Digital Commons Network^™

Generalization Analysis For Supervised Contrastive Representation Learning Under Non‑Iid Settings, Minh Hieu Nong, Antoine Ledent

Research Collection School Of Computing and Information Systems

Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL has remained limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under the assumption that the data tuples used for contrastive learning are independently and identically distributed. However, in practice, we are often limited to a fixed pool of reusable labeled data points, making it inevitable to recycle data across tuples to create sufficiently large datasets. Therefore, the tuple-wise independence condition imposed by previous works is invalidated. In this paper, …

Finir: The 2nd Workshop On Financial Information Retrieval In The Era Of Generative Ai, Fengbin Zhu, Yunshan Ma, Fuli Feng, Chao Wang, Huanbo Luan, Guangnan Ye, Shuo Zhang, Dhagash Mehta, Pingping Chen, Bing Xiang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) …

Grokformer: Graph Fourier Kolmogorov‑Arnold Transformers, Guoguo Ai, Guansong Pang, Hezhe Qiao, Yuan Gao, Hui Yan

Research Collection School Of Computing and Information Systems

Graph Transformers (GTs) have demonstrated remarkable performance in graph representation learning over popular graph neural networks (GNNs). However, self-attention, the core module of GTs, preserves only low-frequency signals in graph features, leading to ineffectiveness in capturing other important signals like high-frequency ones. Some recent GT models help alleviate this issue, but their flexibility and expressiveness are still limited since the filters they learn are fixed on predefined graph spectrum or spectral order. To tackle this challenge, we propose a Graph Fourier Kolmogorov-Arnold Transformer (GrokFormer), a novel GT model that learns highly expressive spectral filters with adaptive graph spectrum and spectral …

What Are Anomalies In A Network?, Kai Ming Ting, Zhong Zhuang, Guansong Pang, Zongyou Liu, Tianrun Liang, Qiuran Zhao

Research Collection School Of Computing and Information Systems

This article examines a collection of assumptions used in the current literature on node anomaly detection in a network. The examination raises the question: What are anomalies in a network? Our attempt to answer this question has provided some interesting findings and led to some open questions. This is the first article which formally defines anomalies in a network and introduces the concept of self-verifiability of a detector without ground-truths in a network. They enable existing detectors to be categorized into two types along the line whether they are self-verifiable or not. We suggest a method to evaluate self-verifiable detectors …

Cracking Aegis: An Adversarial Llm-Based Game For Raising Awareness Of Vulnerabilities In Privacy Protection, Jiaying Fu, Yiyang Lu, Zehua Yang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited. To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis. Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and extract sensitive information from an AI agent, Aegis. A user study (n=22) revealed that players employed diverse deceptive linguistic strategies, including storytelling and emotional rapport, to manipulate Aegis. After playing, players reported connecting in-game scenarios with real-world privacy vulnerabilities, such as phishing and impersonation, and …

Exploring The Capabilities Of Llms For Code-Change-Related Tasks, Lishui Fan, Jiakun Liu, Zhongxin Liu, David Lo, Xin Xia, Shanping Li

Research Collection School Of Computing and Information Systems

Developers deal with code-change-related tasks daily, e.g., reviewing code. Pre-trained code and code-change-oriented models have been adapted to help developers with such tasks. Recently, large language models (LLMs) have shown their effectiveness in code-related tasks. However, existing LLMs for code focus on general code syntax and semantics rather than the differences between two code versions. Thus, it is an open question how LLMs perform on code-change-related tasks.To answer this question, we conduct an empirical study using 1B parameters LLMs on three code-change-related tasks, i.e., code review generation, commit message generation, and just-in-time comment update, with in-context learning (ICL) and parameter-efficient …

Explaining Explanations: An Empirical Study Of Explanations In Code Reviews, Ratnadira Widyasari, Ting Zhang, Abir Bouraffa, Walid Maalej, David Lo

Research Collection School Of Computing and Information Systems

Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accordingly. Different developers might need different explanations in different contexts. Therefore, assisting this process first requires understanding the types of explanations reviewers usually provide. The goal of this article is to study the types of explanations used in code reviews and explore the potential of Large Language Models (LLMs), specifically ChatGPT, in generating these specific types. We extracted 793 code review comments from Gerrit and manually labeled them based on whether they contained a …

How Are We Detecting Inconsistent Method Names? An Empirical Study From Code Review Perspective, Kisub Kim, Xin Zhou, Dongsun Kim, Julia Lawall, Kui Liu, Tegawendé F. Bissyandé, Jacques Klein, Jaekwon Lee, David Lo

Research Collection School Of Computing and Information Systems

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method …

Wa-Fdnet: A Unified Weight Adaptation Network For Multimodal Image Fusion And Object Detection, Yanyin Guo, Ying Luo, Junwei Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Multimodal image fusion and object detection are critical tasks in computer vision, particularly in scenarios requiring robust perception under low illumination conditions. Existing approaches that attempt to combine these tasks often rely on cascaded or loosely coupled designs, which can result in suboptimal performance due to gradient conflicts and task imbalance. In this paper, we propose WA-FDNet, a novel Weight Adaptation Fusion Detection Network that unifies multimodal image fusion and object detection into a single end-to-end framework. WA-FDNet adopts a shared encoder–private decoder architecture, enabling efficient feature sharing while preserving task-specific characteristics. The image fusion branch employs a spatial attention-based …

Financial Data Stewardship: A Librarian's Approach To Data Discovery And A Call For Coding As A Path To Data Comprehension And Verification, Lip Hwe Tee

Research Collection Library

This conference presentation calls to advocate and emphasise coding as a means to understand datasets, to be able to correctly interpret table structure and relevant fields to extract the correct data, to ensure accuracy and relevance.

Ai Assisted Literature Review Tools For Undergraduates: Restrict, Embrace Or Curate?, Aaron Tay

Research Collection Library

As AI-driven literature review tools become widespread, academic librarians must grapple with a fundamental question—should we ban these tools, selectively curate their use, or embrace them fully? This keynote explores the three competing schools of thought shaping AI’s role in undergraduate literature reviews.

The Restrict school argues that students who have not proven capable of writing quality literature review should be restricted from use of such tools. Much like handing a preschooler a calculator before they understand basic arithmetic will affect the learning of arithmetic, premature use of such tools has the potential to shortcut the research learning process. If …

Forging The Future, Kenneth Benoit

Asian Management Insights

How AI is rewriting the rules of knowledge, expertise, and practice.

Strategy And Stewardship In An Uncertain World, Havovi Joshi

Asian Management Insights

This issue, as we continue to celebrate Singapore Management University’s (SMU) 25th anniversary, we explore leadership in higher education, the rise of artificial intelligence (AI), ethical stewardship, and other challenges, highlighting how they intersect in our increasingly complex, fast-changing world.

Reducing Carbon Emissions Through Sustainable Food Consumption: Applying An A-S-I Framework On Beijing Residents, Xuan Yang, Jiaxin Zou, Jingyao Gao, Shu Yang

Student Publications

The food sector represents one of the largest contributors to global climate change, making it urgent for the public to transform their food consumption behavior. However, food consumption behaviors that can facilitate carbon reduction, and how to classify these behaviors from the perspective of behavioral science, have not been deeply explored. This study pioneers a novel categorization of carbon-mitigating food consumption behaviors based on an A-S-I framework (i.e., “Avoid” food loss and waste, “Shift” to low-carbon alternatives, and “Improve” dietary structure). Such a classification not only encompasses the full spectrum of emission-altering behaviors but also enables identification of common determinants …

Rationale And Justification Of The Overarching Principles In The Governance Maturity Framework, Janna Wong, Eugene K. B. Tan, Steve Loh

Lien Centre for Social Innovation: Research

This research addresses the lack of a unified framework for measuring the governance maturity of impact organisations across countries with diverse legal landscapes and cultures, which complicates international assessment and collaboration. In response, we propose a governance framework tailored to the legal context of six countries: India, Indonesia, Malaysia, the Philippines, Singapore, and Thailand. Our analysis identifies six overarching principles embedded in the non-profit laws of the six countries: (1) purpose and impact, (2) leadership, (3) integrity, (4) communication, (5) efficacy, and (6) financial transparency and accountability. These six overarching principles form the proposed framework, which was operationalised and further …

On Sgx’S Voyage To Corporate Sustainability: Exploring Emerging Topics In Multi-Industry Corpora, Xinwen Ni, Min Bin Lin, Simon J.D. Schillebeeckx, Wolfgang Karl Hardle

Research Collection Lee Kong Chian School Of Business

Topic modeling and LDA (Latent Dirichlet Allocation) have proven valuable in various fields as an innovative approach to studying areas of interest and identifying topics in a dynamic content. The underlying assumption is that techniques like LDA can swiftly capture emerging topics in textual documents compared to other categorization tools. These unsupervised approaches have been used to identify new industries and technological domains. However, our study on the nascent topic of “sustainability” within the corpora of SGX-listed companies highlights clear limitations in employing techniques like LDA on sparse data. The dynamic LDA approach, also called DTM (Dynamic Topic Modelling),based on …

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 …

Efficient And Green Large Language Models For Software Engineering: Literature Review, Vision, And The Road Ahead, Jieke Shi, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This article aims to redirect the focus of the research community toward the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance …

Alibaba: Building Advanced Intellectual Property Governance For E-Commerce Marketplaces, Liang Chen, Sin Mei Cheah, Can Huang, Guoqiao Liu

Asian Management Insights

How the global e-commerce powerhouse harnessed artificial intelligence (AI) to balance innovation and intellectual property (IP) rights protection.

Steering The Future, Shai Ganu

Asian Management Insights

Embracing the emergence of corporate stewardship.

Beyond The Binary: Rethinking Work-From-Home Policies, Vineeta Dwivedi, Snehal Shah

Asian Management Insights

The right approach depends on your organisation’s readiness.

Building Urban Resilience, Winston T. L. Chow

Asian Management Insights

Cities can lead on services, resources, and partnerships.

University Leadership, Singapore Management University

Asian Management Insights

The future belongs to institutions that advance impact and prepare minds for lifelong learning. An interview with Dr Andrew Hamilton, President Emeritus of New York University and former Vice-Chancellor of Oxford University.

Irhunter: Universal Detection Of Instruction Reordering Vulnerabilities For Enhanced Concurrency In Distributed And Parallel Systems, Guohua Xin, Guangquan Xu, Yao Zhang, Cheng Wen, Cen Zhang, Xiaofei Xie, Neal N. Xiong, Shaoying Liu, Pan Gao

Research Collection School Of Computing and Information Systems

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

Interactive Generative Modeling: A Pathway For Improved Simulation And Decision Making, Changyu Chen

Dissertations and Theses Collection (Open Access)

This dissertation presents Interactive Generative Modeling (IGM), a unified perspective that integrates interactive paradigm and generative modeling to advance the development of general-purpose intelligent systems. IGM is motivated by the observation that while reinforcement learning (RL) has mastered a wide range of complex simulated tasks, it struggles to generalize in high-dimensional, open-ended tasks. In contrast, generative models excel in such settings due to their expressivity and their ability to serve as powerful priors (e.g., LLMs pretrained on massive corpora). By bridging these two paradigms, IGM offers a promising path forward.

The first direction explored in this dissertation is IGM for …

Resilience In Stem: How Gender-Professional Identity Integration (G-Pii) And Self-Compassion Empower Women To Thrive, Isabelle Chiew

Dissertations and Theses Collection (Open Access)

Fostering resilience is crucial for the retention of women in male-dominated fields such as STEM. This paper investigates the interplay between gender-professional identity integration (G-PII), resilience, and self-compassion through two complementary studies. It is hypothesised that G-PII positively predicts resilience, with higher levels of integration associated with greater resilience. Furthermore, self-compassion is expected to moderate this relationship. Study 1 employs a cross sectional survey design to explore the relationships between G-PII, self-compassion, and resilience, while Study 2 uses an experimental design to assess the causal impact of G-PII on resilience. Study 1 found that G-PII was significantly and positively associated …

Learning And Optimization Under Human-Centric Considerations, Qian Shao

Dissertations and Theses Collection (Open Access)

This dissertation investigates learning and optimization problems shaped by humancentric considerations, such as preferences, demonstrations, behavioral patterns, and resource constraints. As real-world decision-making increasingly involves interaction with human agents, data, and limitations, modeling these factors becomes critical for building practical, adaptive, and robust systems.

The research spans four domains. First, we study preference-aware delivery routing by learning implicit practitioner preferences and incorporating them into a hierarchical route optimization framework. Second, we develop imitation learning methods for cost-constrained settings, enabling agents to mimic expert behavior while respecting safety and resource limitations. Third,we explore early rumor detection in data-limited environments, integrating large …

Trends In The Demographic And Well-Being Indicators Of The Singapore Life Panel Over The Past 9 Years, From 2016-2024, Rachel Wen Yi Ngu, Yi Wen Tan, Yue Qing Chew, Hanif Mohamed Yacob, Yan Er Tan

ROSA Research Briefs

The Singapore Life Panel® (SLP) has been tracking demographic and well-being indicators among older adults in Singapore for nearly a decade. This research brief presents the longitudinal trends observed in the SLP over a nine-year period (2016-2024), with the aim of identifying key shifts in these indicators and drawing attention to salient issues facing Singapore’s ageing population. The findings reveal notable demographic and well-being developments with significant implications for policy domains such as ageing-in-place, employment, and health. We highlight the following prominent trends: (i) a growing proportion of older adults living alone, accompanied by a steady decline in average household …

Rich Models And Methods For On-Demand Same Day Deliveries, Zhiqin Zhang

Dissertations and Theses Collection (Open Access)

Same-day delivery has brought numerous conveniences to people’s lives, but it has also presented challenges in terms of service management. To effectively optimize on-demand same-day delivery operations within urban logistics, intelligent decision-making strategies capable of adapting to rapidly changing circumstances are essential. Employing effective decisionmaking strategies that account for order allocation, route planning, courier scheduling, and other relevant factors, is pivotal in advancing logistics operations, enhancing efficiency, customer satisfaction, and resource utilization in the context of dynamic same-day delivery problems.

The focus of this thesis revolves around different emerging challenges presented by on-demand same-day delivery problems, with a particular emphasis …