Digital Commons Network^™

Towards A Digital Twin For Smart Resilient Cities: Real-Time Fire And Smoke Tracking And Prediction Platform For Community Awareness (Firecom), Kijin Seong, Junfeng Jiao, Ryan Lewis Hardesty, Arya Farahi, Paul Navratil, Nate Casebeer, Braniff Davis, Justice Jones, Dev Niyogi

Research Collection College of Integrative Studies

This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and …

Multi-Period Risk-Aware Procurement Optimization Under Covid-19 Disruption, Jonathan Chase, Hoong Chuin Lau, Jinfeng Yang, Lu Liu

Research Collection School Of Computing and Information Systems

Supply chain resilience has been a topic of active research in the operations research and AI communities for several years, but the COVID-19 pandemic threw the frailties of global supply chains into sharp relief. Disruptions and delays caused by fresh outbreaks leading to lockdowns, put severe strain on supply chains in many industries. In this work we develop lockdown-resilient procurement capabilities for a global technology company. First, through analysis of lockdown data from China we develop a logarithmic regression-based lockdown prediction method to complement a supplier risk metric for conventional risks. Second, we develop a multi-period stochastic optimization model that …

Memory-Efficient 4-Bit Preconditioned Stochastic Optimization, Jingyang Li, Kuangyu Ding, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce 4-bit quantization for Shampoo’s preconditioners. We introduce two key methods: First, we apply Cholesky decomposition followed by quantization of the Cholesky factors, reducing memory usage by leveraging their lower triangular structure while better preserving spectral properties to minimize information loss. To our knowledge, this is the first quantization approach applied to Cholesky factors of preconditioners. Second, we …

Fine-Grained Abnormality Prompt Learning For Zero-Shot Anomaly Detection, Jiawen Zhu, Yew‑Soon Ong, Chunhua Shen, Guansong Pang

Research Collection School Of Computing and Information Systems

Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained visionlanguage models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like ‘damaged’, ‘imperfect’, or ‘defective’ objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt …

Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of …

Real-Time Task Scheduling Strategy For 3d Printing Cloud Platforms In Health Scenes, Jianjia He, Jian Wu, Jingran Ni, Yuning Zhang, Keng Siau

Research Collection School Of Computing and Information Systems

In health scenes, 3D Printing Cloud Platform (3DPCP) needs to cope with unpredictable fluctuations in tasks and resources, but traditional scheduling methods have problems such as incomplete consideration of factors, poor optimization, and weak dynamic adaptability, which make it difficult to meet real-time scheduling requirements. To this end, the real-time task scheduling problem of 3DPCP for health scenes is defined, a real-time task scheduling model is established, the design time of user personalized services is considered, a rescheduling scheme is designed in combination with task variations and device variations, and a scheduling strategy that incorporates dynamic mechanisms and improved multi-objective …

Fcad: Feature-Coupled Anisotropic Diffusion For Continuous Graph Learning, Amitoz Azad, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

In this work, we propose a novel continuous graph neural network called FCAD (Feature-Coupled Anisotropic Diffusion) for the task of node classification on graphs. Our approach is motivated by the success of feature-coupled anisotropic diffusion PDEs in multivalued image restoration. Our method introduces a total variation regularization-inspired anisotropic term to control diffusion between nodes and incorporates a learnable parameterization for feature coupling during the diffusion process. Our model performs competitively against several GNN baselines for both heterophilous and homophilous graphs, demonstrating notable benefits for heterophilous graphs due to the learnable feature coupling.

Art4math: Handwritten Mathematical Expression Recognition Via Multimodal Sketch Grounding, Yang Zhou, Jin Wang, Yuxiao Zhang, Kaixiang Huang, Guodong Lu, Jingru Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

Handwritten Mathematical Expression Recognition (HMER) remains a challenging task due to the structural complexity of mathematical notation and the ambiguity of handwritten symbols-e.g., ''ρ'' vs. ''p'' or ''B'' vs. ''β''. While stroke-based models offer disambiguation via temporal cues, most existing methods are constrained by coarse modality fusion and a lack of fine-grained cross-modal alignment, further hindered by limited annotated data. We introduce Art for Math (Art4Math), a novel framework that leverages the structural richness of human sketches to enhance HMER through fine-grained, modality-aware learning. Art4Math follows a two-stage training paradigm: Art Grounding (A-Grd) and Math Decoding (M-Dec). In A-Grd, the …

Law Grapples With Its Achilles’ Heel? Uncertainties, Digital Experiments, And The Ai Frontier, Heng Wang

Research Collection Yong Pung How School Of Law

The governance of digitalization— which encompasses developments such as artificial intelligence (AI) and central bank digital currencies (CBDCs) — confronts serious challenges. At the core of the challenges are uncertainties, which form the central obstacle to effective governance. These uncertainties range from cyber risks to shifting societal responses. Law, as an institutionalized framework of governance, faces mounting pressure and wrestles with a fundamental vulnerability: traditional legal frameworks struggle to address uncertainties in digitalization.

To tackle the pervasive challenges of uncertainties, this article addresses two connected questions: What uncertainties does law face in governing digitalization shaped by emerging technologies? How can …

Precipitated By The Pandemic: From Remote Witnessing To Electronic Wills, Edward Ti, Hang Wu Tang

Research Collection Yong Pung How School Of Law

The COVID-19 pandemic exposed the rigidity of will-making laws requiring in-person witnessing. Jurisdictions responded differently: some introduced temporary measures, others implemented permanent reforms allowing remote witnessing. This article compares responses in England and Wales, selected Australian states, Singapore, and Hong Kong SAR, tracing developments toward potential recognition of electronic wills. The central argument is that law reform should balance accessibility with safeguards against fraud and undue influence, avoiding overly burdensome formalities that deter compliance. The article concludes by examining how national digital identity systems could be used for the execution of electronic wills.

Beneficial Ownership In Domestic Tax Legislation, Some Clarity, But Far From ‘Well Established’: Hargreaves Property Holdings Ltd V Hmrc [2024] Ewca Civ 365, Vincent Ooi

Research Collection Yong Pung How School Of Law

The concept of beneficial ownership is extensively used in domestic tax legislation, but several decades ofinconsistent case law have muddied the waters as to exactly what it means. With the leading casesstopping short of the apex court, it is difficult to reconcile the cases and come up with a clear definition ofbeneficial ownership. The recent Hargreaves decision by Falk LJ (with whom Nugee and Peter JacksonLJJ agreed) represents the most structured judicial attempt to rationalise the concept to date. This notesuggests that, contrary to Falk LJ’s statement that the concept is ‘well established’, the law pre-Hargreaveswas far from clear. This …

A 3-Item Short Version Of The Fear Of Missing Out (Fomo) Scale For Trait And Time-Intensive Designs: Measurement Invariance And Psychometric Properties, Eva M. Hisham, Andree Hartanto, Nadyanna M. Majeed

Research Collection School of Social Sciences

Research on Fear of Missing Out (FoMO) has gained prominence alongside the rise of digital connectivity and social media, which offer individuals continuous access to others’ experiences in real time. Importantly, it has been increasingly recognised as an important psychological construct, closely associated with various problematic digital behaviours. As the fields of media psychology and human-computer interaction increasingly adopt time-intensive methodologies (e.g., daily and weekly studies) to capture dynamic experiences of technology use, there is a growing need for brief yet psychometrically-sound measures of FoMO suitable for such designs. The present work reports the development of a 3-item FoMO scale …

Southeast Asia Higher Education Changes And Emerging Futures, Muhammad Hali Aprimadya, Lily Kong, Angel Calderon, Hamish Coates

Research Collection School of Social Sciences

This paper unpacks recent developments and future directions in Southeast Asian higher education. It takes stock of higher education systems in three selected countries, Australia, Indonesia and Singapore. It sketches these systems, distils several changes around 2024, and discusses emerging directions for education, research and universities. The paper concludes by looking at what is required to catalyse transformations likely to impel future progress.

Everyday Norms Have Become More Permissive Over Time And Vary Across Cultures, K. Eriksson, P. Strimling, I. Vartanova, Andree Hartanto

Research Collection School of Social Sciences

Every social situation that people encounter in their daily lives comes with a set of unwritten rules about what behavior is considered appropriate or inappropriate. These everyday norms can vary across societies: some societies may have more permissive norms in general or for certain behaviors, or for certain behaviors in specific situations. In a preregistered survey of 25,422 participants across 90 societies, we map societal differences in 150 everyday norms and show that they can be explained by how societies prioritize individualizing moral foundations such as care and liberty versus binding moral foundations such as purity. Specifically, societies with more …

Personality And Personnel Selection: Going Beyond Self-Reports And Linear Relationships, Filip Lievens, Jinyan Fan, Jingyi Li

Research Collection Lee Kong Chian School Of Business

The study of personality in selection and assessment represents one of the most longstanding and voluminous literature in industrial and organizational psychology. In this paper, we review two important recent research streams. First, we review studies on alternatives to self-report personality inventories, namely the use of Situational Judgment Tests (SJTs), assessment center (AC) exercises, and a multi-rater framework. Second, we review studies that go beyond linear relationships between personality and job performance. Generally, our review reveals that these alternative approaches show promises in better understanding personality and how personality relates to performance.

Weak Identification Of Long Memory With Implications For Volatility Modeling;, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.

The Impact Of A Vocational Training ‘Plus’ Programme On Employment And Income, Abu S. Shonchoy, Tomoki Fujii, Selim. Raihan

Research Collection School Of Economics

Following a Randomised Controlled Trial in Northern Bangladesh, this study explores the barriers to rural youth migration to urban employment hubs. By implementing a vocational training “plus” programme facilitating apparel sector employment, the study revealed significant and persistent effects of the programme on income and employment, primarily due to the addition of a stipend and on-the-job training components. Treated participants experienced significant income and remittance increases, alongside reduced income poverty, attributed to the lower job search costs and job–fit risks. Notably, the on-the-job paid apprenticeship offered a benefit–cost ratio of 8.85, demonstrating its cost-effectiveness for programme scalability.

The Motherhood Penalty On Health: Evidence From China, Ang Sun, Fang Xia, Xuan Zhang

Research Collection School Of Economics

Understanding women’s physical and mental well-being following childbirth is critical for informing policies on labor market equity, family support, and fertility. Using event study methods and panel data from China, we document a persistent motherhood penalty in both physical and mental health, whereas the health impacts on fathers are minimal. While mothers experience a temporary reduction in paid work hours, they face a sustained increase in unpaid domestic labor — housework and childcare — resulting in a net increase in total work time. This additional workload likely contributes to deterioration in health. Mothers are also more likely to engage in …

Short-Term Mortality And Healthcare Utilization Consequences Of Pension Income Receipt: Evidence From South Korea, Mimi Jeon, Seonghoon Kim, Kanghyock Koh

Research Collection School Of Economics

We examine short-term consequences of pension income receipt on mortality and healthcare utilization within the monthly payment cycle. Using the national death registry data of South Korea, we document that the mortality rate decreases by 1.2–1.4 percent in the week of the disbursement date. The mortality-reducing effects are larger for causes of death that could have been avoided through timely and effective healthcare interventions. Using healthcare claims data, we document that the number of hospital admissions increases during the disbursement week. We provide suggestive evidence of greater mortality-reducing effects from a smaller but more frequent disbursement than from a monthly …

Product Technology Adoption And Aggregate Innovation, Qiugu He, Xuan Luo, Fan Zheng

Research Collection School Of Economics

We document that a firm's research and development (R&D) expenditure depends on its product diversity. Combining with the fact that Chinese manufacturers often enter new product markets via technology adoption, we develop a quantitative framework of innovation and technology adoption, allowing firms to expand their product scopes. Firms adopt technologies across multiple fields to expand their knowledge base, which in turn serves as an input for subsequent innovation or adoption. Counterfactual analysis from the 2000s reveals that two-thirds of knowledge privately held by all firms is generated through adoption, accounting for one-third of aggregate innovation.

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li

Research Collection School Of Computing and Information Systems

In recent years, applying deep models to automatically learn construction heuristics for vehicle routing problems has achieved remarkable advancements. However, they are less effective in searching solutions due to two primary limitations: relying on deterministic probability distributions and overlooking the strategic advantage of prioritizing nearby unvisited nodes during the route construction process, resulting in suboptimal policies In this paper, we propose a novel lightweight population-based policy optimization (LPPO) framework that learns a diverse population of solution strategies through the utilization of innovative perturbation factors, in order to facilitate search exploration. Moreover, we design a localized attention synthesis (LAS) network to …

What Students Really Think: Unpacking Ai Ethics In Educational Assessments Through A Triadic Framework, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

The rise of AI in educational assessments has significantly enhanced efficiency and accuracy. However, it also introduces critical ethical challenges, including bias in grading, data privacy risks, and accountability gaps. These issues can undermine trust in AI-driven assessments and compromise educational fairness, making a structured ethical framework essential. To address these challenges, this study empirically validates an existing triadic ethical framework for AI-assisted educational assessments, originally proposed by Lim, Gottipati and Cheong (In: Keengwe (ed) Creative AI tools and ethical implications in teaching and learning, IGI Global, 2023), grounded in student perceptions. The framework encompasses three ethical domains—physical, cognitive, and …

Cookingdiffusion: Cooking Procedural Image Generation With Stable Diffusion, Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, Xiang Wang

Research Collection School Of Computing and Information Systems

Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional inputs like cooking styles, ingredients, and recipes are utilized. However, a yet-unexplored challenge is generating a sequence of procedural images based on cooking steps from a recipe. This could enhance the cooking experience with visual guidance and possibly lead to an intelligent cooking simulation system. To fill this gap, we introduce a novel task called cooking procedural image generation. This task is inherently demanding, as it strives to create photo-realistic images that align with cooking steps while …

From Holistic To Localized: Local Enhanced Adapters For Efficient Visual Instruction Fine-Tuning, Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yugang Jiang

Research Collection School Of Computing and Information Systems

Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task diversity and complexity increase, EVIT faces significant challenges in resolving data conflicts. To address this limitation, we propose the Dual Low-Rank Adaptation (Dual-LoRA), a holistic-to-local framework that enhances the adapter’s capacity to address data conflict through dual structural optimization. Specifically, we utilize two subspaces: a skill space for stable, holistic knowledge retention, and a rank-rectified task space that locally activates the holistic knowledge. Additionally, we introduce Visual Cue Enhancement (VCE), a multi-level local feature aggregation module designed …

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

Multimodal models leverage complementary information across modalities to enrich feature representations. While visual information shows potential in representing structure for some combinatorial optimization problems (COPs), its application to complex scheduling like the Flexible Job Shop Scheduling Problem (FJSP) remains underexplored. Current learning-based FJSP solvers predominantly rely on handcrafted state features. This dependence can lead to inconsistencies and may not fully capture the problem's intricate dynamics. Crucially, these methods overlook visual modalities. Visual representations offer a distinct advantage by inherently capturing the global topological structure and complex resource interactions within the FJSP state. Unlike localized handcrafted features, this holistic, structural view …

A Comprehensive Review Of Financial Knowledge Graphs, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge Graphs (KGs) are increasingly used in finance to manage complex, interconnected data and support advanced analytics. This survey provides an overview of how KGs are applied across various financial areas, such as fraud detection, credit risk assessment, anti-money laundering, and regulatory compliance. We examine key techniques for building and using KGs in finance, including graph construction, embedding methods, and machine learning models. The survey also discusses challenges specific to finance, like handling private data, ensuring interpretability, and managing real-time data. Additionally, we explore the emerging combination of KGs with large language models and generative AI, which offers new possibilities …

Polymind: Parallel Visual Diagramming With Large Language Models To Support Prewriting Through Microtasks, Qian Wan, Jiannan Li, Huanchen Wang, Zhicong Lu

Research Collection School Of Computing and Information Systems

Prewriting is the process of generating and organising ideas before a first draft. It consists of a combination of informal, iterative, and semi-structured strategies such as visual diagramming, which poses a challenge for collaborating with large language models (LLMs) in a turn-taking conversational manner. We present Polymind, a visual diagramming tool that leverages multiple LLM-powered agents to support prewriting. The system features a parallel collaboration workflow in place of the turn-taking conversational interactions. It defines multiple ''microtasks'' to simulate group collaboration scenarios such as collaborative writing and group brainstorming. Instead of repetitively prompting a chatbot for various purposes, Polymind enables …