Computer Sciences Commons^™

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

Stability Analysis In The Twist-Bend Nematic Liquid Crystal Model, Zhenqiang Li

Mathematics & Statistics Theses & Dissertations

The recently discovered twist-bend nematic liquid crystal (LC) phase is characterized by a nanoscale helical modulation of the nematic director n, forming a conical helix along the z-axis at an oblique angle θ. While many models assume a constant cone angle and equal elastic constants K11 = K22 = K33, this dissertation removes both assumptions by considering a fully anisotropic elastic energy with K11 ≠ K22 ≠ K33, and allowing θ to vary spatially. We analyze the stability of this system under frustrated and free boundary conditions using variational methods. …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Integrating Computational Thinking Into K–12 Classroom Instruction, Jorge Valenzuela

STEMPS Theses & Dissertations

In 2016, legislation mandated the integration of computer science (CS) and computational thinking (CT) skills across K–12 instruction in the Commonwealth of Virginia. CT integration is also a focus in many schools throughout the United States. Educators must understand CT/CS core concepts and practices, looking for practical ways to integrate CT across the K–12 curricula. To address this, the current study was conducted. The study compares the effects of the Jigsaw technique and teacher-directed instruction on the participants by surveying their self-efficacy for integrating CT in their teaching. This study uses a pre- and post-test design, and data collection took …

“Synchronized Parenting Is Like Mixing Oil And Water”: Reimagining Parental Control For Co-Parenting In The Divorced Households, Prakriti Dumaru, Audrey Flood, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

Children from divorced households are granted access to devices (e.g., smartphones, tablets), helping them to maintain meaningful contact with both parents. However, regulating their device usage across two households presents unique co-parenting challenges, which are little studied in the existing literature on parental mediation. As we begin to address this gap, we used low-fidelity prototype designs, guided by the principles of fostering open communication and instilling self-regulation. We evaluated those designs (presented in the form of storyboards) through semi-structured interviews with 23 divorced parents, whose children are active Internet users and aged 13 years or below. Based on our analysis, …

Cogprog: Utilizing Large Language Models To Forecast In-The-Moment Health Assessment, Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane Cook

Computer Science Faculty Scholarship

Forecasting future health status is beneficial for understanding health patterns and providing anticipatory support for cognitive and physical health difficulties. In recent years, generative Large Language Models (LLMs) have shown promise as forecasters. Though not traditionally considered strong candidates for numeric tasks, LLMs demonstrate emerging abilities to address various forecasting problems. They also provide the ability to incorporate unstructured information and explain their reasoning process. In this article, we explore whether LLMs can effectively forecast future self-reported health state. To do this, we utilized in-the-moment assessments of mental sharpness, fatigue, and stress from multiple studies, utilizing daily responses (N = …

Necrorun, Jeremiah R. Mcdonald

SPARK Symposium Presentations

NecroRun is a fast-paced endless runner game set in a post-apocalyptic world overrun by zombies. The player must avoid obstacles, collect points, and survive as long as possible while navigating a decaying wasteland. You accrue points the longer you stay alive.

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Algorithms For Order Statistics In Farey Sequences: A Computational Study, Connor Weyers

School of Computing: Dissertations, Theses, and Student Research

Farey sequences are the sets of irreducible fractions in increasing order with denominator less or equal to some integer n. They are a well-known concept in number theory problems and are related to many other concepts in number theory including integer factoring, Fibonacci sequences, and Riemann’s Zeta function. In this paper, we investigate some known algorithms to solve certain problems in Farey sequences from a computational perspective. In particular, we implement established algorithms that have not been previously implemented with the goal of creating a package that can be used more broadly. We also develop a new algorithm for rational …

The Use Of Call Graphs And Deep Learning To Improve Software Testing, Ziad A. Al-Sharif, Hemanth G. Chintala, Safwan Omari

Engineering, Computing and Mathematical Sciences Faculty Conferences

Software testing is a critical part of software development, it is essential for preventing failures and enhancing software quality attributes. However, the testing process can be costly and time-consuming, often involving a large number of test cases. Over time, the accumulation of redundant and overlapping test cases can complicate and lengthen the testing time. To address these challenges, this paper utilizes graph similarity and deep learning techniques to optimize test suites. It uses call graphs from test cases to identify redundant and similar test cases. A machine learning model is used to calculate and predict the similarity scores between these …

Enhancing Metacognitive Competencies Through Human-Centered Ai: The Role Of Custom-Trained Intelligent Agents In Workforce Upskilling, James Hutson

Faculty Scholarship

This editorial examines the integration of human-computer intelligent interaction (HCII), specifically through human-centered artificial intelligence (AI) and custom-trained intelligent agents, to foster metacognitive competencies critical for workforce upskilling. With 59% of the workforce projected to require substantial upskilling by 2030, developing personalized AI models tailored to individual cognitive and learning profiles presents an innovative pathway. These custom-trained agents leverage human-computer interaction (HCI) technologies and machine learning methodologies to enhance understanding of one’s own learning processes-metacognition-thus empowering individuals to optimize their future learning and adaptability. This approach not only enhances the individual’s ability to engage effectively with complex tasks in the …

Perspectives On Gaming Throughout The Years, Noh Fekre

ART 108: Introduction to Games Studies

Video games are a lot younger of an art form compared to other mediums like books or movies which means how they fit into our culture isn’t really set in stone yet. They have existed for a while but got their first big boom through arcades and home consoles. I want to look at how video games present themselves differently to other mediums and how that might have affected their perception amongst the general populace while also looking at how that perception has changed over time due to how games have changed themselves.

When The Audience Takes The Controller: How Streaming Is Changing The Way We Play Games, Lea Wilhelmer

ART 108: Introduction to Games Studies

For decades, video games have been understood as solitary experiences. A player would sit alone or with a small group, controller in hand, navigating digital worlds where every action, decision, and outcome was shaped by their own input. At its core, gaming was personal. The lines between the audience and player were clearly drawn. The player was the one actively involved in the game, while everyone else was an observer, watching from the sidelines. This traditional model has seen significant transformation in the last decade, as the rise of streaming platforms like Twitch, YouTube Gaming, and other similar services radically …

Redefining Fun: How Realism And Effort Engage Players In Video Games, Marsel Abdullin

ART 108: Introduction to Games Studies

In the current era of video game development, the gaming industry frequently pursues instant gratification, creating a landscape for immediate and empowering rewards. Current trends clearly show that experiences allow players to become superhuman or extraordinary beings with very little difficulty, satisfying a common desire for escapism and power. This suggests that “fun” is commonly associated with ease, speed, and constant positive feedback. However, my counter-argument is the emergence of games such as Kingdom Come: Deliverance and Red Dead Redemption 2. These games operate with a different game design and philosophy. In fact, they embrace slowness, hardship, and the detailed …

The Evolution Of Video Game Collectibles And Marketplaces, Leland Lee

ART 108: Introduction to Games Studies

Digital Collectibles such as weapon skins, avatar skins, emotes, and in-game art were created as a playful surprise for the video game experience. Many years later, it has produced a multibillion-dollar economy in the gaming industry and is also responsible for the high demand for intriguing in-game collectibles. Although these cosmetic features were initially meant for looks, they have evolved into something of actual value that has completely changed the landscape of games and how they are played. As a result, this has created an intricate environment where players are able to buy, trade, and sell in-game collectibles in both …

Nothing Is Off Limits, But Not Everything Is Done Right: Exploring Ineffability, Alejandro Maciel

ART 108: Introduction to Games Studies

Games are capable of addressing absolutely anything. No topic is too big, too controversial, or too emotional. but not everything is done well; the problem isn't the subject, it's always the delivery. When the developers approach heavy topics without care, they risk doing harm. bwhen they do their research and consult communities and write with purpose, the result can be transformative. Representation, when handled with attention, isn't political correctness; it's simply good storytelling. it's how games become more than entertainment. They become tools for empathy, education, and more valuable connections

The Cost Of Creative Freedom: Comparing Aaa And Indie Game Development, Andrei Morgunov

ART 108: Introduction to Games Studies

The independent development model provides developers with creative freedom and personal fulfillment because it enables them to learn through hands-on experience while creating distinctive experiences and developing diverse skills. It provides them with both long-term ownership of their work and the chance to achieve remarkable success through their own efforts while allowing them to maintain direct communication with players who appreciate their creative output. The choice between AAA and indie depends on individual career goals, but developers who view games as more than just products of entertainment can find greater meaning in the indie path, Long-term triple-A developers who want …

Toward A Global Roadmap: An Analysis Of National Strategies Toward Digital Education Improvements Across Southeast Asia And Southern Africa, Z Alexandra Anderson

Computer Science Undergraduate Theses

The rapid global expansion of digital technologies has created significant opportunities to transform education systems, foster innovation, and reduce inequalities. However, access to and benefits from these technologies remain unevenly distributed, shaped by differences in infrastructure, human capital, governance structures, and economic development. This thesis investigates how systemic approaches to digital education in Cambodia, Thailand, Myanmar, South Africa, Botswana, and Zimbabwe can inform a more inclusive and adaptive global roadmap for digital transformation in education.

Adopting a mixed-methods design, this study combines a comparative quantitative analysis of the CISCO Digital Readiness Index (DRI) with a qualitative content analysis of national …

Causal Models For Realistic Cognitive Reinforcement, Vivek Dhingra, Brandon Bazile, Andrew Forney

Computer Science Undergraduate Theses

Modeling complex hierarchical decision systems can be used for predicting the effects of policy changes before their enactment, such as understanding how new laws might influence students within an educational system. However, understanding the effects of policies on individuals versus populations requires a structured assertion of the system’s causal dynamics. As such, we propose a novel reinforcement learning framework that integrates causal modeling to optimize decision-making in multi-agent environments, like schools. The causal model captures relationships between these levels, providing agents with a structured understanding of how their actions propagate through the system. Compared to traditional reinforcement learning methods, our …

Extending Feature-Based Detection For Artificial Intelligence, Kayla Ahrndt

SPARK Symposium Presentations

AI text generation is rapidly developing, and, as a result, it is becoming increasingly difficult to differentiate it from human written text. Our base study by Leon Fröhling et al. proposed a feature-based detection model trained on GPT2, GPT3, and Grover data, as well as human-generated text. Our work extends their research by training a modified model with four neural networks on word embeddings, select features from the original study, as well as updated data (GPT3, GPT4, and Grover).

Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

A Study Of Preconditions And Postconditions As Design Constraints For Llm Code Generation, Luke Newcomb

Doctoral Dissertations and Master's Theses

Large Language Models (LLMs) have significantly advanced automated code generation, but current methods predominantly rely on natural language descriptions. This approach encounters challenges when handling complex, class-level software generation tasks due to inherent ambiguity and under-specification. Few studies have investigated how more formal software engineering constraints, such as explicit preconditions and postconditions, influence class-level generation tasks. This work addresses this gap through a structured evaluation of six state-of-the-art LLMs generating software implementations from systematically designed class-level specifications. Results demonstrate that incorporating explicit design constraints significantly boosts initial generation accuracy (measured via the pass@k metric), particularly in Python but also in …

Deep Learning For Fine-Grained Digital Histopathology Image Analysis, Joseph Dipalma

Computer Science Technical Reports

As digital pathology becomes increasingly popular, it is critical to develop machine learning solutions to utilize this data. While other image modalities have seen exponential increases in methodology availability, the same has not been true for histopathology images. This is likely in part because histopathology whole slide images possess unique characteristics that prevent simply applying existing methods as-is.

In this thesis, we identify and propose solutions to 3 open problems with histopathology images: 1. large raw image size (up to 150,000×150,000 pixels in size), 2. low class-positivity (low ratio of positive to negative patches), and 3. limited image availability with …

Character Recognition For Greek Squeezes: Annotated Data, Nicholas Howe, Aaron Hershkowitz, Feiran Chang, Isabella Falbo, Tahini Brown, Maura Putzer

Data

No abstract provided.

Research On Quantitative Evaluation Of Artificial Intelligence Policy Texts In The Yangtze River Delta Region, Ying Zhou, Danjie Yang, Mei Jiang, Xiaochun Zhao

Journal of Scientific Information Research

[Purpose/significance]The quantitative evaluation of existing effective artificial intelligence (AI) policies aims to provide reference for government department to formulate scientific and reasonable AI policies and promote the development of AI. [Method/process]Taking 10 AI policies in the Yangtze River Delta region from 2015 to 2024 as the research samples, the text mining method is used to construct the evaluation index system of AI policies in the Yangtze River Delta region, and conduct quantitative evaluation by combining the PMC index model. [Result/conclusion]The study found that from a macro policy text perspective, the average PMC index of the 10 AI policy samples in …

Intelligence Process Of Marine Security: Model Construction And Case Deduction, Xuehui Wang, Feng Hu, Peiwen Wang

Journal of Scientific Information Research

[Purpose/significance]In recent years, our country has been increasingly threatened by security threats from the maritime direction. In this regard, it is necessary to strengthen the intelligence construction in the maritime security system and improve the effectiveness of intelligence in maritime security governance/decision-making.[Method/process]Comprehensively applying the methods of literature research, comparative analysis and case demonstration, on the basis of clarifying the connotation of relevant concepts, sorting out and evaluating the existing intelligence process models in the academic community, combined with the actual scenarios of maritime security incidents, construct an intelligence process model for maritime security assurance and take Fukushima nuclear sewage discharged …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

Binge Buddies, Joshua Uribe

Posters - 2025

Many people struggle to keep track of the shows and movies they’ve watched or plan to watch. Existing streaming platforms often provide limited or cluttered tracking features, making it challenging to stay organized. Binge Buddies addresses this issue by centralizing watchlists and viewing history in one streamlined location. The website is designed to simplify the binge-watching experience, helping users stay on top of their content and discover new shows/movies. Which makes the experience a smoother and more enjoyable experience.