Digital Commons Network^™

Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao

Dissertations and Theses Collection (Open Access)

Graph perturbation, rooted in classical perturbation theory, studies how small topology edits, i.e., adding or deleting edges, affects graph properties (e.g., density, centrality). This fundamental problem underpins applications like bioinformatics, privacy preservation and system defense. While much prior work targets perturbations that influence global graph statistics or model outputs, comparatively little addresses robustness for knowledge discovery and information retrieval. In these settings, graphs are attributed: nodes carry real-world semantics (e.g., locations, people) and edges encode interactions or relationships. This thesis proposes new formulations and algorithms that generate and leverage graph perturbations to make knowledge discovery and retrieval more robust. Specifically, …

Commentary: Delayed Climate Reporting Requirements Threaten Singapore’S Sustainability Leadership, Jiwei Wang, Holly I. Yang, Liandong Zhang

Research Collection School Of Accountancy

In a commentary by SMU Professor of Accounting (Practice) and Director of Master of Professional Accounting (MPA) and Master of Science in Accounting (MSA) programmes Wang Jiwei, SMU Associate Professor of Accounting, Lee Kong Chian Fellow and Co-Director (Academic Research) of the School of Accountancy Research Centre (SOAR) Holly Yang, and SMU Dean of School of Accountancy and Lee Kong Chian Professor of Accounting Zhang Liandong, they suggested that delaying climate disclosure requirements for small and mid-sized enterprises (SMEs) from 2025 to 2030 could weaken Singapore’s sustainable finance leadership, despite regulators saying the extension would help smaller firms build environmental, …

How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …

Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large …

The Resource Characteristics Of Small And Medium-Sized Enterprises (Smes) And The Impact Of Platform Participation On Business Performance, Xue Tan

Dissertations and Theses Collection (Open Access)

Against the backdrop of deeply integrated digital and platform economies, the resource characteristics of small and medium-sized enterprises (SMEs) and platform participation’s impact on business performance have emerged as key interdisciplinary research focuses between strategic management and digital economy studies. Building upon the resource-based view (RBV) and dynamic capabilities theory (DCT), the study establishes a theoretical framework of “resource characteristics-platform participation-business performance.” It systematically examines the relationships and moderating mechanisms among SMEs’ resource endowment, platform embedding strategies, and performance outcomes.

The study adopts a mixed-method design, combining quantitative analysis with qualitative case studies. Using valid samples of 333 Chinese SMEs, …

Symbolic Execution Engine For Dynamic Analysis Of System Software, Pansilu Madhura Bhashana Pitigala Arachchillage

Dissertations and Theses Collection (Open Access)

System software, like any regular software, is prone to errors. It plays a specific role in a computer system by managing the underlying hardware and providing a platform to execute the application software. Defective or vulnerable system software can be exploited by attackers to compromise the entire system. Therefore, the system software must be studied and thoroughly analyzed to evaluate its security. However, due to the inherent complexity and its close interactions with the hardware, analyzing system software is a challenging task. As a result, there is a lack of tools and techniques capable of effectively analyzing system software.

This …

Enhancing Multi-View, Multi-Modal Sensing, Perception And Actuation For Edge Intelligence, Dhanuja Tharith Wanniarachchige

Dissertations and Theses Collection (Open Access)

Artificial Intelligence of Things (AIoT) technologies have ushered in exciting new advances in intelligent sensing, perception, and actuation for many real-world cyberphysical systems (CPS) applications. These technologies have had a formidable impact in domains such as large-scale video surveillance, autonomous transportation and robotics, precision healthcare, and industrial automation. In these applications, sensors and actuators are often collocated with processing nodes, and such nodes are typically interconnected via wireless networks. Vision-based machine intelligence, exemplified by tasks such as object detection, object tracking, and activity analysis, is a very common enabler of such CPS applications. Efficient execution of Deep Neural Network (DNN) …

A Fake Friend? Ai Companions Are Exactly That, Seow Hon Tan

Research Collection Yong Pung How School Of Law

In a commentary, SMU Associate Professor of Law Tan Seow Hon discussed how AI companions, which promise emotionally intelligent companionship, have blurred the line between human and machine relationships by mimicking empathy, memory, and affection. She suggested that while such technologies may ease loneliness, they risk fostering narcissism, diminishing real human connection, and replacing authentic friendship with comforting illusions that erode the capacity for love and community.

Singapore Awakened: How Success – And Flourishing – Shape Family, Seow Hon Tan

Research Collection Yong Pung How School Of Law

Assoc. Prof. (Dr.) Tan Seow Hon delivered the keynote address at Cultivate SG’s second annual conference, “Unfiltered – The Family on Trial”, on 17 November 2025. In her speech titled “Singapore Awakened: How Success – and the Alternative of Flourishing – Shape Family”, Dr Tan reflects on the “Singapore Dream”, the narrow mindset of success in contrast with the concept of flourishing, and how these impact marriage and family. She concludes by offering some thoughts on how to move from success to flourishing.

A Comparison Of Comparisons: Evidence From An International Comparative Study Of 'Smart Cities', Kevin Ward, Teresa Abbruzzese, Tim Bunnell, Paolo Cardullo, I-Chun Catherine Chang, Byron Miller, Ramon Ribera-Fumaz, Haeran Shin, Zachary Spicer, Orlando Woods

Research Collection College of Integrative Studies

Every year the list lengthens of cities with some sort of ‘smart city’ public policy. In some, it emerges as the latest in a long line of urban digital and information communication policies. In others, the introduction of the notion of the ‘smart city’ marks a departure from past approaches to public policy. Additionally, the more studies emerge of actual smart city policies, then the less definitional agreement there seems to be. Nevertheless, that we have witnessed in the last two decades the ‘repeated instance’ of smart cities emerging in cities around the world seems incontrovertible. Like so much urban …

Enhancing Urban Energy Modeling: A Case Study Of Data Acquisition, Enrichment, And Evaluation In Berlin, Felix Rehmann, Martín Mosteiro-Romero, Clayton Miller, Rita Streblow

Research Collection College of Integrative Studies

Urban Building Energy Modeling (UBEM) has become a critical tool for developing local heating and cooling plans, as required by the European Union. Despite growing interest, the reproducibility and reliability of UBEM studies remain limited due to data scarcity and workflow complexity. This paper presents a comprehensive framework to evaluate the data pipeline in UBEM, with a particular focus on data acquisition, enrichment, simulation, calibration, and information application. The approach applies three distinct UBEM workflows (CityEnergyAnalyst, DistrictGenerator, and SimStadt) to the Mierendorffinsel district in Berlin, Germany. We compare the based on quantitative performance metrics and qualitative framework criteria. The results …

Sustainable Llm Inference For Edge Ai: Evaluating Quantized Llms For Energy Efficiency, Output Accuracy, And Inference Latency, Erik Johanne Husom, Arda Goknil, Merve Astekin, Lwin Khin Shar, Andre Kasen, Sagar Sen, Benedikt Andreas Mithassel, Ahmet Soylu

Research Collection School Of Computing and Information Systems

Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption. Model quantization has emerged as a key technique to enable efficient LLM inference by reducing model size and computational overhead. In this study, we conduct a comprehensive analysis of 28 quantized LLMs from the Ollama library, which applies by default Post-Training Quantization (PTQ) and weight-only quantization techniques, deployed on an edge device (Raspberry Pi 4 with 4GB RAM). We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Models are benchmarked on …

Sketch-Sparsenet: Sparse Convolution Framework For Sketch Recognition, Jingru Yang, Jin Wang, Yang Zhou, Guodong Lu, Yu Sun, Huan Yu, Heming Fang, Zhihui Li, Shengfeng He

Research Collection School Of Computing and Information Systems

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, …

Generative Ai And Empirical Software Engineering: A Paradigm Shift, Christoph Treude, Margaret-Anne Storey

Research Collection School Of Computing and Information Systems

The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative, and mixed-method approaches, are well established. However, this paradigm shift introduces novel data types and redefines many concepts in the software engineering process. The roles of developers, users, agents, and researchers increasingly overlap, blurring the distinctions between these social and technical actors within the field. This paper examines how integrating AI into software engineering challenges traditional research paradigms. It …

Pricing Strategies In Global Channels: Considering The Effects Of Parallel Trade, Yuan-Mao Kao, Yang Yang, Shih-Fen Cheng, Cheng-Hung Wu

Research Collection School Of Computing and Information Systems

Pricing a global product differently across multiple regions is a common but controversial practice. Although price differentiation helps capture unique market characteristics, it also encourages parallel trade, which may affect the overall corporate performance of a global company. We study this problem with a single global business unit (GBU) and multiple local business units (LBUs). The GBU manufactures a product and sets a transfer price for supplying the product to all LBUs, and LBUs decide retail prices for their respective regional markets. Customers can purchase products in any region by comparing LBUs’ prices and other parallel-imported factors, and we construct …

Enhancing Spatial Understanding In Mixed-Reality Presentations, Nam-Dang Vo, Van-Vinh Thai, Nam-Hoi Do, Viet-Tham Huynh, Anthony Tang, Khan-Duy Le

Research Collection School Of Computing and Information Systems

Mixed reality (MR) presentations often involve a presenter wearing a head-mounted display (HMD) and an audience watching via a large display, making it difficult for audiences to perceive spatial relationships between the presenter and virtual objects. We report two experiments testing three design variations: (1) scene camera placement (audience-aligned vs. opposite), (2) overlaying the presenter’s first-person view, and (3) highlighting objects in the presenter’s view. Results show that audience-aligned cameras and object highlighting improve spatial understanding, while combining third- and first-person views can further aid perception. We derive design guidelines for configuring MR presentations to better support audience comprehension.

International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate …

Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool, Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny T.W. Choo, Cyrille Jegourel

Research Collection School Of Computing and Information Systems

Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that combines traditional debugging tools with LLM-based techniques to help novice debuggers fix semantic errors while promoting active engagement in the debugging process. We present findings …

Simulated Interactive Debugging, Yannic Noller, Erick Chandra, Srinidhi Chandrashekar, Kenny Choo, Cyrille Jegourel, Oka Kurniawan, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Debugging software, i.e., the localization of faults and their repair, is a key activity in software engineering. Therefore, effective and efficient debugging is one of the core skills a software engineer must develop. However, the teaching of debugging techniques is usually very limited or only taught in indirect ways, e.g., during software projects. As a result, most Computer Science (CS) students learn debugging only in an ad-hoc and unstructured way. In this work, we present our approach called Simulated Interactive Debugging that interactively guides students along the debugging process. The guidance aims to empower the students to repair their solutions …

Seeing Is Fixing: Cross-Modal Reasoning With Multimodal Llms For Visual Software Issue Fixing, Kai Huang, Jian Zhang, Xiaofei Xie, Chunyang Chen

Research Collection School Of Computing and Information Systems

Large language model (LLM)-based automated program repair (APR) techniques have shown promising results in resolving real-world github issue tasks. Existing APR systems are primarily evaluated in unimodal settings (e.g., SWE-bench), relying solely on textual issue descriptions and source code. However, these autonomous systems struggle to resolve multimodal problem scenarios (e.g., SWE-bench M) due to limitations in interpreting and leveraging visual information. In multimodal scenarios, LLMs need to rely on visual information in the graphical user interface (GUI) to understand bugs and generate fixes. To bridge this gap, we propose GUIRepair, a cross-modal reasoning approach for resolving multimodal issue scenarios by …

Adasteer: Your Aligned Llm Is Inherently An Adaptive Jailbreak Defender, Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Despite extensive efforts in safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks. Activation steering offers a training-free defense method but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. To address this, we propose AdaSteer, an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. We identify two key properties: Rejection Law (R-Law), which shows that stronger steering is needed for jailbreak inputs opposing the rejection direction, and Harmfulness Law (H-Law), which differentiates adversarial and benign inputs. AdaSteer steers input representations along both the Rejection Direction …

Exploring Autonomous Agents: A Closer Look At Why They Fail When Completing Tasks, Ruofan Lu, Yichen Li, Yintong Huo

Research Collection School Of Computing and Information Systems

Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-source agent frameworks combined with two LLM backbones, observing a task completion rate of approximately 50%. Through in-depth failure analysis, we develop a three-tier taxonomy of failure causes aligned with task phases, highlighting planning errors, task …

Envisioning Future Interactive Web Development: Editing Webpage With Natural Language, Truong Hai Dang, Jingyu Xiao, Yintong Huo

Research Collection School Of Computing and Information Systems

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., ”center the logo”) remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs …

Interaction2code: Benchmarking Mllm-Based Interactive Webpage Code Generation From Interactive Prototyping, Jingyu Xiao, Yuxuan Wan, Yintong Huo, Zixin Wang, Xinyi Xu, Wenxuan Wang, Zhiyao Xu, Yuhang Wang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmarks only contain static web pages for evaluation and ignore the dynamic interaction, limiting the practicality, usability and user engagement of the generated webpages. To bridge these gaps, we present the first systematic investigation of MLLMs in generating interactive webpages. Specifically, we formulate the Interaction-to-Code task and establish the Interaction2Code benchmark, encompassing 127 unique webpages and 374 distinct interactions across 15 webpage types and 31 interaction categories. Through comprehensive experiments utilizing state-of-theart (SOTA) MLLMs, evaluated via both automatic …

One Planner To Guide Them All! Learning Adaptive Conversational Planners For Goal-Oriented Dialogues, Huy Dao, Lizi Liao

Research Collection School Of Computing and Information Systems

Goal-oriented dialogues, such as recommendation and negotiation, often require balancing multiple, conflicting objectives. Existing methods typically involve training separate models for specific combinations of objectives, leading to computational and scalability issues. In this work, we aim to develop a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining. This raises several challenges in terms of both (1) optimization strategy and (2) knowledge utilization. To address these, we propose a novel learning framework, Preference Adaptive Dialogue Policy Planner (PADPP), for multi-objective goal-oriented dialogues. Specifically, to tackle the former, we introduce a novel policy optimization …

Case Series Overview: Analysis Of Innovation Applications And Value Driven By Aigc Technology—An Early Investment Perspective In The Chinese Market, Siyuan Ma, Mengyu Wang, Tianyi Zhang

Sim Kee Boon Institute for Financial Economics

This case series explores the innovative applications and value analysis driven by AIGC (AI-generated content) technology from the perspective of early-stage investments in the Chinese market. We analyse the influence of core factors such as technology progression, market demand, policy environment, industry cycles and data on AIGC-driven value creation; case studies are provided to illustrate how AIGC has varying applications across different industries depending on market conditions. This series contains four cases: first, we first explore the investment opportunities of AIGC by combining the core technological value and industrial application status of generative AI, using China’s energy industry as a …

3 Of 4: Analysis Of Innovation Applications And Value Driven By Aigc Technology: An Early Investment Perspective In The Chinese Market, Siyuan Ma, Mengyu Wang, Tianyi Zhang

Sim Kee Boon Institute for Financial Economics

This case series explores the innovative applications and value analysis driven by AIGC (AI-generated content) technology from the perspective of early-stage investments in the Chinese market. We analyse the influence of core factors such as technology progression, market demand, policy environment, industry cycles and data on AIGC-driven value creation; case studies are provided to illustrate how AIGC has varying applications across different industries depending on market conditions. This series contains four cases: first, we first explore the investment opportunities of AIGC by combining the core technological value and industrial application status of generative AI, using China’s energy industry as a …

2 Of 4: Impact Of Human Resource Factors On Ai Investment: Ai In The Human Resource Market, Siyuan Ma, Mengyu Wang, Tianyi Zhang

Sim Kee Boon Institute for Financial Economics

This case will explore the implications that AIGC trends have on the development of the Chinese Human Resource (HR) market. While previous generation internet products were based on common needs and provided standardized products, the development of large AI models has opened doors to personalized custom products and services. Take AI applications in online education as an example: AI education models are developing to tailor to individual needs, since almost everything is not only digitalised but also based on dynamic database analysis for each user, including traffic distribution, sales conversion and service evaluation.

1 Of 4: Aigc Technology And Applications: Policy And Industry Development, Siyuan Ma, Mengyu Wang, Tianyi Zhang

Sim Kee Boon Institute for Financial Economics

This case explores the investment opportunities of AIGC by combining the core technological value and industrial application status of generative AI. We then analyse the impacts of these large-scale sectoral changes in a study of AI in China’s energy industry. We are currently in the early stage of the generative AI industry, thus it is analysed more from the perspective of early value investment, including trend investment opportunities brought by macro cycles, inflection point investment opportunities brought by industry transformation and application, and value creation brought by disruptive innovation. The early investment in the Chinese market is also at a …

Technical Appendix: Analysis Of Innovation Applications And Value Driven By Aigc Technology—An Early Investment Perspective In The Chinese Market, Siyuan Ma, Mengyu Wang, Tianyi Zhang

Sim Kee Boon Institute for Financial Economics

Following the introduction of generative AI/AIGC concepts and applications, it is necessary to clarify two related technical concepts: machine vision (CV) and convolutional neural networks (CNN). Computer Vision (CV) is a discipline that studies how to convert inputs such as images, sounds, and videos into a language that computers can understand. Taking 2D images as an example, all images can be broken down into very small pixels, each of which can be converted into a number (based on grayscale) or a combination of numbers. The number represented by each pixel is the feature of that pixel, and combining these numbers …