Digital Commons Network^™

Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong

Research Collection School Of Computing and Information Systems

We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data …

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

The adoption of artificial intelligence (AI) in healthcare is accelerating, yet successful implementations of physician-facing AI systems remain limited and uneven. This paper presents a literature review of 40 peer-reviewed studies published between November 2022 and November 2024, spanning clinical, technical, and human-computer interaction (HCI) domains. Anchored in a socio-technical perspective, the review examines our existing understanding of how technical design, user expertise, and organizational factors shape the effectiveness of AI systems in real-world clinical settings. Our analysis identifies two meta-themes: (1) context as a dynamic, multi-level influence that actively reshapes AI system behavior, and (2) trust as an emergent …

Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observation of environments. However, these methods struggle in real-world applications because of the higher cost of panoramic RGB-D cameras. This paper studies a low-cost and practical VLN-CE setting, e.g., using monocular cameras of limited field of view, which means “Look Less” for visual observations and environment semantics. In this paper, we propose a ThinkMatter framework for monocular VLN-CE, where we motivate monocular robots to “Think More” by 1) generating novel views …

An Investigation Into The Mechanisms, Barriers, Degree And Sphere Of Risk Influence In Corporate Security, Nicola Lockhart

Theses: Doctorates and Masters

This study investigates the sphere of corporate security risk influence within organisations, addressing the conceptual and practical ambiguity surrounding the activity’s capacity to shape organisational decisions, behaviours, and risk priorities. While corporate security’s protective role is widely recognised, its broader organisational risk influence remains under-theorised. The study defines the sphere of risk influence as the range of organisational stakeholders and environments with which the corporate security activity interacts, and within which it may engage, persuade, and mobilise action. This sphere is analytically constituted through the intersection of three dimensions: the mechanisms through which influence is attempted, the barriers that constrain …

Development And Evaluation Of A Gc-Msms Method For The Quantification Of Gamma Hydroxybutyrate In Hair, Jacob Robert Keller

Theses: Doctorates and Masters

Gamma (γ)-hydroxybutyrate (GHB) is an endogenous short-chain fatty acid, and is of forensic importance due to its use in drug facilitated sexual assault (DFSA). Exogenous GHB has a limited window of detection in blood (1-hour) and urine (12-hours). Testing hair allows detection of drugs months after ingestion. This thesis contains a literature review which compares published methods which determine concentration of endogenous GHB in hair to determine which procedures are suitable for experimental comparison. The aims of this thesis were to develop a GC-MS-based method for the detection of GHB, test various methods to improve extraction, determine linearity, precision, accuracy, …

Development Of Virtual Reality Learning Environments In Science To Engage Secondary Students In Hazardous Activities, Luke Spartalis

Theses: Doctorates and Masters

This research examines the need for change in including hazardous activities in education. As different learning technologies develop, platforms that retain authentic outcomes via virtual reality are needed. The main objective of this research is to examine the inclusion of Virtual Reality Learning Environments (VRLEs) to determine their value in areas that include hazardous conditions. The research considered the ways that VR tools could be optimised to support these activities, as well as considering the challenges of VRLE implementation. The study examined whether VRLE’s allowed for authentic experiences to sufficiently drive an acceptance of VR to complement existing teaching and …

Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Graph shrinking has recently emerged as a powerful preprocessing technique for hybrid classical–quantum optimization, enabling variable and constraint reduction before quantum solving. Conventional approaches rely on Semi-Definite Programming (SDP) relaxations to compute vertex correlations, but these methods suffer from high computational overhead, instance-specific tuning, and limited generalizability. In this work, we replace the handcrafted SDP correlation stage with a reinforcement learning (RL) based correlation estimator, trained to predict merge quality directly from graph structure. We reformulate the graph shrinking process as a Markov Decision Process (MDP), design a Graph Neural Network (GNN) policy to guide vertex merging, and integrate the …

Food Recognition With Visual Language Models: Search Re-Ranking Or Retrieval-Augmented Generation?, Kian Yu Gan, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Despite the rapid advances in Visual Language Models (VLMs), these models struggle to recognize culture-specific food items. While VLMs are effective in recognizing popular cultural dishes, their performance is suboptimal for dishes that are unique but not widely known internationally. Specifically, VLMs often generate either generic labels or hallucinated names for dishes that are localized to a particular culture. As a result, retrieval-augmented generation (RAG), which retrieves relevant recipes as references for VLMs, emerges as a promising approach. Nevertheless, recipe retrieval, which is itself imperfect, could mislead VLMs into generating inaccurate or culturally inappropriate dish names. This paper presents a …

Analysis Theories On Artificial Intelligence, Chatgpt, Data Science, And Metaverse: The Case Of Digital Medicine, Yin Yang, Xingyun Liu, Jorge Luis Cuyubamba Dominguez, Yuan Fang, Wen Xie, Bairong Shen, Keng Siau

Research Collection School Of Computing and Information Systems

Healthcare organizations are increasingly adopting digital technologies, with Artificial Intelligence (AI), Data Science, and the metaverse driving significant advancements in smart healthcare. Al facilitates personalized medicine and efficient drug development, while Data Science enables predictive analytics and big data management, enhancing patient outcomes and healthcare quality. The metaverse introduces immersive training and telemedicine platforms, revolutionizing patient engagement and healthcare research. This study conducts' a scoping review of 6,171 articles, analyzing the transformational impact of AI, ChatGPT, Data Science, and the metaverse on healthcare. It highlights the benefits and risks of these technologies, identifies research gaps in their application within the …

Trajlens: Visual Analysis For Constructing Cell Developmental Trajectories In Cross-Sample Exploration, Qipeng Wang, Shaolun Ruan, Rui Sheng, Yong Wang, Min Zhu, Huamin Qu

Research Collection School Of Computing and Information Systems

Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial human effort due to the complex spatial correspondence between each pair of samples. To address this challenge, we first proposed a GNN-based model to predict cross-sample cell developmental trajectories. We then developed TrajLens, a visual analytics system that supports biologists in …

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Scaling Up Cooperative Multi-Agent Reinforcement Learning Through Hierarchical Heterogeneous Modular Architectures, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning enables sophisticated collaborative behaviors in autonomous systems, yet fundamental scalability barriers persist: existing methods struggle to coordinate large agent populations and face challenges with extended decision-making horizons. This research develops hierarchical approaches to scale up multi-agent learning systems through two complementary directions: structural scaling for coordinating increasing numbers of agents and temporal scaling for extending decision-making horizons. This paper presents four integrated contributions: a taxonomic survey establishing hierarchical architectures as the theoretical foundation for scalable multi-agent learning systems, a benchmark for long-horizon multi-objective multi-agent reinforcement learning, a framework integrating self-organizing neural networks with multiple reinforcement learning agents …

Revisiting The Canonicalization For Fast And Accurate Crystal Tensor Property Prediction, Haowei Hua Hua, Jingwen Yang, Wanyu Lin, Pan Zhou

Research Collection School Of Computing and Information Systems

Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining O(3) group tensor equivariance. Such equivariance constraint often requires specialized architecture designs to achieve effective predictions, inevitably introducing tremendous computational costs. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry. In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and …

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

Sketches, as a new solution in multimedia systems that can replace natural language, are characterized by sparse visual cues such as simple strokes that differ significantly from natural images containing complex elements such as background, foreground, and texture. This misalignment poses substantial challenges for zero-shot sketch-based image retrieval (ZS-SBIR). Prior approaches match sketches to full images and tend to overlook redundant elements in natural images, leading to model distraction and semantic ambiguity. To address this issue, we introduce a distraction-agnostic framework, purified cross-domain matching (PuXIM), which operates on a straightforward principle: masking and matching. We devise a visual-cross-linguistic (VxL) sampler …

Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma

Research Collection School Of Computing and Information Systems

Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this work, we present the first comprehensive empirical evaluation of LLMs for AVL. Our study examines 19 leading LLMs suitable for code analysis, including ChatGPT and multiple open-source models, spanning encoder-only, encoder-decoder, and decoder-only architectures, with model sizes from 60M to 70B parameters. We evaluate three paradigms including few-shot prompting, discriminative fine-tuning, and generative fine-tuning with and …

Interpretable Multimodal Zero Shot Ecg Diagnosis Via Structured Clinical Knowledge Alignment, Jialu Tang, Hung Manh Pham, Ignace De Lathauwer, Henk S. Schipper, Yuan Lu, Dong Ma, Aaqib Saeed

Research Collection School Of Computing and Information Systems

Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unseen conditions. To address this, we introduce ZETA, a zero-shot multimodal framework designed for interpretable ECG diagnosis aligned with clinical workflows. ZETA uniquely compares ECG signals against structured positive and negative clinical observations, which are curated through an LLM-assisted, expertvalidated process, thereby mimicking differential diagnosis. Our approach leverages a pre-trained multimodal model to align ECG and text embeddings without disease-specific fine-tuning. Empirical evaluations demonstrate ZETA’s competitive zero-shot classification performance and, importantly, provide qualitative and quantitative evidence of enhanced interpretability, grounding …

The Effectiveness Of Training On The Use Of Project-Based Learning Methods In Developing The Teaching Skills Of Middle School Science Teachers, Essa Dakheel Alrehaly

University of Tabuk Journal for Humanities and Social Sciences

The current research aimed to measure the effectiveness of training on using project-based learning methods in developing teaching skills among intermediate science teachers. To verify the research hypotheses, a cognitive test and an observation card were used. The experimental treatment materials included the training program in light of project-based learning methods to develop teaching skills among intermediate science teachers. The current research used the experimental method with a quasi-experimental design based on a single-group design with a pre-post application to determine the effect of an independent variable on a dependent variable. The research sample consisted of an experimental group of …

Exploit Machina, Andrea Matwyshyn

Faculty Scholarship

Over half a century ago, Hannah Arendt cautioned us to “think what we are doing” when we build new technologies. Engaging with her counsel and a set of historical case studies, this Article frames what it calls exploit machina problems. Exploit machina refers to situations where broken technologies and broken governance combine to irreparably harm the public. In other words, exploit machina involves organizational choices to knowingly leverage technology as part of legally problematic conduct, including various forms of fraud. In the language of data science, exploit machina situations implicate strategic decisions in building and managing artificial intelligence (AI); they …

Mary Cole Nibarger Wts Collection - Accession 1855, Mary Cole Parks Nibarger

Manuscript Collection

The Mary Cole Nibarger Collection consists of materials documenting Mrs. Mary Cole Parks Nibarger’s (1930-2021) teaching experience at the Winthrop Training School. Mrs. Nibarger was a 1951 Winthrop College graduate and was employed with the Winthrop Training School from September 1955 until its close in 1965. She served in many capacities including Supervisor of Mathematics and Freshman Class Sponsor. The collection reflects her commitment to education and dedication to the Winthrop Training School.

The collection includes yearbooks, class reunion correspondence, photographs, newspaper clippings, and miscellaneous items. The yearbooks document the students of WTS in the years 1956-1958, 1960, and 1962-1965. …

David Bancroft Johnson Personal Papers - Accession 2, David Bancroft Johnson

Manuscript Collection

This collection consists of the personal correspondence and papers of Dr. David Bancroft Johnson, founder and first president of Winthrop.  The letters address various subjects and people, both personal and professional, concerning issues from the original funding of Winthrop in 1886 to the minutiae of ordering flour and cotton seed, as well as personal letters to family, friends, and acquaintances. This collection also includes news clippings, brochures, programs, and various documents belonging to Dr. Johnson.  The letters and documents are by organized by each item chronologically.

High Temperature Creep Deformation Mechanism Of Fe28.2ni18.8mn32.9al14.1cr6 High Entropy Alloy And Its Modified Alloys, Edwin S. Jiang, I. Baker

Dartmouth College Ph.D Dissertations

Fe28.2Ni18.8Mn32.9Al14.1Cr6 eutectic high entropy alloy exhibits a good combination of room-temperature and high temperature properties, including tensile strength, ductility, and corrosion resistance, making it a promising candidate for structural applications in extreme environments. However, the creep deformation behavior of this alloy— and high-entropy alloys (HEAs) in general—remains insufficiently understood. This dissertation systematically investigates the high-temperature creep deformation mechanisms of Fe28.2Ni18.8Mn32.9Al14.1Cr6 and its derivatives.

Creep mechanisms and associated microstructural changes were examined across a wide range of strain rates using strain-rate jump and constant-stress tests. Two dominant regimes were identified: dislocation glide/solute-drag at low strain rates, and dislocation climb at high …

The Evolution Of School Accountability In Virginia: Research, Early Results, And Implications For Policy And Practice, David Naff, William L. Parker, Meleah Ellison, Amy Bollt, Andrea Herndon, Paula Muente Mizushima, T'Airra Belcher, Lauren Grob, Lindsay Duncan, Kelly Denny, Emily Leavitt, Gyun Min Jung, Theresa Thompson, Sandra Rucker, Meredith Collier, Jamelle Simmons, Samantha Hope

MERC Publications

This MERC research and policy review focuses on the School Performance and Support Framework (SPSF), Virginia's school accountability system. It synthesizes relevant research literature, student outcome data, and information about accountability systems in other states to provide context, projections, and potential next steps for the SPSF. It concludes with implications and recommendations for state policymakers and education leaders, school boards and division leaders, and school leaders and educators.

Exploring Resiliency, Identity, And Access: Educational Narratives Of Adults Who Experienced Youth Homelessness, Taylor Mestres

University of the Pacific Theses and Dissertations

This study explored the educational experiences of adults who experienced homelessness during their youth, focusing on how instability, stigma, and resilience shaped their K-12 schooling. Guided by Maslow's hierarchy of needs and resiliency theory, the research employed a narrative inquiry approach with seven adult collaborators who reflected on their schooling while experiencing housing instability. Seven themes emerged: living between places, school as sanctuary, concealment and stigma, resilience and survival, the role of educators and support systems, thriving despite adversity, and retrospective recognition. Findings reveal that while housing instability and shame created significant barriers to learning and belonging, schools also functioned …

Inspiring Voices: The Impact Of Poetry Education, Nancy Aide Gonzalez St.Clair

University of the Pacific Theses and Dissertations

Poetry education is frequently viewed as an enrichment activity rather than a critical practice that supports academic, emotional, and social development. This qualitative exploratory case study examined how participation in a nonprofit creative writing program in northern California influenced the cognitive development, emotional well-being, communication skills, empathy, and perspective taking of young adults. The study was guided by constructivism, radical constructivism, and symbolic interactionism. It centered on the lived experiences of six poetry program alumni aged 18 to 25. The researcher collected data through semi-structured Zoom interviews and poetry portfolios containing three poems selected by each participant. I used inductive …

Availability And Utilization Of School Library Resources And Its Impacts On The Academic Excellence Of Secondary School Students In Edo State., Iyobosa Ewere Mrs., Ben Asuquo, Glory Mbah Mrs.

Library Philosophy and Practice (e-journal)

The study examines availability, and utilization of school library resources and its impacts on the academic excellence in secondary school libraries in Edo state. The descriptive survey research was adopted for the study. The population of the study was 2162 senior secondary school students from Idia Girls’ College, Boiling Point Academy and UNIBEN Demonstration Secondary School. The sampling techniques used for the study was Random Sampling Technique, a total of 216 respondents was used as the sample size representing 10% of the total population. A well structured questionnaire was designed and distributed to respondents .A total of 216 copies of …

A Nuclear Terrorism Scenario: Improvised Nuclear Weapons With 60%-Enriched Uranium, Matt Caplan

Faculty publications – Physics

In this work we show that approximately 60–70 kg of 60%-enriched uranium can be used to build a crude nuclear weapon with a kiloton yield. While too large to fit on a missile, such a weapon could be delivered by a shipping container. This analysis is motivated by the June 2025 Israeli and US attacks on Iran, especially the bombings of the nuclear facilities at Natanz, Fordow and Isfahan. The Iranian stockpile of approximately 440 kg of 60%-enriched uranium is, at the time of writing, inaccessible to IAEA inspectors and stored in secret. The rapid clandestine relocation of this material …