Computer Sciences Commons^™

Estimating The Gender Wage Gap: A Comparative Analysis Of Different Estimators, Xinran Zhang

Mathematics, Statistics, and Computer Science Honors Projects

The gender wage gap between males and females has been well studied by labor economists. We take a multi-prong approach to evaluate three estimators —a regression-imputation estimator, a weighting estimator, and a doubly robust estimator—in estimating the gender wage gap. Using the Panel Study of Income Dynamics, we conduct an empirical study of the estimators’ performances. In a simulation study, we evaluate the properties of estimators and study whether bootstrapping is an appropriate measure of the uncertainty of each estimator. The findings show that while the estimators provide different results, the doubly robust estimator provides reliable and consistent results under …

Cyber Warfare And The Future Of Conflict, Victor A. Mercado

Graduate Theses/Dissertations

This thesis examines whether cyber warfare now poses a more immediate threat to U.S. national security than weapons of mass destruction (WMD). Cyber operations have become a dominant instrument of contemporary conflict, with malicious actors operating in a persistent “grey zone” that blurs traditional boundaries between war and peace. These operations target military and civilian entities, both directly and often as collateral damage due to the uncontrollable nature of cyber threats. WMDs, despite their catastrophic destructive potential, remain largely constrained by established deterrence frameworks.

The evolving nature of cyber warfare warrants comparison to WMD effects and impact, as cyber capabilities …

Three-Sided Skyline Counting Queries, Suruchi Kushwaha

Dissertations, Master's Theses and Master's Reports

A two-dimensional point p=(p.x,p.y) dominates another point p'=(p'.x,p'.y) if p.x ≥ p'.x and p.y>p'.y or p.x>p'.x and p.y ≥ p'.y. The skyline of a point set P is a subset P' ⊆ P such that every point in P' is not dominated by any other point in P. An orthogonal skyline counting query Q on a set of points P asks for the number of points on the skyline of P ⋂ Q.

In this work we study data structures that support orthogonal skyline counting queries in the special case when the query range is bounded on three …

The Gender Wage Gap In An Online Labor Market: The Cost Of Interruptions, Abi Adams, Kotaro Hara, Kristy Milland, Chris Callison-Burch

Research Collection School Of Computing and Information Systems

This paper analyses gender differences in working patterns and wages on Amazon Mechanical Turk, a popular online labour platform. Using information on 2 million tasks, we find no gender differences in task selection nor experience. Nonetheless, women earn 20% less per hour on average. Gender differences in working patterns are a significant driver of this wage gap. Women are more likely to interrupt their working time on the platform with consequences for their task completion speed. A follow-up survey shows that the gender differences in working patterns and hourly wages are concentrated amongst workers with children.

Risk Spillover Effect Of China-Asean Supply Chains: Insights Of Industrial Transfer, Zeyang Bian, Yuning Zhang, Keng Siau, Yaqian Zhang, Jianjia He

Research Collection School Of Computing and Information Systems

As labour costs in China increase, labour-intensive industries are migrating to ASEAN countries, attracted by lower labour costs and market potential. This shift not only affects the economies of China and ASEAN but also reshapes the global manufacturing landscape. This paper investigates the correlation and spillover of supply chain risks using production exposure indicators derived from inter-country input-output data and the R-Vine Copula model. We assess the risk spillover of each country within the global supply chain. Our findings indicate that industrial relocation can significantly alter supply chain structures, thereby affecting the concentration and direction of risks. While China's role …

Label Correlated Contrastive Learning For Medical Report Generation, Xinyao Liu, Junchang Xin, Bing Tian Dai, Qi Shen, Zhihong Huang, Zhiqiong Wang

Research Collection School Of Computing and Information Systems

Background and Objective: Automatic generation of medical reports reduces both the burden on radiologists and the possibility of errors due to the inexperience of radiologists. The model that utilizes attention mechanism and contrastive learning can generate medical reports by capturing both general and specific semantics. However, existing contrastive learning methods ignore the specificity of medical data, that is, a patient may suffer from multiple diseases at the same time. This means that the lack of fine-grained relationships for contrastive learning will lead to the problem of insufficient specificity. Methods: To address the above problem, a label correlated contrastive learning method …

Including Individuals' Sense Of Self In Digital Information Privacy, Peter N. Meso, Solomon Negash, Humayun Zafar, Gurpreet Dhillon

Faculty Articles

The nature of contemporary digital ecosystems causes concerns that affect the person, the individual-self, an integral part of an individual’s information privacy calculus and hence a component of individuals’ Information Privacy Concerns (IPC). Yet, prior IPC models overlook self-focused concerns. This study articulates two constructs, termed “loss of autonomy” (i.e., autonomy) and “control over profiling” (i.e., profiling), that reflect individuals’ self-focused privacy concerns. Combining these new constructs with conventional IPC constructs that capture data-focused and device-focused concerns yields an IPC model made up of three dimensions: self-focused concerns, data-focused concerns, and device-focused concerns. The authors first develop instrument items for …

The Effect Of Facial Phenotypes On Differential Performance Of Facial Recognition, Evan R. Garrett

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition technology is utilized in many facets of life. As the use has become more widespread these systems have improved in reliability and performance approaching the level of human accuracy. With these improvements the problem of bias still remains as a persistent problem. Efforts have been made to minimize the bias prevalent in the systems via studies into various demographic factors, creating training datasets that have a more uniform distribution of subjects, and other methods. As facial recognition is one of the most utilized forms of biometric recognition it is vital to analyze potential causes of bias to help …

Symp25s: Can Llm Detect Dementia?, Rishank Singh, Youxiang Zhu, Xiaohui Liang, John A. Batsis, Caroline Summerour

Paul English Applied Artificial Intelligence (AI) Institute Publications

High Cost of Traditional Screening: Formal cognitive assessments for dementia are resource-intensive and not easily accessible for large-scale screening. Speech-Based Alternatives: Existing speech-based methods (e.g., picture description, telephone interviews) aim to address this but have limitations. Lack of Natural Dialogue: These conventional approaches often use rigid, repetitive prompts and do not simulate real conversations. Engagement Issues: Repetition and lack of conversational depth can reduce engagement and affect the accuracy of responses over time. Untapped Potential of LLMs: Large language models (LLMs) are capable of generating natural, coherent, and adaptive dialogue. Research Gap: The application of LLMs for dementia detection through …

Symp25s: Cactas-Ai: Automatic Segmentaion Of Calcified Plaque In Carotid Arteries, Jiehyun Kim, Kevin Wang, Yu Sakai, Youxiang Zhu, Andrew C. Hu, Huy Q. Phi, Nathan Arnett, Grace J. Wang, Brett L. Cucchiara, Jae W. Song, Daniel Haehn

Paul English Applied Artificial Intelligence (AI) Institute Publications

Manual segmentation of calcified plaque, essential for assessing stroke risk, is time-consuming, and conventional methods like 2D and 3D UNet often struggle with the small size. We developed CACTAS-AI, a two-step segmentation process. This approach outperforms baseline methods in plaque segmentation.

Limitations Of Scientific Articles And Navigated Future Directions With Llm And Rag, Ibrahim Al Azher

Graduate Research Theses & Dissertations

Traditional Topic Modeling approaches, as well as zero-shot, few-shot, and fine-tuned Large Language Models (LLMs), have struggled to generate topics alongside relevant text from diverse sources, particularly sections such as Limitations. This thesis investigates automated methods for analyzing and synthesizing key sections of scientific articles using LLMs, exploring multiple dimensions of scientific text analysis.

First, LimTopic is introduced as a method for extracting and modeling the limitations sections of research papers. By integrating LLM-based topic generation with BERTopic, the approach generates descriptive titles and concise summaries that highlight the boundaries and shortcomings of studies, ultimately guiding future research directions.

Second, …

Leveraging Synthetic Data For Efficient Training Of Ai Models For Real-World Object Detection, Reinaldo A. Moraga

Graduate Research Theses & Dissertations

Modern computer vision (CV) systems largely depend on real-world data for training, which is costly in terms of time, materials, and resources. As industries push toward automation and Artificial Intelligence (AI) -driven solutions, the need for enabling more efficient model training is growing. The primary aim of this work is to explore a framework tailored for industrial applications that uses synthetic images generated from 3D models to train a CV model capable of real-world object detection. This approach seeks to reduce the time, cost, and resources typically required for training AI models with real-world data. This work presents a method …

Facial Chick Sexing: An Automated Chick Sexing System From Chick Facial Image, Marta Veganzones Rodriguez, Thinh Phan, Arthur F.A. Fernandes, Vivian Breen, Jesus Arango, Michael T. Kidd, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

Chick sexing, the process of determining the gender of day-old chicks, is a critical task in the poultry industry due to the distinct roles that each gender plays in production. While effective traditional methods achieve high accuracy, color, and wing feather sexing is exclusive to specific breeds, and vent sexing is invasive and requires trained experts. To address these challenges, we propose a novel approach inspired by facial gender classification techniques in humans: facial chick sexing. This new method does not require expert knowledge and aims to reduce training time while enhancing animal welfare by minimizing chick manipulation. We develop …

Navigating The Digital Frontier: New Perspectives On Cybercrime And Governance, Christopher S. Kayser, Thomas Dearden, Katalin Parti, Sinyong Choi

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Modus Operandi And Blockchain Analysis Of Romance Scams: Cryptocurrency-Driven Victimization, Amy Lim, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

The Legal Response To The Intrusion Into Digital Identity In Social Media, Maria González-García Vinuela

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

A Study Of Pattern Of Cybercrime Abuse Of Individual Internet Users In Umuahia North Lga, Abia State Of South-Eastern Nigeria, Ogochukwu Favour Nzeakor, Rita Ngozi Okafor, Chibuike Ndubuisi Nwoke

International Journal of Cybersecurity Intelligence & Cybercrime

Although a number of studies exist on cybercrime and its abuses, little is known about the pattern of cybercrime abuses individual Internet users experience in Nigeria, especially the south eastern region. Using data collected via various methods, this study examines the pattern of cybercrime abuses of individual Internet users in Umuahia, Abia State, of South Eastern Nigeria. The result of the analysis of 1,067 samples drawn from 223,134 Internet users in Umuahia North LGA of Abia Sate showed that: while most users are victims of stolen ICT-gadgets (19%), fraud related offences (17%), and hacking (15%); they rarely fall victims of …

Linking Empirical Data And Numerical Simulation To Characterize Dynamic Fire Behavior Associated With Interacting Firelines, Marta Sergeevna Jerebets

Graduate Student Theses, Dissertations, & Professional Papers

Understanding fuel pattern-fire process relationships is key for predicting fire behavior and effects with follow-on benefits to proactive fire management and model validation. To characterize dynamic fire behavior, this thesis leverages empirical data and numerical simulation through two complementary studies.

In the first study, longwave thermal sensors aboard unmanned aerial systems (UAS) were used to capture fine-scale fire behavior in two experimental grass burns. A novel paired design was used to quantify the effects of fuel arrangement on fire behavior with 3.66 m diameter treatments cut to a height of 0.15 m. The treatments ephemerally reduced fire rate of spread …

Investigating The Impact Of Aerial Firefighting On Rate Of Wildfire Spread, Lindsay Ann Wiard

Graduate Student Theses, Dissertations, & Professional Papers

Aerial retardant drops are widely used in wildfire suppression, yet their effectiveness in slowing fire spread remains difficult to quantify at scale. This study evaluates the impact of aerial suppression on wildfire rate of spread (ROS) using a modeling framework that incorporates both observed (real) and counterfactual (synthetic) drop locations from a sample of 62 wildfires in Oregon. Synthetic drops were generated to simulate a no-suppression baseline, allowing us to compare changes in ROS in the presence and absence of suppression. We trained two random forest classifiers: one using both real and synthetic drops (the full model), and another using …

Binoculars To Bytes: Development And Field Validation Of An Ai-Driven System For Avian Monitoring, Christian J. Dupree

Graduate Student Theses, Dissertations, & Professional Papers

Autonomous camera-trap arrays coupled with artificial-intelligence (AI) vision can lift bird monitoring beyond the spatial, temporal, and labor limits of traditional field surveys. We present Binoculars to Bytes (B2B), an open-source pipeline that turns 180–360° time-lapse imagery into analysis-ready avian data. At Freezout Lake Wildlife Management Area (Montana, USA) the system ran four-hour morning deployments during spring and fall migrations (2023–2024). A YOLO-NAS detector, incrementally refined with a “Specialized Localized Iterative Model” workflow, quadrupled local accuracy and, after confidence-based species-binning, cut false-positive rates in half. Daily AI species lists were benchmarked against contemporaneous eBird citizen-science checklists and recovered ≥ …

Deep Learning And Adaptive Clustering Approaches For Flood Prediction And Efficient Sensor Placement In Missouri, Fahimeh Sharafkhani

Doctoral Dissertations

Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

Uso De Herramientas De Inteligencia Artificial Generativa Por Parte De Docentes En Una Escuela Del Suroeste De Puerto Rico, Glorimar N. Rodríguez Guiliani

Theses and Dissertations

Esta disertación aplicada fue diseñada para investigar el nivel de conocimiento, uso y dificultades que enfrentan los docentes de quinto a duodécimo grado en una escuela privada del suroeste de Puerto Rico respecto a tecnologías emergentes las cuales presentan desafíos significativos para los docentes y los estudiantes. Se exploró la utilización de herramientas de inteligencia artificial generativa (GenAI) como ChatGPT dentro y fuera del aula para actividades pedagógicas y administrativas.

Los hallazgos revelaron una notable carencia en el conocimiento docente sobre el uso y habilidades de la inteligencia artificial. Se identificó, también, una deficiencia en la capacidad de los docentes …

Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali

All Works

This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …

Computer Science For Middle School (Cs4ms), Melani Loney, Lisa Steffian, Tancy J. Vandecar-Burdin

Center for Educational Partnerships Publications

This session will discuss the project Computer Science for Middle School and the associated findings. This Virginia Department of Education funded project provides computer science Professional Development opportunities for in-service teachers, located in Southeastern Virginia, who instruct students in middle school grades 6- 8. Participating teachers learn to integrate computer science standards into core and elective content through online, asynchronous microcredentials and in-person Summer Workshops. The presentation will include project findings and next steps including teacher efficacy for implementing the computer science standards before and after completing the microcredentials and their students’ attitudes about computer science.

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

A Case Study Using The Transparency Framework And Artificial Intelligence To Promote Effective Writing And Student Success In A Writing-Intensive Course, Elizabeth A. Brown, Maria Kronenburg, Ashlee Steeley, Diana Tagbor

Health Behavior, Policy & Management Faculty Publications

Program evaluation data suggest that undergraduate students struggle with writing in a clear and concise manner and appropriately citing. Faculty implemented the plan-do study-act cycle to pilot the Transparency in Learning and Teaching (TILT) project framework and to explore the use of artificial intelligence (AI) and discuss approaches to using AI, along with the TILT framework, in a writing-intensive course to identify the pros and cons of using ChatGPT in an online classroom. The TILT framework reinforces adult learning by helping students clearly understand the assignment's purpose and establish a clear relationship between assignment and students' professional lives. Faculty encouraged …

A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn

Health Behavior, Policy & Management Faculty Publications

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challenges in academic settings, students increasingly seek digital tools that provide holistic support for physical, mental, and financial health. Through focus groups, this qualitative study examines students' preferences for personalized health tracking, educational content, and flexible reminders within a private, supportive community. Key findings emphasize students' desire for a balanced, all-in-one app that integrates health and wellness tools without overwhelming them with notifications. Students also highlighted the importance of social media integration for outreach, though concerns were raised about potential stress …

Error In The Loop: How Human Mistakes Can Improve Algorithmic Learning, Ryan W. Copus, Cait Spackman, Hannah Laqueur

Faculty Works

Algorithms often outperform humans in making decisions, in large part because they are more consistent. Despite this, there remains widespread demand to keep a “human in the loop” to address concerns about fairness and transparency. Although evidence suggests that most human overrides are errors, we argue these errors can provide value: they generate new data from which algorithms can learn. To remain accurate, algorithms must be updated over time, but data generated solely from algorithmic decisions is biased, including only cases selected by the algorithm (e.g., individuals released on parole). Training on this algorithmically selected data can significantly reduce predictive …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …