Digital Commons Network^™

Microplastics In Coastal And Marine Environments: A Critical Issue Of Plastic Pollution On Marine Organisms, Seafood Contaminations, And Human Health Implications, Rebecca Muñiz, Md. Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Plastic has quickly become one of the world's most prevalent environmental pollutants. Population growth has only amplified the demand, resulting in 430 million tons of plastic produced annually, with 11 million tons ending up in our oceans. Without intentional intervention, plastic waste in the oceans is expected to triple, posing severe risks for the aquatic ecosystems, animals, and humans dependent on these waters. As plastic accumulates in oceans and other water environments, the non-biodegradable particles break down into micro- and nano-plastics, invading coastal and marine organisms and causing considerable physiological and morphological damage. This is especially concerning for the millions …

Using Machine Learning To Detect Vault (Anti-Forensic) Apps, Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

Research outputs 2022 to 2026

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault …

Using Natural Language Processing And Machine Learning To Detect Online Radicalisation In The Maldivian Language, Dhivehi, Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

Research outputs 2022 to 2026

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. The …

Crises In Australian Education, The Push For Educational Technology And The Medium-Oriented Perspective Of Neil Postman, Andrew Hutcheon

Research outputs 2022 to 2026

From teacher shortages to declining literacy, the Australian education system faces multiple crises. This paper turns to the work of Neil Postman, who started as a high school teacher but later turned towards communication and media studies, to provide an alternative account of parts of this crisis, especially those around the role of technology in education. In his early works, his concern is attached to the concept of education as a ‘subversive concept’ and then a ‘conserving concept’ – first aiming to create young people engaged in civic society, then aiming to preserve written culture against television. After a middle …

Empowering Through Technology: An Internship Report On Augmentative And Alternative Communication Devices, Olivia G. Macdonald

Communication Disorders and Occupational Therapy Undergraduate Honors Theses

Augmentative and Alternative Communication (AAC) devices play a crucial role in supporting individuals with communication challenges, particularly children with developmental disabilities such as Autism Spectrum Disorder (ASD). This paper provides a comprehensive review of the literature on AAC and offers a detailed report of an internship experience at the University of Arkansas WE CARE Summer Camp. During the internship, firsthand experiences provided valuable insights into how AAC technologies can be applied to enhance communication, foster social connections, and support behavioral regulation.

The literature review explores the different types of AAC systems, including both aided and unaided options, and discusses the …

Assessing Perceived Safety Of Non Motorized Travel With Virtual Reality, Vahid Balali, Sahand Fathi

Mineta Transportation Institute

Cycling is increasingly advocated as a healthier and more sustainable mode of transportation, as reflected in both scholarly literature and policy initiatives. Nonetheless, the escalation in bicyclist crash fatalities underscores deficiencies in extant roadway designs that inadequately safeguard these vulnerable users. A persistent challenge in the examination of bicyclist safety, behavior, and comfort is the paucity of comprehensive cycling data. To enhance understanding of cyclists' behavioral and physiological responses safely and efficiently, this investigation utilizes a bicycle simulator within an immersive virtual environment (IVE). Off-the-shelf sensors are employed to evaluate cyclists' performance metrics (speed and lane position) and physiological responses …

Sensors And Sensibilities: Exploring Interactions For Habitat Comfort With An Environmental-Physiological Sensing Eyewear In The Wild, Sailin Zhong, Patrick Chwalek, Nathan Perry, David Ramsay, Clayton Miller, Denis Lalanne, S. Hamed Alavi, A. Joseph Paradiso

Research Collection College of Integrative Studies

Buildings increasingly incorporate sensing and actuation techniques to automate the regulation of temperature, lighting, ventilation, and more. This trend seeks to minimize human intervention, justified by the promise of enhancing energy optimization. However, it has been widely acknowledged that loss of control over environmental conditions can lead to a diminished perception of comfort and compromised long-term user awareness and satisfaction. How can we envision building systems that can interact with building inhabitants and engage them at the “right” time and place? In this work, we address this challenge through three key contributions: 1) AirSpecs, a novel smart glasses-based system that …

Does The Transit Industry Understand The Risks Of Cybersecurity And Are The Risks Being Appropriately Prioritized?, Scott F. Belcher, Terri Belcher, James Grimes, Lusa Holmstrom, Andy Souders

Mineta Transportation Institute

The intent of this study is to assess the readiness, resourcing, and capabilities of public transit agencies to detect, identify, be protected from, respond to, and recover from cybersecurity vulnerabilities and threats. This study is an update of the 2020 Mineta Transportation Institute (MTI) study, “Is the Transit Industry Prepared for the Cyber Revolution? Policy Recommendations to Enhance Surface Transit Cyber Preparedness.” In the previous study, the authors found that the transit industry was ill-prepared for cybersecurity attacks. Unfortunately, after four years and the development of new, and often free, resources, the situation has not markedly improved. In fact, this …

Latent Variable Dyadic Regression Models For Predicting Over/Under Bets In Sports Betting, Alexcia Trejo

Graduate Theses and Dissertations

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the …

Mathematics-Ai Based Phylogenetic Analysis Of Influenza Virus Mutation Data, Emilee Walden

Mathematical Sciences Undergraduate Honors Theses

The influenza virus is one of the most common viral infections each year and can mutate rapidly. Viral mutations pose significant threats to public health by increasing infectivity and strengthening vaccine resistance. To track these evolving patterns, agencies like the CDC annually evaluate thousands of virus strains to understand viral mutagenesis and evolution in depth. Therefore, a computational method for analyzing high-dimensional, noisy virus data could aid in the rapid identification of antigens essential for an effective influenza vaccine for the upcoming season. Through the integration of genomic analysis, clustering, and dimensionality reduction methods, this study specifically aims to develop …

Some Interpolation Problems In The Projective Plane, Lilah Estes

Mathematical Sciences Undergraduate Honors Theses

Given some set of r general points in the projective plane, we want to better understand: what is the smallest degree of any polynomial passing through the points m times? How many linearly independent equations of this degree pass through the points m times? The investigation of these questions, particularly for the case of m=3 and r< 16, motivates the development of several results. We translate Terracini's inductive argument, a tool for evaluating the expectedness of certain sets of double points, into a version which can be used for triple points, and prove that the argument holds. We compute the minimal graded free resolutions for the ideals corresponding to up to 15 points, for m up to 6, and we conjecture a connection between the expectedness of these ideals and what their resolutions look like. Further, we prove that this conjecture holds when m=1, and we either fully or partially prove that these ideals are …

2024 Annual Operations And Maintenance Report, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Validating An Electronics Cooler Experiment And Optimizing Its Performance Using Ansys Icepak, Daniel V. Curl

Mechanical Engineering Undergraduate Honors Theses

Researchers at the University of Arkansas' Mechanical and Electrical Engineering Research Departments have designed and built a cold plate and substrate design for a 10 kV SiC MOSFET power module. Many variables define the design of the cold plate and substrate system and have quantifiable effects on the device’s performance. These variables include but are not limited to geometry and material selection of the fins and substrate. Manufacturing many test designs, running experiments, and comparing their performance is a time-consuming and expensive design optimization method that may be effective for some low-cost applications. Still, simulation is often a more cost-effective …

Shipnavisim: Data-Driven Simulation For Real-World Maritime Navigation, Quang Anh Pham, Janaka Chathuranga Brahmanage, Akshat Kumar

Research Collection School Of Computing and Information Systems

Maritime traffic management in busy ports faces growing challenges due to increased vessel traffic and complex waterway interactions. Strategies such as e-navigation by the International Maritime Organization aim to enhance navigation safety through traffic digitization. Maritime traffic simulation is essential for these systems, offering a virtual environment to model, analyze, and optimize traffic flows. Unlike road traffic, there are few simulators for maritime traffic, and they often lack realism and multi-ship interactions. In this paper, we (a) present ShipNaviSim, a data-driven maritime traffic simulator that utilizes a large-scale dataset over 2 years and electronic navigation charts to model vessel movements …

Few-Shot Learning On Graphs: From Meta-Learning To Llm-Empowered Pre-Training And Beyond, Yuan Fang, Yuxia Wu, Xingtong Yu, Shirui Pan

Research Collection School Of Computing and Information Systems

Graph representation learning has become central to many graph-based tasks, driving advancements in various domains such as web search, recommendation systems, and social network analysis. Traditionally, these methods rely on end-to-end supervised learning paradigms that require abundant labeled data, which can be costly and difficult to obtain. To address this limitation, few-shot learning on graphs has emerged as a promising approach, allowing models to generalize with minimal supervision and overcome data scarcity in real-world applications. This tutorial offers an in-depth exploration of recent advancements in few-shot learning for graphs, providing a comparative analysis of state-of-the-art methods and identifying future research …

“I Can Run At Night!”: Using Augmented Reality To Support Nighttime Guided Running For Low-Vision Runners, Yuki Abe, Keisuke Matsushima, Kotaro Hara, Daisuke Sakamoto, Tetsuo Ono

Research Collection School Of Computing and Information Systems

Dark environment challenges low-vision (LV) individuals to engage in running by following sighted guide—a Caller-style guided running—due to insufficient illumination, because it prevents them from using their residual vision to follow the guide and be aware about their environment. We design, develop, and evaluate RunSight, an augmented reality (AR)-based assistive tool to support LV individuals to run at night. RunSight combines see-through HMD and image processing to enhance one’s visual awareness of the surrounding environment (e.g., potential hazard) and visualize the guide’s position with AR-based visualization. To demonstrate RunSight’s efficacy, we conducted a user study with 8 LV runners. The …

Unlocking The Planning Capabilities Of Llms Through Maximum Diversity Fine-Tuning, Wenjun Li, Changyu Chen, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated impressive task-solving capabilities through prompting techniques and system designs, including solving planning tasks (e.g., math proofs, basic travel planning) when sufficient data is available online and used during pre-training. However, for planning tasks with limited prior data (e.g., blocks world, advanced travel planning), the performance of LLMs, including proprietary models like GPT and Gemini, is poor. This paper investigates the impact of fine-tuning on the planning capabilities of LLMs, revealing that LLMs can achieve strong performance in planning through substantial (tens of thousands of specific examples) fine-tuning. Yet, this process incurs high economic, time, …

Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose …

2025 May, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for May of 2025.

Fostering Ai Literacy In Undergraduates: A Chatgpt Workshop Case Study, Susan G. Archambault, Nicole L. Murph, Shalini Ramachandran

Librarian Publications & Presentations

This case study explores the design and implementation of a Chat-GPT literacy workshop for undergraduate students, aiming to equip them with the skills and knowledge necessary to navigate the AI-driven information landscape responsibly and critically. Drawing from a comprehensive literature review on AI literacy and employing a postphenomenological lens, the workshop incorporated best practices for teaching ChatGPT skills, including an emphasis on ethical considerations and critical reflection on the human-technology relationship. The postphenomenological perspective allows for a deeper understanding of how ChatGPT shapes students' experiences and practices. Employing a mixed-methods approach, the study assessed the effectiveness of the workshop through …

Customer Flow Prediction At Emirates Id Centers, Humaid Ahmed Saeed Alkhuroosi

Theses

Emirates ID centers face significant resource management challenges due to fluctuating customer traffic, leading to long wait times, customer dissatisfaction, and inefficient resource use. This thesis explores the application of time series analysis to predict customer traffic at Emirates ID centers, focusing on the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The primary research question is: “Can historical queue data from the Qmatic system be effectively used to forecast customer traffic at Emirates ID centers?” To answer this question, 800,000 observations of historical ticket issuance data from the Qmatic queue management system were analyzed. The study …

Beyond Regex – Heuristic-Based Secret Detection, Jesse Burdick-Pless

Theses

Accidental token or credential leakage presents a significant concern within digital environments. Current detection methods of such secrets employ ruleset-based techniques to identify secret information. These methods use pattern recognition within strings (i.e., regex rules) to pinpoint characteristics that resemble various types of secrets. However, regex does not allow for detection of secrets that lack specific patterns, such as passwords. This research addresses the possibility of using heuristics and machine learning to develop a reliable and accurate method for determining if a given string is merely a piece of inconsequential data or a leaked secret requiring timely attention, without the …

Evidence For Distal Bolide Impact And Tsunami Deposits In The Upper Atlantic Coastal Plain Of Moore County (North Carolina, Usa) Generated By The Eocene Chesapeake Bay Bolide Impact, G. Robert Ganis, Ralph H. Willoughby, David J. Cicimurri, G. Richard Whittecar, Steven J. Hageman

OES Faculty Publications

Beds interpreted as Eocene bolide-generated impact and tsunami deposits occur at Paint Hill in the Upper Atlantic Coastal Plain of Moore County, North Carolina, USA. These strata, herein named the Mount Helicon Formation, consist of four distinct beds comprising about one meter of total section. Bed 1 (the basal bed) is approximately 43 cm thick and consists of sandy carbonaceous clay with carbon glass and rock fragments and contains 14-18 parts per billion (ppb) iridium (interpreted as bolide impact ejecta). Bed 2 is approximately 9 cm thick and consists of non-cohesive silt-size particles and loosely bound sand-size accretionary lapilli-like masses …

Achieving Fairness In Zoning Laws With Machine Learning, William Schimitsch

College Honors Program

Zoning is a powerful regulatory tool that determines how municipalities use and develop land. The goal of zoning is to classify land use (e.g., residential, commercial, industrial) to maximize compatibility among neighboring parcels. Local zoning decisions, however, are made by small-sized boards, often through an opaque process, which raises concerns about bias and fairness. In the United States, zoning has historically prioritized single-family housing and thus created economic barriers that limit access to certain communities. Given the task of classifying land use and the wealth of geographical, demographic, and infrastructural data describing each parcel, the problem of bias in zoning …

The State Of Latino-Serving Community-Based Organizations In Massachusetts, Fabián Torres-Ardila, Phillip Granberry, Rachel Paz, Luna Páez Abaunsa, Valentina Valderrama Pérez

Gastón Institute Publications

Massachusetts has a diverse Latino population with large numbers of Puerto Ricans, Dominicans, Salvadorans, Guatemalans, and Mexicans. Nearly a third (32%) of the Latino population is foreign-born. Of this foreign-born population, 61% are not U.S. citizens. Nearly 210,000 Latino children are enrolled in K-12 schools. Of the Latino population over age five who speak Spanish in the home, 33% have limited English proficiency. Among the adult population, 28% have less than a high school education, and 60% are in the labor force.3 The varied needs of this population require a strong ecosystem of local organizations that provide services to support …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

Guest Editorial: When Multimedia Meets Food: Multimedia Computing For Food Data Analysis And Applications, Weiqing Min, Shuqiang Jiang, Petia Radeva, Vladimir Pavlovic, Chong-Wah Ngo, Kiyoharu Aizawa, Wanqing Li

Research Collection School Of Computing and Information Systems

Food is central in our life for its fundamental role in our survival, health, mood and culture. The deployment of various networks (e.g., IoT and mobile networks), devices (e.g., hyperspectral imaging devices, electronic nose/tongue), databases (e.g., nutrition tables and food compositional databases), recipe-sharing websites (e.g., Yummly and Meishijie) and social media (e.g., Twitter and Weibo) has generated unprecedented volumes of multi-modal food data. Such multi-source multi-modal food data provides new perspectives to analyze and understand food consumption via multimedia computing. Riding on the wave of AI, food-oriented multimedia computing integrates AI, multimedia technology and food science to enable a wide …

Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FixDrive, a framework that analyses driving records from near-misses or law …

Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu

Research Collection School Of Computing and Information Systems

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …

Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan

Research Collection School Of Computing and Information Systems

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration …