Digital Commons Network^™

Bibliography For "Ai 2.0: Is Ai A Tool, A Threat, Or A Teammate?", Annikah Carpio, Sally Park, Melody Madrigal

Library Displays and Bibliographies

A bibliography created to support a display about AI 2.0 in August 2025 at the Leatherby Libraries at Chapman University.

Human-Ai-Collaboration-For-Coding, Siddhardha Ravi

Theses, Dissertations and Culminating Projects

AI-generated code, while rapidly producing functional solutions, often falls short in aspects like comprehensive error handling, robust documentation, and optimal architectural design, areas where human expertise excels. Conversely, humans can greatly benefit from AI's rapid code generation capabilities. This project proposes and evaluates "A Framework to Improve Code Quality by Utilizing Generative AI Coding Along With Human-Written Code", designed to create a synergy between AI and human intelligence for enhanced software development. Conducted over four weeks, the research leverages BigCodeBench as its core dataset to rigorously investigate how human intervention can improve AI-generated code quality, identify the most effective human-AI …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Evolving Adaptive Foraging Robot Swarms With Neat In Environments With Obstacles, Tameem Uz Zaman, Pigar Biteng, Qi Lu

Computer Science Faculty Publications

We apply NeuroEvolution of Augmented Topologies (NEAT) to evolve adaptive and efficient swarm foraging behaviors in unknown environments with randomly placed obstacles. By rewarding effective actions and penalizing inefficient ones using the proposed strategy P-NeatFA, the training generates efficient foraging and obstacle avoidance strategies, reducing redundancy and outperforming traditional stochastic foraging algorithms. Optimization is guided by cumulative reward-based fitness, evaluated through simulations involving three types of distributed resources. Foraging performance is assessed in terms of resource retrieval rates. We compare the performance of our proposed P-NeatFA with that of CPFA and NeatFA. Experimental results show that P-NeatFA significantly outperforms the …

Effective Transformer Networks For Undersampled Magnetic Resonance Image Reconstruction, Tahsin Rahman

Open Access Theses & Dissertations

The proliferation of data-driven tools for solving problems in every possible domain, coupled with rapid advances in computing technology, has led to an arms race of AI development and application research in industry and academia. One field of research that stands to gain immeasurably from this revolution is medical imaging. It is a critical part of modern diagnostics, and advancements in this area can directly benefit the average person by making healthcare more accessible, accurate, and affordable. Breakthroughs in mainstream image processing and computer vision have long fueled development in medical imaging, and it is now common to see cutting …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Online Prediction Of Streaming Data, Aleena Chanda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We present two new approaches for point prediction with streaming data based on a) the Count-Min sketch and b) Gaussian Process Priors with random bias. The methods are intended for the most general case where no true model can be usefully formulated for the data stream. In statistical contexts, this is often called the M open problem class. For the Count Min Sketch method we show that the predicted distribution function ^F converges to F under the assumption that the data consists of i.i.d samples from a fixed distribution function F. To implement the Gaussian Process Prior methods, we used …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

A Value Sensitive Design Approach To Reimagining Parental Control Apps, Prakriti Dumaru

All Graduate Theses and Dissertations, Fall 2023 to Present

Parental control apps are often used by families to regulate children's device usage and keep them safe online. However, existing tools focus on monitoring and restricting children, which can lead to tension and mistrust within families. This research takes a new approach by exploring how parental control apps can be redesigned to support positive family values, like encouraging open communication between parents and children and helping children self-regulate their behaviors. Through four in-depth studies, this dissertation looks at how these values play out in different real-life situations. It includes families with children on the autism spectrum, divorced households where parents …

Beyond The Click: Investigating Mental Models, Targeted Attacks, And Behavioral Interventions Against Clickbait, Ankit Shrestha

All Graduate Theses and Dissertations, Fall 2023 to Present

Clickbait is misleading or exaggerated content on social media that tricks people into clicking on links by making them curious. For instance, posts that use headlines such as “This is the worst day to visit a restaurant”. These clickbait posts can lead to fake news, wrong information, and even harmful websites. Even though many people know clickbait can be risky, they often still fall for it. Existing tools to stop clickbait don’t always consider how people think, the situations they are in, or their different needs. This research looks at how people understand and react to clickbait, and develops different …

From Supercomputers To Desktops: An Interactive And Portable System For Particle In Cell Simulation And Visualization Using Commodity Hardware, Kim Peterson

All Graduate Theses and Dissertations, Fall 2023 to Present

Generating and analyzing visual representations of simulation data in real time (i.e., during execution), has become increasingly important for handling the complexity and scale of modern computational models. Traditionally, this approach has been limited to High-Performance Computing (HPC) environments, leaving some researchers, students and educators without resources to explore cutting-edge simulations and analyses. This work attempts to show that powerful scientific simulations and real-time analysis can be made available by using more easily obtained commodity hardware. This approach may enable a broader participation in the scientific computing discovery process.

In this paper, we explore how advanced simulation and visualization frameworks …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Out Of Core And Adaptive Image Blending Approach For Large Scale Image Mosaics, Marcus Quincy

All Graduate Theses and Dissertations, Fall 2023 to Present

When creating large stitched images, like those used in maps made from aerial photos, it’s important to make sure the seams between individual pictures aren’t visible. This process, known as color blending, helps smooth out differences in lighting or weather across the images. But blending very large images, such as those made from many high-resolution aerial photos, can require huge amounts of memory, making it hard to do on a typical computer.

In this work, we developed a method that breaks the problem into smaller pieces, so only a small part of the image needs to be worked on at …

Evaluating The Effects Of 3d User Interactions And Virtual Displays On Human Cognition And Perception For Immersive Analytics, Dongyun Han

All Graduate Theses and Dissertations, Fall 2023 to Present

This research investigates how virtual reality (VR) can be utilized effectively for users to explore data. Traditional data analysis typically takes place on flat, two-dimensional computer screens. In contrast, immersive technologies like VR offer a three-dimensional environment where users can engage with data more naturally and intuitively. Such immersive experiences have the potential to improve comprehension, memory, and insight during data analysis tasks. Despite this potential, several challenges remain in making immersive analytics practical and effective. Key questions include which types of 3D interaction techniques are most helpful and how accurately people perceive visual information in immersive environments. To address …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Onair: Applications Of The Nasa On-Board Artificial Intelligence Research Platform, Evana Gizzi, Conner Firth, Caleb Adams, James Berck, P. Timothy Chase Jr., Christian Cassamajor-Paul, Rachael Chertok, Lily Cloug, Jonathan Davis, Melissa De La Cruz, Matthew Dosberg, Alan Gibson, Jonathan Hammer, Ibrahim Haroon, Michael A. Johnson, Brian Kempa, James Marshall, Patrick Maynard, Brett Mckinney, Leyton Mckinney, Michael Monaghan, Robin Onsay, Hayley Owens, Sam Pedrotty, Daniel Rogers, Mahmooda Sultana, Jivko Sinapov, Bethany Theiling, Aaron Woodard, Caroline Zouloumian

Computer Science: Student Work

Infusing artificial intelligence algorithms into production aerospace systems can
be challenging due to costs, timelines, and a risk-averse industry. We introduce
the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source
software pipeline and cognitive architecture tool that enables full life cycle AI
research for on-board intelligent systems. We begin with a description and user
walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for
both research and deployed onboard applications, detailing their use of OnAIR
and the benefits it provided to the development and function of each respective scenario. Lastly, we describe two upcoming planned deployments …

Rethinking Iterative Proportional Fitting: Scalable And Hybrid Approaches To Joint Distribution Fitting, William Ofosu Agyapong

Open Access Theses & Dissertations

The Iterative Proportional Fitting (IPF) algorithm is widely used in contingency table estimation, survey weighting, and synthetic population generation due to its simplicity and strong theoretical foundation for matching observed marginal distributions. However, in high-dimensional settings, IPF faces substantial computational and memory demands, as well as statistical instability caused by sparse contingency tables. Moreover, IPF is less useful in modern population synthesis tasks that require both scalability and realism because, despite its superiority in matching known marginal distributions, it cannot produce realistic out-of-sample data points. To address these limitations, we first propose a blockwise IPF framework, in which the feature …

Decoding Anisotropic Porous Medium: A Synergy Of Lattice Boltzmann Modelling And Operator Learning To Predict Permeability As A Function Of Orientation, Soumya Shouvik Bhattacharjee

Open Access Theses & Dissertations

Understanding the directional properties of porous media is essential for accurately predicting flow behavior, reactive transport, and fluid-solid interactions in systems ranging from geothermal reservoirs to energy storage devices and biological tissues. Directional variations in permeability - reflecting a medium's response to flow at different angular orientations - are particularly important for complex, inherently anisotropic geometries. In this study, we employ a Lattice Boltzmann (LBM) model to calculate directional permeabilities from porous media images subjected to varying flow inlet angles. Three classes of porous media were investigated: (1) synthetic media with circular grains, serving as isotropic baselines; (2) synthetic media …

Analyzing The Impact Of Approximate Arithmetic On Deep Neural Network Predictions, Johnatan Garcia

Open Access Theses & Dissertations

In recent times, we have seen the use of artificial intelligence in our daily lives. It helps us solve complicated problems. Some of these problems can be large and complex, requiring large models. As models grow in complexity, they require more computations and energy to be trained and tested. The execution of these models relies on floating-point arithmetic, which imposes constraints due to its finite precision. Due to these limitations, many of these computations are not exact. When this happens, computers are forced to round or approximate. We can use several number formats to circumvent this issue. For example, in …

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

Laser Scan Path Design For Controlled Microstructure In Additive Manufacturing With Integrated Reduced-Order Phase-Field Modeling And Deep Reinforcement Learning, Augustine Twumasi

Open Access Theses & Dissertations

Laser Powder Bed Fusion (L-PBF) is a well-established additive manufacturing technique for fabricating intricate metal components with exceptional precision. A significant challenge in L-PBF is the formation of complex microstructures that influence final material properties. We propose a physics-guided, machine learning-aided approach to optimize scan paths for desired microstructure outcomes, such as equiaxed grains. We employed a phase-field method (PFM) to model the evolution of the crystalline grain structure. To reduce computational costs, we trained a surrogate machine learning model, a 3D U-Net convolutional neural network, using single-track phase-field simulations with varying laser powers to predict crystalline grain orientations based …

Low-Level Memory Attacks On Edge Assisted Robotic Applications, William Arnold

Master of Engineering Theses

This thesis investigates how low-level memory faults can undermine edge-assisted robotic systems that rely on memory optimization. As robots are utilized in real world applications, the ability to operate safely and successfully in mission critical deployment becomes important. To help achieve these goals, developers are increasingly starting to place computation nodes at network edges to meet latency and reliability requirements. Edge nodes, however, are resource-constrained and resources conservation techniques such as Kernel Same-page Merging (KSM) are enabled to deduplicate identical pages across processes or virtual machines. This thesis shows that this optimization technique quietly widens the attack surface and can …

Introduction To C++ (Volume I), Hussam Ghunaim Ph.D.

All Open Educational Resources

This book is written as an Open Education Resource (OER) to replace expensive commercial materials currently used at the Department of Computer Science at Fort Hays State University. It has two volumes corresponding to the CSCI 121 and CSCI 221 courses. These courses are developed to introduce college freshmen students to Object-Oriented Programming utilizing C++. The author tried to bridge the gap in the current programming textbooks by avoiding lengthy and, on many occasions, unnecessary details. This book’s main feature is to present the discussed principles in the least wording possible while providing adequate examples and exercises to reinforce students’ …

Advancing Fishery Dependent And Independent Habitat Assessments Using Automated Image Analysis: A Fisheries Management Agency Case Study, Scott Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae Hovey, Marcus Sheaves, Gary A. Kendrick, Lynda M. Bellchambers

Fisheries Research Articles

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level …

Optimal Hypergraph Connectivity With Cut Queries, Hang Liao

Dartmouth College Ph.D Dissertations

Finding connected components in undirected hypergraphs—hypergraph connectivity—is a fundamental problem in computer science. It can be framed as a special case of Symmetric Submodular Function Minimization (SSFM), where the objective is to determine if the non-trivial minimizer is zero. This thesis develops an optimal algorithm for hypergraph connectivity within the $\CUT$ query model, where an algorithm probes a subset of vertices to learn the weight of the hyperedges ``cut" by that partition.

Our approach is constructive, culminating in an optimal algorithm for the general problem by first developing the necessary tools for two foundational subproblems. The main contributions of this …

Low-Cost Monitoring And Fingerprinting Of High-Powered Electric Systems, Kwabena Buamono Aboagye-Otchere

Theses and Dissertations

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and …

Fact-Checker: A Web Application For Leveraging Large Language Models For Fact-Checking Youtube Videos, Andrew R. Craig

Electronic Theses, Projects, and Dissertations

Fact-Checker is a web application that allows users to fact-check YouTube videos. It feeds YouTube’s closed captioning transcript to a large language model (LLM) to extract claims. It then uses multiple LLMs, such as Gemini, Llama, and Claude, to verify these claims. The modular design makes it easy to change to a different LLM or model if needed. The application is built using Python for access to Application Programming Interfaces (APIs) and Streamlit as the front-end framework. The utilization of Docker and Dockerfiles enables easy distribution and deployment. It enables the application to be deployed on almost any hardware platform …

Computational Fact-Checking With Limited Resources, Fengzhu Zeng

Dissertations and Theses Collection (Open Access)

The rapid dissemination of information through online platforms has sparked widespread concern about the propagation of misinformation. Manual fact-checking by pro- fessional fact-checkers is time-consuming and lacks scalability to address the vast volume of daily information. Consequently, computational fact-checking, driven by automated techniques in natural language processing (NLP), has garnered interest as
a potential solution. However, computational fact-checking faces critical challenges limited resources, particularly due to the issues of data scarcity and computing resource constraints. One key challenge is data scarcity, which arises from the constant generation of new information and emerging events on social media. This scarcity manifests in …

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …