Digital Commons Network^™

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

“We Are Trans, But We’Re Not Transitional”: Exploring Safety, Place, And Space In The Lives Of Transgender Individuals In Southeastern Virginia, Susana Avalos

Sociology & Criminal Justice Theses & Dissertations

In general, little research exists on trans people’s spatial experiences despite space being highly relevant to their experiences of violence and perceptions of safety. To date, two criminological studies have examined LGBTQ people’s fear of crime in relation to public or private places and spaces. This is in stark contrast to the sizable place and space literature on cisgender men and women. These topics from the perspectives of trans people are underexplored primarily because most criminological research has historically upheld binarized understandings of gender. Safety should not be privileged or limited to certain groups, individuals, or spaces. Instead, safety should …

The Inside Scoop: Elucidating The Three-Way Relationship Between Schistosoma Mansoni, The Gut, And Vaccines, Mariam A. Mhanna

Biomedical Sciences Theses & Dissertations

Schistosomiasis is a neglected tropical disease that affects over 250 million people worldwide. This blood fluke infection burdens communities and areas with limited to no access to clean fresh water. When a host is exposed to cercariae, the infectious agent, in a body of water, it matures in the host’s circulation into adult female and male worms. Copulated adult worms migrate to the mesentery, in the case of Schistosoma mansoni, and produce eggs that can cross the intestinal barrier and get excreted in the feces to continue the lifecycle. The effects of S. mansoni infection on the host …

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation, Hengameh Fakhravar

Engineering Management & Systems Engineering Theses & Dissertations

Driven by the growing need in the 21st century for integrating rigorous statistical analysis into engineering research, there is a movement to develop an integrated statistical engineering science within statistics and quality communities (Hoerl & Snee, 2010; Anderson-Cook et al., 2012). Systems Statistical Engineering research seeks to integrate the Causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer’s Viable System Model (1972, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. Cotter & Quigley (2018) set forth the Bayesian systemic …

Inclusion, Diversity, Equity, And Access Annual Report, Fiscal Year 2025, April Anderson-Zorn, Emily Baldoni, Karmine Beecroft, Chad E. Buckley, Susan Franzen, Tom Hardy, Ellie Harman, Laura Killingsworth, Heather Koopmans, Maddi Loiselle, Joshua Newport, Ryan Peters, Seth Peterson, Lindsey Skaggs, Caitlin Stewart, Maria Tudela, Eric Willey, Angela Yon

Reports and White Papers - Milner Library

Report of Milner Library's activities and initiatives related to Inclusion, Diversity, Equity, and Access in Fiscal Year 2025. Compiled by the IDEA Committee.

The Impact Of Mindfulness Therapy To Reduce Disruptive Behavior In High School, Wendy Nell Peters

Electronic Theses and Dissertations

The aim of this project was to determine the impact and effectiveness of Gina Biegel’s mindfulness techniques for stressed teens method on adolescent anxiety and the disruptive symptoms that lead to high school adolescents being assigned to detention. This project described a quantitative correlational design research project that investigated a small adolescent sample size, with a goal of decreasing the number of local high school adolescent students who are repeat detention offenders, by implementing Gina Biegel’s mindfulness techniques for stressed teens method on those high school adolescents who were assigned to detention. This was achieved by collecting demographic data from …

Perceived Leadership Behaviors And Workplace Motivation: The Predictive Role Of Generational Cohorts In The U.S. Construction Industry, Leslie D. Klinegardner

Electronic Theses and Dissertations

The purpose of this study was to examine the extent to which perceived leadership behaviors predicted workplace motivation among construction workers across generational cohorts in the Greater Houston, Texas area. Grounded in self-determination theory, the research explored how transformational, transactional, and passive-avoidant leadership behaviors predicted the satisfaction and frustration of psychological needs for autonomy, competence, and relatedness. The analysis also considered whether leadership behaviors and generational cohort served as significant predictors of workplace motivation outcomes. The study followed a quantitative, predictive, cross-sectional design using survey methodology. Data were collected using the Multifactor Leadership Questionnaire (MLQ) and the Basic Psychological Need …

Institutional Betrayal And Military Vaccine Mandates: An Inquiry Into Loss Of Health Autonomy Concerning Unlimited Liability Contractual Employment In The Covid-19 Era, Aaron G. Dehaven

Electronic Theses and Dissertations

The balance between government control and personal liberty is a growing concern in the modern world. Nowhere is this more evident than in discussions surrounding government policy in public health and individual health autonomy. The Covid-19 pandemic forced many institutions to change their standard operating procedures by adapting to rapidly changing health guidance instituted to protect public health. These changes highlighted the role of health autonomy and individuals’ rights to refuse medical treatments. The Department of Defense changed many aspects of how it operated during the pandemic, including the mandate of the Covid-19 vaccine for all service members. For various …

Exploring The Impact Of Generative Ai To Mitigate Educator Burnout, Katie E. Acevedo

Electronic Theses and Dissertations

This qualitative case study examined how sixth–12th grade educators at a single-gender public school in West Texas perceived the role of generative AI in reducing workload, minimizing stress, and supporting professional autonomy. The study focused on concerns related to teacher burnout and the time demands of instructional and noninstructional responsibilities, exploring how emerging technologies might offer practical relief. The study aimed to understand how educators use generative AI tools and identify both the benefits and challenges associated with their integration. Framed by job demand-resource theory, connectivism, and heutagogy, the research explored how generative AI can function as a job resource, …

Overcoming Second-Order Barriers To Teachers’ Technology Integration: A Study Of Positive Impacts, Crystal Ann Hume

Electronic Theses and Dissertations

This study explored K–12 public school teachers’ self-efficacy, value, and pedagogical beliefs about technology. The teachers worked in a medium-sized, semi-affluent public school district in North Texas. The study investigated second-order barriers to technology integration and what might positively impact teachers’ self-efficacy, value, and pedagogical beliefs. This study used a qualitative descriptive methodology to collect qualitative data about their beliefs by interviewing and observing K–12 classroom teachers who had completed a bachelor’s degree through a traditional teacher education program. Teachers with alternative certifications or noncertified classroom instructors were omitted. Findings revealed that external factors, failure, mistakes, confidence, and professional development …

A Study Of Urban High School Teachers' Perceptions Of Multicultural Education, Clifford D. Alexander

Electronic Theses and Dissertations

This dissertation investigated urban high school teachers’ perceptions of multicultural education and how those perceptions related to instructional practices. Guided by culturally responsive pedagogy, the study addressed the persistent achievement gap among minority students and the limited integration of multicultural education in U.S. public schools. The purpose of this study was to evaluate teachers’ perceptions of multicultural education and examine their connection to teaching practices and cultural background. A quantitative cross-sectional survey design was employed with a sample of 331 urban high school teachers. Findings indicated that the majority of participants held positive views toward multicultural education, recognizing its role …

An Exploratory Descriptive Study Of Urban Professional High School Counselors' Work Culture, Burnout, And Mental Well-Being, Alexandria L. Hall

Electronic Theses and Dissertations

This descriptive, exploratory, qualitative study examined urban, public U.S. high school counselors’ perceptions of job-related stress, autonomy, and administrative support within urban school environments. Seven current high school counselors working in urban public schools were recruited through convenience sampling in educator forums on Facebook, followed by snowball sampling to expand the participant pool. Counselors participated in semistructured interviews conducted via Zoom or asynchronous online focus groups hosted on Padlet, depending on the participant’s preference. Data were analyzed using Braun and Clarke’s six-step thematic analysis process, supported by Dedoose software for coding and data management. The analysis revealed four major themes: …

Draining Development: The Changing Nature Of Corruption In Post-Independence India, Chandra Tiwari

Graduate Program in International Studies Theses & Dissertations

The economic growth of emerging economies, particularly India’s, stands at a crossroads as the fight against corruption intensifies. Corruption poses a significant threat to the overall development of a country, necessitating the convergence of technological advances with national and global initiatives to combat the issue. Despite several ongoing efforts taken by international bodies to enforce regulations, challenges persist in curbing this issue due to the fundamental behavioral and structural factors at play. This dissertation investigates the dynamic evolution of corruption within a cash-based economy, a period of trade openness, and the contemporary digitalized age in India through a comparative case …

A Methodology For Model-Based Certification, Jay Albert Silverman

Engineering Management & Systems Engineering Theses & Dissertations

A need for a single source of knowledge for design decisions has led to the development of a new field of systems engineering, model-based systems engineering (MBSE) (Delligati, 2014). System certification is defined as affirming that regulatory requirements for a system have been met (Goodwin & Juzaitis, 2006). System certification is a specific application of system validation. System verification can be defined as ensuring that the system of interest (SOI), as designed, is in accordance with the design inputs/requirements. System validation can be defined as ensuring that the SOI, as designed, meets the defined system needs (Wolfgand, Katz, & Wheatcraft, …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Trust In Healthcare Ai Can’T Just Be Designed – It Must Be Felt By Clinicians And Patients, Adriana Banozic-Tang, Heng Wang

Research Collection Yong Pung How School Of Law

Trust in healthcare AI currently over-relies on system design, not lived medical realities.Continuous feedback loops are necessary to embed trust in healthcare AI that is responsive to clinician and patient needs.Initiatives in South-East Asia show how trust in technology can be extended from policy to practice.

Preface, Special Issue For The 16th International Conference On Graph Transformation (Icgt 2023), Maribel Fernández, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

This special issue contains six extended versions of papers presented at the 16th International Conference on Graph Transformation (ICGT 2023), held in Leicester, UK, on 19–20 July 2023. The conference was part of STAF 2023 (Software Technologies: Applications and Foundations) and was held under the auspices of the European Association for Theoretical Computer Science (EATCS), the European Association of Software Science and Technology (EASST), and the IFIP Working Group 1.3, Foundations of Systems Specification.

Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) – a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client’s state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for diverse clients. We evaluate CAMI’s performance through both automated and expert evaluations, utilizing simulated …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

How To Enable Effective Cooperation Between Humans And Nlp Models: A Survey Of Principles, Formalizations, And Beyond, Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua, Jimmy Huang

Research Collection School Of Computing and Information Systems

With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous NLP tasks in recent years. In this paper, we take the first step to present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. In particular, we introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding …

Fact-Audit: An Adaptive Multi-Agent Framework For Dynamic Fact-Checking Evaluation Of Large Language Models, Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classification metrics, which fail to automatically evaluate the justification production and uncover the nuanced limitations of LLMs in fact-checking. In this work, we introduce FACT-AUDIT, an agent-driven framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities. Leveraging importance sampling principles and multi-agent collaboration, FACT-AUDIT generates adaptive and scalable datasets, performs iterative model-centric evaluations, and updates assessments based on model-specific responses. By incorporating justification production alongside verdict prediction, this framework provides a comprehensive and evolving audit of LLMs’ factual reasoning …

Beware Of Your Po! Measuring And Mitigating Ai Safety Risks In Role-Play Fine-Tuning Of Llms, Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …

Other Orienteering Problem Variants, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In this chapter, different variants of routing problems with profits will be discussed. Based on what is available in literature, mostly variants of the orienteering problem will be discussed. A first variant considers capacity constraints, since these appear frequently in many practical applications. Next, multi-objective orienteering problems, explicitly considering different types of profits separately are discussed. Time-dependent and stochastic travel times are also relevant for most practical applications. These are considered together with time-dependent and stochastic profits. More and more routing problems are considered together with inventory management. For routing problems with profits, this leads to the inventory orienteering problem, …

Causalabstain: Enhancing Multilingual Llms With Causal Reasoning For Trustworthy Abstention, Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to abstain when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce CausalAbstain, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that CausalAbstain effectively …

Advancing Molecular Graph-Text Pre-Training Via Fine-Grained Alignment, Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi

Research Collection School Of Computing and Information Systems

Understanding molecular structure and related knowledge is crucialfor scientific research. Recent studies integrate molecular graphswith their textual descriptions to enhance molecular representationlearning. However, they focus on the whole molecular graph andneglect frequently occurring subgraphs, known as motifs, whichare essential for determining molecular properties. Without suchfine-grained knowledge, these models struggle to generalize to un-seen molecules and tasks that require motif-level insights. To bridgethis gap, we propose FineMolTex, a novel Fine-grained Moleculargraph-Text pre-training framework to jointly learn coarse-grainedmolecule-level knowledge and fine-grained motif-level knowledge.Specifically, FineMolTex consists of two pre-training tasks: a con-trastive alignment task for coarse-grained matching and a maskedmulti-modal modeling task for …

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 …

Graph Positional Autoencoders As Self-Supervised Learners, Yang Liu, Deyu Bo, Wenxuan Cao, Yuan Fang, Yawen Li, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency and scalability. Typically, GAEs take incomplete graphs as input and predict missing elements, such as masked node features or edges. Although effective, our experimental investigation reveals that traditional feature or edge masking paradigms primarily capture low-frequency signals in the graph and fail to learn expressive structural information. To address these issues, we propose Graph Positional Autoencoders (GraphPAE), which employ a dual-path architecture to reconstruct both node features and positions. Specifically, the feature path …

Ai-Assisted Risk Assessment In Generative Ai Governance, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Effective risk assessment is paramount for responsible generative AI (GenAI) deployment. Traditional governance approaches that rely on manual reviews are inadequate given the scale and velocity of GenAI outputs. A risk-based approach incorporating real-time monitoring and governance is paramount. In this research, we examine how the efficacy of suggestive versus supportive explanations for AI’s risk assessment of GenAI outputs is moderated by user domain expertise and AI’s risk assessment in determining user acceptance. We hypothesize that cognitive involvement increases with AI’s risk assessment, with higher risks triggering more critical evaluation. By drawing on the elaboration likelihood model, we hypothesize that …

Labor, Leisure, And Law, Aneil Kovvali, Jonathan S. Masur

Articles

Americans work harder than their counterparts in many other advanced economies. While many predicted that technological progress would eventually make work obsolete, Americans continue to pull long hours. Although stories of worker burnout and “quiet quitting” suggest that this situation is not a good fit for everyone, any problems are largely invisible to the conceptual frameworks that dominate the analysis of law. These frameworks normally assume the goal of maximizing production and often treat the human preference for leisure as a problem to be solved. This Article analyzes the problem of overwork. In doing so, it surfaces legal and policy …

Sex Differences In The Risk Of Developing Alzheimer Disease, Cognitive Trajectories, And Relationships Between Cognition And Pathological Burden, Carling G. Robinson

Arts & Sciences Graduate Student Theses and Dissertations

Prior work suggests females may develop Alzheimer disease (AD) dementia at a higher rate than males, and that this effect may interact with apolipoprotein (APOE) ε4 genotype. However, this work has often been limited by imprecise clinical diagnoses and a lack of AD biomarkers. In addition to the frequency of dementia, there are mixed reports on whether sex impacts rates of cognitive decline. Further, females consistently exhibit greater accumulation of AD pathological biomarkers, particularly tau, than males. It has been theorized that females initially demonstrate resilience to AD pathology, maintaining cognitive functioning longer than males, but experiencing a steeper cognitive …