Digital Commons Network^™

A System Framework To Symbolically Explore Intel Tdx Module Execution, Pansilu Pitigalaarachchillage, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present TDXplorer, the first dynamic symbolic analysis system for Intel's TDX Module, the software trusted computing base of TDX. Without using TDX hardware, an analyzer function on top of TDXplorer can not only apply dynamic analysis to control and instrument the TDX Module's execution, but also carry out symbolic execution for path exploration as well as security and functionality reasoning. The two types of analysis are seamlessly integrated in a way that symbolic execution is conducted directly upon the TDX Module's binary code and runtime states, which are shaped by using dynamic analysis techniques. We implement TDXplorer on Linux …

Developing A Strong Cps Defender: An Evolutionary Approach, Qingyuan Hu, Christopher M. Poskitt, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) are used extensively in critical infrastructure, underscoring the need for anomaly detection systems that are able to catch even the most motivated attackers. Traditional anomaly detection techniques typically do `one-off' training on datasets crafted by experts or generated by fuzzers, potentially limiting their ability to generalize to unseen and more subtle attack strategies. Stopping at this point misses a key opportunity: a defender can actively challenge the attacker to find more nuanced attacks, which in turn can lead to more effective detection capabilities. Building on this concept, we propose Evo-Defender, an evolutionary framework that iteratively strengthens CPS …

Conditional Attribute-Based Pre: Definition And Construction From Lwe, Lisha Yao, Jian Weng, Pengfei Wu, Guofeng Tang, Guomin Yang, Haiyang Xue, Robert H. Deng

Research Collection School Of Computing and Information Systems

Attribute-based proxy re-encryption (AB-PRE) is a crucial variant of proxy re-encryption. It allows a proxy with a re-encryption key to transform a delegator’s ciphertext associated with an access policy into another ciphertext associated with a new access policy, enabling delegatees with matching attributes to decrypt the transformed ciphertext. However, a key limitation of AB-PRE is that the delegator cannot control which ciphertexts are transformed. As a result, the proxy, once given the re-encryption key, indiscriminately transforms all ciphertexts, effectively switching their underlying policies—an issue known as the all-or-nothing problem. It limits the system’s flexibility and practicality in real-world use cases.In …

Fcad: Feature-Coupled Anisotropic Diffusion For Continuous Graph Learning, Amitoz Azad, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

In this work, we propose a novel continuous graph neural network called FCAD (Feature-Coupled Anisotropic Diffusion) for the task of node classification on graphs. Our approach is motivated by the success of feature-coupled anisotropic diffusion PDEs in multivalued image restoration. Our method introduces a total variation regularization-inspired anisotropic term to control diffusion between nodes and incorporates a learnable parameterization for feature coupling during the diffusion process. Our model performs competitively against several GNN baselines for both heterophilous and homophilous graphs, demonstrating notable benefits for heterophilous graphs due to the learnable feature coupling.

Memad: Structured Memory Of Debates For Enhanced Multi-Agent Reasoning, Shuai Ling, Lizi Liao, Dongmei Jiang, Weili Guan

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) demonstrate remarkable in-context learning capabilities but often struggle with complex, multi-step reasoning. Multi-Agent Debate (MAD) frameworks partially address these limitations by enabling iterative agent interactions. However, they neglect valuable historical insights by treating each new debate independently. In this paper, we propose Memory-Augmented MAD (MeMAD), a parameter-free memory-augmented MAD framework that systematically organizes and reuses past debate transcripts. MeMAD stores structured representations of successful and unsuccessful reasoning attempts enriched with self-reflections and peer feedback. It systematically retrieves them via semantic similarity at inference time to inform new reasoning tasks. Our experiments on challenging mathematical reasoning, scientific …

Emoshortcuts: Emotionally Expressive Body Augmentation For Social Mixed Reality Avatars, Hyuna Seo, Youngki Lee, Rajesh Krishna Balan, Thivya Kandappu

Research Collection School Of Computing and Information Systems

We present EmoShortcuts1, a novel social Mixed Reality (MR) framework that enhances emotional expression by dynamically augmenting avatar body gestures to reflect users’ emotional states. While social MR enables immersive remote interactions through avatars, conveying emotions remains challenging due to limitations in head-mounted display (HMD) tracking (e.g., missing lower-body movements, such as stomping or defensive postures), and users’ tendency to deprioritize nonverbal expressions during multitasking. EmoShortcuts addresses these challenges by introducing an augmentation framework that generates expressive body gestures even when users’ physical movements are restricted. We conducted a formative study with 12 participants to identify key challenges in emotional …

Boosting Chart-To-Code Generation In Mllm Via Dual Preference-Guided Refinement, Zhihan Zhang, Yixin Cao, Lizi Liao

Research Collection School Of Computing and Information Systems

Translating chart images into executable plotting scripts-referred to as the chart-to-code generation task-requires Multimodal Large Language Models (MLLMs) to perform fine-grained visual parsing, precise code synthesis, and robust cross-modal reasoning. However, this task is inherently under-constrained: multiple valid code implementations can produce the same visual chart, and evaluation must consider both code correctness and visual fidelity across diverse dimensions. This makes it difficult to learn accurate and generalizable mappings through standard supervised fine-tuning. To address these challenges, we propose a dual preference-guided refinement framework that combines a feedback-driven, dual-modality reward mechanism with iterative preference learning. Our approach introduces a structured …

Art4math: Handwritten Mathematical Expression Recognition Via Multimodal Sketch Grounding, Yang Zhou, Jin Wang, Yuxiao Zhang, Kaixiang Huang, Guodong Lu, Jingru Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

Handwritten Mathematical Expression Recognition (HMER) remains a challenging task due to the structural complexity of mathematical notation and the ambiguity of handwritten symbols-e.g., ''ρ'' vs. ''p'' or ''B'' vs. ''β''. While stroke-based models offer disambiguation via temporal cues, most existing methods are constrained by coarse modality fusion and a lack of fine-grained cross-modal alignment, further hindered by limited annotated data. We introduce Art for Math (Art4Math), a novel framework that leverages the structural richness of human sketches to enhance HMER through fine-grained, modality-aware learning. Art4Math follows a two-stage training paradigm: Art Grounding (A-Grd) and Math Decoding (M-Dec). In A-Grd, the …

Organization Research As An Applied Science: Lessons From Fields That Shape Practice And Policy, Stephen X. Zhang, Gokhan Ertug

Research Collection Lee Kong Chian School Of Business

As an applied science, our field aims to improve organizational effectiveness and solve practical problems (Hideg, DeCelles & Tihanyi, 2020; Pfeffer & Fong. 2002; Pillutla & Thau, 2013). However, despite the increasing sophistication in methods and greater emphasis on theory in recent decades, we have not made commensurate strides in relevance and impact. As we see it, the heart of this challenge is the culture in our field that demands novel theoretical advancement from every paper (Aguinis & Cronin, 2022), which may inadvertently constrain our ability to address pressing organizational challenges. Previous appeals to enhance our impact as an applied …

Detecting Defi Fraud With A Graph-Transformer Language Model, Wei Ma, Junjie Shi, Jiaxi Qiu, Cong Wu, Jing Chen, Lingxiao Jiang, Shangqing Liu, Yang Liu, Yang Xiang

Research Collection School Of Computing and Information Systems

With the rapid development of blockchain technology, the widespread adoption of smart contracts—particularly in decentralized finance (DeFi) applications—has introduced significant security challenges, such as reentrancy attacks, phishing, and Sybil attacks. To address these issues, we propose a novel model called TrxGNNBERT, which combines Graph Neural Network (GNN) and the Transformer architecture to effectively handle both graph-structured and textual data. This combination enhances the detection of suspicious transactions and accounts on blockchain platforms like Ethereum. TrxGNNBERT was pre-trained using a masked language model (MLM) on a dataset of 60,000 Ethereum transactions by randomly masking the attributes of nodes and edges, thereby …

Reimagining U.S. Tort Law For Deepfake Harms: Comparative Insights From China And Singapore, Huijuan Peng, Pey Woan Lee

Research Collection Yong Pung How School Of Law

This Article explores how U.S. tort law can respond more effectively to the distinct harms posed by deepfakes, including reputational injury, identity appropriation, and emotional distress. Traditional tort doctrines, such as defamation, the right of publicity, and intentional infliction of emotional distress (IIED), remain fragmented and ill-suited to the speed, scale, and anonymity of deepfake dissemination. Using a comparative functionalist approach, the Article analyzes how China and Singapore respond to deepfake harms through structurally divergent but functionally instructive frameworks. China’s model combines codified personality rights with intermediary obligations under a civil law regime, while Singapore adopts a hybrid approach that …

Mixsa: Training-Free Reference-Based Sketch Extraction Via Mixture-Of-Self-Attention, Rui Yang, Xiaojun Wu, Shengfeng He

Research Collection School Of Computing and Information Systems

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the …

Neuro-Ins: A Learning-Based One-Shot Node Insertion For Dynamic Routing Problems, Zhiqin Zhang, Jingfeng Yang, Zhiguang Cao, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The rise in instant delivery services necessitates efficient route planning in last-mile delivery scenarios, where new orders arrive dynamically and need to be integrated into existing routes. In such contexts, complete re-optimization of routes are not permitted, and node insertion to existing route sequences is the only viable option. However, many existing heuristics for node insertion, such as the Cheapest Insertion (CI) method, are myopic and often result in suboptimal solutions retrospectively. This paper presents Neuro-Ins, an initial yet novel attempt at harnessing a learning-based framework to handle the insertion of new orders for the Pickup and Delivery Problem (PDP). …

Lighttransfer: Your Long-Context Llm Is Secretly A Hybrid Model With Effortless Adaptation, Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In this work, we propose LightTransfer, a lightweight method that transforms models such as LLaMA into hybrid variants. Our approach identifies lazy layers -- those focusing on recent or initial tokens -- and replaces their full attention with streaming attention. This transformation can be performed without any training for long-context …

Gti: Graph-Based Tree Index With Logarithm Updates For Nearest Neighbor Search In High-Dimensional Spaces, Ruoyao Ma, Yifan Zhu, Baihua Zheng, Lu Chen, Congcong Ge, Yunjun Gao

Research Collection School Of Computing and Information Systems

Nearest neighbor search (NNS) is fundamental for high-dimensional space retrieval and impacts various fields, such as pattern recognition, information retrieval, recommendation systems, and vector database management. Among existing NNS methods, graph-based methods often excel in query accuracy and efficiency. However, these methods face significant challenges, including high construction costs and difficulties with dynamic data updates. Recent efforts have focused on combining graph methods with hashing, quantization, and tree-based approaches to address these issues, but problems with large index sizes and update performance remain unresolved. In response, this paper proposes GTI, a novel, lightweight, and dynamic graph-based tree index for high-dimensional …

Deep Graph Anomaly Detection: A Survey And New Perspectives, Hezhe Qiao, Hanghang Tong, Nanyang Technological University, Irwin King, Charu Aggarwal, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model …

Boosting Symbolic Execution For Vulnerability Detection, Haoxin Tu

Dissertations and Theses Collection (Open Access)

Software systems written by humans tend to be unreliable and insecure, hence, bugs or vulnerabilities in them are inevitable. Symbolic execution has shown considerable potential in detecting diverse types of software bugs and also vulnerabilities that have severe security implications. However, existing symbolic execution engines still suffer from at least three fundamental limitations in memory modeling, path exploration, and structured input generation, which significantly impede existing engines from efficiently and effectively detecting software bugs and vulnerabilities.

The objective of this dissertation is to boost existing symbolic execution engines by designing a new memory model, two new path exploration strategies, and …

Memory-Efficient Graph Processing On Gpus: Reducing Intermediate Data Structure Overhead, Chang Ye

Dissertations and Theses Collection (Open Access)

The increasing scale of real-world graphs in domains such as fraud detection, community detection, and biological analysis demands high-throughput, memory-efficient graph processing solutions. GPUs offer massive parallelism for accelerating such workloads, and numerous frameworks have been developed to leverage their computational power. These frameworks primarily focus on optimizing scheduling to better align graph processing with GPU architectures. It performs well for algorithms with low memory demands, such as BFS, SSSP, and PageRank. However, for algorithms that require substantial memory, such as label propagation, and subgraph counting, the limited memory capacity of GPUs often becomes a significant bottleneck.

This dissertation addresses …

The Dual Impact Of Ict Use On Work Autonomy, Workload, Work Engagement, And Work-Family Conflict: Moderating Effects Of Perceived Organizational Support And Conscientiousness, Shenghui Han

Dissertations and Theses Collection (Open Access)

This study investigates the dual impact of information and communication technology (ICT) use intensity on employee outcomes in the workplace, with a particular focus on work autonomy, workload, work engagement, and work–family conflict (WFC). Drawing upon the job demands-resources (JD-R) model and conservation of resources (COR) theory, I propose a dual-pathway framework in which ICT use intensity acts as both a job resource and a job demand. The study also examines the moderating roles of perceived organizational support (POS) and conscientiousness.

Data were collected from 414 full-time employees across six companies in China through a three-wave survey with two-week intervals …

Supporting Satisfaction, Satisfying Support: Bidirectional Associations Of Social Support And Life Satisfaction, Jonathan Louis Jie Sheng Chia, Andree Hartanto, William Tov

Research Collection School of Social Sciences

Existing literature has explored the impact of social support (SS) on life satisfaction (LS). However, the reciprocal relationship—that is, the influence of LS on SS—remains understudied, despite extant theoretical support. In addressing this gap, the present study employed bivariate latent growth modeling to examine bidirectional associations between SS and LS trajectories among 8,449 middle-aged and older adults in Singapore, over seven waves spanning almost 6 years. Results provided evidence supporting the notion of bidirectional associations. Specifically, baseline SS positively predicted subsequent changes in LS, and baseline LS positively predicted subsequent changes in SS. Findings underscore the potential for interventions and …

The Moderating Role Of Trait Online Disinhibition In Exacerbating The Online Victim-Bully Cycle, Sandeeshwara K Tennakoon Appuhamillage Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

The growth of the internet and social media as a communication platform has increased the incidence of cyberbullying victimisation. While there has been increasing research interest on the antecedents of cyberbullying perpetration, little is known on why some individuals who themself experience cyber victimisation are more likely to become cyberbullying perpetrators. Considering the large number of individuals who experience cyberbullying victimisation in the current, highly digitalised world, this trend of victims turning into bullies poses a worrisome possibility and deserves research attention. One promising individual difference that could potentially moderate the relationship between cyberbullying victimisation can lead to cyberbullying perpetration …

The Return Of Chinese Emigrants: Legal Barriers And Local Practices, Jiaqi M. Liu

Research Collection School of Social Sciences

While China has observed an increasing influx of foreign nationals, a notable phenomenon involves Chinese emigrants opting for return migration to reestablish themselves in their country of origin. The process of emigrants returning to China, referred to as “return and resettlement” (huiguo dingju 回国定居) in official terminology, however, is not automatic. Emigrant returnees must fulfill a set of selective and documentary criteria, implying that only economically affluent and duly documented migrants are permitted to resettle in China. Through a comprehensive review of national policies and an analysis of local practices in a diaspora hometown, this chapter argues that the hukou …

A Multifaceted Nudge-Based Intervention To Reduce Smartphone Use: Findings From A Randomized Cross-Over Trial, K. T. A. Sandeeshwara Kasturiratna, Yi Jing Chua, Andree Hartanto

Research Collection School of Social Sciences

The widespread use of smartphones has raised concerns about problematic usage behaviors, including excessive screentime and frequent phone checking, which can disrupt daily functioning. This study aimed to evaluate the effectiveness of a multifaceted nudge-based intervention in reducing smartphone use through subtle changes to smartphone settings. The intervention incorporated adjustments such as disabling face/touch ID, setting longer passwords, switching to grayscale mode, and removing social media apps from the home screen. A randomized within-subject cross-over trial with counterbalancing was conducted over a two-week period using a daily diary approach paired with objective smartphone usage data to track the impact of …

Prevalence Of Depression, Anxiety, Burden, Burnout, And Stress In Informal Caregivers: An Umbrella Review Of Meta-Analyses, Xun Ci Soh, Andree Hartanto, Naoki Ling, Mayr Reyes, Lester Sim, Nadyanna M. Majeed

Research Collection School of Social Sciences

Informal caregiving is a physically and emotionally taxing role that has a profound impact on caregivers’ emotional and mental well-being. Previous research has examined the mental health of caregivers and found elevated levels of depression, anxiety, burden, burnout, or stress. However, the global rates of the five mental health factors and/or outcomes (i.e., depression, anxiety, burden, burnout, and stress) among caregivers remain largely unclear. Therefore, this umbrella review examines the prevalence of these factors and/or outcomes, including various subgroup factors, such as gender, medical and/or psychological condition of the care recipient, region, and assessment tools. A systematic search was conducted …

Reimagining Academic Assessment In The Age Of Ai, Matthew Hammerton

Research Collection School of Social Sciences

In ‘Reimagining Academic Assessment in the Age of AI’, Matthew Hammerton examines the challenges and opportunities posed by generative AI for higher education assessment. He critiques common responses like banning AI, reverting to in-class exams, or abandoning essays altogether, arguing that they fail to preserve the deeper pedagogical goals of higher order, independent thinking. Instead, Hammerton proposes a guiding principle of intellectual responsibility: students should be accountable for explaining and defending each major choice in their work—regardless of whether they use AI tools. To operationalise this, he advocates for reintegrating oral examinations (‘vivas’) alongside written essays. In this model, students …

The Moderating Role Of Digital Multi-Sided Platform On The Firm Value Of Traditional Value-Chain Firms, Chee Pheng Lim

Dissertations and Theses Collection (Open Access)

This dissertation examines the strategic value and performance implications for traditional value-chain firms (TVCFs) that adopt digital multi-sided platforms (DMSPs) to augment their existing businesses. DMSPs facilitate interactions among multiple user groups through digital intermediaries, generating self-reinforcing network effects that enhance value creation, reduce operational and transaction costs, and enable collective innovation. Despite the prevalence of platform-based firms, 60% of global unicorns and 7 of the world’s 10 most valuable companies operate digital platforms, empirical research on the value-creation mechanisms of DMSPs remains limited. Anchored in the resource-based view and platform theory, this study investigates how DMSP moderates the relationship …

Speculative Bubbles In The Recent Ai Boom: Nasdaq And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi

Research Collection School Of Economics

The recent artificial intelligence (AI) boom covers a period of rapid innovation and wide adoption of AI intelligence technologies across diverse industries. These developments have fueled an unprecedented frenzy in the Nasdaq, with AI-focused companies experiencing soaring stock prices that raise concerns about speculative bubbles and real-economy consequences. Against this background, this study investigates the formation of speculative bubbles in the Nasdaq stock market with a specific focus on the so-called Magnificent Seven (Mag-7) individual stocks during the AI boom, spanning the period from January 2017 to January 2025. We apply the real-time PSY bubble detection methodology of Phillips et …

Guiding Multiple Remote Users In Physical Tasks With Language-Driven Robotic Telepresence, Ruyi Li, Jingfei Guo, Xinyi Zhang, Xuji Zhang, Zeqing Li, Jiannan Li, Jiangtao Gong

Research Collection School Of Computing and Information Systems

Remote assistance through robotic telepresence could involve both control and memory challenges, particularly in one expert to multiple workers situation. In this work, we proposed a novelty language-driven interface to facilitate remote collaboration through telepresence robots. Through operations and maintenance expert interviews and a scenario simulation study, we identified key pain points in executing one-expert-multiple-workers remote guidance using the telepresence robot and proposed two design goals, which together consist of five sub-design goals with corresponding features. These features were integrated into a standard telepresence robot, resulting in the development of a Collaborative LLM-based Embodied Assistant Robot, named CLEAR Robot. A …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Death Penalty For Drug Offenders In Southeast Asia: Weakening Of Resistance To Change?, Wing Cheong Chan

Research Collection Yong Pung How School Of Law

Although the global movement towards abolition of the death penalty has been supported by an increasing number of countries in the world, the number of persons executed for drug offences actually increased in the last few years. Known executions for drug offences now account for more than 40 per cent of all global executions. Most such executions take place in Asia. Of the 34 countries in the world that still retain the death penalty for drug offenders, all nine of those identified as “high application states” (China, Indonesia, Iran, Kuwait, Malaysia, North Korea, Saudi Arabia, Singapore and Vietnam) are found …