Computer Sciences Commons^™

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Intel Oneapis Esimd, Joseph Wassell

Computer Science Theses & Dissertations

The growing popularity of Computational Fluid Dynamics (CFD) simulations among engineers necessitates the use of GPU acceleration for increased efficiency. NASA FUN3D offers GPU accelerated CFD simulations using unstructured grids across the speed regime from incompressible to hypersonic flows involving reentry. This work focuses on the generalized multi-color point implicit solver used in FUN3D, accounting for roughly half of the run time. Specifically, this work focuses on developing three optimized multi-color linear-solver kernels for the Intel Data Center Max 1550 GPU that is available on the Argonne Leadership Computing Facility’s (ALCF) exascale machine, Aurora. These optimized kernels work for a …

Service With A Smile Or Salesperson Mirroring? Understanding The Flow Of Emotional Contagion In Sales Encounters, Vinh Quoc Trong Luong

Theses and Dissertations in Business Administration

This study examines the directionality of emotional contagion in sales interactions, addressing a critical gap in understanding whether emotions flow primarily from the salesperson to the customer, from the customer to the salesperson, or bidirectionally. While prior research emphasizes customer-driven emotional flow or bidirectional alignment, this study challenges these assumptions by employing categorical Cross-Recurrence Quantification Analysis (CRQA) to assess temporal emotional synchronization in sales dialogues. Leveraging automated sentiment analysis and multi-agent AI evaluation for performance metrics, the research analyzes 166 sales interactions to quantify emotional influence dynamics. Results reveal that salespeople predominantly lead emotional exchanges, exhibiting stronger and more stable …

Enhancing Data Usability For People With Visual Impairments, Yash Prakash

Computer Science Theses & Dissertations

Human-Data Interaction (HDI) focuses on how individuals engage with, analyze, and extract insights from data. For blind and visually impaired (BVI) users, interacting with data, whether searching for relevant information from structured data (e.g., web data items) or interpreting visualizations to draw insights (e.g., data charts), presents significant challenges. These challenges arise from the complexity and sheer volume of data which cannot be effectively handled by assistive technologies like screen readers and screen magnifiers. Despite its importance, data usability, the ease, efficiency, and satisfaction with which BVI individuals can interact with the data, has received less attention compared to data …

Unfolding Particle Detector Effects And Solving Qcd Inverse Problem With Generative Ai, Tareq Saeed Alghamdi

Computer Science Theses & Dissertations

Advancements in artificial intelligence (AI) have revolutionized high-energy physics by enabling generative models to address key detector-related Challenges. This work explores the generative model to mitigate smearing, acceptance, and inefficiency in particle detectors, enhancing experimental precision.

We present a generative model-based framework to model and correct detector distortions. Using the Jefferson Lab CLAS g11 experiment as a case study, our approach successfully unfolds detector effects in multi-particle final states while preserving multidimensional correlations despite complex reaction mechanisms. A key focus is addressing the acceptance problem—accurately modeling detector acceptance without computationally expensive simulations. By training generative model-based framework on simulated detector …

Predicting Music Origin With Deep Learning, Fruzsina Ladanyi

Electronic Theses, Projects, and Dissertations

This project explores the usage of a late fusion deep learning architecture to predict the geographic origin of music. Mel-Frequency Cepstral Coefficients (MFCCs) and the language of the music sample are used as features. MFCCs were extracted from audio files to capture sound features. The language was identified using OpenAI’s Whisper model to provide additional context. A late fusion neural network architecture combining Long Short-Term Memory (LSTM) layers for sequential MFCC input and dense layers for non-sequential language features were employed to support both classification and regression tasks. The classification model achieved an accuracy of 33.03% across 56 countries or …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

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 …

Ed-Filter: Dynamic Feature Filtering For Eating Disorder Classification, Mehdi Naseriparsa, Suku Sukunesan, Zhen Cai, Osama Alfarraj, Amr Tolba, Saba Fathi Rabooki, Feng Xia

Research outputs 2022 to 2026

Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarkable challenges for ED classification. To overcome these hurdles, we introduce a novel method, an informed branch and bound search technique known as ED-Filter. This strategy significantly improves the drawbacks of conventional feature selection algorithms such as filters and wrappers. ED-Filter iteratively identifies an optimal set of promising features that maximize the eating disorder classification accuracy. In …

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 …

Classifying Advanced Persistent Threat Stages And Techniques Via Graph-Enhanced Network Flow Representations, Md Taef Uddin Nadim

Graduate Theses and Dissertations

Advanced Persistent Threats (APTs) are complex, stealthy attacks that involve multiple stages and many attack techniques used in each stage, making them difficult to defend against. Although many solutions can detect APTs, most of them only detect the existence of attack, but cannot produce fine-grained classification over the stage of the APT and the specific attack technique used. Some existing solutions can classify the stages of APT, but few of them provide attack technique classification, and existing work do not provide interpretability for the classification or countermeasures for the attack. In this thesis work, we propose a solution named CAPTure, …

Algebraic Multigrid Methods For Nonsymmetric And Indefinite Problems: Theory And Applications, Ahsan Ali

Mathematics & Statistics ETDs

Algebraic multigrid (AMG) is a well-established and highly efficient solver for symmetric positive definite (SPD) systems arising from elliptic and parabolic PDEs, while nonsymmetric systems from hyperbolic PDEs remain a significant challenge. This dissertation develops AMG methods and theory for nonsymmetric problems. First, we develop a novel approach combining mode constraints from energy-minimization AMG with local approximations of ideal restriction in $\ell$AIR, resulting in constrained $\ell$AIR (C$\ell$AIR), which demonstrates scalable convergence across advective and diffusive problems. Second, we extend optimal AMG theory by deriving spectral radius estimates for the two-grid error transfer operator using matrix-induced orthogonality, enabling convergence predictions for …

Gene Regulatory Network Prediction Using Machine Learning, Deep Learning, And Hybrid Approaches, Sai Teja Mummadi, Md Khairul Islam, Victor Busov, Hairong Wei

Michigan Tech Publications

Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also …

Learning From Conditional Data Distributions, Jizhou Huang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traditional machine learning paradigms often rely on a single global model trained on an entire dataset, aiming for broad generalization across all instances. However, in many real-world applications, the underlying data distribution is heterogeneous, and meaningful predictions often require models that focus on specific subpopulations rather than treating the data as a whole. This motivates the study of learning from conditional distributions, a framework where predictive models are designed to capture the structure and properties of restricted subsets of the data, leading to improved accuracy, fairness, and interpretability. This dissertation explores three key subproblems that exemplify different aspects of learning …

Computational And In Vitro Investigation Of P. Crocatum Bioactive Compounds As Pancreatic Lipase Inhibitors, Gusnia Meilin Gholam, Dimas Andrianto, Dewi Anggraini Septaningsih, Mega Safithri

Karbala International Journal of Modern Science

Obesity, a prevalent metabolic disorder characterized by excessive fat accumulation, can severely affect overall health if left untreated. This study investigated the potential of a 70% ethanol extract from Piper crocatum (red betel) leaves as an in vitro inhibitor of pancreatic lipase (PL), supported by computational analyses to identify alternative compounds to orlistat. The phytochemical profile was characterized using LC-MS/MS, revealing alkaloids and terpenoids with contents of 1.1 ± 0.01 mg CE/g and 3.14 ± 0.3 mg UAE/g, respectively. The extract exhibited 49 ± 9.1% inhibition of PL activity. Molecular docking identified three promising compounds: calanolide A (10.43 kcal/mol), myricanone …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Comparative Study Of Machine Learning Models For Predicting The Market Value Of Professional Football Players, Álvaro Salvador López

Master's Theses or Doctor of Nursing Practice

The market value of professional football players is a critical factor in decision-making for clubs, agents, and analysts. Accurate player valuation impacts transfers, contract negotiations, and financial planning. In recent years, data-driven approaches have emerged to support traditional scouting with predictive analytics. This thesis presents a comparative study of machine learning models to estimate the market value of football players based on historical performance and personal attributes.

This thesis presents a comparative study of two independently developed machine learning systems designed to predict the market value of football players for the 2020–2021 season. Both systems were trained using real data …

Exploring Adversarial Threats To Neuralhash: A Perceptual Hashing Algorithm, Gurleen Kaur

Student Theses

Perceptual hashing algorithms are algorithms that generate content-based image hashes by extracting perceptual features from the images. Unlike cryptographic hashes, which exhibit significant changes with even slight input alterations, perceptual hashes do not change when modifications like compression, color correction and brightness are applied to the images. These hashes are designed to remain similar for inputs that are visually or perceptually alike, which has led to their widespread application in detecting duplicate images, finding similar images for reverse image search and to detecting inappropriate content of Child sexual abuse (CSAM) images by comparing image hashes with dataset of known perceptual …

In Silico Prediction Of Cytotoxic T-Cell Epitopes From Helicobacter Pylori Virulence Factors Using An Immunoinformatics Approach, Demy Valerie Chacon, Kiana Alika Co, Daphne Noreen Enriquez, Aubrey Love Labarda, Reanne Eden Manongsong, Edward Kevin B. Bragais

Biology Faculty Publications

Background: Helicobacter pylori infects approximately half of the global population, leading to gastric and duodenal ulcers. Despite the availability of antibiotics, challenges such as patient reluctance, high treatment costs, and antibiotic resistance limit their effectiveness, making vaccination a promising alternative. This study used immunoinformatics to identify candidate epitopes for a multiepitope vaccine construct against H. pylori.

Material and methods: The protein variability server was utilized for conservation analysis. The epitopes were screened for antigenicity, allergenicity, toxicity, cross-reactivity, and population coverage. Selected epitopes were docked with their corresponding human leukocyte antigen (HLA) alleles, and thermodynamic quantities were determined. Five virulence …

Optimizing Distributed Boundary Exchanges For Benchmarks, Solvers And Sparse Matrix Operations, Gerald Collom

Computer Science ETDs

Boundary exchanges dominate the cost of both stenciled codes and those that rely on sparse matrix operations. The performance of large boundary exchanges is limited by synchronization overheads and injection bandwidth limitations. Irregular boundary exchanges incur additional overheads due to the large number of required messages. This thesis investigates multiple methods for improving the performance and scalability of both Cartesian and irregular boundary exchanges. Since boundary exchanges are typically performed iteratively, persistent communication presents an opportunity for optimization by sharing and amortizing setup costs. Partitioned communication is also explored to increase asynchrony, reducing bottlenecks from synchronization overheads and data congestion. …

Reviving The Lost Art: Historical Foundations And Future Pathways For Bespoke Service In Luxury Retail, Andrew Burnstine

Faculty and Staff Publications & Presentations

The mid-20th century witnessed a zenith of deeply personalized, bespoke service within iconic luxury specialty retailers. This paper critically analyzes the foundational principles of this historical bespoke service model, extending Service-Dominant (S-D) Logic and retail evolution theory, to propose a robust framework for its modern revival. Employing a rigorous qualitative, multiple-case study approach, grounded in extensive historical and media analysis of four archetypal American and British luxury boutiques, and uniquely informed by an insider-ethnographic perspective on the central case exemplar ("Martha's"), the study distills six core principles: Profound Client Knowledge, Visionary Curation & Styling, Anticipatory & Proactive Service, The Exclusive …

“Silent Is Not Actually Silent”: An Investigation Of Toxicity On Bug Report Discussion, Mia Mohammad Imran, Jaydeb Sarker

Computer Science Faculty Research & Creative Works

Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resolving defects, yet their inherently problem-focused nature and emotionally charged context make them susceptible to toxic interactions. This study explores toxicity in GitHub bug reports through a qualitative analysis of 203 bug threads, including 81 toxic ones. Our findings reveal that toxicity frequently arises from misaligned perceptions of bug severity and priority, unresolved frustrations with tools, and lapses in professional communication. These toxic interactions not only derail productive discussions but also reduce the likelihood of actionable outcomes, …

Llput: Investigating Large Language Models For Bug Report-Based Input Generation, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Failure-inducing inputs play a crucial role in diagnosing and analyzing software bugs. Bug reports typically contain these inputs, which developers extract to facilitate debugging. Since bug reports are written in natural language, prior research has leveraged various Natural Language Processing (NLP) techniques for automated input extraction. With the advent of Large Language Models (LLMs), an important research question arises: how effectively can generative LLMs extract failure-inducing inputs from bug reports? In this paper, we propose LLPut, a technique to empirically evaluate the performance of three open-source generative LLMs-LLaMA, Qwen, and Qwen-Coder-in extracting relevant inputs from bug reports. We conduct an …

Urban Landscape Recovery And Lulc Analysis: A Deep Learning Approach To Post-Extreme Rainfall Impacts In Dubai, Xin Hong

All Works

From April 14 to 18, 2024, the United Arab Emirates (UAE) experienced its heaviest rainfall in 75 years, resulting in widespread flooding across multiple emirates, including Dubai. This study utilizes high-resolution PlanetScope imagery and a U-Net deep learning model to assess the flood impact and analyze post-rainfall recovery patterns in Dubai’s urban landscape. By integrating Sentinel-2derived land use and land cover (LULC) data to refine the training dataset, a high-accuracy U-Net model was developed through transfer learning that effectively classified pre- and post-rainfall LULC. Post-rainfall LULC change detections indicate that 23.8 km² of land was flooded, which is equivalent …

Secure Frameworks For User Motion Data In Virtual Reality, Jayasri Sai Nikitha Guthula

Theses and Dissertations

Virtual Reality is an innovative technology transforming industries such as gaming, healthcare, and remote collaboration. The increasing deployment of these systems results in the continuous collection of telemetry data, including motion patterns, hand gestures, and spatial interactions. This data is valuable for enhancing user experiences and optimizing system performance. However, it also introduces significant privacy risks. Unlike traditional digital footprints, motion data captures fine-grained physical behaviors that can be linked to individual users, making anonymization ineffective in preventing re-identification.This research introduces secure frameworks for user motion data in virtual reality, each proposed framework addressing privacy preservation from a different angle. …

Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …

Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal

Turkish Journal of Electrical Engineering and Computer Sciences

Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …