Digital Commons Network^™

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …

Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics, Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu

Research Collection School Of Computing and Information Systems

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing "select-then-refine" pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Generative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we propose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. …

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 …

Airaclex: Automated Detection Of Price Oracle Manipulations Via Llm-Driven Knowledge Mining And Prompt Generation, Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu, Rick Siow Mong Goh, David Lo

Research Collection School Of Computing and Information Systems

Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure and fair transactions. However, poorly integrated oracles remain susceptible to manipulation, enabling attackers to exploit smart contract logic for unfair asset valuation and financial gain. While many such vulnerabilities are only detected after deployment, smart contracts are typically immutable once deployed, making post-hoc fixes costly or infeasible. This highlights the critical need for detecting oracle manipulation risks before deployment. In this paper, we propose AiRacleX, a novel LLM-driven framework that enables pre-deployment detection of price oracle manipulation vulnerabilities by leveraging the complementary strengths of multiple large language models …

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 …

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 …

Conduct Of Formative Assessment Of T.E.E.N.Dig Kabataan! Kalusugan Ay Pahalagahan Project, And Development Of Manual Of Procedures (Mop): Final Report, Zaldy C. Collado, Axell M. Alterado, Czarina Labayo-Prieto, Anjo Benedict Fabellon, Rodolfo T. Anacan Jr.

SDRC Projects

This report presents the findings of an independent formative assessment of the T.E.E.N.Dig Kabataan! Kalusugan ay Pahalagahan Project, an adolescent health initiative of the Department of Health (DOH)–CALABARZON Center for Health Development. The assessment examined the project's relevance, implementation coherence, effectiveness, sustainability, and potential for expansion across the five provinces of CALABARZON. Using a mixed-methods approach, the study combined policy and literature reviews, surveys involving 551 respondents, key informant interviews and focus group discussions with 93 participants, observations of 25 school- and community-based T.E.E.N.Dig Centers, and an analysis of adolescent health screening and referral records from 2023 to 2024.

Findings …

Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang

Dartmouth College Ph.D Dissertations

Intelligent systems increasingly anticipate which items a person will prefer, adapt generated text to a familiar style, and carry relevant information across interactions. These capabilities invite the impression that a model knows its user. In the technical sense studied here, a model knows a user when it carries information specific to that user, uses that information in the relevant context, and generalizes beyond the observations used to build the user state. Knowing is thus a measurable relation among user evidence, model state, context, and held-out user-specific behavior. The research community generally studies this family of systems under the term personalization, …

Brief For Jonathan H. Adler As Amicus Curiae Supporting Respondents And Affirmance, Jonathan Adler

Briefs

Global climate change presents many complex and intractable legal and policy questions. Assuming this Court has jurisdiction, the legal questions in this case are comparatively simple and straightforward. Nothing in federal law, let alone the Constitution, preempts state-law claims seeking relief from injuries allegedly caused or exacerbated by petitioners’ conduct.

At issue in this case is whether allegedly aggrieved parties, including subnational governments, may seek redress under state law from defendants otherwise subject to state court jurisdiction for harms allegedly caused or contributed to by petitioners’ conduct. In order to succeed, respondents will ultimately need to show that the damages …

Leading From Where You Are: How Projects Build Leadership Skills In Law Libraries, Susan Azyndar, Yan Yu

Journal Articles

Practical ways to develop leadership skills through collaborative projects–no title required.

Systematic Study On Preparation And Characterizations Of High Performance Bimetallic Pt-Co/Sba-15 Catalyst In N-Heptane Isomerization, Talib M. Albayati, Khalid A. Sukkar, Nisreen S. Ali, Mahir A. Abdulrahman, Basheer A. Abdulhussein, Issam K. Salih, Narges Elmi Fard

ASEAN Journal on Science and Technology for Development

Bimetallic catalysts were prepared by deposition of 1% of loading of (Pt-Co) onto SBA 15 support using heterogeneous reaction for enhancing hydroisomerization and promoting hydrocracking for the decomposition of n-heptane. The catalytic performance of the catalysts was subsequently evaluated thereafter. Characterization of the prepared catalysts was done by various techniques, which include XRD, FTIR, BETBJH, SEM/EDS, and TEM. The temperature range studied for this catalytic activity was 250 to 400 °C, at atmospheric pressure in a plug-flow reactor. The experimental setup kept a very rigorous control over the temperature, flow rate for the reactants, and pressure, while gas chromatographic techniques …

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

Multiclass Skin Lesions Classification And Early Skin Cancer Detection Using An Attention-Enhanced Xception Architecture, Shamira Arshad Shaikh, Yogita Mistry

ASEAN Journal on Science and Technology for Development

Skin disorders are considered one of the major healthcare problems globally and need proper, prompt, effective and early diagnosis to enhance clinical practice. Traditional machine learning and deep learning approaches often exhibit limited generalization capability and reduced effectiveness in extracting discriminative features from complex multiclass dermatological datasets. For this purpose, an interpretable attention-enhanced Xception framework for automatic multiclass skin disease diagnosis based on dermoscopic images has been designed through the use of the International Skin Imaging Collaboration (ISIC) dataset. The dataset includes 2,357 dermoscopic images that belong to nine classes of skin lesions. The proposed framework integrates channel attention and …

Acute Correction Of Acquired Genu Valgum Secondary To Post-Traumatic Growth Arrest At The Time Of Distal Femur Fracture., Josiah Wolf, Nicholas Pelz, Adam Mansour, Christopher Iobst, Allen Kadado

Surgery Articles

A 14-year-old male patient sustained a distal femur fracture after a ground-level fall. His history was notable for severe acquired genu valgum secondary to a childhood lawnmower accident, which had required multiple limb salvage procedures including a Syme amputation and soft-tissue skin grafting. Acute correction of the limb deformity was performed concurrently with fracture fixation using a retrograde intramedullary nail and poller blocking screws. Retrograde intramedullary nailing represents a viable treatment option for simultaneous acute correction of acquired genu valgum and stabilization of distal femur fractures.

Faculty Perceptions Of Engaging Generation Z Students Through Positive Education In The Classroom, Ashley D. Ragsdale

Electronic Theses and Dissertations

This qualitative study examined the engagement gap between Generation Z students and faculty in higher education, emphasizing how positive education approaches can help faculty become more confident and effective instructors. Generation Z students prefer instant internet access and digital learning, have shorter attention spans, and tend to be disengaged when instructors rely on traditional teaching methods. After the COVID-19 pandemic, this engagement gap became more evident. This basic qualitative study used semistructured interviews, reflexive journaling, and nonparticipatory observations. Participants included full-time health science faculty at a private university in central Texas. Each had at least 2 years of teaching experience …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Changepoint Analyses Confirms Global Tropical Cyclone Frequency Decline, Michael Wehner, Thomas Fisher, Norou Diawara, Robert Lund

Mathematics & Statistics Faculty Publications

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

A Composite Narxnn Approach To Photovoltaic Power Forecasting With Integrated Weather Inputs And Uncertainty Quantification, Denisse Urenda Castañeda, Sharmin Abdullah, Jackson Morgan, Honglun Xu, Michael Pokojovy, Tzu-Liang Tseng

Mathematics & Statistics Faculty Publications

Solar photovoltaics (PV) are a major source of sustainable energy. Yet, their power output is highly sensitive to environmental variability, particularly solar irradiance, cloud cover, wind, and temperature. Accurate forecasting of PV power is essential for efficient grid integration and energy planning, especially in applications requiring reliable longer-term forecasting rather than one-step-ahead predictions. This study presents a PV power forecasting approach using Nonlinear Autoregressive models with Exogenous Inputs (NARX), integrating large-scale numerical weather historical data as exogenous variables. Although NARX models effectively capture temporal dependencies, they can become overly dependent on historical power values, reducing responsiveness to real-time weather changes. …