Digital Commons Network^™

Driving Financial Inclusion Through Central Bank Digital Currencies: A Methodology For Cbdc Implementation, Heng Wang

Research Collection Yong Pung How School Of Law

This publication outlines a methodology for the design, testing, and implementation of central bank digital currencies (CBDCs) to advance financial inclusion. As an increasing number of central banks around the world explore this novel form of financial infrastructure, this paper focuses on the potential of CBDCs to address key access and price barriers in developing and emerging economies. CBDCs, especially retail ones, can offer unique opportunities for enhancing financial services accessibility, affordability, and usability for unserved and underserved populations, if implemented responsibly through a structured, inclusive and user-centric approach.

Ai Governance And Algorithmic Auditing In Financial Institutions: Lessons From Singapore, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This paper examines the role of algorithmic auditing as a mechanism for responsible AI development and deployment in the financial sector, with a particular focus on Singapore’s regulatory and institutional initiatives. Against the backdrop of fragmented global artificial intelligence (AI) governance frameworks, the study analyses how Singapore has developed operational tools — such as the Veritas Toolkit, AI Verify, Project Moonshot and Project Mindforge — that go beyond abstract ethical principles to provide measurable, use-case-specific standards for auditing AI systems. These initiatives contribute to standardising audit practices, enhancing transparency and bridging trust gaps between financial institutions, regulators and stakeholders. The …

Treaties Establishing Dispute Settlement Frameworks, N. Jansen Calamita, Stefanie Schacherer, Facundo Perez-Aznar, Aikaterini Florou

Research Collection Yong Pung How School Of Law

The purpose of the present report is to provide an overview and analysis of the measures that States adopt domestically to implement treaties that establish mechanisms for the resolution of international disputes. The report’s scope subsumes both treaties that include dispute settlement mechanisms to resolve disputes concerning the substantive obligations contained therein as well as treaties that establish frameworks for dispute resolution simpliciter which may be used to resolve disputes arising under a variety of substantive treaties (collectively “international dispute settlement mechanism” or “IDSM” treaties). The approach of the research contained within this report has been to gather information about …

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 …

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 …

Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …

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 …

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 …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Learning Spatio-Temporal Dynamics For Trajectory Recovery Via Time-Aware Transformer, Tian Sun, Yuqi Chen, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive GPS points. This sparse characteristic presents challenges for their direct use in GPS-based systems. This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates of GPS trajectories. Previous studies commonly adopt a sequence-to-sequence framework, where an encoder captures the trajectory patterns and a decoder reconstructs the target trajectory. Within this framework, effectively representing the road network and extracting relevant trajectory features are crucial for overall performance. Despite advancements in these models, they fail to fully leverage the …

Towards Uncertainty Aware Task Delegation And Human-Ai Collaborative Decision-Making, Min Hun Lee, Martyn Zhe Yu Tok

Research Collection School Of Computing and Information Systems

Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. In this paper, we investigate the utility of exploring distance-based uncertainty scores for task delegation to AI and describe how these scores can be visualized through embedding representations for human-AI decision-making. After developing an AI-based system for physical stroke rehabilitation assessment, we conducted a study with 19 health professionals and 10 students in medicine/health to understand the effect of exploring distance-based uncertainty scores on users’ reliance on AI. Our findings showed that distance-based uncertainty scores outperformed traditional …

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

Research Collection School Of Computing and Information Systems

No abstract provided.

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 …

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …

Aging And The Law In Singapore And Japan: Adult Guardianship And Other Alternatives, Hang Wu Tang, Yukio Sakurai, Yue-En Chong

Research Collection Yong Pung How School Of Law

Comparative adult guardianship law and other alternatives, especially in an Asian context, is an under-investigated area. This paper attempts to fill the gap in the literature by comparing the adult guardianship law and other alternatives from the perspectives of Singapore and Japan. The central argument of this paper is that in order for the law of adult guardianship to be widely adopted in Asian societies like Singapore and Japan - where much of adult guardianship related issues are governed by informal familial arrangements; this would require governments to do more than the mere enacting of adult guardianship legislation. To encourage …

Revisiting “Mandatory Mediation: An Oxymoron?” Advancing Access To Civil Justice Through Mandatory Adr, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

Access to civil justice, a concept integral to the rule of law, has been an enduring concern in many countries. One prominent strategy to enhance access to civil justice entails the use of alternative dispute resolution (ADR) methods prior to court adjudication. In this regard, efforts to embed ADR within the court system have intensified in both England and Wales and Singapore. In Singapore, the parties have a duty to consider ADR prior to and during civil proceedings. More significantly, the courts have been empowered to order parties to attempt ADR. In a similar vein, the English Court of Appeal …

Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Identifying logical fallacies is essential for maintaining log-ical reasoning and reducing false information in a variety of domains, such as the media, law, and education. We present an extensive study on the use of large language models (LLMs) for logical fallacy detection and provide a comparative overview of model performance across various fallacy classes. We evaluate the logical fallacy detection capabilities of multiple state-of-the-art models (LLaMA, Qwen, Gemma, Phi) utilizing accuracy, precision, recall, and F1-score as assessment measures. Accord-ing to our findings, our models do well on simple fallacies like “circular reasoning,” but they have trouble with more interpretive reasoning …

Perceptions Of Smoking Stigma Among African Americans: A Qualitative Study, Denine R. Crittendon, Alison C. Brecher, Samantha Okere, Richard Hass, Rosemary Frasso, Rickie Brawer, Mph, Phd, Charnita Zeigler-Johnson

College of Population Health Faculty Papers

INTRODUCTION: African Americans/Blacks (AAB) are at increased risk for morbidity and mortality from smoking-related diseases including lung cancer (LC). Smoking stigma is believed to be a primary barrier to health care-seeking for people who smoke. Previous studies illustrate that perceptions of smoking vary across populations. However, little is known about the prevalence of smoking stigmas among AAB. The purpose of this study was to increase understanding of the perception of cigarette smoking by AAB.

AIMS AND METHODS: We conducted free-listing interviews in which individuals listed all-thoughts and feelings regarding smoking and health-related questions with a convenience sample of eligible AAB …

A Vacuolar Proton Pump Controls The Post-Germinative Growth Of Rice (Oryza Sativa Ssp. Japonica), Peter James Icalia Gann, Soumen Nandy, Rodrigo Botelho, Kari Vinzant, Mariya Khodakovskaya, Vibha Srivastava

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

Post-germinative growth in plants is controlled by sucrose mobilization from the endosperm. Regulation of this process, however, is not fully understood. Transcriptional suppression of the rice vacuolar H+ translocating pyrophosphatase (V-PPase) referred to as VPP5 led to enhanced starch content in mature grains. However, vpp5 suppressed lines displayed a slower post-germinative growth rescuable by sucrose, indicating inefficient sucrose mobilization from the endosperm. Biochemical and gene expression analyses showed a slower rate of starch to sucrose conversion and altered expression of the key starch utilization pathway genes. We hypothesized that the modulation of metabolic activities in the vpp5 lines was based …

Media And Power, Bettina Fabos, Christopher R. Martin, Catherine H. Palczewski

Faculty Book Gallery

This handbook guides students through concepts, content, and exercises that help them develop media literacy by understanding media and power. The authors want students to not only gain the ability to critically analyze the languages and discourses – textual, visual, audio, and code – that people use to create and interpret media content, but also to understand the overarching context: media possess immense power in contemporary societies around the world.

Assessment Of Emergency Medicine Residents' Situational Awareness And Perception Of Patient Safety Culture In The Emergency Department, Adriana Olson, Nathan Olson, Nicholas Hartman, Casey Morrone, Morgan Battaglia, Kamna Balhara

Department of Emergency Medicine Faculty Papers

BACKGROUND: Situational awareness (SA) and positive safety climate are crucial to patient safety. Little is known about emergency medicine (EM) trainees' SA and perception of emergency department (ED) safety climate, and the interactions of those factors with safety-related behaviors.

OBJECTIVES: Our objective was to evaluate EM residents' perceptions of ED safety climate, self-reported SA, and comfort with identifying, rectifying, and reporting safety hazards.

METHODS: In this cross-sectional study at two EM residencies, residents completed the Situational Awareness Rating Technique (SART) after ED-based or simulated resuscitations as well as a survey regarding safety climate and comfort with identifying, rectifying, and reporting …

Understanding Disputes Over Digitalization: A Perspective Of Cross-Border Central Bank Digital Currencies, Heng Wang

Research Collection Yong Pung How School Of Law

In a rapidly digitalizing world, complex disputes would arise due to the technological, regulatory and other complexities. Public and private actors—including central banks, financial institutions, tech firms, and law firms—need to understand the complexity of these disputes before determining whether and how to engage with ongoing digitalization. Given the fundamental role of currency in the world economy, this article examines central bank digital currency (CBDC) as a pivotal case study. CBDCs, representing the digitalization of central bank money, are a novel digital form of national currency issued by central banks. Various central banks, including those of the Eurozone, Switzerland, the …

The ‘Double-Exposure’ Of Riverine Communities In Northeast India To Dams And Climate Change, Costanza Rampini

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Developmental Lead Exposure And Aggression In Male Rats: Influences Of Maternal Care And Environmental Enrichment, Shamaila Zafar, Courtney K. Williams, Jaehyun Joo, Blanca E. Himes, Jay S. Schneider

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Developmental lead (Pb) exposure results in a variety of cognitive deficits and behavioral issues including increased antisocial behavior and aggression. This study investigated the effect of developmental Pb exposure on aggression and violent behavior in male rats and the potential modulatory influences of quality of maternal care and enriched/non-enriched housing conditions. Long-Evans male rats with or without Pb exposure (perinatal or early postnatal) from low or high maternal care mothers (based on amounts of licking/grooming and arched- back nursing) were randomly assigned to live in enriched or non-enriched environments at weaning. At post- natal day 120–190, offensive aggression was assessed …

Association Of Sars-Cov-2 With Health-Related Quality Of Life 1 Year After Illness Using Latent Transition Analysis, Lauren Wisk, Michael Gottlieb, Peizheng Chen, Huihui Yu, Kelli O'Laughlin, Kari Stephens, Graham Nichol, Juan Carlos Montoy, Robert Rodriguez, Michelle Santangelo, Kristyn Gatling, Erica Spatz, Arjun Venkatesh, Kristin Rising, Mandy Hill, Ryan Huebinger, Ahamed Idris, Michael Willis, Efrat Kean, Samuel Mcdonald, Joann Elmore, Robert Weinstein

Department of Emergency Medicine Faculty Papers

BACKGROUND: Long-term sequelae after SARS-CoV-2 infection may impact health-related quality-of-life (HRQoL), yet it is unknown how HRQoL changes during recovery. We compared patient-reported HRQoL among adults with COVID-19-like illness who tested SARS-CoV-2 positive (COVID+) with those who tested negative (COVID-).

METHODS: Participants in this prospective, multicenter, longitudinal registry study were enrolled from December 2020 through August 2022 and completed 3-month follow-up assessments until 12 months after enrollment. Participants were adults (≥18 years) with acute symptoms suggestive of COVID-19 who received a Food and Drug Administration-approved SARS-CoV-2 test. Participants received questions from PROMIS-29 (subscales: physical function, anxiety, depression, fatigue, social participation, …