Digital Commons Network^™

Evaluating And Mitigating Linguistic Discrimination In Large Language Models: Perspectives On Safety Equity And Knowledge Equity, Guoliang Dong, Haoyu Wang, Jun Sun, Xinyu Wang

Research Collection School Of Computing and Information Systems

By training on text in various languages, large language models (LLMs) typically possess multilingual support and demonstrate remarkable capabilities in solving tasks described in different languages. However, LLMs can exhibit linguistic discrimination due to the uneven distribution of training data across languages. That is, LLMs are hard to keep the consistency of responses when faced with the same task but depicted in different languages. In this study, we first explore the consistency in the LLMs’ outputs responding to queries in various languages from two aspects: safety and quality. We conduct this analysis with two datasets (AdvBench and NQ) based on …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

Beware Of Your Po! Measuring And Mitigating Ai Safety Risks In Role-Play Fine-Tuning Of Llms, Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …

Browsing Like Human: A Multimodal Web Agent With Experiential Fast-And-Slow Thinking, Haohao Luo, Jiayi Kuang, Wei Liu, Ying Shen, Jian Luan, Yang Deng

Research Collection School Of Computing and Information Systems

Automating web navigation which aims to build a web agent that follows user instructions to complete tasks like booking flights by interacting with websites, has received increasing attention due to its practical value. Although existing web agents are mostly equipped with visual perception, planning, and memory abilities, their reasoning process are still deviate from human cognition. In this work, we study the human thought pattern to empower agent with more human-like abilities in web navigation. To tackle this problem, we propose a novel multimodal web agent framework called WebExperT, which is designed to emulate the human planning process of “thinking …

Optimizing Group Utility In Itinerary Planning: A Strategic And Crowd-Aware Approach, Junhua Liu, Aldy Gunawan, Kristin L. Wood, Kwan Hui Lim

Research Collection School Of Computing and Information Systems

Itinerary recommendation is a complex sequence prediction problem with numerous practical applications. The task becomes significantly more challenging when optimizing multiple factors simultaneously, such as user queuing times, crowd levels, attraction popularity, walking durations, and operating hours. These factors, combined with the dynamic and unpredictable nature of visitor flow, introduce substantial complexities, particularly when accounting for collective user behavior. Existing solutions often adopt a single-user perspective, overlooking critical challenges arising from natural crowd dynamics. For example, the Selfish Routing problem illustrates how individual decision-making can lead to suboptimal outcomes for the group as a whole. To address these challenges, we …

Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee

Research Collection School Of Computing and Information Systems

The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites.In this project, our international and interdisciplinary team will collaborate with …

Advancing Molecular Graph-Text Pre-Training Via Fine-Grained Alignment, Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi

Research Collection School Of Computing and Information Systems

Understanding molecular structure and related knowledge is crucialfor scientific research. Recent studies integrate molecular graphswith their textual descriptions to enhance molecular representationlearning. However, they focus on the whole molecular graph andneglect frequently occurring subgraphs, known as motifs, whichare essential for determining molecular properties. Without suchfine-grained knowledge, these models struggle to generalize to un-seen molecules and tasks that require motif-level insights. To bridgethis gap, we propose FineMolTex, a novel Fine-grained Moleculargraph-Text pre-training framework to jointly learn coarse-grainedmolecule-level knowledge and fine-grained motif-level knowledge.Specifically, FineMolTex consists of two pre-training tasks: a con-trastive alignment task for coarse-grained matching and a maskedmulti-modal modeling task for …

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

Zero-Shot Generalist Graph Anomaly Detection With Unified Neighborhood Prompts, Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight …

Induced Helical Anisotropy In Permalloy Thin Films And Patterns, Md Mahmudul Hasan

LSU New Orleans Theses and Dissertations

The goal of this research was to create thin films with induced helical anisotropy and investigate magnetic properties of such films and their patterns. An alloy of 20% iron and 80% nickel (permalloy) was used because of very small magnetocrystalline anisotropy and magnetostriction. First, an unconventional method was used to deposit films with uniaxial anisotropy by sputtering the films onto silicon substrates in the presence of a uniform field of 50 Oe along the substrate plane. This resulted in magnetic atom pairs ordering along direction of the field during film growth. Shapes of hysteresis loops measured at different angles were …

Funds Of Knowledge And The Transition To School: A Scoping Review, Amelia Ruscoe, Leanne Lavina, Lennie Barblett, Fiona Boylan

Research outputs 2022 to 2026

Children's funds of knowledge (FoK) were recognised in the initial iteration of the Australian Early Years Learning Framework (EYLF) in 2009, the mandated framework for educator decision-making, and their significance was further emphasised in EYLF 2.0 in 2022. However, a unified understanding of children as knowledgeable individuals and how to effectively incorporate their diverse knowledges into early childhood education is still evolving. Recent evidence highlights the link between engaging with children's FoK and supporting successful transitions to formal schooling. Given that childhood is experienced differently across cultures and contexts, achieving consensus on the nature and value of FoK remains a …

Incorporating Ecosystem Service Assessments Into Development Planning − Impact From A Dredging Project In South Australia On Seagrass, Sam Gaylard, Rachel Colella, Matt Nelson, Paul Lavery, Michelle Waycott

Research outputs 2022 to 2026

Major infrastructure development is required for economic development and to improve human well-being, however conflict exists between developers and the community. Environmental impact assessment (EIA) is used in over 100 countries to evaluate potential impacts of major developments across environment, economy, and social benchmarks. However, EIA has been criticized for a lack of transparency and accountability, lack of consultation or participation and inadequate science. An ecosystem service assessment (ESA) recognizes the links between the environment and the socio-economic environment, resulting in a more holistic evaluation of potential impacts and effective community consultation. Despite this, its inclusion within EIA's is rare. …

Cultural And Socioeconomic Determinants Of Hip And Knee Arthroplasty In The Medically Underserved Rio Grande Valley Community, Blake C. Martin, Juan C. Lopez Alvarenga, John M. Gaddis

School of Medicine Publications

Introduction: Joint arthroplasty is a common procedure that is increasing worldwide. In this study, we aimed to discover if there were differences in demographics and social factors associated with individuals undergoing total knee arthroplasty (TKA) and total hip arthroplasty (THA) in the Rio Grande Valley (RGV). We hypothesized that older individuals and those with higher body mass index (BMI) would have an increased risk of TKA.

Methods: We conducted a retrospective chart review using the University of Texas Rio Grande Valley UTHealth electronic database from January 1, 2018, to July 1, 2024. Individuals were selected using Current Procedural Terminology codes …

Does Teach Lead To More Teachers? Evaluating The Effects Of The Federal Teach Program, Daniel Sparks

Counseling, Leadership, and Research Methods Faculty Publications and Presentations

Teachers are critical to education production. Yet, recruiting and retaining teachers has remained challenging, particularly in high need fields, at schools serving a high proportion of students from low-income backgrounds, and from racially diverse backgrounds. To incentivize enrollment in teacher preparation programs and to support teacher candidates through degree completion, the federal government implemented the Teacher Education Assistance for College and Higher Education (TEACH) Grant in 2008. The TEACH Grant offers undergraduate and graduate students up to $16,000 and $8,000 in grant aid, respectively, to pursue degrees in high need teaching fields. I use difference-in-differences analyses to estimate the effects …

Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …

Visceral, Neural, And Immunotoxicity Of Per- And Polyfluoroalkyl Substances: A Mini Review, Pietro Martano, Samira Mahdi, Tong Zhou, Yasmin Barazandegan, Rebecca Iha, Hannah Do, Joel Burken, Paul Ki-Souk Nam, Qingbo Yang, Ruipu Mu

Chemistry Faculty Research & Creative Works

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluoro butane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent …

Comparative Nitrene-Transfer Chemistry To Olefins Mediated By First-Row Transition Metal Catalysts Supported By A Pyridinophane Macrocycle With N4 Ligation, Himanshu Bhatia, Lillian P. Adams, Ingrid Cordsiemon, Suraj Kumar Sahoo, Amitava Choudhury, Thomas R. Cundari, Pericles Stavropoulos

Chemistry Faculty Research & Creative Works

A 12-membered pyridinophane scaffold containing two pyridine and two tertiary amine residues is examined as a prototype ligand (^tBuN4) for supporting nitrene transfer to olefins. The known [(^tBuN4)M^II(MeCN)2]²⁺ (M = Mn, Fe, Co, and Ni) and [(^tBuN4)Cu^I(MeCN)]⁺ cations are synthesized with the hexafluorophosphate counteranion. The aziridination of para-substituted styrenes with PhI=NTs (Ts = tosyl) in various solvents proved to be high yielding for the Cu(I) and Cu(II) reagents, in contrast to the modest efficacy of all other metals. For α-substituted styrenes, aziridination is accompanied by products of aziridine …

Ai In The Judiciary: The Singapore Case, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This paper examines the integration of Artificial Intelligence (AI) within the judicial system of Singapore. Singapore's judiciary has embraced AI not as a tool for adjudication, but as an augmentative instrument for legal research, procedural efficiency, and access to justice. It provides a detailed account of AI use cases in the courts, including case summarization, evidence review, assistance for selfrepresented litigants, and tools like the Divorce Assets Informative Division Estimator. The discussion then turns to the legal profession, exploring how law firms in Singapore are adopting AI technologies. The paper also addresses how AI implementation in the judicial system is …

Crystal Structures Of Escherichia Coli Glucokinase And Insights Into Phosphate Binding, Joseph Andrews, Joshua Sakon, Chenguang Fan

Chemistry & Biochemistry Faculty Publications and Presentations

Here, we report the crystal structure of Escherichia coli glucokinase (GLK), which has phosphate bound in the cleft between the alpha and beta domains adjacent to the active site. A ternary complex consisting of GLK, glucose and phosphate is also reported in this work. Diffraction data were collected at 2.63 angstrom resolution for the phospate-bound form (R-work/R-free = 0.191/0.230) and at 2.54 angstrom resolution for the ternary complex (R-work/R-free = 0.202/0.258), both at 297 K. A B-factor analysis of the phosphate-bound GLK structure revealed consistently lower values for phosphate-interacting basic residues in the alpha 4, alpha 5 and alpha 9 …

The Effect Of The Markle Mill Dam Removal On The Habitat And Riverine Food Web Of Otter Creek, Jenna Blanton

All-Inclusive List of Electronic Theses and Dissertations

Across the United States, dam removals are increasing in frequency. Despite this, the effects of dam removal on freshwater food webs are under-studied. Most existing studies investigate the impacts on fish assemblages, and very few investigate the impact on diatom, crayfish, and turtle communities. Furthermore, low-head dams are more abundant than large hydropower dams, yet the impacts of their removal are less studied than their larger counterparts. I used a Before After Control Impact (BACI) study design to survey populations of benthic diatoms, riffle-dwelling crayfish, and aquatic turtles. Species richness, species evenness, and Shannon Diversity Index values were used to …

Advancing The Utility Of Unmanned Aerial Systems (Uas)-Based Imaging Techniques In Broadacre Agriculture: A Multimodal Case Study On Table Beets, Mohammad Shahriar Saif

Theses

Efficient and sustainable food production and management are growing concerns in the context of an ever-increasing global population. It is in this context that the integration of remote sensing with advanced imaging technologies presents transformative opportunities for data-driven decision-making in agriculture. This study therefore explores the use of unmanned aerial systems (UAS) equipped with multispectral, hyperspectral, and LiDAR sensors for the non-destructive monitoring of table beet (Beta vulgaris) crop traits, with a specific focus on root yield estimation and foliar disease assessment. Table beet, a subterranean crop of increasing commercial and nutritional importance, poses unique challenges for above-canopy sensing due …

Towards A Foundational Framework For Real-World Active Learning: Theory, Algorithms, And Applications, Dayou Yu

Theses

While supervised learning has seen great success in the modern machine learning era, the challenge of obtaining high-quality labeled training data still exists. In many knowledge-rich domains, we still face the problem of letting machine learning models learn well using a limited number of labels. Active learning (AL) has been a prominent learning paradigm that deals with such problems. This thesis reviews the classical challenges of AL, summarizes how our prior work has advanced the field, and charts a course for adapting AL to realistic and challenging scenarios. We begin by discussing past work on standard scenarios for AL, including …

On The Adaptation Of Latent Dynamics Models, Ryan Missel

Theses

Predicting future states of high-dimensional, partially observed dynamical systems - such as cardiac electrical propagation - is crucial for advancing fields like healthcare and physics. While univariate time-series forecasting is well-explored, high-dimensional time-series forecasting presents unresolved challenges. These challenges include the computational burden of processing high-dimensional data and the difficulty of accessing the system’s underlying dynamics directly. Classical optimization and analytical approaches become impractical as the dimensionality increases, leading to a growing interest in data-driven deep learning models, particularly those based on latent dynamics functions. Latent dynamics models provide an efficient way to map high-dimensional observations into lower-dimensional latent spaces, …

Retrofitting An Fdm Printer For 3d Printing With Cotton Yarn: A Novel Approach And Mechanical Property Evaluation, Muhammad Aghead Al Arnaout

Theses

The rapid rise of 3D printing in industry has intensified research into developing advanced and sustainable printing materials, as the properties of the feedstock directly influence the performance of the final product. This thesis investigates the integration of natural cotton yarn into PLA (Polylactic Acid) to enhance mechanical behavior while maintaining cost-effectiveness, using a standard 3D printer without any modifications. Experimental results across three phases demonstrated consistent improvements. In the first phase, cotton–PLA composites exhibited higher ductility (+22.27%) and toughness (+10.51%) than pure PLA, albeit with lower stress values and a slight decrease in Young’s modulus. In the second phase, …

Handling Skewness And Directional Tails In Model-Based Clustering, Cristina Tortora, Antonio Punzo, Brian C. Franczak

Faculty Research, Scholarly, and Creative Activity

Model-based clustering is a powerful approach used in data analysis to unveil underlying patterns or groups within a data set. However, when applied to clusters that exhibit skewness, heavy tails, or both, the classification of data points becomes more challenging. In this study, we introduce two models considering two component-wise transformations of the observed data within a mixture of multiple scaled contaminated normal (MSCN) distributions. MSCN distributions are designed to enable a different tail behavior in each dimension and directional outlier detection in the direction of the principal components. Using the transformed MSCN distributions as components of a mixture, we …

Performance Of The Two Sample Likelihood Ratio Test Under A Nested Dirichlet: A Simulation Study, Edwina Agyeman

Electronic Theses and Dissertations

Compositional data analysis (CoDA) addresses multivariate data constrained to a constant sum, such as proportions or percentages. Originating from early warnings regarding misinterpretation by Pearson (1897), the field was formalized by John Aitchison in 1986, whose foundational work remains highly influential. Over time, new modeling techniques and visualization tools have advanced the field, as noted by Greenacre et al. More recently, Turner et al. proposed an approach based on the Nested Dirichlet Distribution (NDD), which accommodates more flexible dependence structures than the standard Dirichlet model. This thesis builds on the methodology of Turner et al. Chapter 1 introduces the nature …

Evaluating Alpha Spending Functions Applied To Observational Time-To-Event Analysis, Moses Torgbenu

Electronic Theses and Dissertations

This thesis explores the theoretical foundation of the alpha spending approach and extends its application beyond the conventional setting of randomized controlled trials (RCTs) to observational studies with time to event analyses. In these less structured environments, key design parameters such as the total number of events are often unknown, posing challenges for the standard implementation of sequential analysis methods.

Through simulation studies, this research delivers several important contributions. First, it presents a modified approach that uses calendar time to define the timing of interim analyses while relying on event-based information to estimate the correlation among test statistics. This adjustment …

Simultaneous Application Of Multiple Process Control Rules, Tran B. Ngo

Electronic Theses and Dissertations

Statistical Process Control (SPC) charts are tools used in quality control to monitor and analyze the stability of a process over time. This study evaluates the effectiveness of eight individual Western Electric rules, also known as WECO rules, and the various combinations of these rules with Shewhart rule (or WECO rule 1) to SPC charts. As more rules are added to a process control scheme with Rule 1, there is a trade-off: a higher false out-of-control signal rate but an increase in sensitivity, that is the ability of a specified process control scheme to capture a true out-of-control signal. This …

Guided Modes Through The Dual Perspectives Of Geometrical Optics And Wave Equation, Mason Spears

Mathematics and Statistics Undergraduate Departmental Honors Theses

In an optical waveguide, only finitely many electric field intensity profiles travel without loss, and these are aptly named guided modes. This paper uses fundamental concepts from the two paradigms of interpreting light, geometrical and physical optics, to derive guided modes axiomatically. First, basic principles of geometrical optics are demonstrated in a slab waveguide to explain characteristics of optical waveguides such as total internal reflection and numerical aperture. Dispersion curves are assembled from the same principles and used to show that only finitely many guided modes exist for a given waveguide. Next, Maxwell’s equations are used to derive the famous …

Analyzing Sports Motion Using Calculus, Joshua Vidal, Maryam Khazaei Pool

Faculty Research, Scholarly, and Creative Activity

Calculus helps analyze sports motion by modeling a ball's vertical path as a quadratic fuction, capturing initial velocity, gravity, and height. Differentiating this function reveals velocity and acceleration, key to finding peak height, time to peak, and airtime. Using Python, we can accurately calculate and visualize these motions. Linear algebra further connects player skills like strength, technique, and accuracy to kick distance, enabling performance prediction and comparision.