Computer Sciences Commons^™

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

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 …

Advanced Models For Linking Process In Data Washing Machine, Bushra Sajid

Theses and Dissertations

Entity Resolution (ER) is a critical process in data integration and quality improvement that identifies and links multiple records referring to the same real-world entity. As data volumes and heterogeneity increase, traditional ER methods face new challenges, prompting research into more advanced techniques. The Proof-of-Concept Data Washing Machine (DWM), developed under the NSF DART Data Life Cycle and Curation research theme, aims to automatically detect and correct data quality errors through unsupervised entity resolution. Recent research focuses on enhancing DWM's effectiveness by replacing rule-based methods with machine learning and deep learning approaches, particularly in the linking process. Deep learning models, …

An Open-Source, Student-Centric Approach To The Cyber Kill Chain, Justin Lane Wooten

Theses and Dissertations

The cybersecurity landscape demands professionals with practical skills and a deep understanding of attack methodologies. However, many institutions face significant challenges in providing comprehensive cybersecurity education due to the high costs associated with commercial tools and platforms. This thesis presents a student-centric, open-source curriculum for teaching the Cyber Kill Chain, designed to bridge the gap between theoretical knowledge and real-world application while addressing the financial constraints faced by many educational institutions. Our approach leverages freely available tools and hands-on exercises to cover each phase of the Cyber Kill Chain, emphasizing ethical considerations and collaborative learning. We detail the curriculum development …

Analysis Of Cortical Evoked Auditory Response Detection In Adults Using Machine Learning, Pranavi Beerelli

Theses and Dissertations

This study focuses on the use of machine learning (ML) techniques to automate the detection of Cortical Evoked Auditory Responses (CEARs), which are key in understanding how the auditory cortex processes sound stimuli. Traditionally, analyzing these auditory responses has relied on manual interpretation by audiologists, a process that can introduce variability and human error, particularly in complex cases. To address this challenge, the research utilizes advanced deep learning models, including Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) architectures, to analyze Electroencephalography (EEG) data and classify the presence or absence of auditory responses automatically. …

The Implementation Of Artificial Intelligence In University Classrooms: Perspective And Applications, Erika Grodzki, Gary Carlin, Stefanie Powers, Hung Chum Kao

Faculty and Staff Publications & Presentations

This study examined the integration of Artificial Intelligence (AI) in university classrooms, focusing on its benefits, challenges, and the diverse perspectives of academic faculty. While AI was widely embraced in disciplines like animation and design for enhancing creativity and efficiency, traditional fields remained cautious due to concerns about academic integrity and its impact on critical thinking. By analyzing literature and case studies, the presentation highlighted AI’s transformative potential in higher education, fostering dialogue on its strategic adoption to balance innovation with ethical and pedagogical considerations.

La Creatividad En Peligro: Como La Inteligencia Artificial Es Un Reto Para Los Artistas., Nathaly Cisneros

Capstones

Los artistas digitales han creado obras maestras que nos han dejado sin aliento con sus pinceles digitales, lápices y pinturas. Desde retratos que parecen saltar de la pantalla hasta paisajes que nos transportan a mundos desconocidos, su arte ha sido una fuente constante de inspiración.

Pero en los últimos años, una nueva fuerza ha comenzado a cambiar el juego. La inteligencia artificial ha estado avanzando a pasos agigantados y ahora se perfila como una amenaza para el futuro de los artistas digitales. ¿Qué significa esto para el arte y la creatividad?

Link: https://docs.google.com/document/d/1xe8UxDMekX_SwiIppyt_JppK8M-lB-YWNWGyeyShlJM/edit?usp=sharing

Improved Vector Pruning For Partially Observable Markov Decision Processes, Thomas Jonathan Bowman

Theses and Dissertations

Exact dynamic programming algorithms for planning problems that are represented as partially observable Markov decision processes rely on a subroutine that removes, or ``prunes", dominated vectors from sets of vectors that represent piecewise-linear and convex value functions. The classic vector pruning subroutine solves one linear program per vector, where the number of variables is equal to the size of the state space and the number of constraints is equal to the number of vectors shown so far to be undominated. Thus, its scalability is limited not only by the number of linear programs it solves, but especially by their size. …

A Comprehensive Performance Evaluation Of Proprietary And Open-Source Language Models In Closed And Open-Domain Tasks, Abhilash Kanduri

Theses and Dissertations

As the field of Natural Language Processing (NLP) continues to evolve, evaluating the performance of both proprietary and open-source language models has become increasingly critical. This research provides a comprehensive analysis of proprietary models like GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo, alongside open-source models such as FLAN-T5, GPT-Neo, and GPT-2. By leveraging traditional metrics like ROUGE and BLEU, as well as custom metrics including ReGrAde, Contextual Precision, and Faithfulness, the study evaluates these models across closed-domain tasks (e.g., factual question-answering) and open-domain tasks (e.g., creative writing and brainstorming). The proprietary models excelled in structured, fact-based tasks, while the open-source models …

Multi-Cloud Identity Security Utilizing Self-Sovereign Identity, Morgan Lee Reece

Theses and Dissertations

With the increasing use of multi-cloud environments, security professionals face challenges in configuration, management, and integration due to uneven security capabilities and features among providers. As a result, a fragmented approach toward security has been observed, leading to new attack vectors and potential vulnerabilities. Other research has focused on single-cloud platforms or specific applications of multi-cloud environments. Therefore, there is a need for a holistic security and vulnerability assessment and defense strategy that applies to multi-cloud platforms. This dissertation explores risk and vulnerability analysis to identify attack vectors from software, hardware, and the network, as well as interoperability security issues …

Deep Learning - Based Automated Detection And Classification Of Foreign Materials In Poultry Using Color And Hyperspectral Imaging, Rohini Maram

Theses and Dissertations

This thesis explores the use of Deep learning for detection and classification of small foreign materials (FMs) in poultry meat using color and hyperspectral imagery (HSI). The study employs You only look once (YOLO) object detection models on color images for precise localization, and one-dimensional convolutional neural network (1D CNN), two-dimensional convolutional neural network (2D CNN) was used on HSI (600 – 1700 nm) for classification. Twelve different FMs commonly known as polymers including PVC, PET, LDPE and HDPE, were examined using 52 color and 52 hyperspectral images. Four YOLO models (v5x, v7x, v8x, v10x) were implemented, trained, tested and …

Mri Alzheimer’S Disease Prediction, Vikram Ayyawari

Masters Projects

Alzheimer’s Disease (AD) is a significant and growing global health issue, affecting millions of people around the world. Despite ongoing research, there is currently no cure or highly effective treatment for AD, making early detection essential to slowing its progression and alleviating its impact on patients and healthcare systems. This project explores the use of the "Augmented Alzheimer MRI Dataset V2," a specialized dataset developed to improve the precision and reliability of machine learning models for diagnosing and classifying different stages of AD from MRI scans. The dataset includes MRI images categorized into four classes representing different stages of cognitive …

Closed Domain Question Answering With Language Models: Application Of Retrieval-Augmented Generation And Parameter Efficient Fine-Tuning In Healthcare, Aaron Cummings

Master's Theses

Dementia care presents significant challenges for informal caregivers, particularly in managing behavioral symptoms that affect over 90% of individuals with Alzheimer’s Disease and Related Dementias (ADRD) during the moderate-to-severe stages. These symptoms, including agitation, wandering, and repetitive activities, impose emotional and physical burdens on caregivers, often exacerbated by a lack of reliable, accessible, and personalized resources. Non-pharmacological interventions, while evidence-based, are underutilized due to knowledge gaps and the inefficiency of traditional training and information retrieval methods.

This research explores the adaptation of large language models (LLMs) to address these challenges by developing a framework for closed-domain Question Answering (QA) systems, …

Gpu Exploration Using Pycuda, Prudhvi Sai Akulapally

Masters Projects

This project investigates the implementation and optimization of parallel computing techniques using GPU acceleration frameworks such PyCuda. With the increasing demand for high-performance solutions in data-intensive applications, GPUs offer a compelling alternative to traditional CPU-based processing. The primary objective of this work is to harness the computational power of GPUs to achieve significant performance enhancements for complex workloads. The study focuses on two essential research questions: How effectively can PyCuda accelerate computational tasks, and what measurable performance gains can be achieved compared to CPU-based implementations? By addressing these questions, this project explores the applicability of GPU programming in tasks such …

Tag-Based Security In C: Writing And Specifying Flexible Protection, Sean Noble Anderson

Dissertations and Theses

The C language is ubiquitous and insecure. Tag-based security policies offer a flexible toolkit for runtime protection, including policies that mitigate the effects of undetected programming bugs and those that enforce security properties of the program logic. But tag policies can be difficult to define, and the protection that they offer can be difficult to specify and to prove.

This dissertation builds from an assembly-level specification of stack safety, encompassing the essential control-flow structure of C and other high level languages, to a C source-level policy definition framework called Tagged C. Tagged C includes a variant C semantics parameterized by …

A Survey Of Several Python Libraries Foe Computational Geometry, Meghana Kolluru

Masters Projects

This project delves into computational geometry, a crucial area of computer science, by

implementing algorithms to solve geometric problems using Python libraries like Shapely, SciPy,

and Triangle. It begins with foundational tasks such as creating geometric objects (points, lines,

polygons) and performing operations like calculating distances, intersections, and unions. These

basics lay the groundwork for tackling more advanced applications.

Key implementations include the Convex Hull, which computes the smallest convex

polygon enclosing a set of points, aiding in applications like collision detection and GIS. The

project also explores Voronoi Diagrams, which partition a plane into regions based on proximity,

and …

Page Rank Algorithm In Java, Nikkil Bollman

Masters Projects

The PageRank algorithm is introduced by Google, It is used in search engine optimization, and ranking web pages based on their significance and importance based on a graph structure. This project focuses on implementing the PageRank algorithm using Java and Spring Boot, with Apache Spark as the core technologies for graph data processing. The project aims to compute PageRank scores for nodes in a directed graph, simulating real-world scenarios such as web page ranking and link analysis. The implementation is designed to handle large datasets efficiently by leveraging Spark's distributed computing capabilities and GraphFrames' robust graph processing framework. The workflow …

Creating A Colab Notebook Based On The Tutorial Of The Simpy Python Library, Mary Deepika Basani

Masters Projects

This project introduces an interactive Google Colab notebook designed to familiarize users with the core functionalities of the SymPy Python library, a powerful tool for symbolic computation. SymPy offers an extensive range of capabilities essential for mathematical modeling, algebraic operations, calculus, and matrix manipulations. The notebook serves as an educational platform, allowing users to experiment with small examples and explore practical applications of symbolic computation in real time. Organized into sections, the notebook focuses on key SymPy features such as simplification, equation solving, differentiation, integration, and matrix operations. Each section includes clear explanations, practical demonstrations, and interactive exercises, helping users …

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 …

Exfoliated Hydrotalcite-Transition Metal Complex Composite For Eco-Friendly And Efficient Catalytic Degradation Of 4-Nitrophenol, Sidra Khan, Najma Memon, Saima Q. Memon, Yilmaz Yurekli

Karbala International Journal of Modern Science

Nitrophenols are notorious aquatic organic contaminants found as degradation products of various parent compounds, including pesticides and industrial chemicals that persist in the environment and must be removed. Catalytic degradation is one of the feasible routes to clean the contaminated water systems, however, environmental contamination with catalysts is also widespread. Herein, we report an environmentally friendly catalyst based on composited Fe-Schiff’s base with exfoliated layered double hydroxides (LDH) of aluminum and nickel (hydrotalcite). The composite showed agglomerated pleated LDH structures sheathed with Fe(III)SB. Nitrogen adsorption isotherm data exhibited improved surface area and narrow pores patterns for composite as compared to …

Optimal Algorithm For Managing On-Campus Student Transportation, Youssef Harrath, Jood Alyusuf, Zeena Ghulam, Muna Aldoseri

Research & Publications

This study analyzed the transportation issues at the University of Bahrain Sakhir campus, where a bus system with an unorganized and fixed number of buses allocated each semester was in place. Data was collected through a survey, onsite observations, and student schedules to estimate the number of buses needed. The study was limited to students who require to move between buildings for academic purposes and not those who choose to ride buses for other reasons. An algorithm was designed to calculate the optimal number of buses for each time slot, and for each day. This solution could improve transportation efficiency, …

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, …

Enhancing Cybersecurity Strategies Through Automated Cti Extraction, Risk Prioritization, And Privacy-Conscious Information Sharing, Spencer Rian Massengale

Theses and Dissertations

Cybersecurity operations require the ability to collect and analyze large amounts of cyber threat intelligence (CTI) to assess risks and formulate defensive strategies against emerging threats. This task has become increasingly complex due to the rapid evolution of cyber threats and the growing volume of unstructured, natural-language CTI sources. The scale of data and analysis needed to utilize CTI effectively far exceeds humans' manual capacity, especially for organizations with limited resources. This research focuses on leveraging Large Language Models (LLMs) and machine learning techniques to enhance CTI extraction, risk assessment, and data sharing. We utilized LLMs to automate the extraction …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

Identifying Subject Bias In Wifi-Based Human Activity Recognition Evaluation Methods, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

WiFi-based Human Activity Recognition (HAR) has emerged as a promising approach for monitoring and analysing human activities in a non-intrusive manner, leveraging WiFi signals for activity classification. Despite advancements, existing WiFi-based HAR research lacks consideration of subject (human) bias. This results in learning models performing well on individuals used in the training samples but failing to generalise to new/unseen subjects, in contrast to known good practices in machine learning. In this paper, we address this oversight directly by systematically examining the evaluation methodology for the WiFi-based HAR context. Specifically, we investigate the impact of Leave-One-Subject-Out Cross-Validation (LOSOCV) in a hybrid …

Sd-Weat: Towards Robustly Measuring Bias In Input Embeddings For Artificial Intelligence Language Models, Magnus Gray

Theses and Dissertations

Artificial intelligence (AI) is rapidly transforming industries and markets, from healthcare to entertainment, revolutionizing decision-making processes. However, as AI grow more influential, they also risk amplifying existing biases, potentially leading to harmful consequences. Recent advancements in large language models (LLMs), such as GPT-4 and Llama, have heightened concerns about bias in natural language processing (NLP) tasks, driving the need for robust methods to detect and mitigate bias. Current approaches, such as the Word Embedding Association Test (WEAT) and its sentence-level extension the Sentence Encoder Association Test (SEAT) often fall short in capturing the nuances of biases in the input embeddings …

On The Benefits Of Directness In Virtual Characters For Motivational Interviews, Michael O'Mahony, Cathy Ennis, Robert Ross

Conference papers

Understanding the factors influencing successful engagement with Embodied Conversational Agents (ECAs) remains a significant challenge. This understanding could be used to personalise agents to users to improve interactions. Some studies have shown that simulating personalities in healthcare agents can improve effectiveness and engagement. However, it is not yet well understood how variations of agent personality can be leveraged to improve user engagement with Motivational Interviewing (MI) ECAs. Specifically how the balance between agent warmth and directness can be controlled in an MI agent to improve likeability and engagement. We conducted an online Wizard-of-Oz (WoZ) mediated study of two variants of …

A Summer Class Exploring Computer Science With Educators Of Deaf And Hard Of Hearing Students, Meghan L. Mcsherry, Becca A. Leininger, Maria L. Johnson, Annmarie P. Thomas, Susan Outlaw, Douglas C. Orzolek

Journal of Science Education for Students with Disabilities

The purpose of this research project was to design, deliver, and informally assess the content and methodology of an introductory course focused on the block-based coding language, Scratch, for educators of Deaf and Hard of Hearing students (N=20). Prior work by The Playful Learning Lab examined various STEM resources and their effect on student perception of STEM topics and content retention when utilized for Deaf and Hard of Hearing K-12 students. Previous research has shown that computer science and programming resources and curricula available today are not fully accessible for Deaf and Hard of Hearing K-12 students. Educators and teachers …

Unpacking Bias, Accountability, And Ethical Practices In Ai, Manya Chandra, Micol Hebron

Student Scholar Symposium Abstracts and Posters

This study is based on understanding how text-to-image generative AI platforms perpetuate biases such as racism and sexism and decoding how this bias is programmed within large language models and datasets. In this study, the results of generative AI are analyzed through the lens of affect and affect theory, as they are applied to investigate the machine learning and computer theory behind generative AI algorithms. The purpose of the study is to explain why generative AI is biased and whether this bias is generated due to current trends or to deficits and biases within the database that it draws information …

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 …