Digital Commons Network^™

Sparse-To-Dense: A Free Lunch For Lossless Acceleration Of Video Understanding In Llms, Xuan Zhang, Cunxiao Du, Sicheng Yu, Jiawei Wu, Fengzhuo Zhang, Wei Gao, Qian Liu

Research Collection School Of Computing and Information Systems

Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence length grows, posing challenges for the efficient processing of video sequences that are usually very long. We observe that during decoding, the attention scores of most tokens in Video-LLMs tend to be sparse and concentrated, with only certain tokens requiring comprehensive full attention. Based on this insight, we introduce Sparse-to-Dense (StD), a novel decoding strategy that integrates two distinct modules: one leveraging sparse top-K attention and the other employing dense full attention. These modules collaborate to accelerate Video-LLMs without loss. …

Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

The release of OpenAI’s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this …

Simulating Before Planning: Constructing Intrinsic User World Model For User-Tailored Dialogue Policy Planning, Tao He, Lizi Liao, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors. Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for …

Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C’s high performance. Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation and generate unsafe Rust code that is hard for human developers to read, maintain, or even compile. More semantic-aware translation that produces safer, idiomatic, and runnable Rust code is much needed. This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap, based on three key ideas: (1) Utilize LLM’s capabilities to produce idiomatic Rust code from given small pieces of …

Retrieval Augmented Generation For Dynamic Graph Modeling, Yuxia Wu, Lizi Liao, Yuan Fang

Research Collection School Of Computing and Information Systems

Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering relevant insights over time. Traditional approaches primarily rely on graph neural networks with temporal components or sequence generation models, which often focus narrowly on the historical context of target nodes. This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader …

Robust Relevance Feedback For Interactive Known-Item Video Search, Zhixin Ma, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Known-item search (KIS) involves only a single search target, making relevance feedback-typically a powerful technique for efficiently identifying multiple positive examples to infer user intent-inapplicable. PicHunter addresses this issue by asking users to select the top-k most similar examples to the unique search target from a displayed set. Under ideal conditions, when the user's perception aligns closely with the machine's perception of similarity, consistent and precise judgments can elevate the target to the top position within a few iterations. However, in practical scenarios, expecting users to provide consistent judgments is often unrealistic, especially when the underlying embedding features used for …

Physical Work Environments: An Integrative Review And Agenda For Future Research, Bukky Akinsanmi Oyedeji, Yea Hee Ko, Sunkee Lee

Research Collection Lee Kong Chian School Of Business

This review examines the effect of physical work environments—the dedicated, tangible spaces where employees carry out their professional tasks—on organizational processes and outcomes. We synthesize decades of research across various disciplines using a conceptual framework that defines physical work environments along three key dimensions: ambience, spatial configuration, and aesthetics. These dimensions are analyzed for their effects on both internal stakeholders, such as employees, and external stakeholders, including clients, suppliers, and investors. Our analysis reveals two major themes in prior research: (1) task accomplishment, which focuses on how physical work environments influence physical and mental health, motivation and attitudes, as well …

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 …

Stress Reactivity And Sociocultural Learning: More Stress-Reactive Individuals Are Quicker At Learning Sociocultural Norms From Experiential Feedback, Shilpa Madan, Krishna Savani, Pranjal H. Mehta, Desiree Y. Phua, Ying-Yi Hong, Michael W. Morris

Research Collection Lee Kong Chian School Of Business

When interacting with others in unfamiliar sociocultural settings, people need to learn the norms guiding appropriate behavior. The present research investigates an individual difference that helps this kind of learning: stress reactivity. Interactions in an unfamiliar sociocultural setting are stressful, particularly when the actor fails to follow its rules. Although stress is typically considered a liability, more stress-reactive individuals may be more motivated to improve, and, thus, quicker to learn these rules. Consistent with this idea, a pilot study found that people genetically inclined to stress reactivity, as computed by a genetic profile score across 59 single nucleotide polymorphisms on …

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 …

Capacity Investment In The Presence Of Correlated Demand And Production Resource Uncertainties And Its Implications For Financial Hedging, Onur Boyabatli, Guiyun Feng

Research Collection Lee Kong Chian School Of Business

In manufacturing firms, besides demand uncertainty, capacity investment decisions may also be subject to uncertainty in the availability of a production resource (e.g., budget or commodity component) which may become constraining for manufacturing. When the production resource uncertainty is tied to a financial index (e.g., asset or commodity price), the firm can enter into financial hedging contracts at the time of capacity investment to engineer this uncertainty. Our paper characterizes the optimal capacity investment and hedging decisions and examines how production resource uncertainty impacts the firm’s decisions, profitability, and the value of hedging. We identify three key drivers of these …

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 …

Enhancing Vulnerability Detection Via Inter-Procedural Semantic Completion, Bozhi Wu, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Inspired by advances in deep learning, numerous learning-based approaches for vulnerability detection have emerged, primarily operating at the function level for scalability. However, this design choice has a critical limitation: many vulnerabilities span multiple functions, causing function-level approaches to lose the semantics of called functions and fail to capture true vulnerability patterns. To address this issue, we propose VulnSC, a novel framework designed to enhance learning-based approaches by complementing inter-procedural semantics. VulnSC retrieves the source code of called functions for datasets and leverages large language models (LLMs) with well-designed prompts to generate summaries for these functions. The datasets, enhanced with …

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 …

Ntire 2025 Challenge On Event-Based Image Deblurring: Methods And Results, Lei Sun, Et. Al.

Research Collection School Of Computing and Information Systems

This paper presents an overview of NTIRE 2025, the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-toNoise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for singleimage deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of eventbased image deblurring. We anticipate …

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.

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 …

Runtime Backdoor Detection For Federated Learning Via Representational Dissimilarity Analysis, Xiyue Zhang, Xiaoyong Xue, Xiaoning Du, Xiaofei Xie, Yang Liu, Meng Sun

Research Collection School Of Computing and Information Systems

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify …

Nexusgs: Sparse View Synthesis With Epipolar Depth Priors In 3d Gaussian Splatting, Yulong Zheng, Zicheng Jiang, Shengfeng He, Yandu Sun, Junyu Dong, Huaidong Zhang, Yong Du

Research Collection School Of Computing and Information Systems

Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations. Exploiting the inherent epipolar geometry of 3DGS, our method introduces a novel point cloud densification strategy that initializes 3DGS with a dense point cloud, reducing randomness in point placement while preventing over-smoothing and overfitting. Specifically, …

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 …