Digital Commons Network^™

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Scaling Up Cooperative Multi-Agent Reinforcement Learning Through Hierarchical Heterogeneous Modular Architectures, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning enables sophisticated collaborative behaviors in autonomous systems, yet fundamental scalability barriers persist: existing methods struggle to coordinate large agent populations and face challenges with extended decision-making horizons. This research develops hierarchical approaches to scale up multi-agent learning systems through two complementary directions: structural scaling for coordinating increasing numbers of agents and temporal scaling for extending decision-making horizons. This paper presents four integrated contributions: a taxonomic survey establishing hierarchical architectures as the theoretical foundation for scalable multi-agent learning systems, a benchmark for long-horizon multi-objective multi-agent reinforcement learning, a framework integrating self-organizing neural networks with multiple reinforcement learning agents …

Revisiting The Canonicalization For Fast And Accurate Crystal Tensor Property Prediction, Haowei Hua Hua, Jingwen Yang, Wanyu Lin, Pan Zhou

Research Collection School Of Computing and Information Systems

Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining O(3) group tensor equivariance. Such equivariance constraint often requires specialized architecture designs to achieve effective predictions, inevitably introducing tremendous computational costs. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry. In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and …

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

Sketches, as a new solution in multimedia systems that can replace natural language, are characterized by sparse visual cues such as simple strokes that differ significantly from natural images containing complex elements such as background, foreground, and texture. This misalignment poses substantial challenges for zero-shot sketch-based image retrieval (ZS-SBIR). Prior approaches match sketches to full images and tend to overlook redundant elements in natural images, leading to model distraction and semantic ambiguity. To address this issue, we introduce a distraction-agnostic framework, purified cross-domain matching (PuXIM), which operates on a straightforward principle: masking and matching. We devise a visual-cross-linguistic (VxL) sampler …

Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma

Research Collection School Of Computing and Information Systems

Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this work, we present the first comprehensive empirical evaluation of LLMs for AVL. Our study examines 19 leading LLMs suitable for code analysis, including ChatGPT and multiple open-source models, spanning encoder-only, encoder-decoder, and decoder-only architectures, with model sizes from 60M to 70B parameters. We evaluate three paradigms including few-shot prompting, discriminative fine-tuning, and generative fine-tuning with and …

Interpretable Multimodal Zero Shot Ecg Diagnosis Via Structured Clinical Knowledge Alignment, Jialu Tang, Hung Manh Pham, Ignace De Lathauwer, Henk S. Schipper, Yuan Lu, Dong Ma, Aaqib Saeed

Research Collection School Of Computing and Information Systems

Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unseen conditions. To address this, we introduce ZETA, a zero-shot multimodal framework designed for interpretable ECG diagnosis aligned with clinical workflows. ZETA uniquely compares ECG signals against structured positive and negative clinical observations, which are curated through an LLM-assisted, expertvalidated process, thereby mimicking differential diagnosis. Our approach leverages a pre-trained multimodal model to align ECG and text embeddings without disease-specific fine-tuning. Empirical evaluations demonstrate ZETA’s competitive zero-shot classification performance and, importantly, provide qualitative and quantitative evidence of enhanced interpretability, grounding …

Uncovering Mild Drift In Asset Prices With Intraday High-Frequency Data, Shuping Shi, Peter C. B. Phillips

Research Collection School Of Economics

Asset prices are commonly represented as a drift-diffusion process, wherein the drift component denotes the anticipated return of the asset within some time frame, while the diffusion component accommodates random shocks. The drift component has substantial practical significance but accurate estimation is typically challenging and has met with limited success in the existing literature except over large time spans. This paper explores a comprehensive range of drift-diffusion models that include constant, linear, trending, and bursting drift. Conditions are identified under which realized squared drift  is a reliable tool for gauging integrated squared drift when the time span Tn is large …

Assessing Ethereum’S Diversification Role With Bitcoin And Major Equity Index – A Bayesian Mixture Copula Approach, Jackie Li, Jacie Jia Liu

Research Collection School Of Economics

With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, …

International Law, The Courts, And The Political Branches Of Singapore: Painting A Complete Picture, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

In line with Singapore's vision of the separation of powers, the courts' duty is primarily to give effect to domestic law; the political branches take the lead in engaging with international law. A study of Singapore's interface with international law would therefore be incomplete were it to consider only the courts' role and not the political branches' model of international law as primarily a guarantor of Singapore's sovereignty and standing as a participant on the international stage. The political branches have been circumspect in engaging with international law in other areas, such as human rights, preferring a specifically Singaporean vision …

The Shareholder’S Standing To Challenge The Exercise Of Directorial Power: Tianrui (International) Holding Company V China Shanshui Cement Group Ltd, Pearlie M. C. Koh

Research Collection Yong Pung How School Of Law

An enduring problem with the proper purposes duty is the apparent right of the shareholder to enforce the same despite the duty being owed to the company. The cases on the proper purpose duty have thus far simply assumed the right of the shareholder to do so without dealing with the question of standing. In Tianrui (International) Holding Company Ltd v China Shanshui Cement Group Ltd, the Privy Council dealt with this issue directly and affirmed the shareholder's right to bring the action on the basis of the statutory contract constituted by the memorandum and articles of association of the …

Grand Theft Identity: The Privacy Costs Of Digitalization, Kenny Phua, Chishen Wei, Gloria Yang Yu

Sim Kee Boon Institute for Financial Economics

We study whether greater digital engagement increases the risk of identity theft by exploiting bank branch closures as a shock that shifts economic activity online. Using a quasi-natural experiment, we find causal evidence that branch closures increase identity theft, particularly in more vulnerable communities. Exposed consumers spend more time on mobile apps and shift their expenditures from offline to online channels. Adversarial activities associated with identity theft, such as unwanted calls and phishing attempts, increase after branch closures. Overall, our evidence suggests that digitalization offers consumer benefits, but also imposes hidden privacy costs.

Digital Communications Between Firms And Investors: Impact Of Explanatory Responses On Investor Engagement In Online Financial Q&A, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang

Research Collection School Of Computing and Information Systems

The emerging trend of digital communications between firms and investors through online question-and-answer (Q&A) platforms is recognized as a vital strategy for managing investor relations, contributing to enhanced market efficiency and information transparency through increased information exchange. Potential investors can seek responses from firm managers to address their information needs, thereby mitigating market uncertainties. To provide foundational insights, we conduct a survey of investors to assess their awareness, usage, and perceptions of firm-investor Q&A platforms. In the subsequent empirical study, we specifically focus on the substance of managers’ responses, which are primarily aimed at clarifying firm events or information. In …

Coresets For Clustering Under Stochastic Noise, Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi, Runkai Yang, Haoyu Zhao

Research Collection School Of Computing and Information Systems

We study the problem of constructing coresets for $(k, z)$-clustering when the input dataset is corrupted by stochastic noise drawn from a known distribution. In this setting, evaluating the quality of a coreset is inherently challenging, as the true underlying dataset is unobserved. To address this, we investigate coreset construction using surrogate error metrics that are tractable and provably related to the true clustering cost. We analyze a traditional metric from prior work and introduce a new error metric that more closely aligns with the true cost. Although our metric is defined independently of the noise distribution, it enables approximation …

Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le

Research Collection School Of Computing and Information Systems

The nexus between data characteristics and parametric models is fundamental for developing effective and reliable artificial intelligence (AI) systems. Mismatches in data properties for model development may lead to deleterious effects on AI model performance in machine learning practice. This paper proposes a Reliable Data Split (RDS) procedure to learn how to select data points that will generalise the target domain adequately by employing prior knowledge of the data generative process. We introduce a reinforced selection strategy using deep reinforcement learning with diverse black box predictors in maximising ensemble rewards as the proxy of model performance potential while maintaining an …

Scalable Graph Indexing Using Gpus For Approximate Nearest Neighbor Search, Zhonggen Li, Xiangyu Ke, Yifan Zhu, Bocheng Yu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficiently, while graph-based indexes have gained prominence due to their high accuracy and efficiency. However, the indexing overhead of graph-based indexes remains substantial. With exponential growth in data volume and increasing demands for dynamic index adjustments, this overhead continues to escalate, posing a critical challenge.In this paper, we introduce Tagore, a fasT library accelerated by GPUs for graph indexing, which has powerful capabilities of constructing refinement-based graph indexes such as NSG and Vamana. We first introduce …

Does Social Media Undermine Trust? Institutional Trust In Civil Society And Governance Institutions, Christianna Sirindah Parr

Research Collection School of Social Sciences

What is the relationship between social media use and trust in civil society and governance institutions? In many parts of Asia, trust in government remains high despite limited political accountability and civil liberties. This study examines whether online political expression reshapes institutional trust in governance institutions and civil society organizations. The analysis considers the dual role of social media as a site for civic engagement and a channel for disaffection with formal authority. Using data from the Asian Barometer, the findings show that political expression on social media is associated with lower trust in governance institutions and higher trust in …

The Persistence Of Homophobia In Men’S Friendship Norms, Sherrie Y. Xue, Stephanie C. Lin, Christilene Du Plessis

Research Collection Lee Kong Chian School Of Business

Across five studies and one supplementary study (five preregistered; N = 3,215 adults), we found that men, more than women, avoided shared experiences (e.g., going to the movies, sharing food) with individuals of the same gender. Furthermore, persistent societal expectations that men should be unambiguously heterosexual underpinned this pattern: Men felt more apprehensive about signaling same-gender romance in platonic relationships than women did. In turn, romantic prototypicality drove the pattern of men (more than women) avoiding shared activities, above and beyond differences in how hedonic, enjoyable, and feminine the activities were; our findings further suggested that men’s reluctance to share …

A Learning‑Augmented Dynamic Programming Approach For Orienteering Problem With Time Windows, Guansheng Peng, Lining Xing, Fuyan Song Ma, Aldy Gunawan, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Recent years have witnessed a surge of interest in solving combinatorial optimization problems (COPs) using machine learning techniques. Motivated by this trend, we propose a learning-augmented exact approach for tackling an NP-hard COP, the Orienteering Problem with Time Windows, which aims to maximize the total score collected by visiting a subset of vertices in a graph within their time windows. Traditional exact algorithms rely heavily on domain expertise and meticulous design, making it hard to achieve further improvements. By leveraging deep learning models to learn effective relaxations of problem restrictions from data, our approach enables significant performance gains in an …

Semi‑Supervised Graph Anomaly Detection Via Robust Homophily Learning, Guoguo Ai, Hezhe Qiao, Hui Yan, Guansong Pang

Research Collection School Of Computing and Information Systems

Current semi-supervised graph anomaly detection (GAD) methods utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. These methods posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the entire normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of …

The Influence Of Culture + Technology On Night-Time Economy, Jianping Xu

Dissertations and Theses Collection (Open Access)

As a vital component of modern urban economies, the night-time economy has emerged as a crucial pathway for stimulating domestic demand and promoting consumption upgrading. With the rapid development of digital technologies and increasingly diversified cultural demands, the deep integration of 'Culture + Technology' has provided new momentum for the innovative development of the night-time economy. This study focuses on how 'Culture + Technology' empowers the night-time economy, exploring its impact mechanisms, current status, and development pathways through theoretical analysis, case studies, and empirical surveys.

First, this paper constructs a theoretical framework for 'Culture + Technology' empowering the night-time economy …

Copyright In The Generative Ai Era: Reimagining Creativity And Fairness, Huijuan Peng, David Tan

Research Collection Yong Pung How School Of Law

This article examines the need for copyright reform in response to the transformative impact of generative artificial intelligence. It identifies key legal challenges, including the absence of clear criteria for authorship and the infringement risks associated with both inputs (training of large language models) and outputs (AI-generated content). The authors argue that two overarching themes—creativity and fairness—will drive the future development of copyright law. Through a comparative analysis of how three jurisdictions—the United States, China, and Singapore—have addressed these challenges, the article offers practical and forward-looking insights for navigating the complex copyright landscape of the generative AI era.

This article …

Imprisonment When An Offender Cannot Pay A Fine, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

According to a common-law rule in place since the 1993 case of Low Meng Chay v Public Prosecutor [1993] 1 SLR(R) 46, if the court is minded to impose a fine but the offender will clearly be unable to pay a fine, the offender should be sentenced to imprisonment instead (as opposed to a fine coupled with a default imprisonment term). While one can understand why the courts may apply this practice, the practice obscures the crucial distinction between: (a) being sentenced to a fine, then imprisoned in default of payment (which, it is submitted, is the correct course of …

The Impact Of Top Management Team Heterogeneity On Corporate Green Innovation, Xueyan Luo

Dissertations and Theses Collection (Open Access)

At present, green innovation has become a key path for enterprises to achieve sustainable competitive advantage. However, existing research pays limited attention to how internal governance factors—especially top management team (TMT) heterogeneity—affect green innovation. Based on Upper Echelons Theory, managerial cognition theory, and institutional theory, this paper constructs a framework of “TMT heterogeneity–green strategic orientation–green innovation,” and explores the impact of different types of heterogeneity and the moderating role of regional innovation environments. Heterogeneity is categorized into conflict-type (gender, age, financial background) and complementary-type (educational, professional, overseas background) to better explain its mechanisms.

Using a sample of Chinese A-share listed …

A Study On The Influence Of Entity Internet Hospital Platforms And Health Literacy Facilitation On Postoperative Patients’ Platform Usage, Hong Ye

Dissertations and Theses Collection (Open Access)

Entity Internet Hospital (EIH) is emerging online medical service platforms in China. Currently, while EIHs in China are developing rapidly, their usage rates remain low. However, postoperative patients have substantial follow-up consultation needs. Consequently, incentivizing postoperative patients to use EIH from the demand side is a highly challenging proposition in the current era. Grounded in Technology Acceptance Model and Health Belief Model, this study examined the impacts of platform and health literacy facilitation on platform usage (including intention and frequency) among 631 postoperative patients with cardiovascular disease from four medical institutions in Wuhan, and explored the moderating role of literacy …

Research On The Impact Mechanism Of The Development Level Of Smart Agriculture On Farmers' Income, Baoping Liu

Dissertations and Theses Collection (Open Access)

Agriculture is a fundamental and strategic industry essential to national food security and farmers’ well-being. Faced with global population growth, climate change, and resource constraints, the traditional high-input agricultural model is unsustainable. Smart agriculture, empowered by IoT, big data, and AI, has become critical for agricultural transformation and sustainable development. However, existing research overemphasizes technical efficiency and neglects how smart agriculture systematically affects farmers’ income, especially its transmission mechanism and regional heterogeneity.

Against China’s rural revitalization and digital village strategy, this study investigates how smart agriculture development influences farmers’ income, using a hybrid framework of macro empirics and micro analysis. …

A Study On The Impact Of Ceos’ Financial And Technical Backgrounds On The Innovation Strategy Of The Enterprise, Jianyu Cheng

Dissertations and Theses Collection (Open Access)

Amid regional economic integration and market competition, innovation has become a key driver for enterprises to gain competitive advantages and ensure sustainable development. As primary decision-makers in corporate strategy and resource allocation, chief executive officers (CEOs) impact the selection and implementation of innovation strategies through their financial and technical backgrounds, which influence their strategic cognition and risk preferences, leading to heterogeneous innovation behaviors. While existing research has examined the relationship between CEOs and corporate innovation, studies have inadequately explored CEOs’ diverse background characteristics, particularly the interaction between financial and technical backgrounds. Given the rising trend of CFOs and CTOs advancing …

Corporate Strategic Changes In Response To Negative Performance Gaps: The Role Of Listing Board In China, Lei Yu

Dissertations and Theses Collection (Open Access)

This dissertation investigates how Chinese listed companies respond strategically to negative performance gaps under different institutional environments defined by the Main Board and ChiNext Board of China’s capital market. Drawing on Performance Feedback Theory and New Institutional Theory, it explores whether and how listing board heterogeneity moderates the relation between performance shortfalls and corporate strategic transformation, and further examines the boundary effects of internal governance factors including ownership type, CEO power, and resource slack.

Using panel data of A-share listed companies from 2009 to 2019 obtained from the CSMAR database, this study employs multiple regression analysis and interaction modeling to …