Digital Commons Network^™

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 …

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 …

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 …

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 …

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 …

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 …

An Analysis Of The Effects And Implementations Of The Early Literacy Grant In Arizona, Alicia Severiano Perez

Mathematics, Statistics, and Computer Science Honors Projects

Over the years, states have implemented Science of Reading (SoR) frameworks to address low literacy levels. The Early Literacy Grant (ELG) in Arizona funds and supports such frameworks for schools serving low-income students. This paper is the first to explore the grant through interrupted time series modeling to evaluate effectiveness and text analysis to understand its implementation. We do not find clear evidence of positive effects caused by the grant, other than some cases, such as Yuma County. Schools typically allocate funds toward salaries and hiring instructors. These findings raise questions about whether its allocations should be closely monitored.

Machine Learning For Functional Outcome Prediction After Vestibular Schwannoma Surgery: A Systematic Review And Diagnostic Test Accuracy Meta-Analysis, Shiva Nischal, Shaan Patel, Musa China, Kush Kale, Yi Hein Chai, Santosh Guru, William Muirhead, Patrick Grover

Department of Neurosurgery Faculty Papers

PURPOSE: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes.

METHODS: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects …

Interpretable Multimodal Learning For Integrating Neuroimaging And Genetic Data In Alzheimer’S Disease, Kun Zhao, Siyuan Dai, Yingying Zhang, Guodong Liu, Pengfei Gu, Chenghua Lin, Paul M. Thompson, Alex D. Leow, Heng Huang, Haoteng Tang

Computer Science Faculty Publications

Introduction: Early detection of Alzheimer's disease (AD) requires models that combine brain structure changes with genetic risk, but existing methods struggle to align these different data types.

Methods: We present R-GenIMA, an interpretable multimodal large language model that pairs a region-of-interest vision transformer with genetic prompting to jointly analyze structural MRI and single nucleotide polymorphisms (SNPs). Each brain region becomes a visual token and SNP profiles are encoded as structured text, letting the model link regional atrophy to genetic factors through cross-modal attention. Tested on the ADNI cohort, R-GenIMA performs well in classifying four groups: normal cognition, subjective memory concerns, …

Physical Design Automation For Next Generation Ics: Graph And Machine Learning Based Approaches, Dr. Kritanta Saha

Doctoral Theses

Integrated Circuit (IC) technology has progressed significantly over the last six decades, culminating in billions of transistors on a chip. The widely used 193 nm immersion lithography process exceeds modern feature sizes, leading to layout pattern distortions and electrical violations. Mitigation techniques such as Multiple Patterning and Optical Proximity Correction become ineffective below 45 nm, prompting the development of several Next-Generation Lithography (NGL) methods such as Extreme Ultraviolet Lithography (EUVL), Multiple Electron Beam Lithography (MEBL). MEBL employs thousands to millions of e-beams in parallel to overcome throughput limitations. A 2D layout is partitioned into vertical stripes by stitch-lines (SLs), each …

Energy Security Strategy Empowered By Artificial Intelligence, Qiang Ji, Jiaofeng Pan, Yu Song

Bulletin of Chinese Academy of Sciences (Chinese Version)

Against the backdrop of unprecedented changes in a century, geopolitical restructuring has led to the fragmentation of energy game camps, climate change has impacted the resilience of energy infrastructure, and energy transformation has promoted the multidimensional and coordinated expansion of security connotations. Artificial intelligence, with its core advantages such as optimizing geopolitical risk prevention and control, enhancing infrastructure protection, improving energy system efficiency, and accelerating the integration of renewable energy, has promoted the shift of energy security strategy from experience driven to data-driven intelligence, achieving comprehensive risk identification, dynamic evaluation, collaborative response, and full chain monitoring, significantly improving the efficiency, …

Large Models Empowering Cybersecurity: Opportunities And Challenges, Zhuofeng He, Dongbin Hu, Yige Yuan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Cybersecurity serves as a critical pillar for national security and social stability. Large models in cybersecurity are emerging as key enablers for the intelligent transformation of cyber offense and defense systems. As one of the most advanced core technologies in artificial intelligence, large models are introducing new research directions and application paradigms in the cybersecurity domain. This study systematically reviews the current landscape of cybersecurity-oriented large model applications and products, and explores their deployment scenarios in practice. It further analyzes the development trends in model capabilities, industry ecosystems, and trustworthiness, while identifying major practical challenges such as data privacy protection, …

Critical Core Technology Breakthroughs In Large-Scale Models: Industrialization Strategies And Policy Implications, Zhongqi Wu, Yinshan Liu, Tao Dai, Xiaolong Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pivotal direction for breakthroughs in key core technologies within the artificial intelligence domain, large-scale models hold strategic significance in securing national scientific and technological sovereignty. This study employs a multidimensional framework encompassing “technological breakthroughs, industrial transformation, and governance policies” to systematically investigate the developmental trajectories and industrialization bottlenecks of large-scale models. At the technological level, while large-scale models exhibit exponential growth in parameter scale and computing power demands, they face critical challenges including the scarcity of high-quality data, insufficient transfer learning capabilities, and reliability-explainability trade-offs. Industrially, these models are reshaping the global industrial chain landscape through a dual-track …

Evolutionary Trajectory Of Ai And Robotics Integration, Jiannan Zhu, Jianfeng Guo, Siyao Liu, Qi Cao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The convergence of artificial intelligence (AI) and robotics is a cornerstone for the intelligent transformation of the physical world. This study conducts a systematic analysis of over 230,000 publications from the Web of Science and WIPO databases between 1982 and 2025 to dissect the evolutionary trajectory of AI and robotics integration since the 20th century. The findings identify four distinct developmental stages—independent exploration, functional coupling, primary intelligence, and intelligent symbiosis—while delineating the key technological breakthroughs and paradigm characteristics of each phase. Furthermore, seven core research thrusts are distilled, including motion planning and autonomous decision-making and perception & environmental understanding. An …

A Return On Investment (Roi) Evaluation Tool For Quantifying The Value Of Cybersecurity Certifications, Nicolas Meysmans, Kelly Hughes

Journal of Cybersecurity Education, Research and Practice

The growing reliance on cybersecurity certifications has increased the financial and professional stakes associated with certification decision-making for cybersecurity professionals. Despite their widespread use in hiring and career advancement, there is limited objective guidance available to help individuals evaluate the return on investment (ROI) of specific certifications. This gap has created uncertainty regarding which credentials provide the greatest value relative to their cost and market impact. This study presents a data-driven, design science–based tool that supports cybersecurity professionals in evaluating certification ROI using practitioner survey data combined with certification cost and labor-market demand indicators. The paper also demonstrates how such …

From Disruption To Replacement: The 2026 Cae Cybersecurity Community Symposium And The Emerging Federal-Academic Compact For An Ai Workforce, Sunday Oludare Ogunlana

Journal of Cybersecurity Education, Research and Practice

The cybersecurity workforce gap in the United States is estimated at several hundred thousand unfilled positions, and the rapid integration of artificial intelligence into adversary tradecraft and federal cyber operations is widening that gap qualitatively as well as quantitatively, threatening national security and the operational readiness of graduates entering the field. This perspective article synthesizes the principal arguments advanced by five federal and academic speakers at the 2026 CAE Cybersecurity Community Symposium, using verbatim session transcripts, a structured thematic extraction process, and triangulation against published workforce policy and peer-reviewed literature. Findings document a unified speaker thesis that artificial intelligence now …

Trust, Delegation, And Alignment In Human-Ai Decision Making, Erik O. Kimbrough, Brennan Mcdavid, Diba Vazirian

ESI Working Papers

This paper studies delegation to artificial intelligence in a setting where human principals retain the consequences of delegated choices. Participants wrote prompts instructing ChatGPT-4o mini how to choose on their behalf in three canonical economic domains: risky choice, intertemporal choice, and social allocation. We then elicited the compensation participants required to let the AI’s choices count for payment and compared participants’ own choices to choices generated from their prompts. The design produces two central empirical objects: a revealed measure of reluctance to delegate, captured by willingness to accept compensation for AI delegation, and a behavioral measure of alignment, captured by …

Computer Organization And Assembly Language Programming, Chenxi Wang Phd, Muhammad Rashed Phd

Mavs Open Press Open Educational Resources

Computer Organization and Assembly Language Programming is an open textbook written for CSE 2312 students at The University of Texas at Arlington and for anyone who wants to see clearly how high-level code becomes machine operations. The book takes the position that assembly is not a historical curiosity but a working tool: it is where system programming, embedded development, performance tuning, and real debugging skill begin.

Across sixteen chapters, this textbook builds from number systems and base conversion through ALU operations, status flags, and shift operations, then into ARMv7 assembly syntax, the load and store architecture, endianness, addressing modes, branch …

The Honored Children Of Adam: A Muslim Reading Of Magnifica Humanitas In The Age Of Artificial Intelligence, Iqbal Akhtar

The Journal of Social Encounters

No abstract provided.