Physical Sciences and Mathematics Commons^™

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Culminating Experience Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …

Watching The Watchmen: An Ethical Evaluation Of The Behavior Of Modern Software Applications, Joshua Graves

University Honors Program Senior Projects

Software has become a ubiquitous element of modern life around the world. An unprecedented amount of power is bestowed upon the companies that own and operate that software. The obvious question arises: “Do these companies operate in an ethical manner regarding their software?” We derive an ethical code via synthesizing the ethical codes of both the IEEE and the ACM, disregarding principles that cannot be examined by an outside observer. We utilize this ethical code to examine five leaders in the software industry, namely Facebook, Google, Microsoft, Twitter, and Amazon. For each company, we examine four incidents in which they …

Uni-Prover: A Universal Automated Prover For Specificationally Rich Languages, Nicodemus Msafiri John Mbwambo

All Dissertations

Formal software verification systems must be designed to adapt to growth in the scope and complexity of software, driven by expanding capabilities of computer hardware and domain of potential usage. They must provide specification languages that are flexible and rich enough to allow software developers to write precise and comprehensible specifications for a full spectrum of object-based software components. Rich specification languages allow for arbitrary extensions to the library of mathematical theories, and critically, verification of programs with such specifications require a universal automated prover. Most existing verification systems either incorporate specification languages limited to first-order logic, which lacks the …

Design Of Ethical Autonomous Agents For Unmanned Aerial Vehicles Using Fuzzy Logic, Gavin Giovanni Smith

Theses and Dissertations

Autonomous systems have, over the years become part of our everyday lives. These systems have been deployed to executed a diverse range of applications in different industries; finance, healthcare, military, and in particular, the flight industry. With the rise of UAVs, new opportunities arose, but with those opportunities came new pitfalls within any industry. For UAVs, one of the pitfalls came in the form of ethical decisionmaking, which led to a variety of questions. Can the Autonomous systems within UAVs be designed with ethics in mind? Which ethical guidelines would we use to implement such a system? How would we …

Learning To Reason About Code With Assertions: An Exploration With Two Student Populations, Sarah Blankenship

All Theses

Code tracing is fundamental to students’ understanding of a program, and symbolic reasoning that entails learning to use assertions with abstract input and output values, as opposed to concrete values, enhances that understanding. Symbolic reasoning teaches students valuable abstraction and logic skills that will serve them well in all aspects of programming and their software
development careers.
We use lessons integrated into an online educational tool to supplement classroom instruction to help students learn symbolic reasoning. We explore two ways for students to learn about assertions: Writing assertions to capture the behavior of given code and solving Parsons-style problems in …

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

Wildfire Spread Prediction Using Attention Mechanisms In U-Net, Kamen Haresh Shah, Kamen Haresh Shah

Master's Theses

An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression …

A Data Driven Model To Promote Preparedness And Respond Intelligently To Pandemic Outbreaks, Safea Mohammed Al Senani

Theses

The COVID-19 pandemic has had a major effect on various vital sectors of the economy, including education healthcare, and the industry. Governments have imposed strict regulations to reduce the spread of this global disease outbreak. Consequently, working from home, online learning, social distancing and various control measures were enforced. In response, many schools shifted to distance learning, although most of these schools were neither technically ready nor administratively prepared for the online transition. Despite recent progress, countries are still experiencing daunting challenges to control the infection rate and magnitude, stabilize the economy, and relax socialization and public life activities. Decision-makers …

Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann

Doctoral Dissertations and Master's Theses

The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …

Extracting Microservice Dependencies Using Log Analysis, Andres O. Rodriguez Ishida

Electronic Thesis and Dissertation Repository

Microservice architecture is an architectural style that supports the design and implementation of very scalable systems by distributing complex functionality to highly granular components. These highly granular components are referred to as microservices and can be dynamically deployed on Docker containers. These microservice architecture systems are very extensible since new microservices can be added or replaced as the system evolves. In such highly granular architectures, a major challenge that arises is how to quickly identify whether any changes in the system’s structure violate any policies or design constraints. Examples of policies and design constraints include whether a microservice can call …

Towards A Novel And Intelligent E-Commerce Framework For Smart-Shopping Applications, Susmitha Hanumanthu

Electronic Thesis and Dissertation Repository

Nowadays, with the advancement of market digitalization accompanied by internet technologies, consumers can buy products from anywhere in the world. Finding the best-offered deal from numerous e-commerce sites and online stores is overwhelming, time-consuming, and often not very effective. Customers need to visit many online stores to find their desired product at the desired price. Also, the option of finding a product in the future time that is not currently available is limited in the current e-commerce platform. To address these limitations, there is a need to develop a new one-stop e-shopping model that would allow customers to search for …

Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt

Theses and Dissertations

Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory …

A Tool-Supported Metamodel For Program Bugfix Analysis In Empirical Software Engineering, Manal Zneit

Theses and Dissertations

This thesis describes a software modeling approach aimed at addressing empirical studies in software engineering. We build a metamodel that provides an overview of the taxonomy of program bugfixes in deep learning programs. For modeling purposes, we present a prototype tool that is an implementation of the model-driven techniques presented.

The Design And Implementation Of A High-Performance Polynomial System Solver, Alexander Brandt

Electronic Thesis and Dissertation Repository

This thesis examines the algorithmic and practical challenges of solving systems of polynomial equations. We discuss the design and implementation of triangular decomposition to solve polynomials systems exactly by means of symbolic computation.

Incremental triangular decomposition solves one equation from the input list of polynomials at a time. Each step may produce several different components (points, curves, surfaces, etc.) of the solution set. Independent components imply that the solving process may proceed on each component concurrently. This so-called component-level parallelism is a theoretical and practical challenge characterized by irregular parallelism. Parallelism is not an algorithmic property but rather a geometrical …

Deep Learning Edge Detection In Image Inpainting, Zheng Zheng

Electronic Theses, Projects, and Dissertations

In recent years, deep learning has grown rapidly, and it has been creatively implemented for various applications. In 2019, deep learning based EdgeConnect image inpainting algorithm came out and occupied a place in the image inpainting field. Unlike traditional image inpainting methods which mainly read and use the color information of the remaining part of the image to fill the missing regions of the image, EdgeConnect uses the innovative edge-first and color-next approach. It uses an edge detector to generate an edge map of an image with missing regions, then the missing edges are completed by an edge model, finally …

Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning, Keerthi Krishnan

Computational and Data Sciences (MS) Theses

The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental …

Constraint-Aware, Scalable, And Efficient Algorithms For Multi-Chip Power Module Layout Optimization, Imam Al Razi

Graduate Theses and Dissertations

Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM …

Task-Based Runtime Optimizations Towards High Performance Computing Applications, Qinglei Cao

Doctoral Dissertations

The last decades have witnessed a rapid improvement of computational capabilities in high-performance computing (HPC) platforms thanks to hardware technology scaling. HPC architectures benefit from mainstream advances on the hardware with many-core systems, deep hierarchical memory subsystem, non-uniform memory access, and an ever-increasing gap between computational power and memory bandwidth. This has necessitated continuous adaptations across the software stack to maintain high hardware utilization. In this HPC landscape of potentially million-way parallelism, task-based programming models associated with dynamic runtime systems are becoming more popular, which fosters developers’ productivity at extreme scale by abstracting the underlying hardware complexity.

In this context, …

Reputation-Based Trust Assessment Of Transacting Service Components, Konstantinos Tsiounis

Electronic Thesis and Dissertation Repository

As Service-Oriented Systems rely for their operation on many different, and most often, distributed software components, a key issue that emerges is how one component can trust the services offered by another component. Here, the concept of trust is considered in the context of reputation systems and is viewed as a meta-requirement, that is, the level of belief a service requestor has that a service provider will provide the service in a way that meets the requestor’s expectations. We refer to the service offering components as service providers (SPs) and the service requesting components as service clients (SCs).

In this …

Deepcause: Verifying Neural Networks With Abstraction Refinement, Nguyen Hua Gia Phuc

Dissertations and Theses Collection (Open Access)

Neural networks have been becoming essential parts in many safety-critical systems (such
as self-driving cars and medical diagnosis). Due to that, it is desirable that neural networks
not only have high accuracy (which traditionally can be validated using a test set) but also
satisfy some safety properties (such as robustness, fairness, or free of backdoor). To verify
neural networks against desired safety properties, there are many approaches developed
based on classical abstract interpretation. However, like in program verification, these
approaches suffer from false alarms, which may hinder the deployment of the networks.

One natural remedy to tackle the problem adopted …

Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel

Theses and Dissertations

Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …

Optimizing The Performance Of Parallel And Concurrent Applications Based On Asynchronous Many-Task Runtimes, Weile Wei

LSU Doctoral Dissertations

Nowadays, High-performance Computing (HPC) scientific applications often face per- formance challenges when running on heterogeneous supercomputers, so do scalability, portability, and efficiency issues. For years, supercomputer architectures have been rapidly changing and becoming more complex, and this challenge will become even more com- plicated as we enter the exascale era, where computers will exceed one quintillion cal- culations per second. Software adaption and optimization are needed to address these challenges. Asynchronous many-task (AMT) systems show promise against the exascale challenge as they combine advantages of multi-core architectures with light-weight threads, asynchronous executions, smart scheduling, and portability across diverse architectures.

In …

Machine Learning With Big Data For Electrical Load Forecasting, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Today, the amount of data collected is exploding at an unprecedented rate due to developments in Web technologies, social media, mobile and sensing devices and the internet of things (IoT). Data is gathered in every aspect of our lives: from financial information to smart home devices and everything in between. The driving force behind these extensive data collections is the promise of increased knowledge. Therefore, the potential of Big Data relies on our ability to extract value from these massive data sets. Machine learning is central to this quest because of its ability to learn from data and provide data-driven …

Determining American Sign Language Joint Trajectory Similarity Using Dynamic Time Warping (Dtw), Rohith Mandavilli

Computer Science Senior Theses

As American Sign Language (ASL), the language used by Deaf/Hard of Hearing (D/HH) Americans has grown in popularity in recent years, an unprecedented number of schools and organizations now offer ASL classes. Many hold misconceptions about ASL, assuming it is easily learned; however due to its rich, complex grammatical construction, it’s not mastered easily beyond a basic level. Therefore, it becomes ever more important to improve upon existing techniques to teach ASL. The Dartmouth Applied Learning Initiative (DALI) at Dartmouth college in coordination with the Robotics and Reality Lab developed an application on the Oculus Quest that helps D/HH individuals …

Developing A Miniature Smart Boat For Marine Research, Michael Isaac Eirinberg

Computer Engineering

This project examines the development of a smart boat which could serve as a possible marine research apparatus. The smart boat consists of a miniature vessel containing a low-cost microcontroller to live stream a camera feed, GPS telemetry, and compass data through its own WiFi access point. The smart boat also has the potential for autonomous navigation. My project captivated the interest of several members of California Polytechnic State University, San Luis Obispo’s (Cal Poly SLO) Marine Science Department faculty, who proposed a variety of fascinating and valuable smart boat applications.

Rasm: Compiling Racket To Webassembly, Grant Matejka

Master's Theses

WebAssembly is an instruction set designed for a stack based virtual machine, with an emphasis on speed, portability and security. As the use cases for WebAssembly grow, so does the desire to target WebAssembly in compilation. In this thesis we present Rasm, a Racket to WebAssembly compiler that compiles a select subset of the top forms of the Racket programming language to WebAssembly. We also present our early findings in our work towards adding a WebAssembly backend to the Chez Scheme compiler that is the backend of Racket. We address initial concerns and roadblocks in adopting a WebAssembly backend and …

Legislative Language For Success, Sanjana Gundala

Master's Theses

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what …

Wildfire Risk Assessment Using Convolutional Neural Networks And Modis Climate Data, Sean F. Nesbit

Master's Theses

Wildfires burn millions of acres of land each year leading to the destruction of homes and wildland ecosystems while costing governments billions in funding. As climate change intensifies drought volatility across the Western United States, wildfires are likely to become increasingly severe. Wildfire risk assessment and hazard maps are currently employed by fire services, but can often be outdated. This paper introduces an image-based dataset using climate and wildfire data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The dataset consists of 32 climate and topographical layers captured across 0.1 deg by 0.1 deg tiled regions in California and Nevada between …

The Stakeholder-Profile Framework For Tacit Knowledge Acquisition In Requirements Elicitation Interviews, Rasha Eltigani

Master of Science in Software Engineering Theses

The stakeholder’s tacit knowledge is a key crown jewel of requirements elicitation, and in turn software development at large. This critical element holds significant leverage in determining the outcome and the quality of the requirements, and therefore the development endeavor holistically. Due to its very nature of being tacit, it is innately covert and deeply hidden within the stakeholders’ minds, so it is extremely difficult to articulate and relay, as well as even harder to elicit and utilize. Additionally, the literature reports that there is a scarcity of available theorizations and solutions for addressing this challenge, posing a key and …

Privacy Assessment Breakthrough: A Design Science Approach To Creating A Unified Methodology, Lisa Mckee

Masters Theses & Doctoral Dissertations

Recent changes have increased the need for and awareness of privacy assessments. Organizations focus primarily on Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) but rarely take a comprehensive approach to assessments or integrate the results into a privacy risk program. There are numerous industry standards and regulations for privacy assessments, but the industry lacks a simple unified methodology with steps to perform privacy assessments. The objectives of this research project are to create a new privacy assessment methodology model using the design science methodology, update industry standards and present training for conducting privacy assessments that can be …