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Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems, Yulong Lyu, Ruiqi Hong, Jiawan Wang, Jun Sun, Lei Bu Oct 2026

Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems, Yulong Lyu, Ruiqi Hong, Jiawan Wang, Jun Sun, Lei Bu

Research Collection School Of Computing and Information Systems

As autonomous driving systems (ADS) are increasingly deployed in real-world environments, discovering diverse unsafe driving scenarios remains a fundamental yet difficult problem. Existing scenario generation and testing approaches often rely on black-box exploration or externally-observed heuristic feedback, which struggle to effectively guide the search toward high-risk scenarios induced by complex decision-making behaviors. A key difficulty stems from the fact that unsafe behaviors in ADS often arise from internal decision-making logic, which can induce structured and discontinuous responses that are hard to effectively explore using purely black-box guidance. Consequently, current tools tend to repeatedly discover a narrow set of similar unsafe …

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Chatgpt, Can You Generate Multiple-Choice Questions For An Undergraduate Computing Course? An Evaluation Of Its Effectiveness Using Classical Test Theory, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan, Yuhao Zhang Oct 2026

Chatgpt, Can You Generate Multiple-Choice Questions For An Undergraduate Computing Course? An Evaluation Of Its Effectiveness Using Classical Test Theory, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Educators are increasingly adopting large language models such as ChatGPT to generate multiple-choice questions (MCQs), yet empirical evidence on the quality of these items in computing education assessments remains limited. This study compares 30 ChatGPT-generated and 30 manually crafted items from an undergraduate software design course, and evaluates item difficulty, discrimination, and distractor quality using Classical Test Theory. We triangulated two evaluations using within-subjects and between-subjects designs. Our quantitative findings show that all three metrics in both evaluations point in the same direction. ChatGPT-generated items are easier, less discriminating, and exhibit poorer distractor quality than manually crafted items. Distractor quality …

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Restoring Linguistic Grounding In Vla Models Via Train-Free Attention Recalibration, Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen Sep 2026

Restoring Linguistic Grounding In Vla Models Via Train-Free Attention Recalibration, Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen

Research Collection School Of Computing and Information Systems

Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies. However, their reliability under Out-Of-Distribution (OOD) instructions remains underexplored. In this paper, we reveal a critical failure mode in which VLA policies continue executing visually plausible actions even when the language instruction contradicts the scene. We refer to this phenomenon as linguistic blindness, where VLA policies prioritize visual priors over instruction semantics during action generation. To systematically analyze this issue, we introduce ICBench, a diagnostic benchmark constructed from the LIBERO dataset that probes language–action coupling …

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Facevalue: Exploring Real-Time Self-View Overlays To Prompt Meaning-Oriented Self-Awareness In Remote Meetings, Gun Woo (Warren) Park, Anthony Tang, Fanny Chevalier Sep 2026

Facevalue: Exploring Real-Time Self-View Overlays To Prompt Meaning-Oriented Self-Awareness In Remote Meetings, Gun Woo (Warren) Park, Anthony Tang, Fanny Chevalier

Research Collection School Of Computing and Information Systems

In remote video meetings, visual non-verbal cues, such as facial expressions or head movements, are seen continuously but often only partially. This increases ambiguity compared to in-person settings and can cause misinterpretation or misalignment between intended and perceived meaning. Motivated by communication theories, we designed FaceValue, a technology probe that augments the self-view with private, real-time overlays. These overlays are subtle, suggestive prompts intended to help attendees reflect on how their cues might be interpreted by others. To invite personal interpretation, FaceValue avoids behavioral labeling and instead aims to support meaning-oriented self-awareness: recognizing when visible cues may unintentionally (mis)communicate intent. …

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From Withdrawal To Impact: Smu Libraries’ Book Rehoming Initiatives, Kai Leong Heng, Eng Ling Lynn Yeo Sep 2026

From Withdrawal To Impact: Smu Libraries’ Book Rehoming Initiatives, Kai Leong Heng, Eng Ling Lynn Yeo

Research Collection Library

Academic libraries are no strangers to large-scale deselection exercises. Driven by space constraints, evolving curricula, and the shift towards digital resources, the question is no longer whether to withdraw print materials, but what comes next. In the past year, SMU Libraries explored a different answer: instead of recycling withdrawn books, could we reimagine their next chapter, and rehome them to create meaningful impact for the community?

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Learning 1-Bit Lidar-Based Localization With Auxiliary Objective, Kaijie Yin, Zhiyuan Zhang, Tian Gao, Wentao Zhu, Cheng-Zhong Xu, Hui Kong Sep 2026

Learning 1-Bit Lidar-Based Localization With Auxiliary Objective, Kaijie Yin, Zhiyuan Zhang, Tian Gao, Wentao Zhu, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments. Many deep-learning-based localization methods have achieved promising performance so far. However, as one of the always-on modules competing for limited on-board computational resources, the localization module is expected to consume only a small portion of the overall compute budget. Most existing learning-based methods are still too heavy for this purpose. In contrast, binary neural networks (BNNs) offer an appealing solution, but the 1-bit compression causes severe information loss and performance drop. In this paper, we address this challenge by proposing Binarized LiDAR-based Localization (BiLoc), the …

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Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar Sep 2026

Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School of Computing and Information Systems

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …

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Towards More Inclusive Ai Systems In Cities, Siew Ying Shee, Orlando Woods Sep 2026

Towards More Inclusive Ai Systems In Cities, Siew Ying Shee, Orlando Woods

Research Collection School of Social Sciences

Artificial Intelligence (AI) is increasingly embedded in urban infrastructures and governance, shaping how people, spaces, and futures are classified, prioritised, and managed. Yet, most AI systems are developed within a narrow set of linguistic and geopolitical contexts and exported globally, embedding particular epistemic assumptions into diverse urban environments. Even where formal inclusion metrics are met, such asymmetries can render certain populations and realities less legible within algorithmic systems. Prevailing approaches in digital inclusion—centred on fairness metrics, representation, or access—presume technologies as politically inert and bounded. Yet, the adaptive and probabilistic behaviour of contemporary AI disrupts this premise, challenging the idea …

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Heroclix®: A Multiplayer Chess Game For Teaching The Fundamentals Of Human Capital Management, Chin Heng Low, Jiunwen Wang, Paul Lim, Bernie Koh Sep 2026

Heroclix®: A Multiplayer Chess Game For Teaching The Fundamentals Of Human Capital Management, Chin Heng Low, Jiunwen Wang, Paul Lim, Bernie Koh

Research Collection Lee Kong Chian School Of Business

This article describes the adaptation and use of the HeroClix® board game to teach critical real-world principles for effective human capital management within teams. In a HeroClix® game, players pit their team of miniature figurines against other players’ miniatures in combat. This is akin to organizations striving in the business environment today, with well-structured human capital within their teams as a key competitive advantage. In this article, we suggest how the game can be applied in the classroom for undergraduate students to experience, reflect on, and learn more about the prerequisite considerations in proficient people management, the dynamic nature of …

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From Green Presence To Perceived Greenery: Everyday Mobility And Perceptual Availability In Singapore, Ziheng Zeng, Sonny Rosenthal, Orlando Woods Sep 2026

From Green Presence To Perceived Greenery: Everyday Mobility And Perceptual Availability In Singapore, Ziheng Zeng, Sonny Rosenthal, Orlando Woods

Research Collection College of Integrative Studies

Urban greenery may be abundant in transit-oriented cities, yet green presence does not necessarily become perceptually available to people moving through it. This paper distinguishes green supply, perceived greenery, and green experience, and examines the step at which greenery enters awareness during travel. The study combined 21 days of ecological momentary assessment from 68 participants in Singapore, yielding 6,794 in-trip moments, and 25 route-reconstruction interviews. A satellite normalized difference vegetation index (NDVI) provided a top-down baseline of green supply, and a street-level green view index (GVI) from Google Street View (GSV), matched to 5,824 moments, provided a co-located eye-level baseline. …

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The Stickiness Of Meritocratic Beliefs From Adolescence To Adulthood In Singapore, Yen Kiat Chong, Shin Bin Tan, Vincent Chua, Han Ei Chew Sep 2026

The Stickiness Of Meritocratic Beliefs From Adolescence To Adulthood In Singapore, Yen Kiat Chong, Shin Bin Tan, Vincent Chua, Han Ei Chew

Research Collection Office of Impact

The belief in meritocracy has been rising over the past few decades in developed countries. While many studies have focused on the belief in meritocracy, its distribution across groups and its effects, much less is known about how beliefs may change as individuals age and undergo major life transitions. Using linear growth models on three waves of panel data from a national youth survey in Singapore, we found that the major life transitions to adulthood – progressing through the education system (e.g., completing tertiary education), transiting from full-time studies to full-time work, getting married and becoming a parent – predicted …

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Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang Sep 2026

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

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Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber Sep 2026

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …

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Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang Sep 2026

Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized …

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Smart: When Is It Actually Worth Expanding A Speculative Tree?, Lifu Wang, Pan Zhou Sep 2026

Smart: When Is It Actually Worth Expanding A Speculative Tree?, Lifu Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single targetmodel forward pass. However, existing methods prioritize maximizing token-level likelihood or the number of accepted tokens while ignoring a critical “efficiency paradox”: the computational overhead of drafting and verifying big trees can grow super-linearly, particularly at scale. This often leads to negative wall-clock speedup when batch sizes increase or hardware saturation limits are reached. To address this, we propose SMART, a System-aware Marginal Analysis framework for Runtime Tree construction. SMART reformulates tree expansion as a hardware-aware optimization problem that directly maximizes end-toend speedup. …

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Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo Aug 2026

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …

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Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo Aug 2026

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

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Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang Aug 2026

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

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Success Of New Ideas In Online Platforms: An Idea Network Perspective, Yimei Zhou, Qian Tang, Vincent Z.W. Mack Mack, Shao Yi Liaw Aug 2026

Success Of New Ideas In Online Platforms: An Idea Network Perspective, Yimei Zhou, Qian Tang, Vincent Z.W. Mack Mack, Shao Yi Liaw

Research Collection School Of Computing and Information Systems

On online platforms, new ideas often emerge by recombining existing ones within idea networks. Unlike traditional knowledge networks, idea networks represent curated, meaning-based associations among ideas, offering a distinct lens on recombination. Drawing upon a hypergraph perspective, we investigate how new idea success depends on their structural and content attributes, and how collaborative participation shapes these attributes. Using data from an ideation platform, we find that both structural embeddedness and bridging benefit new idea success. Content diversity has no direct effect, but it amplifies the benefits of bridging while constraining those of embeddedness. Both crowd contributions and ideator expertise strengthen …

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Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao Aug 2026

Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we introduce a hybrid propagation model that jointly captures layer-specific diffusion dynamics and probabilistic cross-layer propagation. Based on this model, we formally define the Mlim problem and establish its NP-hardness, monotonicity, and submodularity. To address the Mlim problem, we first propose a greedy baseline Mlim-Greedy, which achieves a (1-1/e) approximation. Since …

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Left: Learnable Fusion Of Tri-View Tokens For Unsupervised Time Series Anomaly Detection, Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin Aug 2026

Left: Learnable Fusion Of Tri-View Tokens For Unsupervised Time Series Anomaly Detection, Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

Research Collection School Of Computing and Information Systems

As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations. A key challenge in unsupervised TSAD is that many anomalies are too subtle to exhibit detectable deviation in any single view (e.g., time domain), and instead manifest as inconsistencies across multiple views like time, frequency, and a mixture of resolutions. However, most cross-view methods rely on feature or score fusion and do not enforce analysis–synthesis consistency, meaning the frequency branch is not required to reconstruct the time signal through an inverse transform, and …

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Timeradar: A Domain-Rotatable Foundation Model For Time Series Anomaly Detection, Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang Aug 2026

Timeradar: A Domain-Rotatable Foundation Model For Time Series Anomaly Detection, Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang

Research Collection School Of Computing and Information Systems

Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (\eg, forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks—such as time series anomaly detection (TSAD), which aims to identify rare, irregular patterns. This limitation arises because such abnormal patterns can closely resemble the regular patterns when presented in the same time/frequency domain. To address this issue, we introduce TimeRadar, an innovative TSFM built in a fractional time–frequency domain to support generalist TSAD across diverse unseen datasets. Our key insight is that …

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Graph Perturbation Analysis For Subgraph Counting, Hanhua Xiao, Yuchen Li, Kyriakos Mouratidis Aug 2026

Graph Perturbation Analysis For Subgraph Counting, Hanhua Xiao, Yuchen Li, Kyriakos Mouratidis

PhD Student’s Publications Collection

Subgraph counting, which involves determining the frequency of a query graph within a data graph, has numerous applications such as query optimization, fraud detection, and evaluating the expressiveness of graph neural networks. Despite its importance, there has been no systematic study on the impact of adversarial graph perturbations on subgraph counts. In this work, we examine the kSub problem, which aims to identify k edge additions that maximize the count of a query graph. We prove that kSub is intractable due to its NP-hardness, even for constant approximation. To address this, we relax the problem into a top-k selection, termed …

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Leading The Change: Staff-Driven Ai Transformation In Smu Libraries’ Collection Team, Siew Khim Lim, Fion Goh Aug 2026

Leading The Change: Staff-Driven Ai Transformation In Smu Libraries’ Collection Team, Siew Khim Lim, Fion Goh

Research Collection Library

No abstract provided.

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Llm-Based Early Rumor Detection With Imitation Agent, Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, Cheng Niu Aug 2026

Llm-Based Early Rumor Detection With Imitation Agent, Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, Cheng Niu

Research Collection School Of Computing and Information Systems

Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive for both training and inference. In this work, we propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for \textit{early time point determination}, while the LLM serves as a powerful \textit{rumor …

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A Deep Reinforcement Learning Framework For Dynamic Order And Rack Scheduling In Robotic Mobile Fulfillment Systems, Lingjun Wang, Bayi Cheng, Zhiguang Cao, Aldy Gunawan Aug 2026

A Deep Reinforcement Learning Framework For Dynamic Order And Rack Scheduling In Robotic Mobile Fulfillment Systems, Lingjun Wang, Bayi Cheng, Zhiguang Cao, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Robotic mobile fulfillment systems play a vital role in modern e-commerce, but their efficiencydepends on overcoming a critical challenge: coordinating orders and racks effectively under dynamicorder arrivals, diverse delivery deadlines, and resource sharing among multiple stations. This papertackles the resulting dynamic order and rack scheduling problem and proposes an intelligentscheduling framework centred on deep reinforcement learning. At its core is TriNet-PPO, whichtrains a scheduling policy using TriNet, a tripartite graph neural network encoder tailored to capturethe many-to-many relationships among orders, racks, and items. Furthermore, we introduce IPCSA,an integer programming-based cooperative scheduling algorithm that maps station-level actionpreferences to conflict-free joint decisions …

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Bank Competition Amid Digital Disruption: Implications For Financial Inclusion, Erica Xuewei Jiang, Gloria Yang Yu, Jinyuan Zhang Aug 2026

Bank Competition Amid Digital Disruption: Implications For Financial Inclusion, Erica Xuewei Jiang, Gloria Yang Yu, Jinyuan Zhang

Research Collection Lee Kong Chian School Of Business

We examine how digital disruption affects bank competition using the staggered rollout of 3G mobile networks. 3G expansion increased mobile banking adoption among tech-savvy households, reducing branch networks—especially in younger counties. Banks' strategies diverged: Less branch-reliant banks closed branches and competed on price, while more branch-reliant banks maintained branches but raised spreads. A structural model shows that perceived digital service improvements among younger consumers drove these shifts, reducing welfare for older savers. Counterfactuals demonstrate that subsidizing adoption for older savers can cost-effectively reduce these disparities, facilitating a smoother digital transition.

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Ai-Ready Libraries Require Ai-Ready Librarians: Building Organisational Capability For Digital Transformation, Salihin Mohammed Ali Aug 2026

Ai-Ready Libraries Require Ai-Ready Librarians: Building Organisational Capability For Digital Transformation, Salihin Mohammed Ali

Research Collection Library

Academic libraries worldwide are rapidly experimenting with artificial intelligence (AI) to enhance research, learning, discovery, operations, and user engagement. However, many institutions continue to approach AI adoption primarily through isolated pilots, individual experimentation, or technology-centric initiatives. While these efforts generate innovation, they often struggle to scale sustainably without corresponding organisational capability development. This presentation argues that AI-ready libraries require AI-ready librarians and proposes an organisational capability approach for sustainable AI transformation in academic libraries. Drawing from the development of a library-wide AI strategy plans at Singapore Management University, the presentation explores how AI capability-building can be operationalised across diverse functional …

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Global Prevalence Of Depression Among University Students: A Comprehensive Umbrella Review, Gilda Yun Kai Sam, Xun Ci Soh, Harshitha Balaji, Gabriel X. D. Tan, Shu Fen Diong, Meilan Hu, Andree Hartanto, Nadyanna M. Majeed Aug 2026

Global Prevalence Of Depression Among University Students: A Comprehensive Umbrella Review, Gilda Yun Kai Sam, Xun Ci Soh, Harshitha Balaji, Gabriel X. D. Tan, Shu Fen Diong, Meilan Hu, Andree Hartanto, Nadyanna M. Majeed

Research Collection School of Social Sciences

Depression has become a major concern for educational institutes due to its increasing incidence and detrimental impacts on students’ well-being. However, its global prevalence in the context of tertiary education remains unclear. This umbrella review summarised existing meta-analyses and systematic reviews to examine the worldwide prevalence of depression among university students. Several sociodemographic and methodological factors, including differences in depression prevalence before, during, and after COVID-19, were examined as moderators. Structured and exhaustive searches were conducted in five bibliographic databases and five journals, alongside two sources of grey literature. A total of 61 meta-analyses and systematic reviews were included in …

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When Your Neighbours Are A Reminder Of Your Ageing, Cynthia Tan, Yi Wen (Chen Yiwen) Tan Aug 2026

When Your Neighbours Are A Reminder Of Your Ageing, Cynthia Tan, Yi Wen (Chen Yiwen) Tan

ROSA Journal Articles and Publications

In a commentary, Cynthia Tan, a retired senior corporate leader, and Tan Yi Wen, a Senior Research Fellow at SMU’s Centre for Research on Successful Ageing (ROSA), questioned whether the low demand for community care apartments is because seniors do not want only other seniors for company. They noted that most older people do not spend their days dwelling on ageing until everyone around them reminds them that they are growing old. They added that prolonged exposure to such an environment may carry subtle emotional and psychological consequences. Citing a national survey by ROSA, the writers mentioned that eight in …

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