Computer Sciences Commons^™

Investigating The Impact Of Outreach Programmes On Second Level Students In Formal And Informal Computer Science Education In Ireland, Karen Nolan

Doctoral

Computer Science (CS) education at second-level in Ireland has undergone significant development, including the introduction of the Leaving Certificate Computer Science (LCCS). However, the existing literature examined demonstrated that access to and participation in CS education is uneven, resulting in many students having limited exposure CS at second-level before making educational choices. At the same time, students who elect to study LCCS require appropriate support as they engage with programming and other aspects of a relatively new formal curriculum. This thesis investigates the function of school-based CS outreach in helping students at these various phases of their second-level CS education …

Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized …

Learning 1-Bit Lidar-Based Localization With Auxiliary Objective, Kaijie Yin, Zhiyuan Zhang, Tian Gao, Wentao Zhu, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments. Many deep-learning-based localization methods have achieved promising performance so far. However, as one of the always-on modules competing for limited on-board computational resources, the localization module is expected to consume only a small portion of the overall compute budget. Most existing learning-based methods are still too heavy for this purpose. In contrast, binary neural networks (BNNs) offer an appealing solution, but the 1-bit compression causes severe information loss and performance drop. In this paper, we address this challenge by proposing Binarized LiDAR-based Localization (BiLoc), the …

Smart: When Is It Actually Worth Expanding A Speculative Tree?, Lifu Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single targetmodel forward pass. However, existing methods prioritize maximizing token-level likelihood or the number of accepted tokens while ignoring a critical “efficiency paradox”: the computational overhead of drafting and verifying big trees can grow super-linearly, particularly at scale. This often leads to negative wall-clock speedup when batch sizes increase or hardware saturation limits are reached. To address this, we propose SMART, a System-aware Marginal Analysis framework for Runtime Tree construction. SMART reformulates tree expansion as a hardware-aware optimization problem that directly maximizes end-toend speedup. …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …

Light-Wdef: Lightweight Deepfake Evaluation Framework For Efficient And Reliable Visual Detection, Muhammad Yousaf, Sarwar Shah Khan, Babar Shah, Muhammad Shehzad Khan, Muhammad Awais Bacha, Mian Saeed Akbar, Ijaz Ali, Fernando Moreira

All Works

Deepfake technology synthesizes and manipulates digital content to produce artificial videos in which an individual’s identity, facial appearance, or actions are convincingly altered. These forgeries can be highly realistic, posing serious risks to trust in digital media and undermining personal reputation, dignity, and the integrity of public discourse. This paper presents the Lightweight Deepfake Evaluation Framework (Light-wDEF)– a resource-efficient, reproducible framework tailored for large-scale evaluation of visual deepfake detectors on modest hardware. Light-wDEF combines deterministic frame sampling, batched MTCNN face extraction, streaming append-only storage with lazy offset indexing, online Albumentations augmentations, stratified splits with class-aware sampling, and robust training (AdamW, …

Constrained Multiview Contrastive Learning For Jointly Supervised Representation Learning, Siyuan Dai, Kai Ye, Kun Zhao, Yang Du, Haoteng Tang, Liang Zhan

Computer Science Faculty Publications

Purpose

Materials and Methods

Bridging Data Gaps In Retinal Imaging: From Structural Domain Adaptation To Topology-Aware Synthesis, Gözde Merve Demirci

Dissertations, Theses, and Capstone Projects

Comprehensive visualization of the retina is essential for diagnosing and monitoring blinding diseases such as Diabetic Retinopathy and Retinopathy of Prematurity (ROP), where pathological changes often extend beyond a single field of view. Despite significant advances in automated retinal image analysis, clinical deployment remains limited by two fundamental data gaps: a structural learning gap, arising from scarce expert annotations and poor generalization across imaging domains, and a spatial coverage gap, caused by the difficulty of acquiring multi-view retinal images in fragile populations. Although these challenges are often addressed independently, this dissertation argues that they are tightly coupled: accurate, …

Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Analysis Of Extended Producer Responsibility In E-Waste Management: Policy Drivers And Challenges In Singapore, Aldy Gunawan, Aidan Marc Wong, Tasaporn Visawameteekul, Minh Phuong Huynh, Linh Chi Tran

Research Collection School Of Computing and Information Systems

This paper examines the role of the Extended Producer Responsibility (EPR) scheme in electronic waste (e-waste) management in Singapore. It investigates the policy drivers and challenges of e-waste management, using data from an online survey to explore the attitudes and behaviors of young consumers, with a particular focus on youth. We employ the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) frameworks to develop a model that examines the relationships among attitudes, perceived norms, awareness, and perceived convenience in relation to EPR awareness and perception. The findings highlight the need for customized policies tailored to different …

Autoqresearch: Llm-Guided Closed-Loop Policy Search For Adaptive Variational Quantum Optimization, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Configuring variational quantum algorithms for combinatorial optimization remains a difficult, expert-driven process requiring coordinated choices over solver family, ansatz, objective, and optimizer. We present AutoQResearch, an LLM-guided closed-loop experimentation framework that casts this task as sequential policy search over a curated design space. Instead of a single static configuration, the framework searches for adaptive solver-control policies that condition future decisions on diagnostics such as feasibility, optimality gap, and convergence stagnation.The system operates through a structured workflow: an LLM agent edits a small policy surface under a fixed evaluation harness, candidate policies are screened using cheap scout evaluations, and only the …

Between Digital Transformation And Regulatory Vacuum: Cybersecurity Of Public Services In Mozambique, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid expansion of digital public services in Mozambique—including e-government platforms, digital health systems, and electronic tax administration—has outpaced the development of a coherent legal framework for cybersecurity. While Law No. 3/2017 (Electronic Transactions Law) of 9 January 2017 introduced foundational data-protection principles, Mozambique long lacked a dedicated cybersecurity regulatory authority, mandatory security standards, and formal incident-notification mechanisms. This regulatory vacuum exposed critical public services to escalating cyber risks as digital transformation was actively promoted as a development priority. This article examines the legal and institutional gaps in Mozambique's cybersecurity governance framework prior to the 2026 Cybersecurity and Cybercrime Laws, …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

Clinic-In-A-Box: A Portable, Software-Defined Cyber Range For Realistic, Scenario-Based Cybersecurity Training, Ethan Chumley, Aaron Nair, Royce Yaezenko, Joshua Payne, Veronika Kyles, Paul Wagner, Robert J. Honomichl, Ryan Straight, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

Realistic, hands-on cybersecurity training has traditionally depended on fixed infrastructure such as dedicated lab hardware, cloud subscriptions, or permanent network connectivity, limiting where and how often it can be delivered. This paper presents the design and implementation of a portable, scenario-based cybersecurity training platform housed in a single travel case and built from commodity hardware, type-1 hypervisor virtualization, containerized service orchestration, and software-defined networking. The platform clones, isolates, and resets complete lab environments on demand, allowing the same physical system to support repeated classroom, workshop, or field deployments with minimal manual reconfiguration. Training scenarios are grounded in generated organizational profiles …

A Statistical Mechanics Approach To Reinforcement Learning, Jacob Adamczyk

Graduate Doctoral Dissertations

Reinforcement learning (RL), the study of optimal decision-making over long timescales in stochastic systems, has recently seen remarkable advances due in large part to the efforts of the deep learning community. RL has witnessed great success in solving problems in video games, robotics, biological control, and language modeling. However, a unified statistical mechanics framework to understand and develop the corresponding algorithms is lacking. To address this issue, we begin by showing that the reinforcement learning problem can be formulated and solved using the tools of statistical mechanics. Drawing on physical principles of free energy minimization and invariance, we address important …

Guardians Of The Record: A Two-Tiered Streaming Cascade With Kafka, Flink, And A Real Llm, Ilan Goodman

Generative AI Teaching Activities

Students build a streaming vandalism detector for live Wikipedia edits in which a fast rule-based tier decides which few of ~1,500 edits per minute are worth escalating to a slow, rate-limited real LLM (Gemini) — confronting the cost, latency, and failure modes of putting AI inside a production data pipeline.

Guardians Of The Record (Cs2 Edition): Heaps, Queues, And A Scarce Oracle, Ilan Goodman

Generative AI Teaching Activities

Students defend Wikipedia from vandals with data structures instead of infrastructure: a sliding-window edit-velocity tracker (hash map of queues), a hand-built binary min-heap, and a budget-bounded top-K selection decide which few suspicious edits earn a question to an expensive, rate-limited Oracle — a stand-in for a real LLM.

Analysis Of The Throttle Settings Under Uncertain Information, Latafat Gardashova, Nihad Afandi

Chemical Technology, Control and Management

Although classical fuzzy logic controllers are capable of modelling non-linear control systems, they fail to consider the reliability of linguistic information, sensor measurements, and expert knowledge. In this paper, an intelligent controller based on the use of Z-numbers is developed for steam-turbine throttle control. Linguistic information and its confidence degree are considered simultaneously in such a controller. The temperature and pressure values are taken as input variables, while the throttle rotation is selected as the controller output variable. At first, the Z-number representation system is constructed to include the credibility of linguistic measurements and control rules. Then, a Mamdani Type-1 …

Nonlinear System Identification Based On Fuzzy Radial Basis Neural Network With Multi-Connected Weight Connections, Kabul Khudaybergenov

Chemical Technology, Control and Management

This paper builds on our earlier radial basis function network with multiple connections (RBFMC) by placing it within a fuzzy inference framework for nonlinear system identification. The idea is inspired by the diversity of neurotransmitters found in biological neurons: instead of a single hidden-to-output weight, RBFMC gives each hidden unit a multi-dimensional connection whose components act as independent filters. Once fuzzy logic is added, each hidden neuron becomes a fuzzy rule, and its antecedent is built from several Gaussian membership functions, one per connection. The resulting Fuzzy RBFMC produces an interpretable, multi-filter description of local regions of the input space …

Enhanced Osseointegration Of Functionally Graded Co˗Cr˗Mo˗Ti/Ha Implants: In Vitro And In Vivo Study In A Rabbit Model, Afrah M. Al Hussainey, Randa Kamel Hussain, Aseel Mustafa Abdul Majeed

Karbala International Journal of Modern Science

    The present investigation aims to evaluate the effects of acid and laser surface treatments on the surface characteristics, biocompatibility, and osseointegration of functionally graded implants fabricated from a Co–Cr–Mo–Ti/HA alloy. The implant surfaces were modified using either an Nd: YAG laser or hydrochloric acid (HCl), while untreated implants served as controls. Atomic force microscopy (AFM) showed that the acid-treated surface exhibited the highest mean surface roughness (Sa) of 58.10 ± 0.24 nm compared to the laser-treated surface (42.64 ± 0.83 nm) and the untreated surface (37.73 ± 0.89 nm). The MTT assay also demonstrated a favourable cellular response on the …

Trustworthy Ai At The Air Quality-Health Nexus: A Systematic Review Of Federated Learning, Distributed Ledger Technologies, And Explainable Ai, Nadia Dahmani, Imen Ben Salem, Syed M Salman Bukhari

All Works

Abstract Air quality (AQ) related health risks are increasing globally, creating urgent demand for artificial intelligence (AI) systems that are privacy-preserving, transparent, and auditable. Although Federated Learning (FL), Distributed Ledger Technologies (DLT), and Explainable AI (XAI) are widely recognized as core components of trustworthy AI, existing research remains divided across technical and application domains. This study conducts a computational, multistage systematic review (SR) of scholarly literature records published between 2011 and 2025 using the Semantic Scholar Graph API with intersection and union search strategies. A total of 13 458 records were retrieved and refined into a research-grade corpus of 7 …

Ai, The Liberal Arts, And Indigenous Languages: Forming Code Into Language, Christina Graebner

Summer Research Showcase

During the Summer, Spanish Professor Adam Coon and I worked on creating an annotated biography on AI and Indigenous languages for the Digital Well at the UMN Morris Library. Through this project, we have dived into conversations and research focusing on using AI as a translator. In recent years, the conversation around AI has created a surge of studies and research around the relationship between Indigenous languages and artificial intelligence. AI will only continue to expand, and it creates new ways to open communication but creates new ethical guidelines needed to be followed. Our project gathers research articles, podcasts, and …

Self Efficacy And Instructional Support Predict Cyber Deception Acceptance In Ics And Ot Cybersecurity, Daniel Ward

Journal of Cybersecurity Education, Research and Practice

Cyber deception can produce high-confidence evidence of unauthorized activity in industrial control systems (ICS) and operational technology (OT), but practitioners must consider the technology useful, safe, understandable, and supported before they will use it. This study reports a secondary quantitative analysis of a deidentified survey of United States-based ICS and OT professionals to determine whether psychological and instructional factors predict adoption readiness and effective utilization beyond education, experience, and sector. Hierarchical ordinary least squares regression with HC3 robust standard errors was conducted on 262 complete cases. The demographics-only model was not significant and explained 2.8 percent of outcome variance. Adding …

Opengrcrmf: A Vendor-Neutral Framework For Teaching And Modeling Rmf Automation, Continuous Authorization, And Zero Trust Governance, Anand Janjal

Journal of Cybersecurity Education, Research and Practice

Abstract—Federal and regulated organizations continue to rely on document-centric Authorization to Operate (ATO) processes even as the NIST Risk Management Framework (RMF), continuous monitoring guidance, Zero Trust Architecture (ZTA), and continuous authorization initiatives require more continuous, evidence-driven risk management [1]-[3], [13], [15]. Manual System Security Plan (SSP) updates, spreadsheet-based Plan of Action and Milestones (POA&M) tracking, and disconnected assessment evidence create governance latency: the delay between operational security events and authorization-ready governance response. This paper presents OpenGRCRMF, a proposed open, vendor-neutral reference framework that models RMF lifecycle activities as workflow states, treats authorization artifacts as structured governance objects, and …

Artificial Intelligence And Social Equities: Navigating The Intersectionalities In A Digital Age (Editorial), Daisuke Akiba, Julie Albright

Publications and Research

This editorial article introduces and synthesizes the Special Issue, “Artificial intelligence and social equities: navigating the intersectionalities in a digital age,” which examines how AI systems intersect with race, ethnicity, and interconnected identity dimensions across global contexts. The eight contributions span healthcare, digital media, higher education, organizational communication, and speculative futures, addressing anti-racist psychiatric algorithms, AI-generated visual disinformation, epistemic injustice between the Global North and South, algorithmically mediated rural–urban divides, culturally untranslated technology transfer, accessibility auditing across the AI lifecycle, and the tension between mechanical objectivity and empathic understanding. Read together, they show that AI is neither inherently …

A Vision For The Future Of Academic Publishing In Sports Analytics, Ryan Elmore, B. Baumer, Brian Macdonald, Gregory J. Matthews, Michael E. Schuckers

Statistical and Data Sciences: Faculty Publications

This article introduces the Journal of Statistics and Data Science in Sports (JSDSS), a Diamond Open Access, peer-reviewed journal. The journal is founded on three core principles. First, our commitment to open access is absolute. Second, reproducibility is critical and fundamental to the journal. Third, we believe sport is a rich and underutilized laboratory for statistical and data science innovation. The aim of the Journal of Statistics and Data Science in Sports is to provide an outlet for original, rigorous, practical, state-of-the-art, reproducible, and peer-reviewed analysis of sports data as well as the data science tools (software, applications, data, etc.) …

Personal Authenticity For Engagement And Transfer In Introductory Cybersecurity Education, Daniel T. Hickey, Ronald J. Kantor

Journal of Cybersecurity Education, Research and Practice

Abstract—This conceptual/theoretical paper explores how personal authenticity might promote generative learning in introductory cybersecurity courses. Generative learning transfers confidently to future educational, professional, personal, and testing situations. This cycle of design-based research addresses the concern that more typical professionally authentic contexts (e.g., hospitals, banks, etc.) may be alien and overwhelming to many students, particularly those in introductory courses and/or from non-professional families and communities. If so, this leads to “inert” knowledge that does not transfer. Personal authenticity is rooted in expansive framing, a modern theory of learning transfer. We reframe expansive framing as personal authenticity to make it …

Nasa’S Ecostress Satellite Reveals Widespread Midday Depression In Ecosystem Evapotranspiration, Jingyi Bu, Jingfeng Xiao, Joshua B. Fisher, Yiqi Luo

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time …