Digital Commons Network^™

Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

Research Collection School Of Computing and Information Systems

Quantizing large language models (LLMs) is essential for reducing memory and computational costs in natural language processing. Existing methods combine quantization with parameter-efficient fine-tuning but often fail to meet practical performance requirements. This paper introduces MeMoTune, a novel fine-tuning framework for quantized LLMs. By employing a measure and moment approach within a low-rank approximation framework in probability measure space, MeMoTune optimizes the objective function for superior fine-tuning results. The update process is further refined through scaled gradient, enhancing convergence efficiency and noise robustness. Experiments on tasks like text generation, summarization, and understanding show MeMoTune significantly outperforms state-of-the-art methods, e.g. fine-tuning …

Inference-Time Gaze Refinement For Micro-Expression Recognition: Enhancing Event-Based Eye Tracking With Motion-Aware Post-Processing, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and …

Enhancing Graph Representation Learning Through Self-Supervision: An Augmentation Perspective, Jianyuan Bo

Dissertations and Theses Collection (Open Access)

Graph representation learning has become fundamental in various domains, from social networks to molecular structures, enabling extraction of meaningful patterns from graph-structured data. While deep learning approaches, particularly graph neural networks, have shown promising results, their effectiveness is often limited by the scarcity of labeled data. This challenge is particularly acute in graph domains where annotation requires specialized expertise and is prohibitively expensive. Self-supervised learning has emerged as a promising direction to address this limitation by creating auxiliary tasks from unlabeled data, with augmentation strategies playing a crucial role in their success.

Current graph self-supervised learning methods face several critical …

Relational History: Correcting Temporal Myopia In Social Network Research, Julia Brennecke, Gianluca Carnabuci, Gokhan Ertug

Research Collection Lee Kong Chian School Of Business

Scholars have used network concepts and methods to explain a wide range of organizational phenomena. Despite this broad interest, prevalent approaches tend to neglect a critical constitutive dimension of network ties: their history. We invoke the concept of relational history to signify the temporal path and biography of a network tie. We argue that a network tie’s history shapes what the relationship means to the parties involved, what can be accomplished in and through the relationship, and how the relationship might further evolve. In directing the attention of organizational network researchers to the relational history of network ties, we aim …

Ts-Diff: Two-Stage Diffusion Model For Low-Light Raw Image Enhancement, Yi Li, Zhiyuan Zhang, Jiangnan Xia, Jianghan Cheng, Qilong Wu, Junwei Li

Research Collection School Of Computing and Information Systems

This paper presents a novel Two-Stage Diffusion Model (TS-Diff) for enhancing extremely low-light RAW images. In the pre-training stage, TS-Diff synthesizes noisy images by constructing multiple virtual cameras based on a noise space. Camera Feature Integration (CFI) modules are then designed to enable the model to learn generalizable features across diverse virtual cameras. During the aligning stage, CFIs are averaged to create a target-specific CFIT, which is fine-tuned using a small amount of real RAW data to adapt to the noise characteristics of specific cameras. A structural reparameterization technique further simplifies CFIT for efficient deployment. To address color shifts during …

Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …

Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write …

Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Android system provides application developers with the ability to define custom permissions, which serve to moderate the sharing of resources and interactions with other applications. However, poor development practices of developers can render the permission mechanism ineffective, weakening the system protection. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, or even component hijacking. To automatically …

The B2biers System: A Content-Based Perspective On Maximizing Influence And Subscription In Social Networks, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

The popular problem of Influence Maximization (IM) asks for the k users who can maximize the influence of a fixed post in a social network. In contrast, the problem of Content- Aware Influence Maximization (CAIM) asks for the k features to form a viral tunable post in a social network starting its diffusion from a fixed set of initial adopters. CAIM paves the way for a number of novel problems to be studied that altogether can lead to the development of a system that would be valuable for advertisers who manage social network pages. This holds since features (brands) in …

Prompttutor: Effects Of An Llm-Based Chatbot On Learning Outcomes And Motivation In Flipped Classrooms, Yuhao Zhang, Eng Lieh Ouh, Chong Jee Adam Ho, Siaw Ling Lo, Kar Way Tan, Feng Lin

Research Collection School Of Computing and Information Systems

This study explores the integration of a Large Language Model (LLM) based chatbot, PromptTutor, into flipped classrooms (FC) for undergraduate Computer Science (CS) education. PromptTutor is designed to provide personalized, immediate feedback to support student learning in FC by incorporating reflective learning and scaffolding strategies. The traditional FC typically lacks this immediate feedback during the pre-class learning phase, risking decreased student motivation according to existing literature. This study examines if students improve in learning outcomes and motivation after using PromptTutor. Through a controlled crossover experiment with 50 students, the study demonstrates statistically significant improvements in students' quiz performance and motivation …

Unified Neural Backdoor Removal With Only Few Clean Samples Through Unlearning And Relearning, Nay Myat Min, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Deep neural networks have achieved remarkable success across various applications; however, their vulnerability to backdoor attacks poses severe security risks—especially in situations where only a limited set of clean samples is available for defense. In this work, we address this critical challenge by proposing ULRL (UnLearn and ReLearn for backdoor removal), a novel two-phase approach for comprehensive backdoor removal. Our method first employs an unlearning phase, in which the network’s loss is intentionally maximized on a small clean dataset to expose neurons that are excessively sensitive to backdoor triggers. Subsequently, in the relearning phase, these suspicious neurons are recalibrated using …

Unveiling Knowledge Boundary Of Large Language Models For Trustworthy Information Access, Yang Deng, Moxin Li, Liang Pang, Wenxuan Zhang, Wai Lam

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries—the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of …

Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …

Dual-Target Disjointed Cross-Domain Recommendation Mediated Via Latent User Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Users often navigate multiple platforms online, each characterized by its own set of scarce data. Recommender systems face a significant challenge in such fragmented environments. This paper proposes a novel approach to enhance recommendation systems by leveraging connections across distinct yet conceptually similar datasets from multiple platforms. We introduce a unique scenario of dual-target overlapping-free cross-platform recommendation, presenting a bridging mechanism to mutually improve across platforms and learn latent user preferences. Our approach addresses the data sparsity prevalent in each platform and enhances recommendation quality by harnessing redundant, rich, and similar domain data. Experiments validate the effectiveness of our method, …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Do Supplier Ceo's National Cultural Origins Affect Supplier-Customer Relationships?, Peng Liang, Hasan Cavusoglu, Nan Hu

Research Collection School Of Computing and Information Systems

This study investigates how the national cultural origins of supplier chief executive officers (CEOs), as characterized by Hofstede’s cross-cultural dimensions, influence the duration of supplier–customer relationships. By analyzing the cultural origins of supplier CEOs from 20 countries over a 25-year period, we find that supplier CEOs with high long-term orientation (LTO) and high uncertainty avoidance (UNA) are associated with longer lasting supplier–customer relationships, while those with high individualism (IND) are associated with shorter relationship durations. These findings are robust to several alternative explanations of customer and supplier CEO variables. To address potential endogeneity—specifically, the concern that CEOs with certain cultural …

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 …

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

Governing Migration, Producing Skills: Emigration And Education In The Philippines, Yasmin Y. Ortiga, Roderick. Galam

Research Collection School of Social Sciences

This article examines skill as an integral yet understudied aspect of emigration governance. To date, migration studies have mainly focused on how the demand for migrant skills drive people's movements across borders, shaping the conditions for entry into popular destinations in the West. In contrast, less is known as to how skills also shape the way governments manage emigration, pushing would-be migrants to acquire certain capacities well before they leave their countries of origin. Drawing from the case of the Philippines, this article discusses how state agencies have used skills training to dominate specific markets for migrant professionals and demand …

Stockpiling, Forecasting And Protecting Skills: Skilling As Technique Of Preparedness In Times Of Crises, Rebecca Ye, Yasmin Y. Ortiga

Research Collection School of Social Sciences

In this comparative and situational analysis, we examine how nation-states use skilling as a technique of preparedness during crisis moments. By analysing two strategically selected country cases, Singapore and Sweden, during a critical phase of a global pandemic, we identify three techniques: stockpiling, forecasting, and protecting skills, and how these were enacted as a means to perform preparedness. Stockpiling skills was observed to take place as a rapid response; forecasting skills was a technique enacted with a view on the longer-term horizon and protecting skills was mobilised as a short-term future technique, where boundaries were assembled around skilling. While there …

Analyzing Google Maps As A Data Source For Urban Research In Dhaka, Xinyi Du

Dissertations and Theses Collection (Open Access)

This paper investigates how mass transit infrastructure shapes urban economic activity by examining the impact of Dhaka's first metro line using novel Google Maps data. Leveraging the opening of Metro Line 6 on December 2022, we employ a difference-in-differences framework to analyze changes in business establishment density around metro stations. Our estimates show that every 1% reduction in distance to the nearest metro station adds, on average, about 0.092–0.111 establishments per 40 m × 40 m grid cell—that is, roughly 57 – 69 additional businesses per square kilometer. The effect is even more pronounced for dining: about 1 – 4 …

Machine Learning For Digital Biomarker-Based Detection Of Cognitive Decline, Seng Khoon The

Dissertations and Theses Collection (Open Access)

Dementia is a neurodegenerative disease with a prevalence rate expected to triple by 2050, posing a significant challenge for health services. To impede the increasing prevalence, medical professionals and scientists are actively investigating technology to detect cognitive decline at a reversible stage known as Mild Cognitive Impairment (MCI). Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies; advanced …

The Rising Returns To R&D: Ideas Are Not Getting Harder To Find, Yoshiki Ando, James Bessen, Xiupeng Wang

Research Collection School Of Economics

R&D has grown robustly, yet aggregate productivity growth has stagnated. Is this because “ideas are getting harder to find”? Using microdata from the US Census Bureau this paper estimates R&D effectiveness in the manufacturing sector from 1976 to 2018. We find that the marginal effect of R&D on output and the gross marginal returns to R&D have risen sharply. But rivals’ R&D has also gotten more effective, raising expected obsolescence. But firm productivity growth rises with growth in the R&D stock. Higher obsolescence slows the accumulation of R&D capital, reducing the contribution to productivity growth and shortening innovation lives.

Evaluating Chatgpt To Answer Multi-Modal Exercises In Computer Science Education, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan

Research Collection School Of Computing and Information Systems

This study investigates ChatGPT-4o's ability to answer multi-modal assessment exercises in computer science (CS) courses. While the use of large language models (LLMs) to answer text-based exercises are extensively researched, their ability to answer exercises involving artifacts of other modalities remains underexplored. To close this gap, we evaluate ChatGPT-4o's answers to 120 multi-modal CS exercises in programming, software design, human-computer interaction, statistical analysis, process analysis, and simulation. The multi-modal artifacts in these exercises include class diagrams, sequence diagrams, user interface images, analytical charts, workflow diagrams and object-flow diagrams. Our comparisons to the expected answers of these exercises show that ChatGPT-4o …

An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi

Research Collection School Of Computing and Information Systems

We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to formula size in the worst case. In this case, the synthesis cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components …

The Role Of Private Law In Promoting Building Safety: The Case Of Singapore, Edward S. W. Ti

Research Collection Yong Pung How School Of Law

Singapore is a tiny granite rock situated just north of the equator. Lacking land, and indeed any kind of natural resource, means that the trend for urban development in the city-state will always be densifi cation; 96 per cent of residents in Singapore already live in multi-owned housing, ie high-rise apartments. 1 Singapore is also a rapidly greying nation. 2 It has been observed that persons over 70 years old are four times more vulnerable in the event of a fi re, 3 and fi re safety is a critical aspect of building safety. Safety features and measures in the …

Digital Economy Innovation In The Indo-Pacific: Towards A 'Singapore Effect'?, J.G. Allen, Qiu Xu Martin Liao

Research Collection Yong Pung How School Of Law

This paper examines the rise of Digital Economy Agreements (DEAs) in the Indo-Pacific as a strategic response to digital trade fragmentation and great power competition. Focusing on Singapore’s leadership, we introduce the ‘Singapore Effect’ – a model of regulatory influence distinct from others, such as the ‘Brussels Effect’ and ‘Beijing Effect.’ Unlike market-driven regulatory diffusion, the Singapore Effect emphasizes interoperability, norm entrepreneurship, and mini-lateralism, enabling middle powers to shape digital trade governance. We analyze DEA formation, implementation challenges in national law, and Singapore’s role as a bridge between diverse regulatory regimes. DEAs’ flexible, modular structure allows for incremental regulatory alignment …

Wild Hogs In The Water: Contested Infrastructural Ecologies Of Reservoir Storage In Texas, Sayd Randle

Research Collection College of Integrative Studies

Reservoirs are developed to store water in reserve for future use. But once built, reservoir sites inevitably hold more than just water, often serving as a key habitat for a range of species. This paper examines how one such animal has transformed water storage facilities and nearby landscapes into contested ground in urbanising areas of Texas, USA. Living around the reservoirs, feral hogs complicate the process of urbanisation by degrading the stockpiled water and infrastructure at the storage sites themselves and by damaging private property throughout the surrounding landscape. Tracking local efforts to manage the hogs, the case study illustrates …

Political Ecologies Of Storage For The 21st Century, Sayd Randle, Matthew Archer

Research Collection College of Integrative Studies

New resource storage arrangements are proliferating rapidly both in terms of physical infrastructures-for the storage of things like "clean" energy, nuclear waste, carbon dioxide, fresh water, and data-and as part of a set of discursive moves that reinforce a vision of a near future world in which problems of climate change mitigation and adaptation in particular (but also issues like energy security, water security, industry growth, etc.) are solved through eco-modernist techno-fixes. This Symposium sketches the contours of a framework we term political ecologies of storage. In doing so, we treat storage as both a potent imaginary and a concrete …