Computer Sciences Commons^™

Procedural Terrain Generation: Noise Functions, Modern Methods, And Style Transfer, Hunter Barton

EWU Masters Thesis Collection

Procedural terrain generation, the algorithmic creation of digital terrain, finds use in multiple types of digital media. As the capabilities of modern computation increase, the ability to create more and more realistic terrains fully procedurally at scale improves. Modern methods of procedural generation have also overlapped with these advances, most notably advances in hardware. To account for this, a survey was done of modern methods for procedural terrain generation. Smooth procedural noise functions are one of the backbones of procedural terrain generation. Perlin noise, value noise, and fractal noise were explored in-depth. These noise functions were also tested for capabilities …

Death By Design: A Biological Approach To Container Security, Alexander Hunter Moomaw

EWU Masters Thesis Collection

Microservice architectures are central to modern cloud computing and have become the dominant design pattern for scalable, distributed applications. Kubernetes is often the tool used for rapid deployment and orchestration of microservices. A known issue for large networks clusters is a malicious actor can compromise a vulnerable cluster in minutes. Their ability to quickly compromise vulnerable clusters highlights the urgent need for stronger security. This project views cluster defense through a preemptive security lens, introducing the Cluster Life cycle Management System (CLMS). Inspired by the biological process of apoptosis, CLMS systematically replaces aging containers and services to prevent exploitation of …

Temporal Team Formation Games With Dynamic Preferences, Cameron Egbert

Theses and Dissertations--Computer Science

In the professional world, it is imperative for management entities to allocate their human resources to a work schedule, and such models of coalition formation games are well-studied. However, most existing literature only considers coalition formation in the context of a single moment in time, without accounting for changing preferences among individuals as they work together. The primary contribution of this thesis is a new team formation game that incorporates skill-based team formation and a dynamic variant of Additively Separable Hedonic Games. These Temporal Team Formation Games with Dynamic Preferences (TTFG-DPs) allow for two psychologically common preference dynamics: a preference …

Scalable Parallel-In-Time Integration For Equations Of Motion, Nathan W. Chapman

All Master's Theses

Physical simulations always need to balance accuracy and run-time. This work implements the Parareal Algorithm using graphics processing units across a distributed system to accurately simulate time-dependent physics while attempting to minimize runtime. Data-transfer latency is identified as the primary bottleneck, for which mitigation methods are provided. Benchmarks comparing single-GPU and distributed implementations on a spectrum of coarse and fine discretizations are analyzed.

Efficient Task Scheduling In Cloud Infrastructures Using Dynamic Score-Based Allocation And Deep Q-Learning, Shadman Sakib

Graduate Theses/Dissertations

Cloud computing has grown rapidly in recent years, mainly due to the sharp increase in data transferred over the internet. This growth makes task scheduling a key and challenging part of cloud systems, as it helps distribute user requests across servers to minimize response time, prevent overloading, and ensure smooth user experience. This thesis proposes two novel approaches for dynamic task scheduling in cloud environments. First, a novel Score-Based Dynamic Load Balancing (SBDLB) strategy is developed, which leverages system parameters to allocate tasks efficiently across virtual machines (VMs) in data centers. SBDLB ensures balanced workload distribution by continuously evaluating VM …

Agentic Ai Systems In Professional Domains: A Probabilistic Framework For Role Suitability And Legal Accountability, Matthew Veach

Master's Theses and Doctoral Dissertations

This thesis presents a cross-sector analysis of agentic AI systems deployed in STEM, education, healthcare, and enterprise domains, with a focus on role suitability, orchestration maturity, and legal accountability. It introduces the Bounded Agentic Suitability Envelope, a dual-bound scoring framework that evaluates deployment viability using weighted assessments of agent capability and decomposed role complexity. Through case studies from Fujitsu, Cleveland Clinic, Carnegie Learning, and Duolingo, the thesis demonstrates measurable gains in efficiency, personalization, and compliance. It argues for an augmentation-first strategy, preserving human roles in high-context domains while enabling targeted replacement in low-complexity workflows. To mitigate risk, the thesis formalizes …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Connecting Worlds: Travelmate’S Bidding System For Personalized Travel Experiences, Suman Khadka

Williams Honors College, Honors Research Projects

TravelMate is a web platform that connects travelers with local guides through a personalized trip posting system along with real time bidding from freelancer guides. This platform utilizes Next.js for a responsive frontend, and Supabase for managing database and backend RESTful API, using PostgREST - a thin API layer on top of Postgres. This platform is ideal for anyone looking to explore new places with a freelancer guide and gain cultural insights from a local expert who understands the place deeply.

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Multitask Learning For Named Entity Recognition And Relationship Extraction, Adrienne D. Hembrick

Theses and Dissertations

Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP), involving the identification of structured information from unstructured text. Two core components of IE—Named Entity Recognition (NER) and Relation Extraction (RE)—are widely used to extract key concepts and the relationships between them across various domains. However, the sequential dependency of RE on the output of NER makes it vulnerable to error propagation: inaccuracies in entity recognition can negatively affect downstream relation extraction.

To mitigate this issue, Multitask Learning (MTL) has been proposed as an approach that jointly models NER and RE, aiming to improve overall performance and reduce …

Utilizing Biometrics And Blockchain For Enhanced Security Of Remote Patient Monitoring (Rpm) Data Sharing, Amaturrahman Raihanah Medlock

Dissertations, Master's Theses and Master's Reports

Advancements in Artificial Intelligence (AI) and Internet of Medical Things (IoMT) technologies have significantly revolutionized the conventional healthcare systems. Through the integration of smart devices, medical sensors, and communication technology, IoMT provides real-time patient’s monitoring data for healthcare providers, thus promoting accurate and timely clinical decisions for patient-centric care. The current healthcare sector is evolving to a connected ecosystem with connectivity and intelligence. While it also incurs increasing security and privacy concerns as integrating IoMT generated patient monitoring data into healthcare information systems. Both blockchain and biometrics are measures that have established reputable names in the security realm. When evaluating …

Further Results On Learning Quantum Measurement Classes: Quantum Pac Model For Povm Hypothesis Classes, Arka Prabha Das

Electronic Theses & Dissertations (2024 - present)

This thesis investigates the problem of learning from quantum systems, where each example consists of a quantum state paired with a classical outcome. The task centers on choosing an effective measurement rule from a fixed set to enable accurate prediction of the classical outcome from the quantum state. A central focus lies in understanding whether joint measurement strategies that cannot be separated into local operations offer a real benefit in terms of the number of examples needed for successful learning. We examine conditions under which a non-separable measurement within a given hypothesis class achieves strictly better sample complexity bounds compared …

Multi-Objective Bike Routing Problem: A Survey And Comparative Evaluation, Dominic Peter Macisaac

EWU Masters Thesis Collection

Multi-objective routing has been studied for over forty years, yet its application to the bike routing problem is a relatively recent development. The Multi-Objective Bike Routing Problem (MOBRP) seeks to optimize a set of bike routes from a single source to a single destination, given multiple cost criteria. This paper makes two primary contributions. First, it consolidates existing research on the MOBRP, including the criteria chosen and associated cost functions, the search algorithms used, and the testing setups and metrics used for evaluation. Second, it implements the most promising search algorithms and evaluates these approaches through comprehensive cross-testing, a topic …

Bytes, Banter, And The Bible: An Interdisciplinary Account Of Objective Meaning, Cameron Bonin

Senior Honors Theses

The claim that the Bible has objective meaning is contested in a postmodern world. This claim can be more persuasively defended when it is addressed by insights from multiple disciplines. In particular, the field of computer science is apt to illuminate the concept of meaning through its reflection on the nature of languages and its concern with the accurate transmission of information. By synthesizing insights from the field of computer science, such as that of Claude Shannon, with Nicholas Wolterstorff’s use of speech-act theory, the concept of meaning can be understood more clearly. Consequently, this synthesis assists in answering questions …

Computer Science For Middle School (Cs4ms), Melani Loney, Lisa Steffian, Tancy J. Vandecar-Burdin

Center for Educational Partnerships Publications

This session will discuss the project Computer Science for Middle School and the associated findings. This Virginia Department of Education funded project provides computer science Professional Development opportunities for in-service teachers, located in Southeastern Virginia, who instruct students in middle school grades 6- 8. Participating teachers learn to integrate computer science standards into core and elective content through online, asynchronous microcredentials and in-person Summer Workshops. The presentation will include project findings and next steps including teacher efficacy for implementing the computer science standards before and after completing the microcredentials and their students’ attitudes about computer science.

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …

Unmanned Aerial Systems (Uas) Image Preprocessing To Reduce Artifacts And Improve Geometric Registration When Generating Orthophoto Mosaics And 3d Models, Eddie Ironsmith

Electronic Theses and Dissertations

Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve …

Real-Time Feedback-Driven Framework For Automated Cybersickness Mitigation, Md Jahirul Islam

Master's Theses

As technologies are becoming more advanced day by day, the embracement of virtual reality (VR) technology among users is also increasing in daily activities for various purposes, and subsequently, the barrier between the real and virtual world is fading. Despite the versatile uses, cybersickness (CS) is a major problem which is induced among users due to the immersive VR experience. There is a plethora of research findings and methods to measure the users’ CS such as virtual reality sickness questionnaire (VRSQ), simulator sickness questionnaire (SSQ), fast motion scale questionnaire (FMS), and others. Recently, machine learning approaches have also been adopted …

How Does Augmentation Affect Feature Space: A Study Using Various Augmentation Methods In Distributed Learning, Nikil Sharan Prabahar Balasubramanian

Computer Science Theses

This thesis examines the impact of data augmentation techniques on model performance within a distributed learning framework, focusing on enhancing feature diversity and improving representation for under-represented classes. Data augmentation, commonly used to address data imbalance, significantly influences the feature space learned by deep learning models, with varied effects in distributed settings where data is split across nodes. Our study reveals that inconsistencies in feature learning across nodes reduce the benefits of local augmentation in capturing complex patterns, leading to suboptimal model performance. To address this, we propose a coherent augmentation approach that embeds consistent transformations in the central server, …

Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego, Franceli L. Cibrian

Student Scholar Symposium Abstracts and Posters

The structure of dance classrooms has remained largely unchanged for years, with minimal integration of technology to enhance teaching. This has motivated our research project, which aims to capture dance movements using wearable sensors and translate the information into meaningful visualizations to help dancers improve their skills. As the first step in addressing the research question—can data from commercial wearables differentiate between the movements of dancers and non-dancers?—we developed DANCETAG (Data Analytics and Notation with Captured Event Tagging), a platform designed for data collection and movement annotation. We utilized Sony’s Mocopi sensors, a motion capture system with six sensors attached …

Q-Learning In Starclash, Hanani Pankaj

2024 Fall Honors Capstone Projects - Archive

Developers create video games using Artificial Intelligence (AI) agents to provide a challenging opponent in a single-player game. However, studies show that when Reinforcement Learning (RL) agents are used, they outperform the AI agents. This project sought to test how RL agents would perform in StarClash, a video game without RL agents, using Q-Learning. This was done by creating two Q-Learning agents: a Simple agent and an Advanced (more complex) agent. These two agents were tested against each other and a Random AI agent. As expected, the Advanced agent did better than the Simple agent but only performed slightly better, …

Patterns Of Interactions In Human-Machine Teams, Kazuhiko Momose

Theses and Dissertations

Increasingly capable machines, including Artificial Intelligence (AI) agents are playing a more important role in a wide range of applications, including human daily activities and safety-critical systems. They can benefit even more when humans and such machines agents work together as a team by leveraging each other's strengths and complementing each other to enhance overall performance. To design high-performing teams, it is critical to analyze the team dynamics and understand how humans and machines interact with each other. Collaboration, Coordination, and Cooperation (3Cs) are terms typically used to describe the behavior of teams. However, these terms tend to be used …

Basic Safety Message Generation Through A Video-Based Analytics For Potential Safety Application, Abyad Enan

All Theses

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the roadway traffic and predict potential risks of crashes in real-time. If any risky situation or behavior is observed that may lead to a crash, then a safety application can send warnings to the vehicles at risk. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle’s location, speed, acceleration, heading direction, etc. …

Innovations In Full-Stack Web Development: Front-End To Back-End, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging technologies within full-stack web development and their potential impact on current front-end and back-end solutions. Both areas employ crucial technologies that determine how end-users access information and navigate web services. Front-end solutions include HTML, JavaScript, and CSS which shape user interaction on websites. Back-end solutions use technologies such as SQL and PHP for the foundation of data processing, retrieval, and storage. The research method involves examining official documentation for these technologies to better understand their key components and to understand how their use in cyberspace has changed. The research will observe several high-traffic websites and domains …

(R2117) Cost Optimization Of Queueing System With Differentiated Vacations And Reneging Of Customers, Poonam Gupta, Rajni Gupta

Applications and Applied Mathematics: An International Journal (AAM)

This manuscript deals with an infinite-capacity queueing system under multiple differentiated working vacations and customers’ impatience. The first vacation is assumed to be a working vacation where the server, instead of being idle, serves the customers at a lower rate. In contrast, the second one is considered a non-working vacation of a different duration. The customers may leave the system at any time due to long delays in service during vacations but, via some convincing mechanisms, they are retained in the system. The operating characteristics of the system are obtained in a steady state. The results obtained are illustrated numerically …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Classifying Supersonic Frequencies For Active Acoustic Side-Channel Exploitation, Destin Hinkel

Graduate Theses and Dissertations (2019 - present)

Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing [1]. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis [1]–[3]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [4]. Furthermore, the SonarSnoop framework presents a novel active approach that involves both …

Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai

Graduate Theses and Dissertations (2019 - present)

An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …

Concert Tickets, Party Matching, Sleeping Barbers, And Single Lane Bridges: Characterizing Student Reasoning About Concurrency, Aubrey Lawson

All Dissertations

Programming with concurrency is challenging both to learn and to teach. A concurrent program has multiple computations happening “at the same time” either simultaneously or in an interleaved manner. It is non-deterministic, imposing only a partial ordering on its decomposed parts. Advantages of concurrency include the potential for increased program throughput, high responsiveness and reduced complexity of program structure. But a concurrent program can be more complex to reason about than a sequential program, in part because the conditions of correctness must hold for all possible execution sequences and also because programmers must implement and reason about synchronization constructs that …

Exploring Secure Methods For Ensuring Data Integrity: A Theoretical Analysis Of Cryptographic And Detection Techniques, Haryam Garcia Martinez

Electronic Theses, Projects, and Dissertations

This study investigates cryptographic methods to ensure data integrity within cloud environments, with a particular focus on comparing the security, performance, and efficiency of MD5 and SHA-256 hash algorithms. Data integrity is critical for protecting sensitive information, especially in sectors like healthcare, where cloud storage solutions are increasingly prevalent. Through a theoretical analysis, the study evaluates the advantages and limitations of MD5 and SHA-256, emphasizing SHA-256’s stronger security capabilities in preventing collision attacks compared to MD5, albeit with higher resource consumption.

The literature review draws from recent advancements in cryptography, blockchain, and artificial intelligence (AI) technologies, presenting a comprehensive view …