Computer Sciences Commons^™

Learning Techniques In Prediction Of Functional Epigenomic Events, Mohammad Shiri

Computer Science Theses & Dissertations

Accurately predicting functional epigenomic events from DNA sequences is critical to understanding gene regulation and the functional impact of non-coding variants. Despite considerable progress, critical challenges hamper the effectiveness and efficiency of existing deep learning approaches. These challenges include negative transfer in multi-task learning (MTL), suboptimal network architectures, and pervasive label noise, particularly the positive-unlabeled problem arising from data sparsity in single-cell assays. This dissertation presents a cohesive framework of novel learning techniques to effectively address these challenges. First, a highly scalable task grouping framework is presented to mitigate negative transfer in deep MTL. This method clusters tasks based on …

Optimization Of Niobium Film For Particle Accelerators And Quantum Applications, Bektur Abdisatarov

Electrical & Computer Engineering Theses & Dissertations

Niobium (Nb) films play a central role in superconducting technologies used in particle accelerators and superconducting quantum circuits. Optimizing the physical properties of Nb films is therefore critical for improving both radiofrequency (RF) performance in superconducting radiofrequency (SRF) cavities and coherence in superconducting qubits. This thesis investigates the relationship between Nb film microstructure, impurity content, and electromagnetic response across these two application domains.

For particle accelerator applications, we studied Nb films deposited using high-power impulse magnetron sputtering (HiPIMS) with DC bias onto a 1.3 GHz elliptical SRF cavity. Nb film cavities exhibit a pronounced medium-field Q-slope, limiting their achievable accelerating …

Dissecting The Etiology Of Alcohol Use Disorder By An Integrative Heritable Component Approach, Ivy Garrenton

Computer Science Theses & Dissertations

Alcohol Use Disorder (AUD) is a pervasive condition characterized by complex interplay among genetic, phenotypic, and environmental factors. Although previous studies have identi fied genetic loci associated with alcohol consumption, these efforts have not captured the genetic heterogeneity and gene-environment interactions underlying AUD pathogenesis. To address this critical gap, we developed a novel statistical methodology that integrates phenotypic, genotypic, and environmental data through an environmentally modified Genetic Relationship Matrix (GRM) to derive AUD-related traits with enhanced heritability.

This approach demonstrated superior performance in both simulated and real-world datasets. Traits derived using the environmentally modified GRM exhibited significantly higher estimated heritability …

Personality Predictors Of Cybersecurity Vulnerability: Insights From Self-Reports And Stimulated Threat Scenarios, Saroja Roy Grandhi

Psychology Theses & Dissertations

In this cyber dependent and enabled era, understanding the role of human factors in digital security is essential. This study investigates the relationship between Big-Five personality traits and cybersecurity behaviors by examining both self-reported and stimulated behaviors in security threat scenarios. Participants completed validated questionnaires to report their personality traits, cybersecurity practices and engage in task-based stimulations to capture behaviors such as phishing detection, password creation, and response to security alerts. The study tested whether higher conscientiousness, openness, and agreeableness would be associated with stronger cybersecurity practices and smaller discrepancies between self-reported and observed behaviors. And, whether greater extraversion and …

Data Tracking And Analytics Within Inventory Management: Coffee Shop And Retail Store Optimization, Mateo J. Moyon

Senior Theses

This thesis examines the application of inventory management theory in the small and medium-sized business context, with a specific focus on the food and beverage industry. Drawing on the foundational academic literature spanning from Harris’s EOQ formula in 1913 through stochastic inventory theory, ABC analysis, and just-in-time strategy, this paper establishes the mathematical and operational bases for modern inventory management practice. Although there are proven value to these frameworks, research demonstrates that small to medium sized businesses adopt inventory management systems at lower rates citing cost and implementation as barriers. This thesis argues that the emergence of low-cost inventory and …

Federated Retrieval-Augmented Generation For Cybersecurity In Resource-Constrained Iot And Edge Environments: A Deployment-Oriented Scoping Review, Hangyu He, Yuan, Kai Wu, Wei Ni

Research outputs 2022 to 2026

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Thesis/ Dissertation Defenses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Taysir Hindi

Thesis/ Dissertation Defenses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). This thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows. The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

Neutrosophic Sets In Neural Networks: Theory, Applications, And Challenges, Vladimir Simic, Dragan Pamucar, Hafiz Muhammad Athar Farid

Neutrosophic Systems with Applications

The integration of neutrosophic sets into neural networks presents a novel approach to handling uncertainty, indeterminacy, and falsity in data. Traditional neural networks typically operate under the assumption of precise and complete data, but real-world applications often involve noisy, incomplete, or ambiguous information. Neutrosophic sets extend fuzzy logic by incorporating three components: truth, indeterminacy, and falsity, allowing for a more nuanced representation of uncertain data. This paper explores the theoretical foundations of neutrosophic sets and their integration with neural networks, highlighting the challenges in computational complexity, training, and optimization. The paper also discusses the potential applications of neutrosophic neural networks …

Neutrosophic Probability With Dynamic Temporal Uncertainty (Nptu), Bhimraj Basumatary, Ashoke Kumar Brahma

Neutrosophic Systems with Applications

This paper introduces Neutrosophic Probability with Dynamic Temporal Uncertainty (NPTU), an extension of classical neutrosophic probability that incorporates the dimension of time. In classical neutrosophic probability, the degrees of truth, indeterminacy, and falsity are considered static. However, real-world uncertainties evolve, and their degrees change as new information becomes available. NPTU models these uncertainties dynamically, allowing for more accurate decision-making in time-varying environments. The paper explores key mathematical properties of NPTU, including entropy, distance measures, similarity measures, and Kullback-Leibler (KL) divergence, to quantify and compare temporal uncertainty states. The proposed framework is demonstrated through a case study on stock price prediction, …

Emergent Operator Logic: A Foundational Framework For Dynamic Reasoning And Generative Intelligence, Mona Gharib, Abduallah Gamal, Muhammad Nawaz, Basma Nasir

Neutrosophic Systems with Applications

This paper introduces Emergent Operator Logic (EOL), a framework that treats propositions as continuous operators $F_p:X \rightarrow X$ on a complete metric state space $( X,d )$ and evaluates truth after action via a continuous valuation $V:X \rightarrow [ 0,1 ]$. Logical composition is realized by three operator-level connectives: sequential $p \circ q$(causal order), parallel $p\parallel q$(1-Lipschitz cooperative blend), and the emergent synthesis $E( p,q ) = \frac12( F_p \circ F_q + F_q \circ F_p )$, which symmetrizes non-commuting actions. We provide a Hilbert-style proof system (sound), an algebraic semantics via E-algebras, and show that the category of E-algebras is …

Double-Valued Complex Neutrosophic Graphs, Suriyakumar G, V. J. Sudhakar, Takaaki Fujita

Neutrosophic Systems with Applications

This paper introduces a novel graph-theoretic framework, called the double-valued complex neutrosophic graph, as an extension of double-valued neutrosophic set theory. Within this framework, we investigate several important classes of such graphs, including self-complementary, strong, and full double-valued complex neutrosophic graphs, and establish a number of their fundamental properties. To clarify the proposed concepts and demonstrate their structural behavior, several relevant illustrative examples are also provided.

A Hybrid Multi-Criteria Decision-Making Approach For Sustainable Forest Fire Monitoring Using Unmanned Aerial Vehicles, Mohamed Eassa, Ahmed Abdelhafeez, Ahmad M. Nagm

Neutrosophic Systems with Applications

Unmanned aerial vehicles (UAVs) have become an effective tool for forest fire monitoring. This study evaluates UAVs for forest fire management, addressing the challenges posed by ambiguous and uncertain factors. Single-valued neutrosophic sets (SVNSs) are employed to model complex uncertainties, as they incorporate three distinct membership values: false, true, and indeterminate. The evaluation of UAVs is a multifaceted task due to the variety of factors involved. To address this complexity, multi-criteria decision-making (MCDM) methods are used. Specifically, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are integrated with SVNS to …

On Fibonacci Ensembles: An Alternative Approach To Ensemble Learning Inspired By The Timeless Architecture Of The Golden Ratio, Ernest Fokoue

Articles

Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony, and recursive stability \citep{Koshy2001Fibonacci, Livio2002GoldenRatio}. From spiral galaxies to the unfolding of leaves, this humble sequence reflects a universal grammar of balance. In this work, we introduce \emph{Fibonacci Ensembles}, a mathematically principled yet philosophically inspired framework for ensemble learning that complements and extends classical aggregation schemes such as bagging, boosting, and random forests \citep{Breiman1996Bagging, Breiman2001RandomForests, Friedman2001GBM, Zhou2012Ensemble, HastieTibshiraniFriedman2009ESL}. Two intertwined formulations unfold: (1) the use of normalized Fibonacci weights -- tempered through orthogonalization and Rao--Blackwell optimization -- …

Shine: Multimodal Machine Learning Approaches For Solar Energetic Particles Events Event Prediction And Posthoc Analysis, Soukaina Filali Boubrahimi

Funded Research Records

No abstract provided.

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

Ai Adoption In Research Administration At Emerging Research Institutions, Dylan Ruediger, Ruby Macdougall, Stefanie Brachfield, Douglas R. Dechow, Jonathan Parker, Jana Remy

Library Articles and Research

"With funding from the National Science Foundation’s GRANTED program (grant #2437518), Ithaka S+R, Chapman University, and Montclair State University organized two workshops to help research administrators consider how to leverage AI to build research capacity at ERIs. Our first workshop, held at Montclair State in September 2025, brought together 31 participants from 13 academic and medical institutions in the New York/New Jersey/Pennsylvania region. Our second workshop, hosted by Chapman University on December 5, 2025, included 32 participants from 13 colleges and universities in Southern California. The approximately 2,600 ERIs in the United States receive a disproportionately small amount of federal …

Enhancing Introductory Cybersecurity Learning: A Design-Based Research Case Study, Manny Niri Dr.

Journal of Cybersecurity Education, Research and Practice

This study employs a design-based research (DBR) framework to examine the impact of a comprehensive curriculum redesign in an introductory Foundations of Security module for undergraduate students in computing and cybersecurity at a UK public university between 2019 and 2025. The redesign aimed to enhance student learning, engagement, and critical thinking through the embodiment of evidence-based pedagogical strategies, including flipped classroom delivery, blended learning, gamified practical exercises, repeated low-stakes mock assessments, and structured problem-solving activities. Student feedback, assessment outcomes, attendance records, and faculty reflections were analysed to evaluate the effectiveness of the redesign. The results indicate substantial improvements in student …

The Core-Modulation Architecture (Cma): A Structural Overview Of A 14-Paper Research Program (Preprint), Griselda Poe

Publications and Research

This document provides a structural overview of the Core-Modulation Architecture (CMA), a 14-paper research program on cognition, communication, and AI interaction.

The series specifies the conditions under which cognition operates, terminates, fails, and generates structure. Rather than describing cognition by its contents (beliefs, emotions, decisions), it defines cognition through its underlying architecture: constraint-governed processing across layers with distinct termination conditions.

The framework introduces a layered model consisting of Core processing (constraint preservation and structural coherence) and Modulation (affective calibration and social interface adjustment), extended by a Prior layer as the source of constraints. Across the series, phenomena such as miscommunication, …

The Effects Of Affordability And Quality Of Care On Utilization Of Primary Healthcare Among Rural Residents In Twifo Ati-Morkwaa District, Ghana: A Qualitative Study, Peter Ansah Boakye, Francis Tei-Nartey, Gideon Owusu

Michigan Tech Publications

Objective: This qualitative study examines how affordability and quality of care influence the utilization of primary healthcare (PHC) services among rural residents in the Twifo Ati-Morkwaa District, Ghana. While Ghana has implemented policies such as CHPS and NHIS to improve access, subjective experiences of rural residents regarding total costs of care and perceived quality remain underexplored. Methods: We conducted a qualitative cross-sectional study between April and September 2024 using purposive and snowball sampling. Ten gender-segregated focus group discussions (FGDs; n = 90 residents) and six in-depth interviews (IDIs) with PHC providers were conducted. Data were collected in Twi, transcribed and …

Classification Of Land Cover In Sentinel-2 Imagery Using Machine Learning Models, Ehsan Ali Al-Zubaidi, Mohammed Ridha Hammoodi, Ahmed Naser Alzurfi

Al-Bahir

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO …

Trogs-26 Test Images, Aaron Hershkowitz, Nicholas Howe, Bebe Cosgrove, Tajhini Brown

Data

No abstract provided.

When Ai Writes The Doctoral Thesis: Reclaiming The Oral Defence As A Learning Development Intervention, Valerie A. Storey

All Faculty and Staff Scholarship

Large language models have fundamentally challenged traditional methods of verifying doctoral competency as AI-generated text becomes increasingly difficult to distinguish from human scholarship. This paper argues that thesis committees and doctoral supervisors must reclaim the oral defence as a critical checkpoint for assessing authentic threshold crossing rather than a ceremonial rite of passage. Drawing on historical examples from medieval oral disputations through to the rise of written theses, this paper asserts the necessity of returning to rigorous oral assessment. Given the limitations of detection technologies and the growing use of AI in thesis writing, oral defences must move from confirmatory …

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

A Comprehensive Survey Of Watermarking Techniques For Copyright Protection And Integrity Verification On Dnns And Generative Models, Xinyun Liu, Ronghua Xu

Michigan Tech Publications

Deep neural networks (DNNs) have made remarkable progress in recent years and are widely applied across many fields. Trained DNNs are valuable assets due to their dependence on large volumes of quality data, expensive computational resources, and the development of sophisticated architectures. However, their increasing vulnerability to intellectual property (IP) infringement, including unauthorized use, replication, and redistribution, underscores the critical need for adequate copyright protection and integrity verification. DNN model watermarking has emerged as a promising solution to these challenges by embedding imperceptible identifiers into models. This survey provides a concise yet comprehensive state-of-the-art review of watermarking techniques focusing on …

A Comprehensive Survey Of Watermarking Techniques For Copyright Protection And Integrity Verification On Dnns And Generative Models, Xinyun Liu, Ronghua Xu

Michigan Tech Publications

Deep neural networks (DNNs) have made remarkable progress in recent years and are widely applied across many fields. Trained DNNs are valuable assets due to their dependence on large volumes of quality data, expensive computational resources, and the development of sophisticated architectures. However, their increasing vulnerability to intellectual property (IP) infringement, including unauthorized use, replication, and redistribution, underscores the critical need for adequate copyright protection and integrity verification. DNN model watermarking has emerged as a promising solution to these challenges by embedding imperceptible identifiers into models. This survey provides a concise yet comprehensive state-of-the-art review of watermarking techniques focusing on …

Pong Revised: Network-Based Competitions Through Secure Socket Services, Noah T. Jennings, Destiny D. Hale, Jared D. Williams, Michael J. Lively-Scholz

Knowledge and Creativity Expo

We aim to provide a safe, thrilling, locally hosted, and educational multiplayer experience that can be quickly replicated in modern Capture The Flag (CTF) events.

How Much Does Shape Matter: Investigating The Impact Of Marine Particle Morphological Features On In-Situ Settling Velocities Using Pca And Various Ml Models, Huanqing Huang, Alexander B. Bochdansky

Knowledge and Creativity Expo

Particle settling velocity serves as an essential component in ocean biological pump, as it determines particle retention time in the water column. Stokes’ law has been widely used to predict particle settling velocities by particle size and excess density in aquatic environments. However, an increasing number of studies suggest that Stokes’ law fits poorly in the size-velocity relationship of observations on small oceanic particles. Here, we present a series of novel approaches to investigate the relative contribution of settling velocities by the particle shape and optical densities using machine learning (ML) models and principal component analysis (PCA), based on 3906 …

What Does Next-Generation Mass Spectrometry Offer For Proteomics? A Comprehensive Platform Comparison, Filipa Blasco Tavares Pereira Lopes, Daniela Schlatzer, Tara Sudhadevi, Anantha Harijith, Marzieh Ayati, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

Next-generation mass spectrometry platforms (Orbitrap Astral, timsTOF Ultra) are reshaping proteomics by enhancing analytical depth and sensitivity. We compared these platforms against Orbitrap Exploris 480 using neonatal mouse lung tissues from a bronchopulmonary dysplasia model (n = 12), employing four acquisition strategies: Exploris 480 DDA/DIA, Astral HR-DIA, and timsTOF Ultra DIA-PASEF. All platforms identified ∼4000 proteins in common, with 98% proteome coverage of data-dependent acquisition (DDA) identifications using data-independent (DIA) methods and 92% concordance between next-generation systems. Orbitrap Astral and timsTOF Ultra quantified >225,000 peptides and 13,000 proteins, representing ∼800% and ∼300% greater depth than Exploris 480 DDA, respectively. …