Physical Sciences and Mathematics Commons^™

A Roadmap For Applying The Contextual Integrity Framework In Qualitative Privacy Research, Priya C. Kumar, Michael Zimmer, Jessica Vitak

Computer Science Faculty Research and Publications

Privacy is an important topic in HCI and social computing research, and the theory of contextual integrity (CI) is increasingly used to understand how sociotechnical systems-and the new kinds of information flows they introduce-can violate privacy. In empirical research, CI can serve as a conceptual framework for explaining the contextual nature of privacy as well as an analytical framework for evaluating privacy attitudes and behaviors. Analytical applications of CI in HCI primarily employ quantitative methods to identify appropriate information flows but rarely engage with the full CI framework to evaluate such flows. In this paper, we present a roadmap to …

Unveiling Gene Interactions In Alzheimer's Disease By Integrating Genetic And Epigenetic Data With A Network-Based Approach, Xiangning Chen, Zhongming Zhao

Faculty, Staff and Student Publications

Alzheimer’s Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD’s pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted …

Digital Health Technologies For High-Risk Pregnancy Management: Three Case Studies Using Digilego Framework, Amy Franklin, Deevakar Rogith, Jerrie Refuerzo

Faculty, Staff and Student Publications

OBJECTIVE: High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.

METHODS: We describe three studies that leverage core methods from

RESULTS: Scalable social computing models using deep learning classifiers …

Identification Of Novel F2-Isoprostane Metabolites By Specific Udp-Glucuronosyltransferases, Warda Amin, Sarah Thomas, Camille Oger, Jean-Marie Galano, Harvey J Murff, Gong Yang, Thierry Durand

Faculty, Staff and Student Publications

UDP-glucuronosyltransferases (UGTs) catalyze the conjugation of glucuronic acid with endogenous and exogenous lipophilic small molecules to facilitate their inactivation and excretion from the body. This represents approximately 35 % of all phase II metabolic transformations. Fatty acids and their oxidized eicosanoid derivatives can be metabolized by UGTs. F2-isoprostanes (F2-IsoPs) are eicosanoids formed from the free radical oxidation of arachidonic acid. These molecules are potent vasoconstrictors and are widely used as biomarkers of endogenous oxidative damage. An increasing body of evidence demonstrates the efficacy of measuring the β-oxidation metabolites of F2-IsoPs rather than the unmetabolized F2-IsoPs to quantify oxidative damage in …

Social And Behavior Factors Of Alzheimer's Disease And Related Dementias: A National Study In The Us, Yinan Zheng, Hua Xu, Xiaoqian Jiang, Kun Huang, Kelley Pettee Gabriel, Hong-Wen Deng, Mary P Gallant, Kai Zhang

Faculty, Staff and Student Publications

Introduction: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated.

Methods: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by …

Evaluating And Improving The Usability Of A Mhealth Platform To Assess Postoperative Dental Pain, Nicholas Skourtes, David Holmes, Joel White, Elsbeth Kalenderian, Muhammad F Walji

Faculty, Staff and Student Publications

OBJECTIVES: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application.

MATERIALS AND METHODS: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and

RESULTS: The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by …

Learning From Data In Dentistry: Summary Of The Third Annual Openwide Conference, Alfa Yansane, Bunmi Tokede, D Brad Rindal, Heiko Spallek, Joel White, Muhammad Walji

Faculty, Staff and Student Publications

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) …

The Eliahou-Kervaire Resolution Over A Skew Polynomial Ring, Luigi Ferraro, Alexis Hardesty

School of Mathematical & Statistical Sciences Faculty Publications

In a 1987 paper, Eliahou and Kervaire constructed a minimal resolution of a class of monomial ideals in a polynomial ring, called stable ideals. As a consequence of their construction they deduced several homological properties of stable ideals. Furthermore they showed that this resolution admits an associative, graded commutative product that satisfies the Leibniz rule. In this paper we show that their construction can be extended to stable ideals in skew polynomial rings. As a consequence we show that the homological properties of stable ideals proved by Eliahou and Kervaire hold also for stable ideals in skew polynomial rings.

Enhancing Health Analytics: Secure And Private Federated Learning Solutions, Nisha Thorakkattu Madathil

Dissertations

Federated Learning (FL) is a collaborative method allowing individuals to train a model jointly without sharing their local datasets. It utilizes decentralized data sources to protect privacy, making it particularly promising in medical contexts where data confidentiality is paramount. FL facilitates the use of diverse datasets from various healthcare organizations while upholding patient confidentiality. It also plays a crucial role in advancing medical research and healthcare services while adhering to data distribution and compliance requirements. The primary challenges within federated healthcare encompass privacy preservation among sensitive distributed data, ensuring efficient communication, addressing data heterogeneity, and ultimately guaranteeing model accuracy. To …

Balancing Act: Insights Into Telangana Farmers’ Perspectives On Intensive Vs. Sustainable Agriculture, Srinivas Katherasala, Ram Shepherd Bheenaveni

School of Social Work Faculty Publications

High-input agriculture presents several drawbacks that can adversely affect both environmental sustainability and human health. A primary concern is soil degradation, which results from the excessive use of chemical fertilizers and pesticides. These substances can deplete soil nutrients, impair crop growth, and ultimately render the land barren and unproductive. This study, therefore, aims to explore the perceptions and practices of farmers in the Kumuram Bheem Asifabad district of Telangana State, India. The research involved a sample of 382 farmers across six mandals within this district. A stratified random sampling method was utilized within a survey-descriptive research framework. Data collection incorporated …

Implementation And Evaluation Of Ai-Based Citizen Question-Answer Recommender (Acqar) To Enhance Citizen Service Delivery In Singapore Public Sector: A Case Study, Hui Shan Lee

Dissertations and Theses Collection (Open Access)

Government agencies prioritize citizen service delivery to foster trust with the public. Technological advancements, particularly in Artificial Intelligence (AI), hold promise for improving service provision and aligning government operations with citizens' needs. Yet the inherent inflexibility of Service Level Agreements (SLAs) often overlooks the nuances of human emotions and the varied nature of citizen inquiries, exacerbated by a lack of tools to guide appropriate responses. This dissertation aims to address the gaps of overlook of human emotions and non-support for appropriate responses, by exploring the following questions: (1) Can a predictive model incorporating both numeric and textual data effectively forecast …

Can Organizational Focus On Responsible Ai Lead To Improved Ai Adoption By Employees?, Seema Chokshi

Dissertations and Theses Collection (Open Access)

The duality inherent in Artificial Intelligence technology entails that while AI has the potential to bring about transformative benefits to organizations, unintended consequences of AI applications could lead to biased and discriminatory outcomes, which could have negative consequences for the organization and society in general. Concerns about such unintended consequences are an impediment to AI adoption where unwilling employees and practitioners often fear ethical breaches, thereby, negatively impacting their engagement with AI driven applications. In response to these concerns various organizations and regulatory bodies have developed governing frameworks broadly known as Responsible AI standards, that set guidelines to design, …

Sequential Recommendation: From Representation Learning To Reasoning, Lei Wang

Dissertations and Theses Collection (Open Access)

The recommender system is a crucial component of today's online services. It helps users navigate through an overwhelmingly large number of items and discovering those that interest them. Unlike general recommender systems, which recommend items based on the user's overall preferences, sequential recommender systems consider the order of user-item interactions. Sequential recommendations aim to predict the next item a user will interact with, given a sequence of previously interacted items, while considering the short-term and long-term dependencies among items.

In this thesis, we focus on sequential recommendation methods: from representation learning to large language model (LLM)-based reasoning. On the one …

Non-Native Rhizophora Mangle As Sinks For Coastal Contamination On Moloka’I, Hawai’I, Geoffrey Szafranski, Elise F. Granek, Michelle L. Hladik, Mia Hackett

Environmental Science and Management Faculty Publications and Presentations

Coastal mangrove forests provide a suite of environmental services, including sequestration of anthropogenic contamination. Yet, research lags on the environmental fate and potential human health risks of mangrove-sequestered contaminants in the context of mangrove removal for development and range shifts due to climate change. To address this, we conducted a study on Moloka'i, Hawai'i, comparing microplastic and pesticide contamination in coastal compartments both at areas modified by non-native red mangroves (Rhizophora mangle) and unmodified, open coastline. Sediment, porewater, and mangrove plant tissues were collected to quantify microplastic and pesticide concentrations across ecosystem type. Average microplastics were similar between …

Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox

Faculty Publications

This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface …

Urban Flooding: Legal Tools To Address A Growing Crisis, Michael B. Gerrard

Faculty Scholarship

While sea level rise has long received a great deal of attention, another impact of climate change is now getting greater notice — increases in extreme precipitation. As the atmosphere warms, it can hold more moisture; for every increase of one degree Celsius, seven percent more water vapor is carried in the air. When that moisture is quickly released as intense rainstorms, stormwater systems can be overwhelmed, and cities can suffer devastating floods.

The frequency and intensity of heavy precipitation events have increased since the 1950s over most of the world's land areas. One 2023 study — generally consistent with …

Pulsed-Power Neutron Production With Deuterated Polymer Accelerator Targets, Anthony O. Hagey, Juan J. Manfredi, Whitman T. Dailey, S. L. Jackson, E. R. Kaiser, J. W. Schumer

Faculty Publications

This document presents an investigation of the effect of deuterated accelerator targets on the neutron fluence from a local mass injection dense plasma focus (LMIDPF) driven by the United States Naval Research Laboratory's (NRL) Hawk pulsed-power generator. Deuterated targets were made using two methods: a well-established thin-film casting technique for flat targets and a more novel additive manufacturing technique that allowed targets to be three-dimensional (3D) printed in both flat and conical geometries. These targets were then tested during neutron-producing experiments conducted using Hawk, and theneutron fluences measured for the various target types were compared. Additive manufacturing was used as …

Impact Of Climate Change On Carbon And Nitrogen Balance In Zostera Marina L. (Eelgrass), Malee Jinuntuya

OES Theses and Dissertations

Seagrasses face vulnerability to both global stressors like Ocean Acidification (OA) and climate warming compounded by local stressors such as eutrophication that reduces light availability, leading to a complex dynamic of positive and negative effect on their growth and survival. Increased dissolved aqueous CO₂ (CO_2(aq)) benefits seagrasses by enhancing photosynthetic and growth rates, but it may increase nutrient demand, potentially depleting nutrient supply, especially in oligotrophic environments.

In this study, the long-term impact of CO₂ on Zostera marina L. (eelgrass) were investigated across a gradient of CO_2(aq) concentrations (55 – 2200 µM CO_{2(aq)) …}

Uni Science Education Update Conference, Save The Date Flier, Spring 2024, University Of Northern Iowa. Science Education Program.

Science Education Update Conference Documents

Announcement for the 2024 Science Education Update Conference.

Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Test migration, which enables the reuse of test cases crafted with knowledge and creativity by testers across various platforms and programming languages, has exhibited effectiveness in mobile app testing. However, unit test migration at the source code level has not garnered adequate attention and exploration. In this paper, we propose a novel cross-language and cross-platform test migration methodology, named MUT, which consists of four modules: code mapping, test case filtering, test case translation, and test case adaptation. MUT initially calculates code mappings to establish associations between source and target projects, and identifies suitable unit tests for migration from the source …

Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language …

Integrating Artificial Intelligence For Automated Storytelling In Turn-Based Strategy Games, Timothy Ripper

Theses

This project is inspired by turn-based strategy games, Final Fantasy Tactics, X-Com 2, and modern turn-based strategy games. This project is structured around the use of artificial intelligence for storytelling within strategy games. The focus of this project utilizes artificial intelligence in creating a quest generation system for storytelling. The resulting quest system creates new quests dynamically after communicating with an artificial intelligence allowing players to potentially experience an ever-expanding story from quests

Lyraquist: Language Learning Via Music App, Vivian D'Souza, Siri Avula, Mahi Patel, Tanvi Singh, Ashley Bickham

Senior Theses

Lyraquist is a new language learning mobile app that encourages the practice of a foreign language through music. Language learners can connect their Spotify Premium account to Lyraquist to listen to music in their target languages and utilize tools such as translation and vocabulary lists to facilitate language practice. Through integration with Spotify, users can import and create Spotify playlists and search the service’s entire catalog. By combining daily listening habits with several tasks associated with language learning in one place, Lyraquist hopes to be a useful language learning tool.

Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem

Senior Theses

The USC Faculty Dashboard is a web application designed to revolutionize how department heads, professors, and instructors monitor progress and make decisions, providing a centralized hub for efficient data storage and analysis. Currently, there’s a gap in tools tailored for department heads to concisely manage the performance of their department, which our platform aims to fill. The USC Faculty Dashboard offers easy access to upload and view student evaluation and research information, empowering department heads to evaluate the performance of faculty members and seamlessly track their research grants, publications, and expenditures. Furthermore, professors and instructors gain personalized performance analysis tools, …

On Adaptivity And Randomness For Streaming Algorithms, Manuel Stoeckl

Dartmouth College Ph.D Dissertations

A streaming algorithm has a limited amount of memory and reads a long sequence (data stream) of input elements, one by one, and computes an output depending on the input. Such algorithms may be used in an online fashion, producing a sequence of intermediate outputs corresponding to the prefixes of the data stream. Adversarially robust streaming algorithms are required to give correct outputs with a desired probability even when the data stream is adaptively generated by an adversary that can see all intermediate outputs of the algorithm. This thesis binds together research on a variety of problems related to the …

On Generating Bijections For Permutations And Inversion Sequences, Melanie J. Ferreri

Dartmouth College Ph.D Dissertations

Given an algebraic proof of a combinatorial identity, we use recursive methods to construct a bijection demonstrating the identity.

Our first application centers around derangements and nonderangements. A derangement is a permutation with no fixed point, and a nonderangement is a permutation with at least one fixed point. There is a one-term recurrence for the number of derangements of n elements, and we describe a bijective proof of this recurrence which can be found using a recursive map. We then show the combinatorial interpretation of this bijection and how it compares with other known bijections, and show how this extends …

Design, Analysis, And Drop Assembly Of Interlocking Rigid Bodies, Amy K. Sniffen

Dartmouth College Ph.D Dissertations

This work presents a system of interlocking blocks that can be used to build a wide variety of structures. The blocks slide together to form structures that interlock geometrically like a puzzle to form semi-permanent structures without the need for cement or friction lock. The blocks are designed to be easy to fabricate, assemble, and disassemble. Contributions of the block designs include a novel interlocking joint structure; the joints are wedge-shaped, allowing for error mitigation during assembly and allowing structures to be assembled without jamming even if there is manufacturing error. We introduce planar, 3D, and volumetric designs using these …

Methods, Analyses, And Applications Of Multilayer Temporal Link Prediction In Networks, Xie He

Dartmouth College Ph.D Dissertations

Many applications stem from the possibility of accurately predicting links in various types of networks. In this thesis, we present methods, analyses, and applications for static, temporal, and multilayer networks. The first part of this thesis demonstrates how static network features serve as efficient and accurate predictors for link prediction in temporal networks. It includes an ensemble learning method we developed and presents experimental results on 90 synthetic stochastic block models and 19 real-world datasets. The second part closely follows, showcasing 20 different sampling methods and their effects on nine different link prediction algorithms for 250 real-world networks across 6 …

Scaled And Graduated Learning In Deep Relu Networks And Reconstructing Depp Inelastic Scattering Kinematics, Abdullah Ayar Farhat

Mathematics & Statistics Theses & Dissertations

To address computational challenges in learning deep neural networks, properties of deep RELU networks were studied to develop a multi-scale learning model. The multi-scale model was compared to the multi-grade learning models. Unlike the deep neural network learned from the standard single-scale, single-grade model, the multi-scale neural networks use low scale information from all hidden layers, and thusly provide a robust approximation method that requires fewer parameters, lower computational time, and is resistant to noise. It is shown that the multiscale method is not subject to issues arising from the vanishing gradient problem. This allows very deep multi-scale networks to …

Dynamic Soil-Structure Response And Failure Of Wood Utility Poles Under Hurricane-Force Wind And Non-Linear Cable Loads, Ramani Ayakannu

Civil & Environmental Engineering Theses & Dissertations

This dissertation presents the outcome of a study of the dynamic soil-structure interaction response of tapered wood utility poles embedded in various foundation soils subject to quasi-static and dynamic wind loads. The study includes the effects of nonlinear soil behavior with varying quantities of moisture and nonlinear cable tension loads on a class H1 wood pole. The natural frequency response of the pole is determined using a finite element formulation. The dynamic response in the presence of wind-induced forcing function is studied using a SAP2000 finite element scheme with wind speeds up to 220 mph. The natural frequency of the …