Digital Commons Network^™

Making Explanations Make Sense: Xai For Smishing Detection, Eleni Alexandra Katsarakes

Psychology Theses & Dissertations

Explainable Artificial Intelligence (XAI) is a key component of effective human-AI collaboration, particularly in high-stakes domains such as cybersecurity. While AI tools hold promise for mitigating threats such as SMS-based phishing (SMiShing), their real-world effectiveness may hinge not just on detection accuracy, but on whether users can make sense of the system’s outputs. As SMiShing attacks grow in both frequency and sophistication, so does the urgency of designing human-centered AI systems that support user decision-making under uncertainty. This study examined how four distinct AI explanation types - Normative (rule-based), Attributive (feature-based), Exemplar (case-based), and Recommendation-Only - influence user performance, confidence, …

Sediment Burial Negatively Impacts The Growth Of Seagrass Posidonia Sinuosa, Chanelle Webster, Nicole Said, Natasha Dunham, Simone Strydom, Kathryn Mcmahon

Research outputs 2022 to 2026

Burial disturbances affect foundation plant species in marine ecosystems. Deposition of dredge spoil can bury seagrass meadows yet we have limited threshold information to predict the trajectory of impact from burial or possible recovery. To investigate the response to burial by dredge spoil, established seagrass ramets of Posidonia sinuosa were collected from a population in Western Australia and exposed to cutter suction dredge spoil sediment. Plant responses were measured during a burial phase after 2, 4 and 8 weeks to assess the influence of duration to burial depths (0, 1, 4, 8 and 16 cm). Sediments were then removed, and …

Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

A New Goodness-Of-Fit Test For Azzalini’S Skew-T Distribution Based On The Energy Distance Framework With Applications, Joseph Njuki, Abeer M. Hasan

Mathematics and Statistics

In response to the growing need for flexible parametric models for skewed and heavy-tailed data, this paper introduces a novel goodness-of-fit test for the Skew-t distribution, a widely used flexible parametric probability distribution. Traditional methods often fail to capture the complex behavior of data in fields such as engineering, public health, and the social sciences. Our proposed test, based on energy statistics, provides practitioners with a robust and powerful tool for assessing the suitability of the Skew-t distribution for their data. We present a comprehensive methodological evaluation, including a comparative study that highlights the advantages of our approach over traditional …

Climate Change Has Increased Global Evaporative Demand Except In South Asia, Saeed Karimzadeh, Arman Ahmadi, Dennis Baldocchi, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Climate change alters how strongly the atmosphere draws water from the land, yet a consistent global assessment of this evaporative demand has been lacking. Here, we analyze 45 years of climate data and global models to quantify trends in the key drivers—air temperature, humidity, radiation, wind speed, and cloud cover—that determine the atmosphere’s drying power. We find that evaporative demand has increased worldwide, indicating a stronger atmospheric thirst, except in South Asia, where it has declined. There, widespread irrigation has increased soil and air moisture, enhanced cloud formation, and reduced sunlight reaching the surface, counteracting the global signal. These contrasting …

Benchmarking Dna Foundation Models For Genomic And Genetic Tasks, Haonan Feng, Lang Wu, Bingxin Zhao, Chad Huff, Jianjun Zhang, Jia Wu, Lifeng Lin, Peng Wei, Chong Wu

School of Medicine Faculty Publications

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less …

Improving Glycemic Control Among Indonesian Urban Adults: A Digital And Behavioral Extension Of The Information–Motivation–Behavioral Skills Model, Imelda Appulembang

Kesmas

Management of type 2 diabetes mellitus (T2DM) in Indonesia continues to face challenges due to behavioral, informational, and technological gaps among patients. This study analyzed the influence of self-regulatory competence and information, motivation, family support, and digital health literacy on glycemic control behavior. A cross-sectional survey was conducted from February to April 2025 among 587 adults aged >30 years with T2DM enrolled in the Chronic Disease Management Program at primary health care in six major cities: Jakarta, Surabaya, Yogyakarta, Medan, Makassar, and Banjarmasin. Data were collected through structured questionnaires and analyzed using partial least squares structural equation modeling. The findings …

Development Of A Digestion Procedure Using Fe2+ Ions For Electrochemical Detection Of Mno2 Particles In Drinking Water, Kayla Elliott, Sarah Jane Payne, Zhe She

Journal of Electrochemistry

Developing methods for detection contaminants in drinking water is essential to ensuring that safe and acceptable quality drinking water is delivered to consumers. While manganese (Mn) was previously known only as a mere aesthetic issue, recent epidemiological data has shown to have negative neurological effects on humans, especially on children, prompting new health-based guidelines by Health Canada and the World Health Organization. In drinking water, Mn exists predominantly as Mn(II) and Mn(IV), and is regulated based on total Mn levels. Interestingly, measurement of Mn particulate using electroanalytical methods has not yet been reported in the literature. Herein, a digestion procedure …

Biocompatible And Antibacterial Pcl-Tio2@Ag/Γ-Cd Mof Nanocomposite Coating For Corrosion Resistance Of Magnesium Alloy In Simulated Body Fluid, Sara Dehghan-Chenar, Hamid R. Zare, Zahra Mohammadpour, Maryam Sadat Mirbagheri-Firoozabad

Journal of Electrochemistry

Magnesium alloys are promising candidates for bio-implant applications due to their biodegradability and biocompatibility. However, their rapid corrosion remains a critical limitation. This study presents the development of a multifunctional nanocomposite coating designed to enhance the corrosion resistance and antibacterial properties of magnesium alloy implants. The coating comprised γ-cyclodextrin metal-organic frameworks (γ-CD MOFs) decorated with TiO₂@Ag core-shell nanoparticles, embedded in a polycaprolactone (PCL) matrix. Immersion tests in a simulated body fluid (SBF) revealed an initially higher corrosion rate for the PCL-TiO₂@Ag/γ-CD MOF coating compared to the coating without TiO₂@Ag nanoparticles; however, it demonstrated significant …

Transdisciplinary Perspectives On Ai: The Fourth Annual Conference Of The European Culture And Technology Laboratory, Connell Vaughan, Ioana Madalina Moldovan, Silivan Moldovan, Noel Fitzpatrick

Books/Book Chapters

The fourth annual conference of the ECT Lab+ was hosted by Technical University of Cluj-Napoca over two days in October 2024 at the Cluj Innovation Park. The conference brought together experts from the Arts, Humanities, Social Sciences, Technology, and other fields to discuss and reflect on the advent of Artificial Intelligence and how the associated technologies are transforming how we live, work and study. Under the title Transdisciplinary perspectives on AI: Alternative Histories, Current Practices and Possible Futures the conference moved beyond simplistic technophila and technophobia to consider whether we can co-evolve with these new technologies which combine machine learning …

Tannin Supplementation Alters Foraging Behavior And Spatial Distribution In Beef Cattle, Bashiri Iddy Muzzo, R. Douglas Ramsey, Kelvyn Bladen, Juan J. Villalba

Wildland Resources Student Research

Beef production on chemically uniform grass monocultures can limit nutrient synchrony and contribute to uneven pasture use. We evaluated whether supplementing tannins with bioactive plant secondary compounds improves foraging dynamics and landscape use by beef cattle grazing a meadow bromegrass monoculture in ways aligned with rangeland sustainability. Twenty-four Angus cow–calf pairs were allocated to six 3.6-ha paddocks (four pairs/paddock), randomly assigned to Control (Ctrl; n = 3) or Tannin treatment (TT; n = 3). Animals received 1 kg/cow/day of DDGs, with TT receiving an added 0.4% tannins (2:1 condensed:hydrolyzable). Grazing occurred during four 15-day periods (July– September) across two years. …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol

All Works

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …

Field Validation Of Multiple Species Distribution Models Shows Variation In Performance For Predicting Aedes Albopictus Distributions At The Invasion Edge, Anna V. Shattuck, Brandon D. Hollingsworth Ph.D., Jared Skrotzki, Scott R. Campbell, Christopher L. Romano, Courtney C. Murdock

Faculty Publications

Background

Climate and land use changes have resulted in range expansion of many species. In this shifting disease landscape, it is important to leverage tools that can predict the distributions of invading vectors to target surveillance and control efforts and identify at-risk populations. Species distribution models (SDMs) are used to predict ranges of invasive species; however, invasive species often violate assumptions of equilibrium and niche conservatism. Moreover, these studies are rarely validated using independent data.

Methods 

We use long-term surveillance data for Aedes albopictus, a highly invasive mosquito capable of transmitting several arboviruses, at its range edge to evaluate a …

Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Exploring The Link Between Emotional States And Coding Task Quality: A Pilot Study, Aquib Reshad, Valentina Nino, Maria Valero, Adriane Randolph, Yang Shi

Faculty Articles

Emotions play a crucial role in shaping cognitive performance, yet their influence on programing remains understudied. This pilot study investigates the relationship between emotional states and coding task quality. Ten participants completed a programing task while their brain activity was recorded using electroencephalography (EEG), with frontal alpha asymmetry (FAI) applied as a neural marker of emotional valence. Emotional self-reports were collected using the Scale of Positive and Negative Experience (SPANE), and coding quality was evaluated through a structured rubric. Preliminary findings indicate a potential association between FAI and coding performance, whereas self-reported affect showed weaker or inconsistent patterns. Given the …

Gc-0258 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur- Rahman, Soarov Borty, Shakib Quddus

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) is one of the leading causes of global health concern, but current risk assessments are limited to episodic clinical visits. Most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of …

Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics​, Syeda Umme Salma

C-Day Computing Showcase

Caregivers face distinctive emotional and logistical burdens, yet many mental-health apps overlook their needs and show usability issues. We introduce an automated pipeline that analyzes 317K app-store reviews from 9 apps, mapping them to Nielsen’s usability components and heuristics, together with sentiment. To assess reliability, we run a human–AI agreement study (N=50) where a domain expert (A2) and a non- expert (A1) label reviews. For heuristics, the pipeline achieves 66% exact agreement and moderate κ=0.579 with the expert, outperforming human–human agreement; components remain harder, revealing a need to refine the codebook (e.g., learnability vs satisfaction). Complementary clustering and sentiment analyses …

Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan

C-Day Computing Showcase

Edge Artificial Intelligence (AI) refers to running AI inference directly on local devices such as wearables, sensors, and mobile systems rather than relying on cloud computing. The growth of Edge AI has created strong demand for efficient inference on resource-limited devices. Edge AI devices must perform real-time inference while operating under strict battery constraints. Although significant model optimizations exist for managing power-intensive inference models, operating system (OS) level support is limited. Existing OS schedulers often neglect energy limits in edge devices as they prioritize fairness or throughput. In this research we proposed an OS level framework to bridge this gap …

Grp-1230 Environmental Protection: Development Of A Real-Time Multi-Stream Water Quality Monitoring System, Faruk Muritala

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha

C-Day Computing Showcase

This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.

Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold

C-Day Computing Showcase

Our project is about creating a basic cloud-native pipeline that can build and deploy an application in a more automated way. We will also try to add some security checks and monitoring tools so that we can see how everything is working. The goal is to get hands-on experience with the process and show a working demo at the end of the semester.

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty

C-Day Computing Showcase

Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, …

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

Uc-0223 Predicting Nba Player Re-Injury Using Net Rating, Anaya Tention

C-Day Computing Showcase

This project examines whether player performance data can signal injury risk before an absence occurs. Using game-by-game net rating trends, I applied an exponentially weighted control-chart approach to detect early shifts in performance that might indicate a rising risk of re-injury. The method successfully identified 71% of re-injury cases with an average 20-game lead, suggesting that performance declines can serve as an early warning signal. While the false-alarm rate was high, the results show that performance-based monitoring has potential value for teams seeking proactive player-health insights.