Digital Commons Network^™

Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection Against Multipath Routing, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Zhipeng Liu, Tingting Li, Jiongchi Yu, Fan Zhang, Binbin Chen

Research Collection School Of Computing and Information Systems

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multipath routing phenomenon that …

Institutional Dual-Holders And Corporate Disclosures: A Natural Experiment, Lin Cheng, Qiang Cheng, Liwei Weng, Mark Yuzhi Yan

Research Collection School Of Accountancy

This study examines the impact of the presence of institutional dual-holders, whose portfolios hold both loans and equity securities of the same firms, on those firms' voluntary disclosures. Using mergers between institutional shareholders and lenders to the same firms as exogenous shocks to identify firms with institutional dual-holders that have high relative equity ownership, we document that such firms are less likely to provide management forecasts and disclose fewer voluntary 8-K items. In cross-sectional analyses, we find that the reduction in voluntary disclosures is more pronounced when institutional dual-holders have higher board representation and when firms have lower litigation risk. …

Understanding Gender Differences In Digital Readiness During Digital Transformation In Accounting Functions: Accounting Professionals’ Perspective, Benjamin Lee, Clarence Goh, Gary Pan, Poh-Sun Seow

Research Collection School Of Accountancy

Research aims: To examine gender differences in digital readiness during digital transformation in accounting functions, addressing a knowledge gap in current accounting literature. Design/Methodology/Approach: Survey of 297 accounting professionals (180 men, 117 women) with 27 questions on digital readiness perceptions, analysed using exploratory factor analysis and one-way analysis of variance (ANOVA) tests. Research findings: Women exhibited higher overall digital readiness than men, particularly feeling more empowered by senior management and better equipped to continue job tasks during digital transformation. No gender differences were found in perceptions of knowledge and skills, barriers, or organisational strategy. Theoretical contribution/Originality: Challenges traditional stereotypes about …

Alibaba: Building Advanced Intellectual Property Governance For E-Commerce Marketplaces, Liang Chen, Sin Mei Cheah, Can Huang, Guoqiao Liu

Asian Management Insights

How the global e-commerce powerhouse harnessed artificial intelligence (AI) to balance innovation and intellectual property (IP) rights protection.

Bridging Worlds, Singapore Management University

Asian Management Insights

How philosophy, culture, and ethics shape leadership and learning.

Local Insights Into Global Value Chains, Pao-Li Chang

Asian Management Insights

Leveraging firm-level data to inform anticipatory economic policy decisions.

Steering The Future, Shai Ganu

Asian Management Insights

Embracing the emergence of corporate stewardship.

Beyond The Binary: Rethinking Work-From-Home Policies, Vineeta Dwivedi, Snehal Shah

Asian Management Insights

The right approach depends on your organisation’s readiness.

Unlocking Private Equity Investing: A Primer, Yin Wang, Steve Balaban

Asian Management Insights

Private equity might provide higher returns, but investors should be aware of the associated risks.

Building Urban Resilience, Winston T. L. Chow

Asian Management Insights

Cities can lead on services, resources, and partnerships.

University Leadership, Singapore Management University

Asian Management Insights

The future belongs to institutions that advance impact and prepare minds for lifelong learning. An interview with Dr Andrew Hamilton, President Emeritus of New York University and former Vice-Chancellor of Oxford University.

How “Green” Are Green Acquisitions Really?, Sri V Srinivasan

Dissertations and Theses Collection (Open Access)

Our study finds that 43.14% of global green mergers & acquisitions (GMAs) from 2001 to 2020 may be susceptible to Green Embellishment—the gap between corporate environmental representations and actual performance, regardless of intent. Using a multi-criteria theoretical framework, we show that financial constraints strongly drive Green Embellishment in GMAs, with regulatory stringency and national culture moderating this effect. GMAs in weak regulatory environments are especially vulnerable, particularly under high stakeholder pressure. We further provide empirical evidence that financially constrained GMAs with lower green profiles and those in “browner” industries exhibit a higher propensity for Green Embellishment. Even in stringent …

Exploring How The High Or Low Of Firm Transparency And Speed Of Adjustment In Its Capital Structure Relates To The Implied Cost Of Capital Of A Firm, Yung Chee Leong

Dissertations and Theses Collection (Open Access)

This dissertation tackles a core challenge in corporate finance — how firms can optimise both their capital structure decisions and the way those decisions are communicated to investors. It shows that while financial responsiveness (SOA) and transparency each help lower a firm’s implied cost of capital (ICC), the most meaningful reduction occurs when both are present and working together. For CFOs, the research offers a practical way to connect capital structure planning with communication strategy to improve market understanding and reduce financing costs. For investors and analysts, it introduces the idea of a transparency–SOA alignment measure to better assess firm …

Cutting Through The Infodemic Efficiently: News Claims Surveillance And Llm-Based Lightweight Fact Verification, Xuan Zhang

Dissertations and Theses Collection (Open Access)

In the context of the current infodemic, the rapid spread of misinformation poses a severe threat to social stability and public health. Recently, the rise of deep learning technologies has offered the potential for accelerating the development of automated misinformation detection and verification. However, current technological capabilities and computational resources often prove inadequate for the exhaustive scrutiny required, rendering the enhancement of processing efficiency a critical imperative. Given the vast amount of data on the internet, current technology and computational power often fall short in timely and accurate scrutiny of each piece of information, making the improvement of processing efficiency …

Rich Models And Methods For On-Demand Same Day Deliveries, Zhiqin Zhang

Dissertations and Theses Collection (Open Access)

Same-day delivery has brought numerous conveniences to people’s lives, but it has also presented challenges in terms of service management. To effectively optimize on-demand same-day delivery operations within urban logistics, intelligent decision-making strategies capable of adapting to rapidly changing circumstances are essential. Employing effective decisionmaking strategies that account for order allocation, route planning, courier scheduling, and other relevant factors, is pivotal in advancing logistics operations, enhancing efficiency, customer satisfaction, and resource utilization in the context of dynamic same-day delivery problems.

The focus of this thesis revolves around different emerging challenges presented by on-demand same-day delivery problems, with a particular emphasis …

The Association Between Different Succession Methods And Innovation Investments In Family Businesses In China, Lei Wu

Dissertations and Theses Collection (Open Access)

Amidst the deepening of economic globalization, countries around the world are being progressively steered toward paths of innovative development, laying a solid foundation for enhancing national comprehensive strength and elevating international standing. Especially after the implementation of the reform and opening-up policy in our country, technological innovation has become the main direction of our transformation. According to the requirements of the 20th National Congress of the Communist Party of China, innovation-driven development will be fully applied to the country's development, creating favorable conditions for the realization of strategic development goals. National development and social progress are directly linked to corporate …

Interactive Generative Modeling: A Pathway For Improved Simulation And Decision Making, Changyu Chen

Dissertations and Theses Collection (Open Access)

This dissertation presents Interactive Generative Modeling (IGM), a unified perspective that integrates interactive paradigm and generative modeling to advance the development of general-purpose intelligent systems. IGM is motivated by the observation that while reinforcement learning (RL) has mastered a wide range of complex simulated tasks, it struggles to generalize in high-dimensional, open-ended tasks. In contrast, generative models excel in such settings due to their expressivity and their ability to serve as powerful priors (e.g., LLMs pretrained on massive corpora). By bridging these two paradigms, IGM offers a promising path forward.

The first direction explored in this dissertation is IGM for …

Essays On Empirical Asset Pricing, Ruxue Gong

Dissertations and Theses Collection (Open Access)

The dissertation consists of three chapters on behavioral finance, arbitrage activities, cryptocurrencies and ETFs. The first chapter examines the existence and trading behavior of triangular arbitrageurs in cryptocurrency markets. Using a novel account-level orderbook dataset from an Indian cryptocurrency exchange, we document persistent price deviations between USD- and INRquoted token pairs, and use the triangular arbitrage opportunities to identify arbitrageurs and noise traders. Our results show that arbitrageurs significantly outperform noise traders on their holding-based returns, after adjusting for market returns and trading fees. However, arbitrageurs are not immune to behavioral biases when making trading decisions. Using a composite behavioral …

Resilience In Stem: How Gender-Professional Identity Integration (G-Pii) And Self-Compassion Empower Women To Thrive, Isabelle Chiew

Dissertations and Theses Collection (Open Access)

Fostering resilience is crucial for the retention of women in male-dominated fields such as STEM. This paper investigates the interplay between gender-professional identity integration (G-PII), resilience, and self-compassion through two complementary studies. It is hypothesised that G-PII positively predicts resilience, with higher levels of integration associated with greater resilience. Furthermore, self-compassion is expected to moderate this relationship. Study 1 employs a cross sectional survey design to explore the relationships between G-PII, self-compassion, and resilience, while Study 2 uses an experimental design to assess the causal impact of G-PII on resilience. Study 1 found that G-PII was significantly and positively associated …

Learning And Optimization Under Human-Centric Considerations, Qian Shao

Dissertations and Theses Collection (Open Access)

This dissertation investigates learning and optimization problems shaped by humancentric considerations, such as preferences, demonstrations, behavioral patterns, and resource constraints. As real-world decision-making increasingly involves interaction with human agents, data, and limitations, modeling these factors becomes critical for building practical, adaptive, and robust systems.

The research spans four domains. First, we study preference-aware delivery routing by learning implicit practitioner preferences and incorporating them into a hierarchical route optimization framework. Second, we develop imitation learning methods for cost-constrained settings, enabling agents to mimic expert behavior while respecting safety and resource limitations. Third,we explore early rumor detection in data-limited environments, integrating large …

Using Multiculturalism To Combat The Negative Creativity Effect Of Cultural Tightness: How It Is Done Matters, Jung Kiu Choi

Dissertations and Theses Collection (Open Access)

This study investigates the multifaceted influence of cultural tightness and multiculturalism on the global commercial success of pop music. Addressing an under-explored moderating effect of multicultural factors on creative output under tight culture, this research analyzes Billboard Global 200 and Spotify Top 50 chart data from 2013-2021. The empirical analysis reveals that cultural tightness negatively impacts pop song’s success. Conversely, multiculturalism, specifically through diverse languages and performance teams, generally enhances global success. A key finding is that while multicultural production teams do not directly correlate with success, they critically moderate the detrimental effects of cultural tightness, a buffering effect unique …

Peak Policy Lab Or Chasing Windmills? The Overlooked Issue Of Misaligned Policy Design, Adam Wellstead, Sarah Giest, Ishani Mukherjee

Research Collection School of Social Sciences

Policy innovation labs (PILs) are relatively new policy actors and are part of a larger global “labification” movement. They are touted as spaces for the novel development and testing of policy solutions. PILs have evolved into various forms–including those at different levels of government (central, sub-national, and local), sectoral (such as food, transportation, and environment), and cross-sectoral labs (social innovation and data labs). After a decade, some practitioners lament the effectiveness of their efforts and question if policy labs are indeed engines of innovation and change. We argue that the approach to policy design by PILs, in part, is an …

Irhunter: Universal Detection Of Instruction Reordering Vulnerabilities For Enhanced Concurrency In Distributed And Parallel Systems, Guohua Xin, Guangquan Xu, Yao Zhang, Cheng Wen, Cen Zhang, Xiaofei Xie, Neal N. Xiong, Shaoying Liu, Pan Gao

Research Collection School Of Computing and Information Systems

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts …

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

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

Research Collection School Of Computing and Information Systems

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

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 1, Summary Report, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Regtrieve: Reducing System-Level Regression Errors For Machine Learning Systems Via Retrieval-Enhanced Ensemble, Junming Cao, Xuwen Xiang, Mingfei Cheng, Bihuan Chen, Xinyan Wang, You Lu, Chaofeng Sha, Xiaofei Xie, Xin Peng

Research Collection School Of Computing and Information Systems

Multiple machine learning (ML) models are often incorporated into real-world ML systems. However, updating an individual model in these ML systems frequently results in regression errors, where the new model performs worse than the old model for some inputs. While model-level regression errors have been widely studied, little is known about how regression errors propagate at system level. To address this gap, we propose RegTrieve, a novel retrieval-enhanced ensemble approach to reduce regression errors at both model and system level. Our evaluation across various model update scenarios shows that RegTrieve reduces system-level regression errors with almost no impact on system …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …