Computer Sciences Commons^™

Mmwave Rat Optimization: Mac Layer Initial Access Design And Transport Layer Integration, Suresh Srinivasan

Dissertations and Theses

MmWave Radio Access Technology (RAT) is a promising technology for wireless communication due its large bandwidth and is already being deployed in 5G cellular and emerging WiFi technologies. MmWave systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. A mmWave radio can be used either in a standalone mode (where all radios use the same technology) or simultaneously with other technologies such as LTE and low frequency WiFi in a communication mode commonly referred to as integrated mode. This thesis proposes two methods to optimize mmWave RAT performance in both …

Energy Auction With Non-Relational Persistence, Michael Ramez Howard

Dissertations and Theses

As the current landscape for electric vehicles changes, options for remote charging are expanding to keep up. In the United States alone, sales of electric vehicles grew 85% from 2020 until hitting 450,000 units by the end of 2021. While these growing sales are encouraging, commercial charging stations have a long way to go before they are as ubiquitous as gasoline stations are today. The peer-to-peer energy auction helps fill the gap in underserved areas by allowing private homeowners to share their charging facilities with other electric vehicle drivers. The auction framework wraps existing charging outlets with a Cloud-connected microcontroller. …

Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten

Dissertations and Theses

For the safety of both equipment and human life, it is important to identify the location of orphaned radioactive material as quickly and accurately as possible. There are many factors that make radiation localization a challenging task, such as low gamma radiation signal strength and the need to search in unknown environments without prior information. The inverse-square relationship between the intensity of radiation and the source location, the probabilistic nature of nuclear decay and gamma ray detection, and the pervasive presence of naturally occurring environmental radiation complicates localization tasks. The presence of obstructions in complex environments can further attenuate the …

A Deep Hierarchical Variational Autoencoder For World Models In Complex Reinforcement Learning Environments, Sriharshitha Ayyalasomayajula

Dissertations and Theses

Model-based reinforcement learning (MBRL) approaches leverage learned models of the environment to plan and make optimal decisions, reducing the need for extensive real-world interactions and enabling more efficient learning in complex domains such as robotics, autonomous systems, and resource allocation problems. They also provide interpretability and insight into the underlying dynamics, facilitating better decision-making and system understanding.

The world model is a model-based RL approach that employs generative neural network models to learn a compressed spatial and temporal representation of the environment. This work explores world models and a simple single-layered RNN model to learn a simple policy based on …

Implementing A Functional Logic Programming Language Via The Fair Scheme, Andrew Michael Jost

Dissertations and Theses

This document presents a new compiler for the Functional Logic programming language Curry based on a novel pull-tabbing evaluation strategy called the Fair Scheme. A simple version of the Fair Scheme is proven sound, complete, and optimal. An elaborated version is also developed, which supports narrowing computations and other features of Curry, such as constraint programming, equational constraints, and set functions.

The Fair Scheme is used to develop a new Curry system called Sprite, a high-quality, performant implementation whose aims are to promote practical uses of Curry and to serve as a laboratory for further research. An important aspect of …

Toward Efficient Rendering: A Neural Network Approach, Qiqi Hou

Dissertations and Theses

Physically-based image synthesis has attracted considerable attention due to its wide applications in visual effects, video games, design visualization, and simulation. However, obtaining visually satisfactory renderings with ray tracing algorithms often requires casting a large number of rays and thus takes a vast amount of computation. The extensive computational and memory requirements of ray tracing methods pose a challenge, especially when running these rendering algorithms on resource-constrained platforms, and impede their applications that require high resolutions and refresh rates. This thesis presents three methods to address the challenge of efficient rendering.

First, we present a hybrid rendering method to speed …

Scaling Epa-Rimm With Multicore System Management Interrupt Handlers, Alexander K. Freed

Dissertations and Theses

Continuous runtime integrity measurement mechanisms (RIMMs) can be used for timely detection of kernel and hypervisor rootkits. Researchers have proposed running RIMMs in privileged execution environments, such as the x86 architecture’s System Management Mode (SMM), to detect interference from rootkits that have gained control of the host operating system. However, the extended amount of time in SMM required to perform inspections can cause severe disruption to the host. A previously proposed RIMM design called EPA-RIMM addresses this by decomposing long inspections across multiple System Management Interrupts (SMI), the interrupt used to invoke SMM.

EPA-RIMM is intended for deployment on server-class …

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl

Dissertations and Theses

Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …

Toward Analyzing The Diversity Of Extractive Summaries, Aaron David Hudson

Dissertations and Theses

As the amount of text generated across the internet continues to increase, developing methods for processing that text to glean valuable insights is paramount. Automatic text summarization is one such method that aims to provide a concise and representative summary of input text, allowing users access to the most salient points from a large amount of textual data. However, in working with these summaries, especially those generated from social media data, questions arise about not only the quality of a summary, but also its ability to reflect the diversity of user perspectives. This work examines the quality of summaries with …

Unpaired Style Transfer Conditional Generative Adversarial Network For Scanned Document Generation, David Jonathan Hawbaker

Dissertations and Theses

Neural networks are a powerful machine learning tool, especially when trained on a large dataset of relevant high-quality data. Generative adversarial networks, image super resolution and most other image manipulation neural networks require a dataset of images and matching target images for training. Collecting and compiling that data can be time consuming and expensive. This work explores an approach for building a dataset of paired document images with a matching scanned version of each document without physical printers or scanners. A dataset of these document image pairs could be used to train a generative adversarial network or image super resolution …

Making Curry With Rice: An Optimizing Curry Compiler, Steven Libby

Dissertations and Theses

In this dissertation we present the RICE optimizing compiler for the functional logic language Curry. This is the first general optimizing compiler for a functional logic language. Our work is based on the idea of compiling through program transformations, which we have adapted from the functional language compiler community. We also present the GAS system for generating new program transformations, which uses the power of functional logic programming to provide a flexible framework for describing transformations. This allows us to describe and implement a wide range of optimizations including inlining, shortcut deforestation, unboxing, and case shortcutting, a new optimization we …

Scenario Acceleration Through Automated Modelling: A Method And System For Creating Traceable Quantitative Future Scenarios Based On Fcm System Modeling And Natural Language Processing, Christopher W.H. Davis

Dissertations and Theses

Scenario planning is used extensively in strategic planning because it helps leaders broaden their perspectives and make better decisions by presenting possible futures in story form. Some of the benefits of using scenarios include breaking away from groupthink, creating better products, acceleration of organization learning and reducing bias. Product development teams, particularly for digital products, are gaining more autonomy in organizations and tend to manage risk by undergoing very short development iterations on their products while leaning on their consumers for feedback -- a process known as agile development. This method tends to limit the perspective of the team and …

Using Intrinsically-Typed Definitional Interpreters To Verify Compiler Optimizations In A Monadic Intermediate Language, Dani Barrack

Dissertations and Theses

Compiler optimizations are critical to the efficiency of modern functional programs. At the same time, optimizations that unintentionally change the semantics of programs can systematically introduce errors into programs that pass through them. The question of how to best verify that optimizations and other program transformations preserve semantics is an important one, given the potential for error introduction. Dependent types allow us to prove that properties about our programs are correct, as well as to design data types and interpreters in such a way that they are correct-by-construction. In this thesis, we explore the use of dependent types and intrinsically-typed …

An Automated Zoom Class Session Analysis Tool To Improve Education, Jack Arlo Cannon Ii

Dissertations and Theses

The recent shift towards remote education has presented new challenges for instructors with respect to teaching evaluation. Students in traditional classrooms send signals to instructors which provide feedback for the effectiveness of a given lecture. Virtual learning environments lack some of these communication channels and require new ways of collecting feedback. This work presents a suite of analysis tools for the virtual instructor. Given the transcript and video files for a Zoom meeting, this tool summarizes student sentiment and speaking characteristics. Sentiment scores are derived using state of the art Natural Language Processing (NLP) models. The video file is used …

Situate: An Agent-Based System For Situation Recognition, Max Henry Quinn

Dissertations and Theses

Computer vision and machine learning systems have improved significantly in recent years, largely based on the development of deep learning systems, leading to impressive performance on object detection tasks. Understanding the content of images is considerably more difficult. Even simple situations, such as "a handshake", "walking the dog", "a game of ping-pong", or "people waiting for a bus", present significant challenges. Each consists of common objects, but are not reliably detectable as a single entity nor through the simple co-occurrence of their parts.

In this dissertation, toward the goal of developing machine learning systems that demonstrate properties associated with understanding, …

From Mdp To Alphazero, David Robert Sewell

Dissertations and Theses

In this paper I will explain the AlphaGo family of algorithms starting from first principles and requiring little previous knowledge from the reader. The focus will be upon one of the more recent versions AlphaZero but I hope to explain the core principles that allowed these algorithms to be so successful. I will generally refer to AlphaZero as theses [sic] core set of principles and will make it clear when I am referring to a specific algorithm of the AlphaGo family. AlphaZero in short combines Monte Carlo Tree Search (MCTS) with Deep learning and self-play. We will see how these …

Efficient Neuromorphic Algorithms For Gamma-Ray Spectrum Denoising And Radionuclide Identification, Merlin Phillip Carson

Dissertations and Theses

Radionuclide detection and identification are important tasks for deterring a potentially catastrophic nuclear event. Due to high levels of background radiation from both terrestrial and extraterrestrial sources, some form of noise reduction pre-processing is required for a gamma-ray spectrum prior to being analyzed by an identification algorithm so as to determine the identity of anomalous sources. This research focuses on the use of neuromorphic algorithms for the purpose of developing low power, accurate radionuclide identification devices that can filter out non-anomalous background radiation and other artifacts created by gamma-ray detector measurement equipment, along with identifying clandestine, radioactive material.

A sparse …

Automated Statistical Structural Testing Techniques And Applications, Yang Shi

Dissertations and Theses

Statistical structural testing(SST) is an effective testing technique that produces random test inputs from probability distributions. SST shows superiority in fault-revealing power over random testing and deterministic approaches since it heritages the merits from both of them. SST ensures testing thoroughness by setting up a probability lower-bound criterion for each structural cover element and test inputs that exercise a structural cover element sampled from the probability distribution, ensuring testing randomness. Despite the advantages, SST is not a widely used approach in practice. There are two major limitations. First, to construct probability distributions, a tester must understand the underlying software's structure, …

Quantum Grover's Oracles With Symmetry Boolean Functions, Peng Gao

Dissertations and Theses

Quantum computing has become an important research field of computer science and engineering. Among many quantum algorithms, Grover's algorithm is one of the most famous ones. Designing an effective quantum oracle poses a challenging conundrum in circuit and system-level design for practical application realization of Grover's algorithm.

In this dissertation, we present a new method to build quantum oracles for Grover's algorithm to solve graph theory problems. We explore generalized Boolean symmetric functions with lattice diagrams to develop a low quantum cost and area efficient quantum oracle. We study two graph theory problems: cycle detection of undirected graphs and generalized …

Proximal Policy Optimization For Radiation Source Search, Philippe Erol Proctor

Dissertations and Theses

Rapid localization and search for lost nuclear sources in a given area of interest is an important task for the safety of society and the reduction of human harm. Detection, localization and identification are based upon the measured gamma radiation spectrum from a radiation detector. The nonlinear relationship of electromagnetic wave propagation paired with the probabilistic nature of gamma ray emission and background radiation from the environment leads to ambiguity in the estimation of a source's location. In the case of a single mobile detector, there are numerous challenges to overcome such as weak source activity, multiple sources, or the …

Information Security Maturity Model For Healthcare Organizations In The United States, Bridget Joan Barnes Page

Dissertations and Theses

This research provides a maturity model for information security for healthcare organizations in the United States. Healthcare organizations are faced with increasing threats to the security of their information systems. The maturity model identifies specific performance metrics, with relative importance measures, that can be used to enhance information security at healthcare organizations allowing them to focus scarce resources on mitigating the most important information security threat vectors. This generalizable, hierarchical decision model uses both qualitative and quantitative metrics based on objective goals. This model may be used as a baseline by which to measure individual organizational performance, to measure performance …

A Method For Comparative Analysis Of Trusted Execution Environments, Stephano Cetola

Dissertations and Theses

The problem of secure remote computation has become a serious concern of hardware manufacturers and software developers alike. Trusted Execution Environments (TEEs) are a solution to the problem of secure remote computation in applications ranging from "chip and pin" financial transactions to intellectual property protection in modern gaming systems. While extensive literature has been published about many of these technologies, there exists no current model for comparing TEEs. This thesis provides hardware architects and designers with a set of tools for comparing TEEs. I do so by examining several properties of a TEE and comparing their implementations in several technologies. …

Storing Intermediate Results In Space And Time: Sql Graphs And Block Referencing, Basem Ibrahim Elazzabi

Dissertations and Theses

With the advancement of data-collection technology and with more data being available for data analysts for data-intensive decision making, many data analysts use client-based data-analysis environments to analyze that data. Client-based environments where only a personal computer or a laptop is used to perform data analysis tasks are common. In such client-based environments, multiple tools and systems are typically needed to accomplish data-analysis tasks. Stand-alone systems such as spreadsheets, R, Matlab, and Tableau are usually easy to use, and they are designed for the typical, non-technical data analyst. However, these systems are limited in their data-analysis capabilities. More complex data …

Forecasting Optimal Parameters Of The Broken Wing Butterfly Option Strategy Using Differential Evolution, David Munoz Constantine

Dissertations and Theses

Obtaining an edge in financial markets has been the objective of many hedge funds, investors, and market participants. Even with today's abundance of data and computing power, few individuals achieve a consistent edge over an extended time. To obtain this edge, investors usually use options strategies. The Broken Wing Butterfly (BWB) is an options strategy that has increased in popularity among traders. Profit is generated primarily by exploiting option value time decay. In this thesis, the selection of entry and exit BWB parameters, such as profit and loss targets, are optimized for an in-sample period. Afterward, they are used to …

Automated Test Generation For Validating Systemc Designs, Bin Lin

Dissertations and Theses

Modern system design involves integration of all components of a system on a single chip, namely System-on-a-Chip (SoC). The ever-increasing complexity of SoCs and rapidly decreasing time-to-market have pushed the design abstraction to the electronic system level (ESL), in order to increase design productivity. SystemC is a widely used ESL modeling language that plays a central role in modern SoCs design process. ESL SystemC designs usually serve as executable specifications for the subsequent SoCs design flow. Therefore, undetected bugs in ESL SystemC designs may propagate to low-level implementations or even final silicon products. In addition, modern SoCs design often involves …

Exploring The Potential Of Sparse Coding For Machine Learning, Sheng Yang Lundquist

Dissertations and Theses

While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifications of test images, including being susceptible to adversarial examples. Additionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples.

In this dissertation, I investigate whether the use …

Novel View Synthesis - A Neural Network Approach, Hoang Le

Dissertations and Theses

Novel view synthesis is an important research problem in computer vision and computational photography. It enables a wide range of applications including re-cinematography, video enhancement, virtual reality, etc. These algorithms leverage a pre-acquired set of images taken from a set of viewpoints to synthesize another image at a novel viewpoint as if it was captured by a real camera. To synthesize a high-quality novel view, these algorithms often assume a static scene, or the images were captured synchronously. However, the scenes in practice are often dynamic, and taking a dense set of images of these scenes at the same moment …

A Computer Science Academic Vocabulary List, David Roesler

Dissertations and Theses

This thesis documents the development of the Computer Science Academic Vocabulary List (CSAVL), a pedagogical tool intended for use by English-for-specific-purpose educators and material developers. A 3.5-million-word corpus of academic computer science textbooks and journal articles was developed in order to produce the CSAVL. This study draws on the improved methodologies used in the creation of recent lemma-based word lists such as the Academic Vocabulary List (AVL) and the Medical Academic Vocabulary List (MAVL), which take into account the discipline-specific meanings of academic vocabulary. The CSAVL provides specific information for each entry, including part of speech and CS-specific meanings in …

Leveraging Model Flexibility And Deep Structure: Non-Parametric And Deep Models For Computer Vision Processes With Applications To Deep Model Compression, Anthony D. Rhodes

Dissertations and Theses

My dissertation presents several new algorithms incorporating non-parametric and deep learning approaches for computer vision and related tasks, including object localization, object tracking and model compression. With respect to object localization, I introduce a method to perform active localization by modeling spatial and other relationships between objects in a coherent "visual situation" using a set of probability distributions. I further refine this approach with the Multipole Density Estimation with Importance Clustering (MIC-Situate) algorithm. Next, I formulate active, "situation" object search as a Bayesian optimization problem using Gaussian Processes. Using my Gaussian Process Context Situation Learning (GP-CL) algorithm, I demonstrate improved …

Smart Contract Vulnerabilities On The Ethereum Blockchain: A Current Perspective, Daniel Steven Connelly

Dissertations and Theses

Ethereum is a unique offshoot of blockchain technologies that incorporates the use of what are called smart contracts or DApps -- small-sized programs that orchestrate financial transactions on the Ethereum blockchain. With this fairly new paradigm in blockchain, however, comes a host of security concerns and a track record that reveals a history of losses in the range of millions of dollars. Since Ethereum is a decentralized entity, these concerns are not allayed as they are in typical financial institutions. For example, there is no Federal Deposit Insurance Corporation (FDIC) to back the investors of these contracts from financial loss …