ground …

A Framework For Incorporating Insurance Into Critical Infrastructure Cyber Risk Strategies, Derek R. Young

Theses and Dissertations

Critical infrastructure owners and operators want to minimize their cyber risk and expenditures on cybersecurity. The insurance industry has been quantitatively assessing risk for hundreds of years in order to minimize risk and maximize profits. To achieve these goals, insurers continuously gather statistical data to improve their predictions, incentivize their clients' investment in self-protection and periodically refine their models to improve the accuracy of risk estimates. This paper presents a framework which incorporates the operating principles of the insurance industry in order to provide quantitative estimates of cyber risk. The framework implements optimization techniques to suggest levels of investment for …

Deception In Game Theory: A Survey And Multiobjective Model, Austin L. Davis

Theses and Dissertations

Game theory is the study of mathematical models of conflict. It provides tools for analyzing dynamic interactions between multiple agents and (in some cases) across multiple interactions. This thesis contains two scholarly articles. The first article is a survey of game-theoretic models of deception. The survey describes the ways researchers use game theory to measure the practicality of deception, model the mechanisms for performing deception, analyze the outcomes of deception, and respond to, or mitigate the effects of deception. The survey highlights several gaps in the literature. One important gap concerns the benefit-cost-risk trade-off made during deception planning. To address …

Cross-Subject Continuous Analytic Workload Profiling Using Stochastic Discrete Event Simulation, Joseph J. Giametta

Theses and Dissertations

Operator functional state (OFS) in remotely piloted aircraft (RPA) simulations is modeled using electroencephalograph (EEG) physiological data and continuous analytic workload profiles (CAWPs). A framework is proposed that provides solutions to the limitations that stem from lengthy training data collection and labeling techniques associated with generating CAWPs for multiple operators/trials. The framework focuses on the creation of scalable machine learning models using two generalization methods: 1) the stochastic generation of CAWPs and 2) the use of cross-subject physiological training data to calibrate machine learning models. Cross-subject workload models are used to infer OFS on new subjects, reducing the need to …

A Misuse-Based Intrusion Detection System For Itu-T G.9959 Wireless Networks, Jonathan D. Fuller

Theses and Dissertations

Wireless Sensor Networks (WSNs) provide low-cost, low-power, and low-complexity systems tightly integrating control and communication. Protocols based on the ITU-T G.9959 recommendation specifying narrow-band sub-GHz communications have significant growth potential. The Z-Wave protocol is the most common implementation. Z-Wave developers are required to sign nondisclosure and confidentiality agreements, limiting the availability of tools to perform open source research. This work discovers vulnerabilities allowing the injection of rogue devices or hiding information in Z-Wave packets as a type of covert channel attack. Given existing vulnerabilities and exploitations, defensive countermeasures are needed. A Misuse-Based Intrusion Detection System (MBIDS) is engineered, capable of …

Poco-Moea: Using Evolutionary Algorithms To Solve The Controller Placement Problem, Scott I. Harned

Theses and Dissertations

One of the central tenets of a Software Defined Network (SDN) is the use of controllers, which are responsible for managing how traffic flows through switches, routers, and other data-passing devices on a computer network. Most modern SDNs use multiple controllers to divide responsibility for network switches while keeping communication latency low. A problem that has emerged since approximately 2011 is the decision of where to place these controllers to create the most 'optimum' network. This is known as the Controller Placement Problem (CPP). Such a decision is subject to multiple and sometimes con_icting goals, making the CPP a type …

Statistic Whitelisting For Enterprise Network Incident Response, Nathan E. Grunzweig

Theses and Dissertations

This research seeks to satisfy the need for the rapid evaluation of enterprise network hosts in order to identify items of significance through the introduction of a statistic whitelist based on the behavior of the processes on each host. By taking advantage of the repetition of processes and the resources they access, a whitelist can be generated using large quantities of host machines. For each process, the Modules and the TCP & UDP Connections are compared to identify which resources are most commonly accessed by each process. Results show 47% of processes receiving a whitelist score of 75% or greater …

Analysis Of Software Design Patterns In Human Cognitive Performance Experiments, Alexander C. Roosma

Theses and Dissertations

As Air Force operations continue to move toward the use of more autonomous systems and more human-machine teaming in general, there is a corresponding need to swiftly evaluate systems with these capabilities. We support this development through software design improvements of the execution of human cognitive performance experiments. This thesis sought to answer the following two research questions addressing the core functionality that these experiments rely on for execution and analysis: 1) What data infrastructure software requirements are necessary to execute the experimental design of human cognitive performance experiments? 2) How effectively does a central data mediator design pattern meet …

Pointing Analysis And Design Drivers For Low Earth Orbit Satellite Quantum Key Distribution, Jeremiah A. Specht

Theses and Dissertations

The world relies on encryption to perform critical and sensitive tasks every day. If quantum computing matures, the capability to decode keys and decrypt messages becomes possible. Quantum key distribution (QKD) is a method of distributing secure cryptographic keys which relies on the laws of quantum mechanics. Current implementations of QKD use fiber-based channels which limit the number of users and the distance between users. Satellite-based QKD using free-space channels is proposed as a feasible secure global communication solution. Since a free-space link does not use a waveguide, pointing a transmitter to receiver is required to ensure signal arrival. In …

Framework For Evaluating The Readiness Of Cyber First Responders Responsible For Critical Infrastructure Protection, Jungsang Yoon

Theses and Dissertations

First responders go through rigorous training and evaluation to ensure they are adequately prepared for an emergency. As an example, firefighters continually evaluate the readiness of their personnel using a defined set of criteria to measure performance for fire suppression and rescue procedures. From a cyber security standpoint, however, this same set of criteria and rigor is severely lacking for the professionals that must detect, respond to and recover from a cyber-based attack against the nation's critical infrastructure. This research provides a framework for evaluating the readiness of cyber first responders responsible for critical infrastructure protection. The framework demonstrates the …

Position And Volume Estimation Of Atmospheric Nuclear Detonations From Video Reconstruction, Daniel T. Schmitt

Theses and Dissertations

Recent work in digitizing films of foundational atmospheric nuclear detonations from the 1950s provides an opportunity to perform deeper analysis on these historical tests. This work leverages multi-view geometry and computer vision techniques to provide an automated means to perform three-dimensional analysis of the blasts for several points in time. The accomplishment of this requires careful alignment of the films in time, detection of features in the images, matching of features, and multi-view reconstruction. Sub-explosion features can be detected with a 67% hit rate and 22% false alarm rate. Hotspot features can be detected with a 71.95% hit rate, 86.03% …

"Hour Of Code”: Can It Change Students’ Attitudes Toward Programming?, Jie Du, Hayden Wimmer, Roy Rada

Information Technology: Faculty Publications

The Hour of Code is a one-hour introduction to computer science organized by Code.org, a non-profit dedicated to expanding participation in computer science. This study investigated the impact of the Hour of Code on students’ attitudes towards computer programming and their knowledge of programming. A sample of undergraduate students from two universities was selected to participate. Participants completed an Hour of Code tutorial as part of an undergraduate course. An electronic questionnaire was implemented in a pre-survey and post-survey format to gauge the change in student attitudes toward programming and their programming ability. The findings indicated the positive impact of …

Handwriting Recognition Through Distance-Based Morphing And Energy Minimization, Dr. William Barrett

Journal of Undergraduate Research

During the past year, I have had the opportunity to mentor two undergraduate students as we performed research for improving technologies used for family history. The specific projects each student worked on, the outcomes of the projects, and the mentoring are described below.

Detecting And Tracking Attacks In Mobile Edge Computing Platforms, Abderrahmen Mtibaa, Khaled A. Harras, Hussein Alnuweiri

Computer Science Faculty Works

Device-to-device (d2d) communication has emerged as a solution that promises high bit rates, low delay and low energy consumption which represents the key for novel technologies such as Google Glass, S Beam, and LTE-Direct. Such d2d communication has enabled computational offloading among collaborative mobile devices for a multitude of purposes such as reducing the overall energy, ensuring resource balancing across device, reducing the execution time, or simply executing applications whose computing requirements transcend what can be accomplished on a single device. While this novel computation platform has offered convenience and multiple other advantages, it obviously enables new security challenges and …

Teaching Critical Media Literacy Through Videogame Creation In Scratch Programming, Elizabeth Anne Gregg

LMU Theses and Dissertations

Critical media literacy (Kellner & Share, 2005) may better equip children to interpret videogame content and to create games that are nonviolent and socially just. Videogames are growing in popularity in classrooms. Yet educators and parents have concerns about the violent and stereotypical content they include. An earlier study based on the curriculum Beyond Blame: Challenging Violence in the Media (Webb, Martin, Afifi, & Kraus, 2009) examined the value of a media awareness curriculum. In this mixed-method study, I explored the effectiveness of a critical media literacy program that incorporated collaboratively creating nonviolent or sociallyjust games in teaching fourth-grade students …

Using A Dynamic Domain-Specific Modeling Language For The Model-Driven Development Of Cross-Platform Mobile Applications, Christopher A. Jones

College of Computing and Digital Media Dissertations

There has been a gradual but steady convergence of dynamic programming languages with modeling languages. One area that can benefit from this convergence is modeldriven development (MDD) especially in the domain of mobile application development. By using a dynamic language to construct a domain-specific modeling language (DSML), it is possible to create models that are executable, exhibit flexible type checking, and provide a smaller cognitive gap between business users, modelers and developers than more traditional model-driven approaches.

Dynamic languages have found strong adoption by practitioners of Agile development processes. These processes often rely on developers to rapidly produce working code …

Random Forest Algorithm For Land Cover Classification, Arun D. Kulkarni, Barrett Lowe

Computer Science Faculty Publications and Presentations

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree …