Physical Sciences and Mathematics Commons^™

Cellebrite Reliability In Digital Forensics, Christina Huynh

Shelby Hall Graduate Research Forum Posters

Forensic tools like Cellebrite are commonly used in court to gather and interpret raw data for evidence. Cellebrite does not only collect data but creates and interprets the artifacts of data to create a scene of the process it has been through. With this, evidence can be influenced by software designs and not just the data on the mobile device. Courts and police use Cellebrite to gather evidence and reconstruct it to create an easily readable dataset. These tools lack reproducibility, transparency, integrity, and chain of evidence command. Cellebrite is often used in court and by police without further vetting …

Brain Computer Interfaces: Enhancing Low-Cost Eeg Performance Through Deep, Anwar Rassoul

Shelby Hall Graduate Research Forum Posters

The field of Brain Computer Interfacing (BCI) has traditionally been confined to clinical and research environments due to the high cost and complexity of medical-grade EEG systems. However, the emergence of low-cost hardware exemplified has catalyzed a shift toward accessible, portable BCI applications. While these devices lower the barrier to entry for developers and researchers, they often suffer from a lower signal-to-noise ratio (SNR). This increased noise makes it difficult to extract the clean neural signatures required for high-accuracy control, particularly when operating in non-shielded, real-world environments.

This research focuses on Steady-State Visually Evoked Potentials (SSVEP), a robust BCI paradigm …

Algorithm For Detecting Luks2-Encrypted Containers In Forensic Images, Nicholas Flynn, Michael Black

Shelby Hall Graduate Research Forum Posters

As technology becomes increasingly integrated into daily life, the way in which society interacts with digital content continues to rapidly change. Though there is growth in advancements that help the average user, there is a similar upward trend in crimes committed involving a computer. Figure 1 illustrates the growth in research across many disciplines of digital forensics reflecting the demand for tools which can combat a wide variety of cyber crimes.In the past two decades, with a massive spike since 2017, there has been much literature produced in response to this demand. It can be inferred from the Federal Bureau …

Improving Consensus In Blockchain, Nelson Navas

Shelby Hall Graduate Research Forum Posters

Called the 4th industrial revolution, Industry 4.0 is the latest paradigm for implementing industrial applications. This new approach relies heavily on increased automation, smart machines, human-machine interaction, AI, and telecommunications. Industry 4.0 applications introduce the idea of the smart factory. The integration of information technology (IT) and operational technology (OT) is a key factor that promotes efficiency in the supply chain. All this is predicated in the generation, sharing, and storage of large quantities of data and transactions to facilitate management, traceability, and control of industrial processes. Increased reliance on interconnectedness causes cybersecurity challenges. Access to machinery, infrastructure, IT systems, …

Machine Learning On The Edge: Performance And Security Evaluation Of Cnn Implementations In Embedded Systems, Krista Stacey

Shelby Hall Graduate Research Forum Posters

Embedded systems increasingly integrate Machine Learning (ML) for real-time decision-making across loT, infrastructure, and critical systems. However, ecosystems differ significantly in: Latency, Throughput, Energy use, Accuracy of Models Security exposure. Most research evaluates performance or security, not both together. There is a need for a unified cross-platform performance-security evaluation framework

Trophic Structure And Mercury Bioaccumulation In Walleye And Yellow Perch In The Upper And Lower Red Lake Basins, Marissa Pribyl

Biology Graduate Theses

Mercury is a persistent global contaminant that biomagnifies through aquatic food webs, with dietary and environmental factors serving as the primary drivers of accumulation in fishes. Trophic structure and methylmercury dynamics in Walleye (ogaa; Sander vitreus) and Yellow Perch (asaawens; Perca flavescens) were investigated in the Upper and Lower Red Lake basins in Red Lake, Minnesota, during 2024-2025. Diets of Walleye and Yellow Perch were assessed through stomach dissections, and tissue samples from both species were analyzed for total mercury concentrations. Additional analyses included shiners (gigoozens; Notropis spp., Hudsonius spp.) along with a variety of freshwater fish and …

Wayne E. Sabbe Arkansas Soil Fertility Studies 2025, Nathan A. Slaton

Arkansas Agricultural Experiment Station Research Series

Rapid technological changes in crop management and production require that the research efforts be presented in an expeditious manner. The contributions of soil fertility and fertilizers are major production factors in all Arkansas crops. The studies described within will allow producers to compare their practices with the university’s research efforts. Additionally, soil-test data and fertilizer sales are presented to allow comparisons among years, crops, and other areas within Arkansas.

Comparison Of Analytical Techniques From The Extraction Of Bioactive Compounds From Kratom, Curry, Ginger, And Turmeric, Riley Bruno

Honors Program: Senior Projects (Public)

Plants, herbs, and spices have been used as medicines for thousands of years. Early civilizations often attributed healing properties of plants to magical or divine forces. However, as chemistry and analytical technology advanced between the 16th and 18th centuries, scientists began to understand that bioactive compounds within the plants actually caused these effects. Today, natural products remain extremely important in drug discovery. As the number of newly developed synthetic drugs declines, there has been renewed interest in identifying biologically active molecules from plants. Bioactive molecules are a group of diverse chemical compounds that stimulate a response in living tissues. The …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Homogeneous Syringe-Sharing Network, Seun Ale, Que Thi Nguyet Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model assumes homogeneous mixing among syringe-sharing agents, without any form of heterogeneity in the agents interactions or syringe-sharing attitude. All syringe-sharing PWID are treated as identical in terms of their interaction frequency and syringe-sharing probability. Interactions are generated dynamically using proximity-based sampling at each timestep (one day), allowing agents to form syringe-sharing interactions based on spatial closeness. The number of daily interaction events is fixed at the population level, and each syringe-sharing agent has the same probability …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (M4), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing …

Stard-Net: Spatiotemporal Attention For Robust Detection Of Tiny Airborne Objects From Moving Drones, Hasibur Rahman, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

The rapid adoption of drones across various domains, alongside advancements in computer vision, has driven growing interest in vision-based airborne object detection from moving aerial platforms. However, this task remains challenging due to the small scale of objects, camouflage within cluttered backgrounds, and occlusions. To address these challenges, we introduce an end-to-end detection framework that integrates a Drone Receptive Field Block (DRFB) to extract multiscale and geometrically diverse features, specifically designed to enhance the detection of small and camouflaged airborne objects. To model motion patterns over time while preserving spatial structure, particularly for detecting camouflaged, cluttered and occluded objects with …

Argument-Based Consistency In Toxicity Explanations Of Llms, Ramaravind K. Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, Shion Guha

Health Services and Informatics Research

The discourse around toxicity and LLMs in NLP largely revolves around detection tasks. This work shifts the focus to evaluating LLMs’ reasoning about toxicity—from their explanations that justify a stance—to enhance their trustworthiness in downstream tasks. Despite extensive research on explainability, it is not straightforward to adopt existing methods to evaluate free-form toxicity explanation due to their over-reliance on input text perturbations, among other challenges. To account for these, we propose a novel, theoretically-grounded multi-dimensional criterion, Argument-based Consistency (ArC), that measures the extent to which LLMs’ free-form toxicity explanations reflect an ideal and logical argumentation process. Based on uncertainty quantification, …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Theses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). The thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows.

The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Securing Ai Models Against Backdoors, Jailbreaks, And Prompt Injections, Myat Min Nay

Dissertations and Theses Collection (Open Access)

Deep neural networks (DNNs) and large language models (LLMs) in safety-critical settings face a family of related threats: an adversary can corrupt training so a hidden trigger flips a model’s behavior (backdoor), craft inference-time inputs that override safety alignment (jailbreaks) or smuggle in task-overriding instructions (prompt injections), or steer the model’s stance on whole concepts (semantic inconsistencies). Current defenses address each of these in isolation. This dissertation argues they admit a unified treatment through a shared diagnostic design principle: a matched perturbation, probing, or operating condition exposes a method-specific diagnostic signal in the model’s internal computation or observable behavior, and …

Microstructural Evolution Of Zirconium Diboride Irradiated With 5–10 Mev Au Ions At Room Temperature And 570 °C, Narrie Loftus, José Olivares, Miguel Crespillo, Esther Enríquez Pérez, Jeremy Watts, Eric W. Bohannan, Gregory Hilmas, William Fahrenholtz, Joseph Graham

Materials Science and Engineering Faculty Research & Creative Works

Zirconium diboride was irradiated with 5 MeV Au^2 + , 7 MeV Au⁴⁺ and 10 MeV Au³⁺ ions at room temperature and 570 °C to doses from 1 to 8 displacements per atom (dpa). Grazing incidence X-ray diffraction (GIXRD) analysis revealed no secondary phase formation. Rietveld analysis of the GIXRD data indicated an accumulation of microstrain at low dpa and room temperature. Dislocations observed in transmission electron microscopy (TEM) cross-sections are likely the main contributor to the microstrain. High dpa and high-temperature samples exhibit lower lattice distortion than lower dpa samples, suggesting the presence of enhanced defect …

A Mineralogical And Geochemical Characterization Of The Amo Meteorite, Karinn Johnson '27, Kenneth Brown, Chad Byers, Thomas Grier

Annual Student Research Poster Session

Meteorites provide a wealth of information about solar system evolution, planetary formation and differentiation, and provide clues to the origins of water and life on Earth. On December 10th, 2024 (4:04 EST), a meteor fall was observed approximately 50 km west of Indianapolis, over the small town of Amo, Indiana (Hendricks County). Several pieces of the meteor ranging from about 1g to >60kg were recovered. A local Greencastle, IN resident collected a 1451.3g sample, of which a 7.5g piece was donated to DePauw University for detailed textural, mineralogical, and geochemical characterization using stereomicroscopy, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), …

Iowa Waste Reduction Center Newsletter, March 2026, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Welcome, IWRC Advisory Committee
--- Growing Iowa’s Composting Movement to National Leadership
--- Diane Albertson Memorial Scholarship
--- P2 Green Brewery Webinar Registration is Now Open
--- Potential Hazards of Lithium-Ion Batteries in Landfills

Hybrid Deep Learning For Anti-Money Laundering: Unsupervised Detection Of Emerging Schemes Via Feature Fusion And Explainable Artificial Intelligence, Cosmas Ochieng Kungu, Kennedy Senagi, Evans Omondi

All Peer-Reviewed Publications

Traditional rule-based anti-money laundering (AML) transaction monitoring systems suffer from high false-positive rates and rigidity in detecting complex emerging risk. This limitation has prompted changes to the Financial Action Task Force (FATF) recommendation 16, mandating the use of advanced systems for detecting money laundering schemes in cross-border payments. This study developed a hybrid framework integrating VAE-learned behavioural latent factors, GNN-captured relational network signals, and rule-based heuristics for enhanced anomaly detection. The model was evaluated on 54,258 real-world cross-border transaction records from an East African commercial bank. The One-Class SVM, optimised via a rigorous grid search proved superior compared to Isolation …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

A Cryptographic Perspective On The Verifiability Of Quantum Advantage, Nai-Hui Chia, Honghao Fu, Fang Song, Penghui Yao

Computer Science Faculty Publications and Presentations

In recent years, achieving verifiable quantum advantage on a NISQ device has emerged as an important open problem in quantum information. The sampling-based quantum advantages are not known to have efficient verification methods. This article investigates the verification of quantum advantage from a cryptographic perspective. We establish a strong connection between the verifiability of quantum advantage and cryptographic and complexity primitives, including efficiently samplable, statistically far but computationally indistinguishable pairs of (mixed) quantum states (EFI), pseudorandom states (PRS), and variants of minimum circuit size problems (MCSP). Specifically, we prove that a) a sampling-based quantum advantage is either verifiable or can …

In What Style Shall I Confront Them? The Role Of Social Relationships In Social Correction Of Misinformation Among The Uk And Arab Social Media Users, Muaadh Noman, Mohamed B. Almourad, Ala Yankouskaya, Firoj Alam, Raian Ali

All Works

This study investigates how social factors influence the likelihood of employing direct or indirect communication styles when correcting misinformation on social media in two different cultural contexts, the United Kingdom (UK) and the Arab Gulf Cooperation Council (GCC) countries. We conducted an online survey, supported by vignettes, that involved 686 participants, 367 from the UK and 319 from the Arab GCC countries. Participants were presented with a misinformation scenario and asked about their likelihood of using direct or indirect communication styles to correct their acquaintances. The survey captured variations in gender similarity (same vs. different gender), social status (lower vs. …

Generative Ai For Text-To-Video Generation: Recent Advances And Future Directions, Kadhim Hayawi, Sakib Shahriar

All Works

Text-to-video (T2V) generation has recently emerged as a transformative technology within the field of generative AI, enabling the creation of realistic, temporally coherent videos based on natural language descriptions. This paradigm provides significant added value in many domains such as creative media, human-computer interaction, immersive learning, and simulation. Despite its growing importance, systematic discussion of T2V is still limited compared with adjacent modalities such as text-to-image and image-to-video. To alleviate the scarcity of discussions in the T2V field, this paper provides a systematic review of works published from 2024 onward, consolidating fragmented contributions across the field. We survey and categorize …

Identification Of Microplastics And Additives From The Lake Erie Watershed And The Cuyahoga River Via Maldi-Ms And Dart-Ms, Chrys Wesdemiotis, Calum Bochenek, Luciana Rivera Molina, Robert Brand

University Research

Micro(nano)plastics have received increased attention as environmental contaminants due to their harmful effects on ecosystems and human health. Conventional extraction methods for microplastic analysis are often lengthy and complicated, resulting in the loss of important chemical components such as additives. In this study, we provide a complementary approach, utilizing matrix-assisted laser desorption/ionization (MALDI) and direct analysis in real time (DART) mass spectrometry (MS) techniques, with minimal extraction and sample preparation to preserve both polymer and additive information. MALDI-MS was applied to aqueous samples collected along the shores of Lake Erie and the Cuyahoga River, successfully identifying the base polymer(s) in …

No Name Ditch Filter Request, Chad Pritchard, Sonja Durr

EWU PFAS Research Collection

This is a one-page request showing the need for filtration at No Name Ditch at the SE corner of Fairchild Air Force Base (FAFB), West Plains, Spokane County, Washington.  Surface water levels in the ditch and stormwater ponds that feed the system can exceed 20,000 ppt for PFOS while groundwater can be up to 2,000 ppt for PFOS, per a remedial investigation report for FAFB in 2025.  The federal drinking water standard for PFOS is 4 ppt. While surface water and stormwater are not regulated for PFAS levels, these polluted waters enter the groundwater just off base and likely contaminate …

Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …

Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do …

Interpretable Machine Learning For Personalized Profiling Of Mild Cognitive Impairment From Daily Activities, Budhitama Subagdja, Ah-Hwee Tan, Kenneth Kwok, Iris Rawtaer

Research Collection School Of Computing and Information Systems

Continuous monitoring of individual daily activities is essential to detect mild cognitive impairment (MCI) wherein timely intervention can still be applied to prevent more severe mental decline. Recent approaches in predicting MCI are mostly considering digital biomarkers across individuals but often neglecting specific indicators from a single person over a long period of time. Making this personalized, dynamic, and highly noisy prediction model with irregular distribution of missing information to be explainable and actionable for clinical use, remains a challenge. This paper presents a study on a personalized MCI prediction and profiling from an in-home and mobile cognitive health monitoring …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …