Digital Commons Network^™

Unpacking Singapore's Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti

Research Collection School Of Economics

In Singapore, most land is state-owned, with the state generally issuing leasehold estates via state leases of not more than 99 years1, depending on the intended land use. Naturally, the value of a leasehold estate, which erodes over time as the lease approaches the end of its term, is a key component of the premium charged for lease renewals, or the tax imposed for permission given in relation to a development that would increase the value of the land. By law, the state valuation of leasehold land is prescribed by a leasehold relativity table colloquially known as ‘Bala’s Curve’ or …

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 …

Rethinking Teaching Evaluation Reports: Designing Ai-Transformed Student Feedback For Instructor Engagement, Ruoxi Shang, Keri Mallari, Au Wei Bin Yeong, Ken Yasuhara, Anthony Tang, Gary Hsieh

Research Collection School Of Computing and Information Systems

Student feedback is critical for improving teaching, yet instructors often avoid reading evaluations due to emotional burden and information overload. We present a systematic exploration of how language models can distill and transform student evaluations into adaptive, actionable insights. Through a systematic design space exploration combining 4 feedback strategies (removing harmful content, paraphrasing criticism, sandwiching negatives, adding constructive suggestions) with 4 presentation formats (themes, cards, letters, chatbots), we created six AI-augmented prototypes of teaching evaluations. Interviews with 16 post-secondary instructors revealed that effective use of AI in feedback processing should: (1) support action formation through focused views and divergent thinking, …

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 …

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 …

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 …

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.

Interpreting The Private‑ And Public‑Sector Service Criteria For Singapore’S Aspiring Presidential Candidates, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

The Singapore President's role is not only ceremonial: from 1991, it has encompassed the substantive function of serving as a guardian of public reserves and the integrity of the public service. A person must meet certain qualifying criteria in order to run in a Presidential election. This article focuses on one set of qualifying criteria, namely, the "service criteria": the requirement that candidates have had certain experience in serving in certain roles in the public sector or the private sector. The service criteria were last amended in 2016, and came to the fore in the 2023 Presidential Election following George …

The Excellent Legal Scholar, Seow Hon Tan

Research Collection Yong Pung How School Of Law

The Excellent Legal Scholar: This article considers how virtues pan out in the life of the legal scholar, bearing in mind the purpose of legal scholarship and the identity of the legal scholar, who plays multifarious roles in today's research landscape. I consider how vision is important for the excellent legal scholar, bearing in mind that an aretaic account should be attentive to eudaimonia. I conclude with soul-searching questions for the legal scholar who endeavours to live an examined life that stands up to aretaic appraisal.

Review Of Victor Fan, “Cinema Illuminating Reality: Media Philosophy Through Buddhism”, Teng-Kuan Ng

Research Collection College of Integrative Studies

This is a book review of Victor Fan, Cinema Illuminating Reality: Media Philosophy through Buddhism (Minneapolis: University of Minnesota Press, 2022)

On Modeling Of Multiplicative Bias Factor For Multivariate Degradation Data, Bingxin Yan, Qiuzhuang Sun, Zhisheng Ye

Research Collection College of Integrative Studies

Multivariate degradation data are increasingly common due to advances in sensor technology. They provide rich information for reliability analysis and remaining useful life (RUL) prediction. The multivariate measurement typically contains actual degradation of multiple performance characteristics (PCs) and measurement errors. The multiple PCs often have different physical meanings with different orders of magnitude, making the associated measurement errors vary in the orders of magnitude as well. It is highly desirable to have a parsimonious model accounting for the possible correlation in degradation measurements, so that more flexibility can be reserved for modeling the actual multivariate degradation. This study proposes a …

Early Signs The Eu Carbon Border Adjustment Mechanism Is Reshaping Eu-India Steel Trade, Gian Luca Vriz, Theodor Florian Cojoianu, Carolyn Fischer, Luca Taschini

Research Collection College of Integrative Studies

This study examines early signals of the EU CBAM's impact on iron and steel exporters from India by integrating firm-level export data with third-party estimates of manufacturers' emissions and production. Changes in trade flows and pricing during the CBAM reporting phase indicate that high-emission-intensity firms have decreased both their average unit prices and shipment sizes to the EU, while low-emission-intensity firms have not changed shipment behavior and may have benefited from increasing prices. Such supply chain adjustments are in line with EU policy intentions, creating opportunities for low-emission producers while reducing reliance on high-emission suppliers to the EU.

Make Yourself Comfortable: Nudging Urban Heat And Noise Mitigation With Smartwatch-Based Just-In-Time Adaptive Interventions (Jitai), Clayton Miller, Xuan Yun Chua, Matias Quintana, Binyu Lei, Filip Biljecki, Mario Frei

Research Collection College of Integrative Studies

Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework is based on the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with …

Hlcg: A Hierarchical Lane-Changing Gaming Decision Model For Heterogeneous Traffic Flow On Two-Lane Highways, Tianyi Wang, Chong He, Hao Li, Yixuan Li, Yiming Xu, Yangyang Wang, Junfeng Jiao

Research Collection College of Integrative Studies

Discretionary lane-changing behavior is one of the most common highway operations, which seriously affects traffic efficiency and safety. Nowadays, connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. As a result, a mixed traffic environment with traditional human-driven vehicles (HDVs) and CAVs will persist for the foreseeable future. To achieve effective automatic lane-changing maneuvers, it’s necessary to propose a lane-changing decision model for heterogeneous traffic flow on two-lane highways. This paper firstly extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using quintic polynomial curves to accommodate heterogeneous traffic flow, and …

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 …

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 …

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 …

Information Provision And Search Frictions: Evidence From The Taxi Industry In Singapore, Sumit Agarwal, Shih-Fen Cheng, Jussi Keppo, Long Wang, Yang Yang

Research Collection School Of Computing and Information Systems

Search frictions and misallocation are common in decentralized transportation markets. Using novel trip-level data of taxis in Singapore, this paper examines the impactof real-time demand information at airport terminals on search frictions. The information reduces taxi supply misallocation, increasing deadheading speed by 16.3% and decreasing deadheading time by 10.77%, benefiting both passengers and drivers. It raises daily earnings by $3.70 USD and adds 6.2 minutes of operational time per airport-trip taxi. Spatial spillovers are primarily observed among drivers in adjacentdistricts. Taxis from the Budget Terminal and drivers with fewer prior airport pickups benefit more from this information.

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 …

Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …

Ivycross: A Privacy-Preserving And Concurrency Control Framework For Blockchain Interoperability, Ming Li, Jian Weng, Jia-Si Weng, Yi Li, Yongdong Wu, Dingcheng Li, Guowen Xu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design …

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 …

Information-Bottleneck Driven Binary Neural Network For Change Detection, Kaijie Yin, Zhiyuan Zhang, Shu Kong, Tian Gao, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input …

Auxiliary Prompt Tuning Of Vision‑Language Models For Few‑Shot Out‑Of‑Distribution Detection, Wenjun Miao, Guansong Pang, Zihan Wang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Recent advancements in CLIP-based out-of-distribution (OOD) detection have shown promising results via regularization on prompt tuning, leveraging background features extracted from a few in-distribution (ID) samples as proxies for OOD features.However, these methods suffer from an inherent limitation: a lack of diversity in the extracted OOD features from the few-shot ID data.To address this issue, we propose to leverage external datasets as auxiliary outlier data (i.e., pseudo OOD samples) to extract rich, diverse OOD features, with the features from not only background regions but also foreground object regions, thereby supporting more discriminative prompt tuning for OOD detection. We further introduce …