Digital Commons Network^™

The Good Life Paradox, Matthew Hammerton

Research Collection School of Social Sciences

Picture two people on their deathbeds. The first lived comfortably, surrounded by loving family and friends, enjoying diverse pleasures and achievements throughout a long life. The second dedicated herself entirely to fighting injustice, achieving remarkable social change, but at great personal cost. Who lived the better life?Your answer might depend on what you mean by ‘better’. Philosophers have long recognized that when we call a life ‘good’ we can mean different things. So we could be talking about a life’s moral goodness – how virtuous the person was – or its prudential goodness – how well the life went for …

Effectiveness Of Cognitive And Behavioral Interventions In The Treatment Of Schizophrenia: An Umbrella Review Of Meta-Analyses, Gabriel Xing Da Tan, Andree Hartanto, Zoey Khai Yee Eun, Meilan Hu, Kean J. Hsu, Nadyanna M. Majeed

Research Collection School of Social Sciences

Background: Cognitive and behavioral interventions have risen in popularity both as an adjunctive treatment to antipsychotic medication and as an alternative treatment for schizophrenia. With the growing number of such interventions, we performed an umbrella review to provide a comprehensive summary comparing the effectiveness of the different interventions among populations with schizophrenia. Methods: This umbrella review included meta-analyses evaluating cognitive and behavioral interventions for schizophrenia. Following PRISMA guidelines, the initial search yielded 4888 records, and after a three-stage screening procedure, 33 meta-analyses met the inclusion criteria for the final analysis. Results: Our findings from the 33 meta-analyses support the efficacy …

Contextual Stress Profiles And Trajectories Of Internalizing Symptoms Among Adolescents In Mexican Immigrant Families, Su Yeong Kim, Wen Wen, Kiera M. Coulter, Wei Xiang Sim, Tianlu Zhang, Jingyi Shen, Yang Hou, Yishan Shen, Ka I. Ip

Research Collection School of Social Sciences

Contextual stress experiences in early adolescents may predict distinct trajectories of depressive symptoms and anxiety across the course of adolescence. Data from 604 Mexican-origin adolescents collected from 2012 to 2020 (54% female; Mage = 12.92, SD = 0.92) showed that youth with high stress levels in multiple domains (Broadly Stressed) and those with high stress in language brokering domain only (Language Brokering Stressed) had similar levels of internalizing symptoms in early adolescence, and both groups reported higher stress than youth experiencing occasional levels of stress across domains (Occasionally Environmentally Stressed). However, youth in the Language Brokering Stressed group experienced slightly …

Reliability And Validity Of A Self-Administrated Online Assessment Of Intrinsic Capacity: A Singapore Cohort Study, Paulin Tay Straughan, William Tov, Lay Khoon Lau, Li Ling Grace Cheong, Chong Sheng Micah Tan, William Tov, Yew Yoong Ding, Wee Shiong Lim

Research Collection School of Social Sciences

Objective: To ascertain the construct validity and reliability of a self-administered web-based assessment of intrinsic capacity (IC). Design: Cross-sectional data analysis of a prospective cohort study. Settings and Participants: We included data from 6,434 respondents (age, mean (SD): 65.33 (5.81) years; 52.4% women) of the Singapore Life Panel population study who participated in the online surveys in March 2022 and May 2022.MethodsIncremental nested factor structures of IC were modelled with confirmatory factor analysis (CFA) and their goodness-of-fit were assessed mainly with root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square …

Attachment To Artificial Intelligence: Development Of The Ai Attachment Scale, Construct Validation, And The Psychological Mechanisms Of Human-Ai Attachment, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) systems are increasingly integrated into daily life, not only as tools but also as social partners that people may turn to for interaction and support. This raises important questions about whether, how, and why individuals form attachment-like bonds with AI, and the psychological implications of such attachments. Across five studies involving 1259 unique participants from Singapore and the U.S., the current work developed and validated the 15-item AI Attachment Scale and investigated the dispositional and motivational factors associated with attachment to AI, as well as its emotional and social outcomes. The AI Attachment Scale displayed strong psychometric …

Privately Owned Companies Dominate Renewable Energy Generation Ownership Around The World, Dyaran Bansraj, Theodor Florian Cojoianu, Xi Hu, Khaladdin Rzayev, Francisco Urzua

Research Collection College of Integrative Studies

Global sustainable finance policies are premised on publicly listed companies driving decarbonization through transparency, investor pressure, and capital market access. Analysing c. 20,000 corporate owners of renewable and fossil-fuel assets worldwide, we show the opposite: private firms own approximately 75% of global renewable generation capacity. This private dominance holds across all major technologies and regions, with listed ownership of renewable assets being the majority only in the oil & gas and technology sectors. The Paris Agreement did not alter this balance. Instead, ownership of the energy transition reflects countries' financial structures, with similar patterns observed across manufacturing, construction, and financial …

The Impact Of Value Co-Creation On Organizational Resilience In Small And Medium-Sized Traditional Foreign Trade Enterprises, Dan Yan

Dissertations and Theses Collection (Open Access)

Against the backdrop of increasing uncertainty in the global business environment, international scholarly attention to individual, group, and organizational resilience has risen significantly. Chinese export trading enterprises—especially small and medium-sized export trading enterprises(SMETEs)—face disadvantages such as small scale, weak financing capacity, limited access to information, and low brand strength. After experiencing shocks from digital transformation, the COVID-19 pandemic, the Russia–Ukraine war, and the European energy crisis, these firms have been compelled to reconsider how to enhance their organizational resilience in order to adapt to an increasingly turbulent external environment.

First, this study selects Company H as a representative case of …

Striking A Balance Between Personal Data Protection And Transactional Efficiency In The Context Of Data-Driven Mergers, Jun Hu

Dissertations and Theses Collection (Open Access)

The proliferation of data-driven mergers and acquisitions (M&A) has created a critical tension between the commercial value of integrated data assets and the fundamental privacy rights of users. This dissertation addresses the underexplored micro-foundations of user consent when personal data is transferred wholesale to an acquiring firm. It develops and empirically tests a sequential mediation model, anchored in Privacy Calculus and Signaling Theory, to explain how users' perceptions and intentions are shaped by different corporate data protection strategies.

A between-subjects experimental design was employed (N=532), exposing participants to one of three distinct protection mechanisms: procedural safeguards (emphasizing fairness and control), …

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 …

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. …

Copyright Ownership And Duration Of Ai-Authored Works, Cheng Lim Saw

Research Collection Yong Pung How School Of Law

On the assumption that Parliament has endorsed the notion of AI authorship and the prospect that copyright may well subsist in works created autonomously by the AI itself, this essay further explores allied issues surrounding the ownership and duration of copyright in AI-authored works.

Stableguard: Towards Unified Copyright Protection And Tamper Localization In Latent Diffusion Models, Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He

Research Collection School Of Computing and Information Systems

The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling …

Public Health Trade-Offs During A Pandemic: Mobility Decline And Hypertension Risk In India, Arpita Khanna, Tomoki Fujii

Research Collection School Of Economics

The Covid pandemic dramatically altered patterns of daily life, particularly through widespread declines in mobility due to lockdowns and social distancing measures. While the economic and epidemiological effects of these restrictions have been widely studied, their broader health implications remain underexplored. This paper investigates the relationship between mobility decline during the Covid pandemic and hypertension outcomes in India. Leveraging district-level variation in mobility from Google Community Mobility Reports and health data from the 2019–2021 Indian Demographic and Health Survey (DHS), we find that a 1 percentage point reduction in mobility over the 30 days prior to interview is associated with …

Local Problems, Global Solutions? Making It Rain In Hong Kong C. 1890-1930, Fiona Williamson

Research Collection College of Integrative Studies

The late nineteenth to early twentieth century saw a small but dedicated rise in experimental rainmaking. The possibility that humanity might one day be able to control the weather – especially to alleviate drought – was very attractive to governments and private investors. The late nineteenth century was an era of scientific optimism and a number of rainmaking experiments across the world had brought the potential for weather control out of the realms of discourse and literature and further into tangible near-future science. There has been a small but thorough historiographical literature on this subject, focusing largely on American, British, …

Public Views Towards Lifespan, Healthspan, And Healthy Longevity Medicine In Singapore: A Qualitative Study From The Healthy Longevity (Helo) Initiatives, Belinda Wang, Anna Szucs, Elena Sandalova, Zhi Meng Lim, E. J. Horberg, Paul A. O’Keefe, Louis Island, Sonny Rosenthal, Andrea B. Maier

Research Collection College of Integrative Studies

Introduction: Healthy Longevity Medicine (HLM) offers a strategy to reduce the healthspan-lifespan gap, yet public perspectives remain unclear. This study refines the Healthy Longevity (HELO) framework through a qualitative exploration of public views towards lifespan, healthspan, and HLM.Methods: Individuals living in Singapore participated in semi-structured group or individual discussions to explore (a) their understanding of lifespan and healthspan, (b) motivational factors for health behaviours, and (c) their awareness of HLM. Sampling maximised variation across age, sex, and ethnicity. Data obtained through 13 discussions were analysed with a mixed, inductive-deductive approach employing the HELO framework.Results: Thirty-six participants (mean age = 49.4 …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

Bim-To-Brick: Using Graph Modeling For Iot/Bms And Spatial Semantic Data Interoperability Within Digital Data Models Of Buildings, Filippo Vittori, Fu Chuan Tan, Laura Anna Pisello, Adrian Chong, Cristina Piselli, Clayton Miller

Research Collection College of Integrative Studies

The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models (BIM), all aimed at environmental well-being. Data interoperability is a key component to eliminate silos of information, and using semantic web technologies like the BRICK schema, an effort to standardize semantic descriptions of the physical, logical, and virtual assets in buildings and the relationships between them, is a suitable approach. However, current data integration processes can involve significant manual interventions. This paper presents a methodology to automatically collect, assemble, and integrate information from a building …

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 …

Backdoorllm: A Comprehensive Benchmark For Backdoor Attacks And Defenses On Large Language Models, Yige Li, Hanxun Huang, Yunhan Zhao, Xingjun Ma, Jun Sun

Research Collection School Of Computing and Information Systems

Generative large language models (LLMs) have achieved state-of-the-art results on a wide range of tasks, yet they remain susceptible to backdoor attacks: carefully crafted triggers in the input can manipulate the model to produce adversaryspecified outputs. While prior research has predominantly focused on backdoor risks in vision and classification settings, the vulnerability of LLMs in open-ended text generation remains underexplored. To fill this gap, we introduce BackdoorLLM1 , the first comprehensive benchmark for systematically evaluating backdoor threats in text-generation LLMs. BackdoorLLM provides: (i) a unified repository of benchmarks with a standardized training and evaluation pipeline; (ii) a diverse suite of …

Griffin: Effective Token Alignment For Faster Speculative Decoding, Shijing Hu, Jingyang Li, Xingyu Xie, Zhihui Lu, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference in large language models (LLMs) by generating multiple draft tokens simultaneously. However, existing methods often struggle with token misalignment between the training and decoding phases, limiting their performance. To address this, we propose GRIFFIN, a novel framework that incorporates a token-alignable training strategy and a token-alignable draft model to mitigate misalignment. The training strategy employs a loss masking mechanism to exclude highly misaligned tokens during training, preventing them from negatively impacting the draft model’s optimization. The token-alignable draft model introduces input tokens to correct inconsistencies in generated features. Experiments on LLaMA, Vicuna, Qwen and Mixtral models …

Sopo: Text-To-Motion Generation Using Semi-Online Preference Optimization, Xiaofeng Tan, Hongsong Wang, Xin Geng, Pan Zhou

Research Collection School Of Computing and Information Systems

Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor highquality, human-preferred motions—a critical yet largely unexplored problem. In this work, we theoretically investigate the DPO under both online and offline settings, and reveal their respective limitation: overfitting in offline DPO, and biased sampling in online DPO. Building on our theoretical insights, we introduce Semi-online Preference Optimization (SoPo), a DPO-based method for training text-to-motion models using “semi-online” data pair, consisting of unpreferred motion from online distribution and preferred motion in …

Hybrid-Balance Gflownet For Solving Vehicle Routing Problems, Ni Zhang, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Existing GFlowNet-based methods for vehicle routing problems (VRPs) typically employ Trajectory Balance (TB) to achieve global optimization but often neglect important aspects of local optimization. While Detailed Balance (DB) addresses local optimization more effectively, it alone falls short in solving VRPs, which inherently require holistic trajectory optimization. To address these limitations, we introduce the Hybrid-Balance GFlowNet (HBG) framework, which uniquely integrates TB and DB in a principled and adaptive manner by aligning their intrinsically complementary strengths. Additionally, we propose a specialized inference strategy for depot-centric scenarios like the Capacitated Vehicle Routing Problem (CVRP), leveraging the depot node's greater flexibility in …

Uniteformer: Unifying Node And Edge Modalities In Transformers For Vehicle Routing Problem, Dian Meng, Zhiguang Cao, Jie Gao, Yaoxin Wu, Yaqing Hou

Research Collection School Of Computing and Information Systems

Neural solvers for the Vehicle Routing Problem (VRP) have typically relied on either node or edge inputs, limiting their flexibility and generalization in real-world scenarios. We propose UniteFormer, a unified neural solver that supports node-only, edge-only, and hybrid input types through a single model trained via joint edge-node modalities. UniteFormer introduces: (1) a mixed encoder that integrates graph convolutional networks and attention mechanisms to collaboratively process node and edge features, capturing cross-modal interactions between them; and (2) a parallel decoder enhanced with query mapping and a feed-forward layer for improved representation. The model is trained with REINFORCE by randomly sampling …

Large Language Models As End-To-End Combinatorial Optimization Solvers, Xia Jiang, Yaoxin Wu, Minshuo Li, Zhiguang Cao, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requiring significant domain expertise. While large language models (LLMs) have shown promise in automating CO problem solving, existing approaches rely on intermediate steps such as code generation or solver invocation, limiting their generality and accessibility. This paper introduces a novel framework that empowers LLMs to serve as end-to-end CO solvers by directly mapping natural language problem descriptions to solutions. We propose a two-stage training strategy: supervised fine-tuning (SFT) imparts LLMs with solution generation patterns from domain-specific solvers, while a feasibility-and-optimality-aware reinforcement learning …

Multi-Task Vehicle Routing Solver Via Mixture Of Specialized Experts Under State-Decomposable Mdp, Yuxin Pan, Zhiguang Cao, Chengyang Gu, Liu Liu, Peilin Zhao, Yize Chen, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutilize the compositional structure of VRP variants, each derivable from a common set of basis VRP variants. This critical oversight causes unified solvers to miss out the potential benefits of basis solvers, each specialized for a basis VRP variant. To overcome this limitation, we propose a framework that enables unified solvers to perceive the shared-component nature across VRP variants by proactively reusing basis solvers, while mitigating the exponential growth of trained neural solvers. Specifically, we introduce a State-Decomposable MDP …

Learning Memory-Enhanced Improvement Heuristics For Flexible Job Shop Scheduling, Jiaqi Wang, Zhiguang Cao, Peng Zhao, Rui Cao, Yubin Xiao, Yuan Jiang, You Zhou

Research Collection School Of Computing and Information Systems

The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are more effective in approaching optimality. However, the flexible machine allocation in FJSP poses significant challenges to the application of this framework, including …

Island-Based Evolutionary Computation With Diverse Surrogates And Adaptive Knowledge Transfer For High-Dimensional Data-Driven Optimization, Xianrong Zhang, Yuejiao Gong, Zhiguang Cao, Jun Zhang

Research Collection School Of Computing and Information Systems

In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantly when applied to large-scale optimization problems (LSOPs). To address the challenge, this paper proposes an offline DDEA named DSKT-DDEA. DSKT-DDEA leverages multiple islands that utilize different data to establish diverse surrogate models, fostering diverse subpopulations and mitigating the risk of premature convergence. In the intra-island optimization phase, a semi-supervised learning method is devised to fine-tune the surrogates. It not only facilitates …

Robust Hallucination Detection In Llms Via Adaptive Token Selection, Mengjia Niu, Hamed Haddadi, Guansong Pang

Research Collection School Of Computing and Information Systems

Hallucinations in large language models (LLMs) pose significant safety concerns that impede their broader deployment. Recent research in hallucination detection has demonstrated that LLMs’ internal representations contain truthfulness hints, which can be harnessed for detector training. However, the performance of these detectors is heavily dependent on the internal representations of predetermined tokens, fluctuating considerably when working on free-form generations with varying lengths and sparse distributions of hallucinated entities. To address this, we propose HaMI, a novel approach that enables robust detection of hallucinations through adaptive selection and learning of critical tokens that are most indicative of hallucinations. We achieve this …

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 …