Computer Sciences Commons^™

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Two-Group Structural Heterogeneous Syringe-Sharing Network (M2), 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 extended a baseline homogeneous model by incorporating structural heterogeneity via a two-group interaction framework. The syringe-sharing population in the model is divided into inner and outer circle groups representing individuals with differing levels of syringe-sharing interaction intensity. While all agents share the same syringe-sharing probability and epidemiological processes remain identical across agents, the number of daily interaction opportunities differs between the two groups. Interactions in the model are generated dynamically using proximity-based sampling at each timestep …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Three-Group Structural Heterogeneous Syringe-Sharing Network (M3), 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. 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. While the syringe-sharing rate and all epidemiological processes remain identical across agents, the number of daily interaction opportunities differs by agent grouping, capturing variation in structural position within the syringe-sharing network. Interactions are generated dynamically using proximity-based sampling at each …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb

Research Collection School Of Accountancy

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …

Explainable Deep Reinforcement Learning For Real-Time Network Intrusion Detection, Sebastian Bustamante

Shelby Hall Graduate Research Forum Posters

This research aims to enhance current Deep Reinforcement Learning (DRL)-based Intrusion Detection System (IDS) models by adding transparency using Explainable Artificial Intelligence (XAI). This study proposes a DRL-based IDS architecture that incorporates explainability to provide interpretable reasons for IDS decisions. A Deep Q-Network (DQN) agent will be trained in a simulated network using well-known datasets to learn traffic behavior. XAI methods will be applied to extract feature importance and allow users to understand why alerts were generated. The outcomes of this research will contribute to improving network security by providing more insight on how XAI could be adopted into modern …

Integrating Nonlinear Phase Space Analysis And Image-Based Representation For Network Intrusion Detection, Chakriya Suon

Shelby Hall Graduate Research Forum Posters

With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology by protecting and defending against threat agents. Our research applies novel machine learning (ML)techniques to detect network intrusions effectively. Our primary focus is to extend prior research, which has used network flows that are processed by a nonlinear phase space algorithm (NLPSA). The NLSPA approach has proven extremely effective in detecting anomalous or malicious traffic patterns on representative data but requires extensive training time.

Our contribution integrates deep learning into the anomaly detection approach by creating image-based representations of the adjacency …

Deconstructing Digital Disinformation: Social Media Data Preparation And Analysis For Healthcare Research, Russell W. Cantrell, Matt Campbell

Shelby Hall Graduate Research Forum Posters

The spread of medical misinformation poses significant threats to public health, healthcare system stability, and the quality of patient care. Our research examines misinformation targeting the U.S. healthcare system. It uses a mixed-methods approach that includes social media data analysis, surveys of practicing nurses, and agent-based simulation. This poster focuses on the initial phase, which attempts to detect potential misinformation and disinformation by analyzing patterns in social media posts and user account behaviors. A detailed account of the data preparation process lays the groundwork for examining how disinformation operates online. This phase draws on the Pushshift repository, which offers historical …

Detecting Sensor Data Manipulation, Ricky Green, Michael Black

Shelby Hall Graduate Research Forum Posters

The integration of Information Technology (IT) and Operational Technology (OT) have made OT devices vulnerable to threats that have been successfully exploited with devastating results. Many modern techniques for hardening and securing enterprise IT systems are either incompatible with OT components in an Industrial Control System (ICS), reduce the efficiency of processes, or are prohibitively expensive to implement. Research in the area of ICS security focuses on a top-down approach, such as intrusion prevention by securing the perimeter of the network at layers 3 – 5 of the Purdue model by hardening IT systems. This approach is useful in Enterprise …

Temporal Eclectic Rule Extraction: Exploring Trustworthy Explainable Artificial Intelligence For Recurrent Neural Networks, Micah Israel

Shelby Hall Graduate Research Forum Posters

Enhancing temporal neural network interpretability can greatly increase the effectiveness of Intrusion Detection Systems (IDS). While explainable Deep Neural Networks (DNN) have been researched heavily in the literature for intrusion detection, explainable temporal neural networks lack the same attention. Current state-of-the-art XAI techniques rely on black-box surrogate explainers, which attempt to generate post-hoc explanations without valuable information inside the model's hidden neurons. To address this, this proposal introduces a novel white-box XAI method, Temporal Eclectic Rule Extraction (TERE), which is designed to provide explainable rules directly from temporal models. TERE aims to enhance decision transparency in IDS by offering interpretable …

Evaluating The Effects Of Anti-Forensic Activities In Additive Manufacturing Devices, Daniel B. Miller

Shelby Hall Graduate Research Forum Posters

Additive Manufacturing (AM) is a newer famlily of production tecchologies that constructs objects by fusing layers of material into the desired shape. Methods for achieving this as described in Gibson et al. [1] are varied and include Fused Filament Deposition, Selective Laser Sintering, Stereolithography (SLA), and Powder Bed Fusion. Computers are integral to the processes being responsible for creating and decoding design instructions, collecting and processing sensor data, and, ultimately, directing the activity of the machines which implement the process. Additionally, the AM industry is rapidly expanding, worth an estimated $23 billion in 2023 and projected to reach $88 billion …

Detecting Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton Purdy

Shelby Hall Graduate Research Forum Posters

From around the globe, malicious actors continually probe critical infrastructure assets for weaknesses. Their backgrounds, goals, and motives may vary, but the purpose of their attacks is the same: to damage, undermine, or exploit the functionality of these assets [2]. At a fundamental level, critical infrastructure is any essential system and asset vital to national security. Critical infrastructure includes assets such as power, transportation, telecommunications, water and wastewater systems (WWS), and many more [3].

Law Library Blog (March 2026): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

A Survey On Heterogeneous Computing Using Smartnics And Emerging Data Processing Units, Nathan Tibbetts, Sifat Ibtisum, Satish Puri

Computer Science Faculty Research & Creative Works

The emergence of new, off-path smart network cards (SmartNICs), known generally as Data Processing Units (DPU), has opened a wide range of research opportunities. Of particular interest is the use of these and related devices in tandem with their host's CPU, creating a heterogeneous computing system with new properties and strengths to be explored, capable of accelerating a wide variety of workloads. This survey begins by providing the motivation and relevant background information for this new field, including its origins, a few current hardware offerings, major programming languages and frameworks for using them, and associated challenges. We then review and …

Exploring Neural Network Structure Code Reuse In The Open-Source Community For Improving Maintenance, Xiaoning Ren, Yuekun Wang, Chongyang Liu, Yueming Wu, Qiang Hu, Lijun Zhang, Yinxing Xue

Research Collection School Of Computing and Information Systems

Neural networks (NNs) have rapidly advanced, demonstrating exceptional performance across various fields, leading to a surge in open-source NN projects. The complexity and rapid growth of these projects pose significant challenges for maintenance within the open-source community. Given that NN architecture code is the core asset of NN projects, understanding its reuse in the open-source community is essential for effective maintenance, such as reducing redundancy and identifying potential intellectual property violations. While prior studies have examined code reuse in open-source projects, they have two key limitations: They do not specifically address NN structure code, and they rely on manually selected …

Compositions Of Variant Experts For Integrating Short-Term And Long-Term Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user’s immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users’ …

Video Generation Techniques For Novel View Synthesis With Flow-Matching Transformers, Xiuyuan Qiu

Master's Theses

Novel view synthesis (NVS) aims to generate images of a scene from unseen camera viewpoints. Recent work, such as Stable Virtual Camera, shows that large-scale image diffusion models like Stable Diffusion can be adapted for pose-conditioned view synthesis by incorporating video-generation techniques with camera conditioning. In this thesis, we introduce MVFlow, a new NVS model that extends this approach to a different image generation architecture: a flow-matching diffusion transformer, specifically FLUX.1, which has demonstrated strong performance in image synthesis. We evaluate MVFlow under varying input view counts and pose distance settings. Our results show that this architectural transfer is feasible; …

How Agile Became The Design Philosophy Of Ai Fishbowl Under Real-World Constraints, Jad Saad

University Honors Theses

This capstone review examines the development of AI Fishbowl, a public-facing, interactive artificial intelligence system, as a case study in how Agile methods evolve from a project management tool into a design philosophy under real-world constraints. Although the project adopted an Agile workflow early on through a Kanban-style task management approach, the initial system design and architecture were still shaped by a largely plan-first mindset. This created a mismatch between flexible process and rigid design assumptions, which became increasingly apparent as the team moved from high-level architecture into implementation.

A critical turning point occurred when early architectural plans proved difficult …

Transfer Learning Neural Networks For Nuclear Forensic Image Morphology Using Image Splitting Techniques, Niko A. Petrocelli, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley

Faculty Publications

Manual morphological analysis of actinide particles from scanning electron microscope (SEM) imagery is a critical component of nuclear forensics but is prone to significant inter-analyst variability. To address this challenge, this work develops and evaluates an automated classification method using deep learning. We introduce a methodology based on partitioning 1906 SEM images, representing 13 classes of uranium compounds, into smaller patches for analysis. Three convolutional neural network (CNN) architectures of increasing complexity were compared: a custom baseline CNN, a simple transfer learning model using ResNet50v1, and a complex model featuring hierarchical feature extraction and a spatial attention mechanism built upon …

The Application, Construction, And Validation Of Hidden Markov Model Profiles For Carbonic Anhydrase Enzymes, Samuel F. Kaplan

Master's Theses

Carbonic anhydrases (CAs) catalyze the reversible hydration of CO2 and have evolved independently at least eight times, resulting in structurally distinct enzyme families (α, β, γ, δ, ζ, η, θ, ι). Traditional sequence alignment methods struggle to classify these convergently evolved proteins because their sequential similarity does not reliably indicate functional or evolutionary relationships. Many CA sequences in public databases are annotated generically without family assignments, and prior computational approaches have focused predominantly on the three well characterized families (α, β, γ), leaving the five recently discovered classes without robust classification tools. Family level assignment is often a prerequisite for …

Designing For Trust In Chat-Based Question Answering Systems: An Exchange-Based Retrieval Approach, Nathan Mccutchen

Master's Theses

Community chat platforms such as Discord and Slack support spontaneous, collaborative communication but make it difficult to retrieve previously discussed information. As conversations accumulate, valuable exchanges become buried, leading to repeated questions and sustained burden on experienced community members.

This work contributes a set of design requirements for question-answering systems operating over unstructured chat data, a Discord bot prototype implementing those requirements named Echo, and an empirical evaluation of how such a system affects user trust. Rather than encoding discrete question-answer pairs or generating synthetic responses with a language model, Echo indexes conversation topics for semantic retrieval and presents results …

A Scalable Iteration Of The Horizon Simulation Framework Using Multithreading Techniques, Jason E. Beals

Master's Theses

The Horizon Simulation Framework (HSF) occupies a unique space in the modern aerospace modeling landscape, enabling flexible, modular modeling of mission-level agent behavior through an object-oriented, hierarchical design. HSF's hallmark breadth-first search scheduling algorithm explores a "multiverse" of possible mission execution pathways, enabling exhaustive evaluation of schedule combinations against user-defined heuristics.

As aerospace systems become increasingly complex, HSF faces critical challenges in establishing verifiable, deterministic behavior. The framework's core scheduling algorithm had not undergone systematic validation, leaving questions about temporal consistency, state management correctness, and reproducibility across different program executions. Furthermore, the exponential growth of schedule combinations creates computational bottlenecks …

Stock Market Price Prediction Using Big Data Models Comparison Analysis, Vibhor Pal

Shelby Hall Graduate Research Forum Posters

The stock market consists of complex financial datasets, and achieving stock price real time prediction needs an efficient big data framework for processing. This paper compares big data distributed data processing frameworks for forecasting stock prices using Graph Neural Networks (GNNs) - Apache Flink and Apache Spark. We analyze 70 publicly traded companies’ monthly data for the last 5 years from Yahoo Finance, ranked by Price-to-Earnings (P/E). In the companies’ datasets, there may be a connection or similarity between companies, and this can lead to similar stocks’ price behavior. These interfirm relationships are maintained by GNNs models, and their output …

Evaluating Software-Based Hardware Abstraction As A Fault Injection Countermeasure, Tristan Clark, J. Todd Mcdonald

Shelby Hall Graduate Research Forum Posters

Despite how theoretically secure a system may be, it can be compromised if an adversary has physical access to the device. This can be done by injecting hardware corruptions directly into the physical system, which is called a fault injection (FI) attack. To combat this, there is a need for robust fault-tolerant countermeasures. One such countermeasure is obfuscating the program to introduce redundancy and complexity, particularly through software-based hardware abstraction (SBHA).

This research proposes using SBHA as a countermeasure to fault injection attacks. By transforming point-function password programs, the proposed countermeasure aims to increase the difficulty of conducting successful FI …

Text Corpus Combined Method And Tools For Music Textual Analysis, Yuwei Lu

Shelby Hall Graduate Research Forum Posters

While there has been a great deal of research conducted on how to search images and video using text, there has been less focus on how to retrieve music using multiple facets such as mood and lyrical content. or its features This is due in part to the historical lack of available musical corpuses. A musical corpus is a specialized text corpus specifically designed to capture information about music. Recently, several musical corpora for music information retrieval have been built and made available. However, these corpora and the tools designed to exploit them are typically designed to facilitate only one …

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

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 …