Digital Commons Network^™

Bogging Down Investors: An Unintended Consequence Of Litigation Risk, Siwen Fu, Ke Wang, Liandong Zhang, Liu Zheng

Research Collection School Of Accountancy

Securities litigation risk is a well-recognized yet underexplored source of financial reporting complexity or unreadability. This study examines the effect of litigation risk on the readability of corporate financial reports. The 1999 Silicon Graphics Inc. (SGI) court ruling unexpectedly reduced litigation risk for firms within the Ninth Circuit Court’s jurisdiction. Using a difference-in-differences design centered on the SGI court ruling, we find that, while the readability of financial reports generally declines over the sample period, treated firms in the Ninth Circuit experience a comparatively smaller decline in readability than control firms in other states after the ruling. Put differently, treated …

The Vietnam War And The Regional Context, Wen-Qing Ngoei

Research Collection School of Social Sciences

This chapter traces Southeast Asia’s overall pro-U.S. trajectory from before and through the American war in Vietnam, a process in which the region’s anticommunist nationalists collaborated with the United States and Britain to gain, and remain in, power. Between the late 1940s through the early 1960s, indigenous anticommunist elites in Thailand and the Philippines rose to political dominance with U.S. assistance; in Malaya and Singapore they did so with British support. The United States and Britain, with their Malayan and Singaporean allies, would influence developments within Indonesia that precipitated the Indonesian Army’s rightwing coup of 1965, a transformative event that …

Discontent In The World City Of Singapore, Gordon Tan, Jessie P. H. Poon, Orlando Woods

Research Collection College of Integrative Studies

A burgeoning literature on ‘left behind’ places has emerged that captures the backlash against globalisation and highlights the locales that lag world cities. This paper integrates the ‘left behind’ and world cities literatures through the lens of discontent in the context of Singapore, using sentiment analysis and topic modelling as well as interviews with local professionals to unpack the multidimensional aspects of discontent. Focusing on the Singapore–India Comprehensive Economic Cooperation Agreement that spurred discontent directed at foreign Indian professionals, we show that the worlding generated by transnational flows has accentuated intra-urban inequality through racialisation and spatialisation of financial business and …

No Consistent Evidence For Between- And Within-Person Associations Between Objective Social Media Screen Time And Body Image Dissatisfaction: Insights From A Daily Diary Study, Adalia Y. H. Goh, Andree Hartanto, K. T. A. Sandeeshwara Kasturiratna, Nadyanna M. Majeed

Research Collection School of Social Sciences

With the abundance of social media content that promotes unrealistic beauty standards, there are growing concerns about the potential negative impact of social media use on body image satisfaction. While some studies highlight negative associations, others present null effects, pointing to methodological limitations like biased and unreliable self-reported screen time measures and a focus on singular platforms. Addressing these gaps, our study employed a daily diary method to objectively measure social media screen time across six major platforms (Twitter, Reddit, TikTok, YouTube, Instagram, and Facebook), alongside daily body image dissatisfaction among 252 young adults (Mage = 21.67 years, 67.77% female) …

Learning By Teaching With Deliberate Errors Promotes Argumentative Reasoning, Sarah Shi Hui Wong

Research Collection School of Social Sciences

Reasoning and arguing well lies at the core of thinking and constructing knowledge about complex, controversial issues. Leveraging the techniques of learning by teaching and deliberate erring, the present study developed and tested a novel intervention—learning by misteaching—to boost argumentative reasoning. University students (N = 208) were trained on argumentation strategies and studied a dual-position argumentative text on a controversial topic using one of three learning methods: notetaking, correct teaching, or misteaching. The notetaking group prepared to be tested and wrote study notes while generating good arguments about the topic, whereas both teaching groups prepared to teach and wrote a …

Mapping The End Of Coal: How Policy Strength, Ownership, Finance Shape Global Plant Retirements, Jiwon Kim, Theodor Florian Cojoianu, Theodor F. Cojoianu, Maretno Agus Harjoto, Dariusz Wojcik

Research Collection College of Integrative Studies

This study examines how national environmental performance, institutional investor divestment from fossil fuels, investment in renewable energy, and ownership type influence the global phase-out of coal power plants, and whether these effects are constrained by geographical boundaries. Using data on coal plant retirements across 90 countries, we test whether domestic and cross-border factors shape the timing and likelihood of asset closure. We find that companies headquartered in countries with stronger environmental performance are more likely to retire domestic but not foreign plants. While institutional investors divestment is often assumed to exert cross-border influence, our findings show that divestment pressure by …

Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: Testing Adequacy: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. Interpretability: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which …

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …

Separability Doctrine: Perspectives From China, England And The Model Law, Jia Jun Benjamin Goh

Research Collection Yong Pung How School Of Law

In this article, the author compares the application of the separability doctrine in the People's Republic of China, England and Wales, and select Model Law jurisdictions (including Singapore and Hong Kong). This article argues that the proper scope of the doctrine must include situations of both contractual validity and existence are in question. Additionally, the doctrine should apply in finding that the law of the arbitration agreement is different from the law of the main contract. Finally, the doctrine must also serve to protect party autonomy to shield an arbitration agreement from self defeating main agreements.

Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …

Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin

Research Collection School Of Computing and Information Systems

With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage …

Constitutional Comparisons And Language, De Maartje Visser

Research Collection Yong Pung How School Of Law

Language can be seen as an intuitive challenge for those who embark on comparative constitutional research: its intricate entanglement with law is often overlooked and not engaged with explicitly. This chapter argues that this is a missed opportunity at best, and arguably presents a hazard for the collective quality of the scholarly discourse. It discusses four ways in which language matters when engaging in constitutional comparisons: the problem of literal translation; the problem of legal-conceptual translation; a multilingual discourse that is marked by fragmentation; and expectations in communication with a foreign or international readership. The approach taken is constructive, with …

Ai Characterisations And Their Legal Implications, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

This chapter examines the difficult legal characterisation problems that artificially intelligent systems raise and explores how different characterisations of artificial intelligence (AI) shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The chapter thus argues that a range of plausible AI characterisations …

Attribute-Based Encryption With Searchable Encryption, Yang Yang

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with searchable encryption is a notion that inherits the advantages of both ABE (Goyal et al., 2006) and searchable encryption (SE) (Boneh et al., 2004; Boneh and Waters, 2007) mechanisms to realize secure share and search for the outsourced data. ABE creates fine-grained access control system to prevent sensitive data from being accessed by unauthorized user or external attacker (Bethencourt et al., 2007). SE generates encrypted index for outsourced data such that it can be searched by a keyword trapdoor (or token) (Shi et al., 2007; Naveed et al., 2014), where the outsourced content and search keyword(s) …

The Effectiveness Of Local Updates For Decentralized Learning Under Data Heterogeneity, Tongle Wu, Zhize Li, Ying Sun

Research Collection School Of Computing and Information Systems

We revisit two fundamental decentralized optimization methods, Decentralized Gradient Tracking (DGT) and Decentralized Gradient Descent (DGD), with multiple local updates. We consider two settings and demonstrate that incorporating local update steps can reduce communication complexity. Specifically, for  $\mu$-strongly convex and $L$-smooth loss functions, we proved that local DGT  achieves communication complexity {}{$\tilde{\mathcal{O}} \Big(\frac{L}{\mu(K+1)} + \frac{\delta + {}{\mu}}{\mu (1 - \rho)} + \frac{\rho }{(1 - \rho)^2} \cdot \frac{L+ \delta}{\mu}\Big)$}, where $K$ is the number of additional local update}, $\rho$ measures the network connectivity and $\delta$ measures the second-order heterogeneity of the local losses. Our results reveal the tradeoff between communication and …

Financial Named Entity Recognition: How Far Can Llm Go?, Yi-Te Lu, Yintong Huo

Research Collection School Of Computing and Information Systems

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight …

Robust Learning With Probabilistic Relaxation Using Hypothesis-Test-Based Sampling, Zilin Wang

Dissertations and Theses Collection (Open Access)

In recent years, deep learning has been a vital tool in various tasks. The performance of a neural network is usually evaluated by empirical risk minimization. However, robustness issues have gained great concern which can be fatal in safety-critical applications. Adversarial training can mitigate the issue by minimizing the loss of worst-case perturbations of data. It is effective in improving the robustness of the model, but it is too conservative, and the plain performance of the model can be unsatisfying. Probabilistic Robust Learning (PRL) empirically balances the average- and worst-case performance while the robustness of the model is not provable …

Towards Privacy-Aware Iot Communications: Delegable, Revocable, And Efficient, Pengfei Wu, Jianfei Sun, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is widely recognized for its potential to enhance efficiency and productivity across various industries. However, its increasing prevalence has also made it a more attractive target for cybercriminals. While many advanced cryptographic solutions have been developed to secure IoT, some practical security and privacy issues such as self-sovereign delegation, flexible revocation, and lightweight access remain inadequately addressed in existing solutions. In this paper, we propose PLIC, a Privacy-aware Lightweight IoT Communication scheme, which not only enables any authorized user to flexibly delegate their lightweight access privileges to other delegatees, such that they can also access …

Towards Unified Multimodal Editing With Enhanced Knowledge Collaboration, Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic …

Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …

Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …

A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. Firstly, the absence of a unified evaluation platform for defining and describing tools’ supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity. Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose …

Regulatory Approaches To Consumer Protection In The Financial Sector And Beyond: Toward A Smart Disclosure Regime?, Nydia Remolina Leon, Yvonne Ai-Chi Loh, David R. Hardoon

Research Collection Yong Pung How School Of Law

Traditionally, consumer and data protection policies evolved from issues of consent and information disclosure. The purpose of these regulatory approaches is the protection of consumers by reducing some contracting failures, such as asymmetries of information and a lower bargaining power, especially in transactions involving complex issues such as financial products and sensitive personal data. In the past, regulators have responded to privacy and consumer protection by adopting what this paper refers to as an “imperfectly informed regime”, in which consumers do not receive full information about the risks associated with their decisions, even if they are still protected through a …

The Research On The Impact Of Listing Regime On Innovation For The Biotech Industry: Inspiration And Experience From Mb Rules 18a Published By Hkex, Yueying Dong

Dissertations and Theses Collection (Open Access)

The development of the Biotech Industry is not only connected to the national economy and livelihood of the people, but also affects the technology development, economy growth and national security of the country. The development depends on the technology innovation, with the essence for the continuous process of financial ecology guiding and technology refresh driving. For high-tech intensive industries of biotech companies, the development of the equity market is an important support for promoting the technology innovation. The capital market in guiding resource allocation has the responsibility of optimizing the listing regime, providing more external financing channels and promoting the …

Causality Analysis For Neural Network Security, Bing Sun

Dissertations and Theses Collection (Open Access)

While neural networks are demonstrating excellent performance in a wide range of applications, there has been a growing concern on their reliability and dependability.Similar to traditional decision-making programs, neural networks inevitably have defects that need to be identified and mitigated at times. Neural networks are usually inherently black-boxes and do not provide explanations on how and why decisions are made. As a result, these defects are more ``hidden" and more challenging to eliminate. It is thus crucial to develop systematic approaches to identify and mitigate defects in a neural network in a rigorous way.

In this dissertation, we focus on …

Towards Robust, Secure, And Privacy-Aware Large Language Models Of Code, Zhou Yang

Dissertations and Theses Collection (Open Access)

The field of software engineering has witnessed a surge in large language models specifically tailored to understand and process code, which we call large language models for code (LLM4Code). The increasing popularity of LLM4Code is inseparable from three key factors: the availability of extensive datasets compiled from diverse data sources, the advancements in deep learning algorithms and computational power that facilitate the training of these powerful models, and the active engagement and collaboration within the research community fostering innovation and the rapid exchange of ideas and methodologies. As evidenced by a series of studies, LLM4Code has been experiencing rapid development …

Political Embeddedness And Organizational Resilience: Empirical Investigation And Theorisation Of Institutional Translation Mechanisms In Chinese Private Enterprises, Shuai Shao

Dissertations and Theses Collection (Open Access)

China is governed by the Communist Party of China (CPC), which embodies the nation's core values, mission, and ideological framework. The CPC organization embeddedness represents a distinctive governance arrangement, reflecting the state's Political Integration Imperatives. In the context of significant external shocks such as Covid-19, how does this political embeddedness influence private enterprises' organizational resilience - specifically their capacity to withstand disruptions and their ability to recover? What are the underlying mechanisms driving these effects?

This study employs a mixed-methods approach to investigate how CPC organization embeddedness shapes organizational resilience within China's unique institutional context, and the mechanisms through which …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …