Digital Commons Network^™

Conv4rec: A 1‑By‑1 Convolutional Autoencoder For User Profiling Through Joint Analysis Of Implicit And Explicit Feedbacks, Antoine Ledent, Petr Kasalický, Rodrigo Alves, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Second, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as ”implicit feedback”). It can also make separate predictions for the probability of consuming content and the likelihood of granting it a high rating …

Robface: A Test Suite For Efficient Robustness Evaluation Of Face Recognition Systems, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Face recognition is a widely used authentication technology in practice, where robustness is required. It is thus essential to have an efficient and easy-to-use method for evaluating the robustness of (possibly third-party) trained face recognition systems. Existing approaches to evaluating the robustness of face recognition systems are either based on empirical evaluation (e.g., measuring attacking success rate using state-of-the-art attacking methods) or formal analysis (e.g., measuring the Lipschitz constant). While the former demands significant user efforts and expertise, the latter is extremely time-consuming. In pursuit of a comprehensive, efficient, easy-to-use, and scalable estimation of the robustness of face recognition systems, …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Bridging The Great Wall: China’S Evolving Cross-Border Data Flow Policies And Implications For Global Data Governance, Sheng Zhang, Henry S. Gao

Research Collection Yong Pung How School Of Law

Despite the rapid expansion of the digital economy, the global regulatory framework for data flows remains fragmented, with countries adopting divergent approaches shaped by their own regulatory priorities. As a key player in the Internet economy, China’s approach to cross-border data flows (CBDF) not only defines its domestic digital landscape but also influences emerging global norms. This paper takes a comprehensive view of the evolution of China’s CBDF regime, examining its development through both domestic and international lenses. Domestically, China’s regulation of CBDF has evolved from a security-first approach to one that seeks to balance security with economic development. This …

The Density Distribution Of The Material Around Betelgeuse, Iman Behbehani

Dissertations, Theses, and Capstone Projects

Betelgeuse is a red supergiant star visible in the constellation Orion. Its windy and highly convective surface results in a complicated mass loss pattern difficult to understand and replicate in simulations. The ejected mass can form a shell around the star we consider the circumstellar material (CSM). In this study, we use ALMA interferometric observations to find the structure of Betelgeuse's CSM, and connect to the mass loss mechanisms that could form it. We measure a bipolar circumstellar structure with a position angle of 42.3$\pm 7.0^\circ$. We observe asymmetries in the form of hot spots in the north east of …

Towards Automated Evolution Of Imperative Deep Learning Programs, Tatiana Castro-Vélez

Dissertations, Theses, and Capstone Projects

Software engineering (SE) is increasingly intersecting with data-centric domains such as machine learning (ML) and deep learning (DL). Similar to bugs in traditional software systems, defects can emerge in ML and DL systems. ML, including DL, systems are now widespread and rely on dynamic models defined by input data. Developers face the challenge of building dependable systems while addressing the demand for scalable software.

Efficiency is essential to support responsiveness with respect to ever-growing datasets. Traditional DL frameworks achieve scalability through deferred execution, enabling symbolic, graph-based deep neural network (DNN) computation. While efficient, this approach is error-prone, cumbersome, and difficult …

Reinforcement Learning Based Resource Management In Edge Systems, Motahare Mounesan

Dissertations, Theses, and Capstone Projects

Modern end-user applications that are highly compute- and data-intensive, while being extremely latency- and accuracy-sensitive, are increasingly reliant on distributed computing. This paradigm spans a range of architectures, from cloud computing to in-device processing. Cloud computing, though scalable, often incurs high latency and cost, constraints that are particularly problematic for time-sensitive applications. In contrast, in-device computing on end or IoT devices is limited by resource constraints, making it inadequate for many complex workloads. Edge computing presents a compelling alternative by bringing computation closer to data sources, thereby reducing end-to-end latency and improving responsiveness. However, the inherent decentralized and dynamic nature …

Can Artificial Intelligence Models Provide Reliable Medical Counselling To Fertility Patients?, Idan Alcalay, Ariel Weissman, Hadas Ganer Herman, Avi Tsafrir, Matan Friedman, Eran Weiner, Raoul Orvieto, Nikolaos P. Polyzos, Michael H. Dahan, Alex Polyakov, Robert Fischer, Sandro C. Esteves, Baris Ata, Jason M. Franasiak, Yossi Mizrachi

Department of Obstetrics and Gynecology Faculty Papers

RESEARCH QUESTION: Can generative artificial intelligence (AI) models provide reliable counselling to fertility patients regarding real-world clinical questions?

DESIGN: In this cross-sectional study, 12 clinical questions were developed to reflect common, real-life dilemmas encountered during fertility workup and treatment. Responses to each question were generated by two experienced fertility specialists, and two AI models - ChatGPT and Gemini. Eight leading internationally recognized fertility experts, blinded to the source of each reply, independently rated all the responses on a scale from 1 (strongly disagree) to 10 (strongly agree). Ratings were compared across all four repliers using non-parametric statistical tests.

RESULTS: The …

Machine Learning And Crime Prevention, Emily Lizewski

Student Theses

Predictive policing uses machine learning to analyze crime patterns and help law enforcement better efficient use their resources. These tools can improve accuracy by highlighting complex trends in large sets of data. While this technology has its advantages, it also raises important ethical and social questions. Within this paper we looks at how predictive policing works, focusing on the machine learning models often used such as decision trees, random forests, gradient boosting, and models that factor in both time and location. It also explores how these tools might unintentionally reinforce biases already present in historical crime data. In reviewing the …

Quantum Pseudorandom Primitives Beyond Pseudorandom States, Chuhan Lu

Dissertations and Theses

Quantum pseudorandomness is an emerging research area. Ji, Liu, and Song defined pseudorandom states (PRSs) and pseudorandom unitaries (PRUs) as quantum analogs of pseudorandom generators and pseudorandom functions. A unitary oracle separation result between one-way functions and PRSs/PRUs, established by Kretschmer, suggests that certain quantum primitives may remain secure even if classical cryptography is compromised. This insight has spurred extensive work on quantum pseudorandomness and its applications in quantum cryptography.

Many constructions of PRSs have been established under standard assumptions, yet building a secure PRU was a long-standing open problem. This dissertation aims to narrow the gap between PRSs and …

A Comprehensive Review On Gamification In Neurocybersecurity, Ms. Kritika

Journal of Cybersecurity Education, Research and Practice

The research investigates gamification methods as it applies to the emerging interdisciplinary domain that brings together cybersecurity with neuroscience and psychology. Neurocybersecurity implements neural concepts to protect digital systems from security threats while targeting the human vulnerabilities that present as the strongest points of attack. Several researchers have examined gamification techniques which incorporate game design elements to enhance cybersecurity training outcomes by improving user participation and knowledge retention and user conduct compliance. The research incorporates Self-Determination Theory along with Cognitive Load Theory to explain the design principles for efficient gamified interventions. The implementation of both cognitive performance improvement and neuroplasticity …

A Backtracking Algorithm For Determining The Existence Of Regular Graphs Of Specified Girth And Excess, Stetson Ray Bosecker

Doctoral Dissertations

The study of cages focuses on finding (k, g)-graphs of minimal order. This dissertation generalizes the problem of finding cages to the determination of graphs with specified excess, thereby broadening the significance of the results. The (k, g, ε)-graph problem seeks to determine the existence or nonexistence of k-regular graphs with girth g and excess ε = n(G)−M(k, g) (where M(k, g) represents the Moore bound for cage graphs). Motivated by heuristic methods used to determine properties within the study of cages, we present a backtracking algorithm capable of constructing (k, g, ε)-graphs or determining their nonexistence. Chapter 2 provides …

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …

Modeling The Importance Of Life Exposure Factors On Memory Performance In Diverse Older Adults: A Machine Learning Approach, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Ruijia Chen, Omonigho M. Bubu, Rachel Whitmer, Paola Gilsanz, Zvinka Z. Zlatar

Moss-Magee Rehabilitation Papers

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and …

Machine Learning Research On Time Series Data, Zeyi Fan

Lingnan Theses (MPhil & PhD)

Time series generated by complex systems, such as industrial IoT and user behavior systems, confront two core challenges: structured missingness (e.g., continuous or periodic gaps) that disrupt temporal dependencies, and the difficulty in effectively modeling dynamic long- and short-term temporal dependencies inherent in evolving patterns (e.g., user interests). Traditional approaches struggle to balance the preservation of local dependency continuity and the rational association of global long-range dependencies in structured missing scenarios, often incurring high computational costs. In temporal pattern modeling, the lack of adaptive mechanisms to fuse evolving long- and recent behavior trends (e.g., stable interest inertia vs. short-term preference …

The Implications Of Insecure Use Of Fonts Against Pdf Documents And Web Pages, Junjie Xiong, Mingkui Wei, Xiao Han, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

This paper identifies the importance of the safe use of fonts in web and document security. We find multiple attack surfaces that can be exploited by an adversary using malicious fonts. We conduct a comprehensive evaluation of Portable Document Format (PDF) documents collected from the real world to investigate how an attacker can bypass PDF signatures. We further evaluate the potential security threats that an attacker can bring to web-based emails. Our study shows that various security issues may be caused by the inappropriate use of fonts, which are nevertheless overlooked in the past years. As such, guidelines promoting the …

Combining Code Analysis And Pedagogical Guidance: Automated Tools For Teaching Debugging In Introductory Programming, Yana Malysheva

McKelvey School of Engineering Graduate Student Theses & Dissertations

The ability to debug code is critical to being a programmer and represents a distinct skill from writing code. Yet debugging is rarely explicitly taught in introductory programming and Computer Science courses. Instead, novices typically develop their own debugging habits and strategies when they encounter bugs in their code, which are often less effective than those of expert programmers. When students do seek help with debugging, they traditionally turn to office hours conducted by Teaching Assistants (TAs) or, increasingly, to Large Language Models such as ChatGPT. However, both sources of assistance have limitations. TAs are often students themselves with limited …

Code Stories For Software Evolution, John Joseph Allen

McKelvey School of Engineering Graduate Student Theses & Dissertations

Programmers spend more than half of their time comprehending code, and in particular struggle to answer questions about the rationale, intent, and history behind software artifacts. Through this dissertation, I explore how history-aware tools can help programmers understand unfamiliar software artifacts. First, I investigated how providing additional context -- historical code changes grouped by the original developer's stated subgoals and the web foraging activity of the original developer impacted the process of code reuse. I found that programmers utilized these resources to 1) make better analogies between their reuse scenario and what code was already written, and 2) anchor into …

Towards Graph Foundation Models: Few-Shot And Zero-Shot Learning On Graphs, Hao Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Graphs naturally model complex relationships and interactions across various domains, including social networks, biological systems, and recommender platforms. Graph Neural Networks (GNNs) have emerged as powerful tools for learning effective graph representations through iterative message passing, significantly improving performance in tasks such as node classification, link prediction, and graph classification. However, the success of GNNs largely depends on abundant labeled data, posing challenges in practical scenarios where labeled data is scarce or unavailable. This dissertation addresses these challenges by exploring few-shot and zero-shot learning within the graph domain. We first propose COLA, a self-supervised few-shot node classification method that exploits …

Computational Imaging Under Incomplete Information, Weijie Gan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Computational imaging is a pivotal field that synergizes physical measurement principles with advanced algorithms to generate visual information. An important task in this field is solving imaging inverse problems that aim to reconstruct high-quality images from observed measurements. Model-based deep learning (MBDL) has emerged as a particularly powerful tool for tackling these inverse problems by integrating machine learning (ML)-driven priors with knowledge of the imaging physics. This dissertation focuses on the pervasive challenge of informational incompleteness in computational imaging, arising from various practical and physical limitations, that hinder the widespread adoption of ML-driven computational imaging algorithms in practice. This includes: …

Multimodal Representation Learning For Geospatial Soundscape Mapping, Subash Khanal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Sound is one of the fundamental senses that helps us reason about our environment. There exists an intricate relationship between the visual appearance of a location and the distribution of sounds present there. We propose leveraging this relationship to formulate the task of soundscape mapping—predicting the most probable distribution of sounds that could be perceived at a given geographic location, as observed in its overhead imagery. To support research on this task, we curated a comprehensive dataset, GeoSound, which consists of geotagged audio recordings from various sources, paired with both low- and high-resolution overhead imagery. We approach the soundscape mapping …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

Autonomous Uav Swarm Formation Utilizing Gradient-Driven Contour Mapping For Radiation Source Localization, Edgar Amalyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents a drone swarm for radiation mapping to aid source localization. The Department of Energy advocates employing UAVs for this task, but existing approaches remain inefficient and impractical in real-world scenarios. Three custom drones are built and flight-tested. A control algorithm to follow a contour, a constant-intensity path, is designed using a gradient fit. By knowing the source’s direction, the drone swarm can fly in the optimal trajectory at every step, leaving nothing to assumption. A program is created that implements formation flight and autonomous navigation. It is tested via a software-in-the-loop simulation utilizing radiation sources and detectors …

Advanced Robotic Multimodal Imaging With Real-Time Motion Compensation For Dynamic Structural Health Monitoring, George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Chris Ruberg, Shawn Owens, Nikolaos Michailidis

Publications and Research

Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures.

This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it …

Cybersecurity: Digital Stewardship In A Violent World, Rocky K. C. Chang

University Faculty Publications and Creative Works

This paper defines cybersecurity based on the principle of biblical stewardship. The focus of this principle is God, not the technologies, who is the creator and owner of cyberspace and everything in it. Humankind is called by God to steward them by protecting the digital property of our neighbors in cyberspace from malicious attacks. In the context of cybersecurity, a neighbor can be any individual cyber user, practitioner, educator, organization, tech company, or even hacker. However, as argued from the perspectives of human sin and human finitude, the defending stewards can never win over evil in this spiritual battle. Instead, …

Digital Trading Platform Selection Under Neutrosophic Numbers And Multi-Criteria Decision-Making Methodology: An Illustrative Example, Eman Sayed, Karam M. Sallam, Ibrahim Alrashdi

Neutrosophic Systems with Applications

Uncertainty, vagueness, and incomplete information are pervasive in real-world decision-making scenarios, particularly in multi-criteria decision-making (MCDM) contexts involving expert judgments. To address these challenges, this study introduces a comprehensive decision-making framework that integrates Triangular Neutrosophic Numbers (TNNs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method. The framework begins by capturing expert evaluations using TNNs, which effectively represent the degrees of truth, indeterminacy, and falsity inherent in subjective assessments. These evaluations are then transformed into crisp values using a novel score function that preserves the embedded uncertainty. The resulting decision matrices are aggregated into a unified structure to ensure consistency …

Integrated Algorithm And Hardware Design For Hybrid Neuromorphic Systems, James Seekings, Mahsa Ardakani, Peyton Chandarana, Arshia Eslami, Mohammadreza Mohammadi, Ramtin Zand

Faculty Publications

This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build and train hybrid models that integrate spiking neural networks (SNNs) and artificial neural networks (ANNs) using the PyTorch and Lava frameworks. We explore the effects of quantization on ANN models to assess its impact on both accuracy and energy efficiency. Additionally, we address the challenges of deploying hybrid models on hardware by implementing individual components on specific edge platforms. We also propose an accumulator circuit to bridge the spiking and non-spiking …

Land8fire: A Complete Study On Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, And Extensive Benchmarking, Anh Tran, Minh Tran, Esteban Marti, Jackson Cothren, Chase Rainwater, Sandra Eksioglu, Ngan Le

Electrical Engineering and Computer Science Faculty Publications and Presentations

Early and accurate wildfire detection is critical for minimizing environmental damage and ensuring a timely response. However, existing satellite-based wildfire datasets suffer from limitations such as coarse ground truth, poor spectral coverage, and class imbalance, which hinder progress in developing robust segmentation models. In this paper, we introduce Land8Fire, a new large-scale wildfire segmentation dataset composed of over 20,000 multispectral image patches derived from Landsat 8 and manually annotated for high-quality fire masks. Building on the ActiveFire dataset, Land8Fire improves ground truth reliability and offers predefined splits for consistent benchmarking. We evaluate a range of state-of-the-art convolutional and transformer-based models, …

Jazz Scale Patterns With Abjad And Lilypond, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Background

Jazz method books often advise students to “learn it in all keys,” yet most present examples in only one or two keys (if that, as they start from C, the easiest key, and usually stop there). For many "classically-trained" players—especially those who check fingerings, enharmonics, and voice-leading by reading—the absence of complete, notated materials is a barrier. While most scales can be internalized as Whole (W) / Half (H) step patterns, important exceptions (e.g., harmonic and melodic minor, octatonic, whole tone, and blues) are aided by having notated patterns in front of us.

Aims

To generate clear, consistent notation-first …

Bring Back The Blue-Book Exam: In An Age Of Ai, We Need To Return To Handwritten Assignments., Katie Day Good

University Faculty Publications and Creative Works

When ChatGPT was released three years ago, its ability to mimic human writing unsettled me. I’m a professor of communication; what did it mean that my students now had access to a machine that could communicate for them? My initial unease led to a half-joke with my colleagues. Universities could survive this threat, I ventured, but only if we reverted back to 19th-century teaching methods like Socratic dialogue, oral defenses, and lengthy essay exams. This once-laughable scenario is now a serious consideration for many faculty, including me.

Like many professors, I’ve recently abandoned take-home essays in favor of blue-book exams. …