Digital Commons Network^™

Dementia, Advance Directives, And Second-Order Volitions, Rand Hirmiz

Research Collection School of Social Sciences

This paper contributes to the ongoing debate over the authority of advance directives in cases where patients with dementia express desires that conflict with their earlier wishes. Drawing on Harry Frankfurt’s concept of second-order volitions, I argue that the preferences of the pre-dementia self (the “then-self”) should, in most cases, take precedence over those of the post-dementia self (the “now-self”) –  particularly in instances where the now-self has lost the capacity to form second-order volitions and is no longer able to meaningfully repudiate prior values and commitments.

Grammar Expert: A University-Based Grammar Application To Support Grammar Learning And Writing Instruction, Ivy Chan, Yin Teng Chong

Research Collection College of Integrative Studies

Technologies like web-based grammar applications have been integrated into classroom writing pedagogy to enhance students’ learning experiences. Drawing from qualitative findings from students’ interviews and reflection journals over one semester, this technology review focuses predominantly on university students’ perceptions towards an internally developed grammar application, Grammar Expert. Adopting the Technology Affordances and Constraints Theory (TACT) framework, the review identifies that GE’s affordances include promoting engagement in grammar learning, offering a flexible and encouraging environment, and ensuring real-world applicability, while its constraints involve aspects related to a limited user interface. Subsequently, the authors will present their pedagogical narrative on integrating this …

Scaling Up Multi-Agent Reinforcement Learning For Large Agent Teams And Long-Horizon Tasks: A Survey, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) empowers multiple autonomous agents to acquire effective policies for collaborative problem-solving. Over the last decade, MARL has seen significant advancements, with numerous algorithms achieving impressive performance across various benchmarks and real-world applications. Nevertheless, the scalability of multi-agent systems, in terms of the number of agents and the length of the task horizon, remains a critical consideration for applying MARL methods to complex problem-solving. Given that a dedicated review of the existing approaches and challenges in scaling up multi-agent systems remains largely absent, this survey aims to bridge this gap by delivering a comprehensive review of MARL …

Benchmarking Gaslighting Negation Attacks Against Multimodal Large Language Models, Bin Zhu, Yinxuan Gui, Huiyan Qi, Jingjing Chen, Chong-Wah Ngo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have exhibited remarkable advancements in integrating different modalities, excelling in complex understanding and generation tasks. Despite their success, MLLMs remain vulnerable to conversational adversarial inputs. In this paper, we systematically study gaslighting negation attacks—a phenomenon where models, despite initially providing correct answers, are persuaded by user-provided negations to reverse their outputs, often fabricating justifications. We conduct extensive evaluations of state-of-the-art MLLMs across diverse benchmarks and observe substantial performance drops when negation is introduced. Notably, we introduce the first benchmark GaslightingBench, specifically designed to evaluate the vulnerability of MLLMs to negation arguments. GaslightingBench consists of multiple-choice …

Not Too Early, Not All At Once: Design Tensions In Ai-Mediated Self-Disclosure In Online Dating, Pei-Hua Tsai, Tianyi Zhang, Emran Bin Elias Poh, Anthony Tang, Yung-Ju Chang

Research Collection School Of Computing and Information Systems

Online dating relies on self-disclosure, yet initial conversations are fragile: users must navigate uncertainty around timing, boundaries, and reciprocity with little shared context. While advances in AI raise the possibility of mediating disclosure, how such support might reshape the experience of early-stage relational disclosure remains underexplored. We conducted 29 semi-structured interviews to examine how daters envision AI-mediated self-disclosure in online dating. Our findings surface recurring design tensions rather than simple opportunities or risks. Participants welcomed guidance that could pace disclosure, support reflection, and reduce social awkwardness, but stressed preserving agency and authorship. They valued interpretive assistance for sense-making of ambiguous …

Sok: Understanding Zkvm: From Research To Practice, Guomin Yang, Yunbo Yang, Yuejia Cheng, Haibo Tang, Bingsheng Zhang, Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge virtual machine (zkVM) is a powerful infrastructure for proving the correctness of a program execution with a succinct proof, attracting significant interest from researchers, developers, and users. It has been widely used in applications such as blockchain rollups, privacy-preserving machine learning, and off-chain computation. As the field grows, a wide range of zkVMs have been proposed. However, they adopt different choices in instruction formats, trace layouts, and proving backends, which results in a highly heterogeneous design landscape and makes it difficult to understand the relations among these systems.To bridge this gap, we provide a comprehensive study of zkVMs that …

Anatomical Domain Shifts: Test-Time Heterogeneous Adaptation For 3d Human Pose Prediction, Qiongjie Cui, Pan Zhou, Jingjing Chen, Na Zhao

Research Collection School Of Computing and Information Systems

The research frontier in human pose prediction (HPP) is advancing toward continual test-time adaptation (TTA), where models must self-adapt to dynamic test distributions. To date, the homeostatic continual TTA remains the sole viable solution, which isolates the model parameters and update domain-sensitive ones. Despite mitigating full-body domain gaps, human anatomical heterogeneity (domain shifts often localize to specific regions) is ignored. This anatomical-agnostic approach forces uniform parameter adaptation across kinematically distinct segments, causing: over-adaptation of stable regions and under-adaptation of shift-prone articulations. To address it, we introduce TT-HA, a novel Test-Time Heterogeneous Adaptation that implicitly estimates domain changes for anatomical segments, …

Enhancing Pointing Gestures Of Non-Hmd Users In Asymmetric Collocated Mixed Reality Collaboration, Nam-Dang Vo, Van-Vinh Thai, Anthony Tang, Khanh-Duy Le

Research Collection School Of Computing and Information Systems

A common collocated group setting in mixed-reality (MR) collaboration is a person wearing a MR headset (HMD user) and presenting MR contents to audiences who are not provided with such specialized devices (Non-HMD users). In this setting, while Non-HMD users can view the MR environment shown on a large physical display, it still remains challenging for the HMD user to interpret their pointing gesture when they spatially refer to objects in the MR environment. To address this, we designed and evaluated two pointing techniques—SCREEN and SCREEN+SPACE—that support Non-HMD users in referring to MR content. Screen pointing allows users to refer …

How Do Machine Learning Models Change?, Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández

Research Collection School Of Computing and Information Systems

The proliferation of Machine Learning (ML) models and their open source implementations has transformed AI research and applications. Platforms like Hugging Face (HF) enable this evolving ecosystem, yet a large-scale longitudinal study of how these models change is lacking. This study addresses this gap by analyzing over 680,000 commits from 100,000 models and 2,251 releases from 202 of these models on HF using repository mining and longitudinal methods. We apply an extended ML change taxonomy to classify commits and use Bayesian networks to model temporal patterns in commit and release activities. Our findings show that commit activities align with established …

Ai In Healthcare: Regulatory Guidelines And Judge-Made Negligence Principles For Ai Implementers, Gary K. Y. Chan

Research Collection Yong Pung How School Of Law

The use of artificial intelligence (AI) in healthcare may, notwithstanding its potential benefits, result in harm to patients from allegedly negligent acts or omissions by hospitals and medical doctors. In such circumstances, how should the principles in the tort of negligence (duty of care, breach, causation, remoteness of damage, and defences) respond to AI innovations in healthcare? In particular, how may the standard of care expected of hospitals and medical doctors be informed by regulatory guidelines? We refer to case law precedents and regulatory guidelines on the roles and responsibilities of doctors and hospitals as AI implementers. Importantly, they prompt …

Factors Influencing Patient Recall For Follow-Up In The Multi-Stage Deployment Of Medical Intelligent Screening Systems, Wenjie Chen

Dissertations and Theses Collection (Open Access)

With the deep integration of artificial intelligence (AI) technology in the healthcare sector, intelligent screening systems have achieved significant breakthroughs in predictive accuracy; however, they commonly face a management challenge in clinical implementation: accurate early warnings but limited patient recall. To address this ineffective technological empowerment issue in practice, this study proposes a theoretical framework featuringbidirectional interactions between "technological empowerment" and"relational empowerment" based on empowerment theory to systematicallyinvestigate the dynamic evolution mechanisms and boundary conditions of patient recall willingness under multi-stage, cross-departmental deployment of medical intelligent screening systems. Using a leading municipal hospital as the primary experimental setting, the study …

The Impact Of Successor Differences In Family Businesses On Firm Performance: An Empirical Analysis Based On China Listed Company Data, Xulong Zhao

Dissertations and Theses Collection (Open Access)

Family businesses are an important pillar of global economic development. Currently, Chinese private enterprises are ushering in an unprecedented wave of intergenerational succession. At this critical historical juncture, "who" takes over and how the succession behavior affects firm performance have become core issues of common concern to academia and practice. Traditional principal-agent theory usually posits that introducing professional managers can break the limitations of family governance and improve corporate efficiency. However, Chinese family businesses are deeply rooted in specific institutional environments and cultural soils, where blood-based "relational trust" and socioemotional wealth (SEW) play irreplaceable roles in power transitions. Therefore, how …

Func: Reducing The Impact Of Android Framework Evolution On Malware Detection, Hailong Yu, Tiantian Wang, Lwin Khin Shar, Hanmeng Li, David Lo

Research Collection School Of Computing and Information Systems

Android malware detection approaches commonly use APIs and permissions as features for classifying malware. However, since the release of the first Android operating system in 2008, the Android framework has undergone numerous version updates. The evolution of the Android framework over time has led to changes in APIs and permissions, including deprecations and replacements. These changes can result in inaccurate characterization of Android malware, thereby affecting performance of malware detectors. There is a lack of methods to mitigate the impact of Android framework evolution on malware detection. To fill this gap, we conduct a systematic study of the impact of …

Essays On High-Frequency Dynamics Of Asset Prices, Yuhong Zhu

Dissertations and Theses Collection (Open Access)

This dissertation studies econometric inference for high-frequency financial data, with a focus on detecting nonstandard drift and volatility dynamics in continuous-time models.

The first chapter proposes a new framework for uniform inference on explosive drift in high-frequency data, where conventional Gaussian approximations can fail due to the non-Gaussian behavior of short-window spot statistics. Under fixed-window asymptotics, these statistics are coupled with dependent t variables, and their maximum converges to a Fréchet distribution. We establish an anti-clustering condition for dependent t-statistics under overlapping windows and develop a feasible coupling-based test. Simulation results demonstrate better size control, and the empirical findings suggest …

From Catching Up To Surpassing—A Multi-Case Study Of Chinese Latecomer Technology Firms, Baohua Chen

Dissertations and Theses Collection (Open Access)

In recent years, a number of Chinese latecomer technology firms have grown rapidly and, in some cases, have even moved into leading positions in global competition. As global competition is being reshaped, technologies are evolving more rapidly, and traditional catch-up paths are becoming less viable, how these Chinese latecomer technology firms catch up with leading incumbents has become an important issue in strategic management research. Drawing on the theoretical lens of asymmetrical competition, resource orchestration, and dynamic capabilities, this study adopts an Eisenhardt-style multiple-case design to conduct a longitudinal comparative analysis of six firms—Insta360, Dreame, DJI, Unitree, Transsion, and Royole—and …

Log Off To Level Up: The Effect Of Social Media Hiatus On Goal Pursuit, Nekysha L. Y. Jalil, Yong Han Tan, Andree Hartanto, Eddie M. W. Tong

Research Collection School of Social Sciences

Concerns are growing that social media, with its constant notifications and attention-grabbing content, may distract individuals and hinder progress toward important goals. Despite its pervasive presence in our lives, the impact of social media on goal achievement has not been thoroughly studied. This within-subject experimental study aims to investigate the effects of social media hiatus (SMH), a short abstinence from social media usage, on goal pursuit processes. Participants (N= 107) completed daily diaries over three consecutive days of SMH and three days of regular social media use. SMH was manipulated by blocking participants’ access to multiple social media platforms. The …

Sevoauth: Secure Voiceprint Authentication With Hash-Based Feature Transformation, Rui Zhang, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

While voiceprint authentication offers convenient user authentication and access control through voice feature recognition, a critical research gap remains: existing voiceprint authentication systems fail to simultaneously achieve sound security against replay, spoofing, and adversarial attacks, preserve voice privacy leakage, and satisfy usability demand. Previous efforts have struggled to balance these issues comprehensively. To bridge this gap, we present SeVoAuth, a cloud-based Voiceprint Authentication as a Service (VAaaS) system designed to provide privacy preservation, robust security, and enhanced usability. SeVoAuth stores a synthesized voiceprint of a user in the cloud during user registration, thereby safeguarding the privacy of the real voiceprint …

Synthesis And Evaluation Of Long-Term History-Aware Medical Dialogue, Hebin Hu, Renke Dai, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

An effective healthcare agent must be able to recall and reason over a patient’s longitudinal medical history. However, the absence of datasets with realistic long-term dialogue timelines limits systematic evaluation. Real clinical text is constrained by privacy and ethics, while existing benchmarks focus on isolated interactions, failing to capture cross-session reasoning. We introduce a framework for synthesizing high-quality, long-term medical dialogues with LLMs. Our approach entails a knowledge-guided decomposition into three stages: constructing synthetic patient profiles with diverse disease and complication trajectories, generating multiturn dialogues per encounter, and integrating them into a coherent longitudinal history dataset, MediLongChat. We establish three …

Generation Of Elaborated, Targeted And Effective Feedback For Novice Programmers Using Llm, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Programming errors and misconceptions are pervasive in novice programmers which causes difficulty in the learning of computer programming. Large Language Models (LLMs), with their ability to comprehend and generate programming codes have shown promising results in the automatic identification of errors. This can potentially benefit student programmers by providing them with timely formative feedback at efficiencies and scale that were not attainable previously. In this study, we leveraged an LLM - OpenAI o4-mini for the generation of elaborated, targeted feedback for novice programmers across PHP and JavaScript exercises. We contend that the feedback needs to be effective and targeted other …

Perception Of Socioeconomic Status: A Meta-Analysis Of Manipulations, Jacinth Jia Xin Tan, Yong En Amos Tai

Research Collection School of Social Sciences

The causal effects of one’s socioeconomic status (SES) on outcomes are typically examined by experimentally manipulating SES self-perceptions based on one of three SES dimensions—absolute resource, relative resource, and general social position. We investigated the efficacy of these manipulations by systematically meta-analyzing their effects on SES self-perceptions. Based on 107 eligible samples (N = 26,203), manipulations of SES self-perceptions across the three SES dimensions were effective overall (g = 0.56–0.95). Explicit priming of absolute resource and relative resource manipulations comparing high versus low SES were consistently effective—although bias-corrected effects were attenuated—suggesting the importance of salient SES information and social comparisons. …

An Empirical Study On The Impact Of Eco-Friendly Selling Points On The Purchase Intention Of Young Chinese Consumers, Yang Liu

Dissertations and Theses Collection (Open Access)

Industry consumer research widely portrays Generation Z as an identity-driven cohort that enacts sustainability values through consumption. This narrative is often generalized across cultural contexts, assuming that Western-developed green consumption frameworks can be directly transferred without structural modification. Yet Chinese Gen Z’s e-commerce purchase patterns depart notably from this depiction, raising core questions: Can Western green consumption frameworks be applied to Chinese Gen Z? If so, what theoretical and structural adaptations are necessary?

This dissertation develops and tests an integrated three-pathway model to explain green purchase intention among Chinese Gen Z consumers. The model places utilitarian benefits, self-expression benefits, and …

Securing Cloud-Native Systems: From Vulnerability Analysis To External And Insider Threat Detection, Jiongchi Yu

Dissertations and Theses Collection (Open Access)

Cloud-native systems have become the backbone of modern software infrastructure. However, their dynamic resource orchestration and complex configurability introduce a large attack surface and intricate security challenges. Adversaries can externally exploit vulnerabilities in cloud components or perform insider movement within cloud environments to launch attacks. As these systems increasingly support critical services, security breaches can lead to severe operational and economic consequences.

Despite extensive efforts in vulnerability detection and attack monitoring, existing approaches struggle to remain effective in cloud-native environments characterized by rapid evolution and inherent heterogeneity. In particular, they exhibit three fundamental limitations: (1) Insufficient understanding of defect patterns …

Improving Estimation Efficiency Via Regression-Adjustment In Covariate-Adaptive Randomizations With Imperfect Compliance, Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang

Research Collection School Of Economics

We study how to improve efficiency via regression adjustments with additional covariates under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We first establish the semiparametric efficiency bound for the local average treatment effect (LATE) under CARs. Second, we develop a general regression-adjusted LATE estimator which allows for parametric, nonparametric, and regularized adjustments. Even when the adjustments are misspecified, our proposed estimator is still consistent and asymptotically normal, and their inference method still achieves the exact asymptotic size under the null. When the adjustments are correctly specified, our estimator achieves the semiparametric efficiency bound. Third, we derive the optimal linear …

Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Reflective learning enhances understanding, especially when instructors promptly address difficulties raised in student reflections. Automated doubt detection can reduce time for instructors, yet existing classification approaches take substantial time for manual annotation and model training. This paper investigates whether large and small language models (LLMs, SLMs) can automate doubt detection without time-consuming training. Using a dataset of anonymized student reflections, we evaluate zeroshot, few-shot prompting, and multi-step reasoning against prior supervised classification baselines. We show that LLMs (GPT-4o, Claude-4, Gemini-2.5) surpass earlier F1 scores without prompting, while prompting further improves their performance. However, using proprietary LLMs can raise cost and …

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find …

Brief Virtual Reality And Mixed Reality Mindfulness Breathing Exercise For Emotional Well-Being And Cognitive Functions In University Students: Within-Subjects Experimental Design Study, Zoey Khai Yee Eun, Charmaine Jiali Koh, Hwajin Yang, Adalia Yin Hui Goh, Meilan Hu, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Background: Mindfulness has been shown to enhance emotional well-being and cognitive performance, yet much of this evidence stems from interventions requiring prolonged practice, making them time-consuming and less accessible. Recent studies suggest that brief mindfulness sessions may also yield positive outcomes, but the effectiveness of such interventions in virtual reality (VR) and mixed reality (MR) remains underexplored. Objective: This study investigates the effects of brief mindfulness breathing exercises delivered through VR and MR on attentional and emotional restoration and self-control capacity. Methods: Using a within-subjects experimental design, 102 undergraduate participants (n=83, 81.4% female; mean age 20.87, SD 1.89) completed a …

Research "Junkification" Is Caused By Researchers, Not Journals, Simon Sebastian Groome, Jose C. Yong, Norman P. Li

Research Collection School of Social Sciences

Concerns about declining research quality have increasingly been framed as “junkification,” whereby academic output expands in volume while declining in epistemic value. A recent paper by Rhodes and Linnenluecke (2025) attributes this trend to changes in publishing, arguing that research quality erodes as journals and publishers become increasingly oriented toward output quantity and metrics, driven by monetization. In 2009, Nobel Laureate James Watson remarked that scientific publishing in the mid-twentieth century was both easier and of a higher standard than today (Web of Stories, 2017). If publishing is much easier of late, as Rhodes and Linnenelucke (R&L) argued, then how …

Meritocracy And Equal Access To The Public Sector: A Comparative Analysis Of Accessibility To Public Sector Jobs And Public Services, Beomgeun Cho, Heasun Choi

Research Collection School of Social Sciences

This study investigates the impact of meritocracy on equal access to the public sector, focusing on public sector employment and services. While meritocratic principles promote impartiality and fairness in administrative processes, their effectiveness in improving equal accessibility remains understudied and contested. Drawing on the literature linking meritocracy to democratic stability, we argue that strong meritocratic systems enhance equal access to the public sector regardless of social groups and gender by fostering professional stewardship in the bureaucracy and strengthening checks and balances between bureaucrats and politicians. Our findings, drawn from a panel dataset consisting of 161 countries over 49 years, suggest …

Miserly Thinking: Understanding The Factors Shaping Public Support Towards Project Wolbachia In Singapore, Shirley S. Ho, Agnes S. F. Chuah, Fiona J. W. Goh, Nova Mengxia Huang, Mengxue Qu, Hye Kyung Kim, Sonny Rosenthal

Research Collection College of Integrative Studies

Project Wolbachia is a vector control method that releases male Wolbachia-infected Aedes mosquitoes to curb dengue transmission in Singapore. While research has primarily focused on its efficacy, few studies have examined public opinion. This study draws upon the cognitive miser model and scientific literacy model to examine the factors shaping public support for Project Wolbachia. A nationally representative door-to-door survey was conducted in Singapore (N = 1,000) using multi-stage stratified random sampling. Our findings revealed that while knowledge about Project Wolbachia explains public support, laypeople may often rely on cognitive shortcuts, such as value predispositions, media attention, and pre-existing perceptions, …