Digital Commons Network^™

The Lesson Of Confucius For Today’S America: Education In Ethics Is The Road To Building A Just And Peaceful Society, Yeomin Yoon

KAUPA Letters

According to Confucius, what confers humans their special status and dignity among beings in nature is the moral virtues they can achieve. Humans should build good character, for which they need education to acquire values. He found no correlation between the ability to learn and the socio-economic status. He believed that people with the ability to learn deserved an opportunity to learn. Confucius would not approve of the prevalent American conception of education as transmitting technical knowledge and skills to students without an education in the values to promote by their use. Purely technical education would be an oxymoron for …

Why English Matters: The Case For Majoring In It, John J. Han

KAUPA Letters

No abstract provided.

The Challenges Of Academic Publishing, Young B. Choi

KAUPA Letters

No abstract provided.

Words Of Wisdom, Semoon Chang

KAUPA Letters

No abstract provided.

We Need Another Voice — Taoism To Zen Buddhism, Don S. Lee

KAUPA Letters

No abstract provided.

Unlocking Private Equity Investing: A Primer, Yin Wang, Steve Balaban

Asian Management Insights

Private equity might provide higher returns, but investors should be aware of the associated risks.

Building Urban Resilience, Winston T. L. Chow

Asian Management Insights

Cities can lead on services, resources, and partnerships.

Pacific Rxtracts - June 2025, Pacific Rxtracts

Pacific RxTracts

Articles:

• Atzumi Receives FDA Approval as First Nasal Powder DHE for Migraine Treatment
• IMAAVY: First and Only FDA-approved Neonatal Fc Receptor Inhibitor for gMG
• New Prophylactic Treatment Approved for Hemophilia A or B, With or Without Factor Inhibitors
• Cytisinicline: The Next Smoking Cessation Aid
• FDA Approves Dupilumab for the Management of Chronic Spontaneous Urticaria
• Study Shows Promise in Combining Behavioral Treatment with Phentermine
• Metformin for Osteoarthritis
• A New Direction in Atopic Dermatitis Treatment
• Orforglipron: A Potential Oral Alternative to Injectable GLP-1 Agonists
• Beyond GLP-1s: MariTide’s Dual Mechanism in Weight Loss Therapy
• Atrasentan Gains FDA Approval to Combat Proteinuria in Primary …

Aeroallergen Exposure As Short-Term Predictor Of Respiratory Viral Infections In Two Health Regions (Caguas And San Juan) In Puerto Rico: A Seasonal And Machine Learning Approach, Félix E. Rivera-Mariani, Armando J. Borrero-Aponte, Benjamín Bolaños-Rosero

Faculty and Staff Publications & Presentations

In this late-breaking abstract, we investigated whether seasonal and short-term variations in airborne fungal spores and pollen are associated with, and predictive of, influenza and COVID-19 incidence in the San Juan and Caguas health regions of Puerto Rico from 2022 to 2024. Using correlation analyses, lag modeling, logistic regression, and machine learning approaches, the study found that fungal spore concentrations—but not pollen—were consistently associated with and predictive of high-incidence influenza and COVID-19 days, particularly during the fall season. Peak associations occurred at short lags of 2–4 days, and random forest models demonstrated strong predictive performance for both influenza and COVID-19 …

Associations Between Vitamin D Deficiency And Sociodemographic Factors, Lifestyle Behaviors, And Metabolic Conditions: A Cross-Sectional Study From The Hnnhs In Greece, Asmae Mohamad Sadek

Theses

Vitamin D deficiency (VDD) is a widespread global health concern associated with various sociodemographic, lifestyle, dietary, and metabolic factors. While previous studies examined VDD in Greece, few provided a comprehensive analysis across multiple determinants using representative national data. This study aimed to assess the prevalence and predictors of VDD among Greek adults and to examine its associations with sociodemographic, dietary, behavioral, and metabolic variables using data from the Hellenic National Nutrition and Health Survey (HNNHS). A cross-sectional analysis was conducted among 978 adults (≥19 years) from the HNNHS. Serum 25-hydroxyvitamin D [25(OH)D] concentrations < 20 ng/mL were defined as deficient and ≥20 ng/mL as normal. The median age of participants was 40.2 years, and 62.0% were female. The overall prevalence of VDD was 46.0%, with the highest rate observed among adults aged ≥60 years (62.2%). VDD was significantly associated with older age (p < 0.001), retirement status (62.9% among retired; p < 0.001), low and sedentary physical activity (32.7% and 32.6% vs. 50.8% among highly active; p < 0.001), higher HDL (median 52.0 vs. 49.0 mg/dL among VDD vs Normal; p = 0.001), elevated serum calcium (median 9.4 vs. 9.2 mg/dL among VDD vs Normal; p = 0.020), and lower insulin levels (median 7.5 vs. 8.0 μIU/mL among VDD vs Normal; p = 0.044). Dietary intake (total energy and macronutrients) and body composition were not significantly associated with VDD. In the multivariate model, significant independent predictors of VDD included being retired (AOR = 2.383; 95% CI: 1.596–3.558; p < 0.001), having higher serum calcium levels (AOR = 1.563; 95% CI: 1.078–2.266; p = 0.019), and higher HDL levels (AOR = 1.016; 95% CI: 1.005–1.027; p = 0.001). Physical activity level was also a key determinant; participants classified as sedentary or low active had significantly higher odds of deficiency compared to highly active individuals (AOR = 0.468; 95% CI: 0.319–0.690; p < 0.001). This counterintuitive result may be influenced by factors such as increased indoor physical activity, and warrants further investigation. Dietary intake and body composition were not significantly associated with VDD. The findings underscore the importance of incorporating sociodemographic and lifestyle factors into prevention strategies. Interventions should particularly focus on older adults, retired individuals, even if they have higher HDL and calcium levels, and are physically active.

Development Of A Machine-Learning-Driven Digital Teaching Assistant That Utilises Student Engagement Data To Improve Access To And Success In K-12 Stem Education, Kelly Shreeve, Kathryn Jessen Eller, Michael Cassidy, Beth Price, Brandy Jackson, Anthony M. Perry, Leo Celi, Ismini Lourentzou, Luk Hendrik

Teaching, Leadership & Professional Practice Faculty Publications

Student engagement is a key predictor of academic achievement and is closely linked to career awareness, interest, and preparedness. Measuring student engagement during STEM learning is challenging for teachers, given the dynamic and ever-changing nature of these learning environments. Even when engagement data can be collected, leveraging this information to refine and personalise instruction requires significant experience and time. To address this, we are developing Scoutlier EngagEd, a digital teaching assistant that embeds in existing Learning Management Systems (LMS) to automatically and invisibly gather multidimensional data on student engagement and performance during STEM learning. These data are being leveraged to …

Predictors Of Nursing Students' Stress, Anxiety, And Depression During The Covid-19 Pandemic In A Hispanic-Serving University In South Texas: A Cross-Sectional Study, Maria I. Diaz, Eleftherios Gkioulekas, Nancy Nadeau

School of Mathematical & Statistical Sciences Faculty Publications

Background: In nursing education, there have been several studies on the impact of the COVID-19 pandemic on the ability of nursing students to cope while in nursing school.

Purpose statement: The goal of this study is to assess undergraduate nursing students' support mechanisms as predictors of stress, anxiety, and depression during the COVID-19 pandemic within a Hispanic-serving institution in South Texas.

Methods: Across-sectional design was used in this study. An online survey using self-reported questionnaires was used to gather data from an undergraduate nursing student cohort during the Fall 2021 semester. Linear regression was used to identify the predictors of …

Facial Recognition Ai, Margaret Hu

Faculty Publications

The integration of algorithmic decisionmaking and artificial intelligence (“AI”) into facial recognition technology poses new, unprecedented risks to privacy and individual autonomy rights, particularly in urban settings. The murder of Brian Thompson, CEO of UnitedHealthcare, in New York City on December 4, 2024, provides a timely case study to examine the deployment of facial recognition systems by the New York Police Department and other law enforcement agencies to identify the suspect. New York City deploys some of the most sophisticated surveillance architecture in the nation, put into place following the terrorist attacks of September 11, 2001. This Article explores the …

The Financial Effects Of Enrolling Students With Disabilities In Catholic High Schools In States That Offer Vouchers Or Scholarships For Students With Disabilities, Susan M. Noonan

Electronic Theses and Dissertations

This study investigated the financial effect of enrolling students with disabilities in Catholic high schools located in states that offer vouchers or scholarships for these students. Furthermore, the research aimed to understand the school’s experience in collaborating with the U.S. Department of Education to meet the criteria for voucher acceptance. It is important to note that enrollment and tuition revenue is the key to a school’s financial stability. Using an exploratory case study approach, information was gathered from a variety of stakeholders including the superintendent, administration, business manager, admissions coordinator, teachers, and counselors. The findings indicated that parents of children …

A Standardized Bathing Protocol To Prevent Central Line–Associated Bloodstream And Methicillin-Resistant Staphylococcus Aureus Infections, Lindsy L. Phillips

Electronic Theses and Dissertations

Central line–associated bloodstream infections (CLABSIs), including methicillin-resistant Staphylococcus aureus (MRSA) infections, in medical–surgical inpatients increase mortality, length of stay, and readmissions. In this project, the investigator sought to decrease CLABSIs and MRSA infections through quality improvement by implementing a standard chlorhexidine gluconate (CHG) bathing process. An urban hospital in the Midwestern United States experienced more CLABSIs and MRSA infections than expected in 2024. This unit had a standardized infection ratio of 1.4, with three CLABSIs rather than the 2.1 predicted. Also, they had 10 hospital-acquired MRSA isolates, which was higher than the 3.3 average of the other medical–surgical units. The …

Exploring New Mexico 4-H Volunteers’ Acceptance And Use Of Technology: A Qualitative Approach, Kayla Dawn Hinrichs

Electronic Theses and Dissertations

Integrating technology into youth development programs like 4-H is increasingly vital for enhancing communication, education, and engagement. However, technology adoption among 4-H volunteers remains inconsistent. This qualitative study explored the barriers to technology adoption among New Mexico 4-H volunteers. The researcher aimed to understand the factors influencing their willingness and ability to integrate digital tools into their volunteer roles. Guided by Rogers’s (2003) diffusion of innovation theory and framed by Creswell and Poth’s (2024) qualitative research design, this study employed semistructured interviews with a purposive sample of 4-H volunteers across the state. Following Braun and Clarke’s (2006) six- phase approach, …

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …

Runtime Backdoor Detection For Federated Learning Via Representational Dissimilarity Analysis, Xiyue Zhang, Xiaoyong Xue, Xiaoning Du, Xiaofei Xie, Yang Liu, Meng Sun

Research Collection School Of Computing and Information Systems

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify …

A Multimodal Fusion Model Leveraging Mlp Mixer And Handcrafted Features-Based Deep Learning Networks For Facial Palsy Detection, Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segments) and a feed-forward neural network to process structured data (i.e. facial landmark coordinates, features of facial expressions, or handcrafted features) for detecting facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of …

Event-Based Eye Tracking: Event-Based Vision Workshop 2025, Qinyu Chen, Et. Al.

Research Collection School Of Computing and Information Systems

No abstract provided.

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Based on ten semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for …

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

Outperforming The Best With Minimal Effort: Algorithm Selection For Constrained Multi-Objective Optimization, Mustafa Misir, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The present study performs algorithm selection on a suite of optimization algorithms targeting the constrained multi-objective optimization problems. The idea is to utilize the existing, relevant algorithmic experience in the literature to deliver an improved solver with limited effort. The reason being that algorithm development, in general, is a challenging and time-consuming process, especially with the goal of outperforming the existing methods from varying perspectives such as performance, speed, and robustness. Concerning the multi-objective optimization problems, the required development efforts happen to be even harder than addressing the single-objective ones. Furthermore, referring to the fact that the number of candidate …

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang

Research Collection School Of Computing and Information Systems

AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …

De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do not produce any error messages. To address this problem, multiple white-box techniques were proposed, but they are inapplicable in many practical scenarios. Black-box techniques are more practical, but the existing ones are less effective as they often rely on irrelevant syntactic information. To bridge this gap, we propose a novel black-box technique (BLADE), which …

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 2, Ten In-Depth Interviews, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

On-Demand Scenario Generation For Testing Automated Driving Systems, Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin, Yonggang Luo, Jucheng Yang, Ming Fan, Chao Yang, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Traditional testing approaches often prioritize either natural scenario sampling or safety-critical scenario generation, resulting in overly simplistic or unrealistic hazardous tests. In practice, the demand for natural scenarios (e.g., when evaluating the ADS's reliability in real-world conditions), critical scenarios (e.g., when evaluating safety in critical situations), or somewhere in between (e.g., when testing the ADS in regions with less civilized drivers) varies depending on the testing objectives. To address this issue, we propose the On-demand Scenario Generation (OSG) Framework, …

A Comprehensive Study Of Oop-Related Bugs In C++ Compilers, Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from …