Digital Commons Network^™

Beyond Religious Narcissistic Identification: Agnostic And Atheistic Narcissism, Magdalena Żemojtel-Piotrowska, Jarosław Piotrowski, Bartłomiej Nowak, V. Saroglou, John Maltby, Constantine Sedikides, Mladen Adamovic, Nur Amali Aminnuddin, Seth Christopher Yaw Appiah, Rahkman Ardi, Zana Babakr, Einar Baldursson, Sergiu Bălţătescu, M. Bilgehan Aytaç, Li, Norman P., Bruno Bonfá-Araujo, Matthias Burghart, Phatthanakit Chobthamkit, Marilyn Clark, Magali. Clobert

Research Collection School of Social Sciences

Agnosticism and atheism are often grouped simply as nonreligious identities, yet emerging research highlights their distinct psychological profiles and social implications. Among these distinctions, collective narcissism—characterized by strong attachment to one’s group, exceptionalism, and grievance for recognition—offers a framework for understanding identity processes in both nonreligious groups. We examined whether agnostics and atheists exhibit collective narcissism and its forms (agentic—focused on exceptional effectiveness; communal—focused on exceptional morality) similarly to believers. We explored crossdenominational variance in agentic and communal collective narcissism levels relying on data from 77 countries (N = 3,570; 1227 agnostics, 2343 atheists). Agnostics and atheists from secular countries …

“From Remembering To Shaping”: Narrating Shared Experiences By Co-Designing Cultural Heritage Artifacts In Collaborative Vr, Yushang Yang, Fanxu Meng, Fiona Fui-Hoon Nah, L. C. Ray

Research Collection School Of Computing and Information Systems

The ways people remember and recall places reveal an invisible aspect of cultural heritage (CH), reflecting how individuals and communities relate to these places. Heritage is communal, emerging through collaboratively constructed narratives rather than individual records. To probe how people may share collective memories, we designed an immersive two-person workflow for collaboratively co-designing 3D artifacts and environments in virtual heritage locations, using Generative AI (GenAI) to instantiate these intangible memories. Observations of the co-creation process revealed that participants merged prompts and model placements when negotiating different perspectives. They used spatial operations to compose scenes, and also to express personal and …

Context Matters: Auditing Gender Bias In T2i Generation Through Risk-Tiered Use-Case Profiles, Jose Luis Luna Campoverde, Yankun Wu, Xiaofei Xie, Noa Garcia

Research Collection School Of Computing and Information Systems

Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting to be integrated into higher-impact pipelines. Since generated images tend to reinforce stereotypes, producing representational erasure via “default” depictions and shaping perceptions of who belongs in certain roles, a growing body of work has proposed metrics to quantify gender bias in T2I outputs. Yet existing evaluations remain fragmented. Metrics are often reported without a shared view of what they measure, what assumptions they entail, or how their results should be interpreted under different deployment contexts. This limits the usefulness of gender …

“Megadeal” Subsidies, Local Spillovers And Corporate Innovation, Yoojin Lee, Shaphan Ng, Aruhn Venkat

Research Collection School Of Accountancy

We examine whether the largest place-based, firm-specific corporate subsidies (“Megadeals”) awarded by state and local governments affect local firms’ innovation. First, we document that 1) subsidy firms innovate in the subsidized county and 2) subsidy firms bring inventors from other counties into the subsidized county, consistent with subsidy firms generating new knowledge locally. In our main test, we use a stacked cohort design with stringent fixed effects to document that local firms increase patenting following a Megadeal. Cross-sectionally, effects are increasing 1) in subsidy firm innovativeness, 2) in the technological closeness of subsidy firms and local firms, 3) when subsidy …

To Go Far, Go Together: Force2026 Brings The World’S Scholarly Communication Community To Singapore, Singapore Management University

SMU Press Releases and News

Singapore Management University Libraries (SMU Libraries) hosted FORCE2026, the annual conference of FORCE11, from 3 to 5 June 2026 in our city campus.

FORCE11 is a global community dedicated to advancing how scholarly knowledge is created, shared, and used. The conference commemorated FORCE11’s 15-year journey of building a global community committed to improving scholarly communication and research assessment.

FORCE2026 marked the first time the conference was held in Asia, positioning Singapore and SMU Libraries at the centre of global conversations on the future of research, scholarly communication and research assessment. By bringing the conference to the region, SMU Libraries …

Parasocial Relationships With Artificial Intelligence (Ai): A Systematic Review Of Benefits And Risks, Jing Wen Hung, Charlotte K. Y. Lee, K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Advances in artificial intelligence (AI) have transformed AI systems into increasingly interactive and relational agents, raising questions about the consequences of the perceived sense of intimacy and reciprocity formed with AI systems. The present systematic review synthesised empirical evidence on the benefits and risks of AI parasocial relationships across diverse AI agents and contexts. Following PRISMA guidelines, 39 empirical records were identified and analysed using narrative thematic analysis. Through this, five key benefits of AI parasocial relationships were identified, namely personal development and adaptive functioning, emotional support, social needs fulfilment, enjoyment and entertainment, and community participation and social belonging. However, …

Towards Efficient Continual Learning: From Memory Optimization To Foundation Models, Zilin Luo

Dissertations and Theses Collection (Open Access)

Continual learning, also termed lifelong learning, enables machine learning models to incrementally acquire new knowledge while mitigating the degradation of previously learned information—a capability essential for adapting to dynamic, real-world data environments. This dissertation investigates the core challenges of continual learning and extends its application to enhancing training efficiency in the era of foundation models. The first part of this dissertation addresses the constraints of few-shot exemplar storage with a novel compression framework. While leveraging class activation maps to downsample non-discriminative pixels, we introduce an adaptive masking model, optimized through bilevel optimization, to store more exemplars efficiently. The second part …

How To Save The Take-Home Essay With Oral Assessments, Matthew Hammerton, Jacqueline Ho

Research Collection School of Social Sciences

In a commentary, the authors opined that pairing take-home essays with oral assessments is a more effective response to AI than policing its use. Students who cannot adequately explain their work can be marked down, reducing incentives to rely on AI. They noted that oral exams help preserve key elements of university education – intellectual effort, ownership, and human relationships – while allowing take-home essays to remain relevant in an AI-driven landscape that demands greater emphasis on understanding, responsibility, and dialogue.

Daily Problematic Smartphone Use Predicts Decreases In Self-Control Capacity: Evidence From Random-Intercept Cross-Lagged Panel Model, Adalia Yin Hui Goh, Andree Hartanto

Research Collection School of Social Sciences

Amidst the widespread increase in global smartphone screen time and the phenomenon of ‘The Great Exhaustion’, where multiple surveys indicate pervasive tiredness and feelings of being drained, there is growing concern about the link between problematic smartphone use and the capacity for self-control. While numerous studies have investigated the relationship between problematic smartphone use and self-control capacity, they are mostly cross-sectional. Thus, it is unclear whether the lack of capacity for self-control is an antecedent or consequence of problematic smartphone use. Addressing the research gap, we conducted a 7-day diary study to investigate the bidirectional relationship between problematic smartphone use …

Patchfuzz: Patch Fuzzing For Javascript Engines, Junjie Wang, Zhihua Xie, Xiaofei Xie, Xiaoning Du, Xiangwei Zhang

Research Collection School Of Computing and Information Systems

Context: Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript (JS) engines with considerable success, these efforts have been limited to using ordinary test cases or publicly available vulnerability PoCs (Proof of Concepts) as seeds, and the sustainability of these approaches is hindered by the challenges associated with automating the PoC collection. Objective: To address these limitations, we propose an end-to-end sustainable approach for JS engine patch fuzzing, named PatchFuzz. Method: It automates the collection of PoCs of a broader range of …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

When Politics Meets Digital Assets: Gender Identity Salience And Nft Pricing After Roe V. Wade, Xiang Liu, Yao Zhao, Ping Fan Ke

Research Collection School Of Computing and Information Systems

Major sociopolitical events can reshape public attention toward identity-related issues, potentially influencing valuation patterns in digital markets where identity-related characteristics are embedded in digital assets. Using the overturning of Roe v. Wade as an exogenous policy shock, this paper examines how gender attributes represented in non-fungible token (NFT) avatars affect market outcomes. Using transaction data from six major avatar-based NFT collections traded on Etherscan in 2022, we apply a quasi-experimental design combining propensity score matching and a difference-in-differences model. The results indicate that the policy shock significantly increased the resale prices of NFTs representing female avatars. These findings suggest that …

On-The-Fly Generation-Quality Enhancement Of Deep Code Models Via Model Collaboration, Weifeng Sun, Naiqi Huang, Meng Yan, Zhongxin Liu, Hongyan Li, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

The growing prominence of deep code models in automating software engineering tasks is undeniable. However, their deployment encounters significant challenges in on-the-fly performance enhancement, which refers to dynamically improving the performance of deep code models during real-time execution. Conventional techniques, such as retraining or fine-tuning, are effective in controlled pre-deployment scenarios but fall short when adapting to on-the-fly adjustments post-deployment. CodeDenoise, a notable on-the-fly performance enhancement technology, leverages uncertainty-based methods to identify misclassified inputs and applies an input modification strategy to rectify classification errors. While effective for classification tasks, this approach is inapplicable to generative tasks due to two key …

Videocreator: An Agentic System For Multi-Turn Video Production, Zhengyang Liang, Yan Shu, Cathal Gurrin, Nicu Sebe, Lizi Liao

Research Collection School Of Computing and Information Systems

Recent advances in video generation models enable visually compelling single clips. However, real-world video creation is inherently continuous and iterative: creators refine content over multiple rounds while maintaining narrative, style, and entity consistency. Existing standalone generators are largely stateless and lack memory of previously generated segments, making it difficult to produce a coherent and consistent video project. To address this gap, we present VideoCreator, a unified video agent that integrates generation and understanding with a project-level memory system. VideoCreator leverages understanding capabilities to perform fine-grained analysis of newly produced content and uses persistent memory to retain and reuse prior context …

Sam3-Litetext: An Anatomical Study Of The Sam3 Text Encoder For Efficient Vision-Language Segmentation, Chengxi Zeng, Yuxuan Jiang, Ge Gao, Shuai Wang, Duolikun Danier, Bin Zhu, Stevan Rudinac, David Bull, Fan Zhang

Research Collection School Of Computing and Information Systems

Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over-provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large-scale anatomical analysis of text prompting in vision–language segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on a low-dimensional manifold despite high-dimensional representations. Motivated by these findings, we …

Frozen Lvlms For Micro-Video Recommendation: A Systematic Study Of Feature Extraction And Fusion, Huatuan Sun, Yunshan Ma, Changguang Wu, Yanxin Zhang, Pengfei Wang, Xiaoyu Du

Research Collection School Of Computing and Information Systems

Frozen Large Video Language Models (LVLMs) are increasingly employed in micro-video recommendation (MVR) due to their strong multimodal understanding. However, existing apporches typically deploy LVLMs as fixed black-box feature extractors without systematically comparing alternative representation strategies. To address this gap, we present the first systematic empirical study on various feature extraction paradigms and integration strategies, along with hierarchical representations from frozen LVLMs for MVR. Extensive experiments on representative LVLMs reveal that hidden states from multiple decoder layers provide richer and more effective representations for MVR. Guided by this insight, we propose the Dual Feature Fusion (DFF) Framework, a lightweight approach …

Co-Designing With Autistic Livestreamers: Care, Constraints, And Trade-Offs In Livestreaming, Terrance Mok, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Autistic livestreamers use platforms like Twitch for social connection, self-expression, and community, but these spaces also impose ongoing social and emotional demands. Prior work has documented these experiences, but less is known about what autistic creators themselves envision for the tools and platforms they use. We address this gap through a Research through Design (RtD) co-design study with three autistic Twitch streamers, using speculative artefacts as discussion prompts to explore how participants reasoned about potential livestreaming technologies. Across three co-design activities, we identify three overarching tensions shaping autistic streaming practice: Expression versus Misinterpretation and Harm; Public Participation versus Control and …

“Grandpa, Can You Speak Nicer?”: Envisioned Chatbot Roles And Design Tensions In Intergenerational Communication Conflicts, Tianyi Zhang, Emran Bin Elias Poh, Yueyue Hou, Yi-Chieh Lee, Renwen Zhang, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across …

Group Conversational Agents: A Review Of Designs That Support And Shape Group Interaction, Shunyi Yeo, Tianyi Zhang, Scott Bateman, Gary Hsieh, Young-Ho Kim, Simon Tangi Perrault, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in …

The Stars Align: Modeling User Rating Calibration With Sparse Semantic Review Features, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

User ratings are often treated as comparable across users, although identical scores may reflect different experiences. We study whether ratings can be viewed as user-specific discretizations of a shared semantic continuum derived from review text. Our method maps reviews into sparse semantic features with a sparse autoencoder and learns user-specific filters for each rating level. On Amazon Electronics, the learned embeddings align along a shared low-dimensional rating axis. Users differ mainly in how they anchor and partition this continuum, while preserving its overall ordinal structure. These findings support a semantic view of calibration beyond scalar bias correction.

Hide-And-Sweep: Detecting Concealed Cameras Via Led Illumination Sweeps, Jonghyuk Yun, Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, Jun Han

Research Collection School Of Computing and Information Systems

Hidden cameras have increasingly infiltrated hotel and Airbnb rooms, posing serious privacy risks. Detecting such cameras is challenging because they are visually inconspicuous and often embedded inside everyday objects. Even worse, existing handheld detectors are manual and also rely on single-angle illumination and hence suffer from high false-positive rates. We present SweepLED (pronounced "sweepled")1, a practical hidden camera detection system that operates on a commodity smartphone augmented with an unobtrusive LED-embedded case. SweepLED performs LED sweeping - a controlled sequence of multi-angle illumination - while the user simply holds the phone still by hand, enabling the camera to capture how …

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

Towards Auto-Evaluation For Large Language Models, Jiahao Ying

Dissertations and Theses Collection (Open Access)

The rapid advancement of large language models (LLMs) has created an urgent need for evaluation methodologies that are timely, scalable, reliable, and informative. Conventional evaluation benchmarks, although essential for measuring model capabilities and guiding model development, are often constructed and maintained through labor-intensive human annotation. As LLMs continue to improve through increases in model scale, training data, and computational resources, static benchmarks may quickly lose discriminative power. Moreover, the growing use of large and diverse training corpora increases the risk of benchmark leakage, which can inflate evaluation results and obscure the true capabilities of models. These challenges call for a …

Compellingness In Nash Implementation, Shurojit Chatterji, Takashi Kunimoto, Paulo Daniel Salles Ramos

Research Collection School Of Economics

A social choice function (SCF) is said to be Nash implementable (in pure strategies) if there exists a mechanism in which every pure-strategy Nash equilibrium induces outcomes specified by the SCF. The main objective of this paper is to assess the impact of considering mixed-strategy equilibria in Nash implementation. We define compelling Nash implementation as a case where the implementing mechanism possesses a pure-strategy equilibrium that strictly Pareto dominates any undesired mixed-strategy equilibrium. We show that if the finite environment and the SCF to be implemented jointly satisfy what we call Condition COM, then we can construct a finite …

The External Influence Of International Business Scholarship: Growth, Stabilization, Or Decline?, Gokhan Ertug, Andrew Delios, Yi Li, Zhengchu Zhang

Research Collection Lee Kong Chian School Of Business

The influence of international business (IB) scholarship is an active debate (e.g., Bello & Kostova, 2012; Cantwell et al., 2014, 2016; Doh et al., 2023; Tung et al., 2023). Although IB research has expanded in the number of publications, in its sophistication of research design, and in its thematic breadth over the past decades, questions remain regarding its influence beyond the core ecosystem of IB journals. Anecdotally, a pessimistic view has become more commonplace. The concern is that IB has become increasingly self-referential, relying more heavily on IB-related theory extensions and context-specific settings, which could limit its resonance in the …

Settlement Manipulation In Prediction Markets, David Dai, Ruizhe Jia, Shihao Yu

Research Collection Lee Kong Chian School Of Business

Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, causing large price reversals after settlement. Manipulators capture a large amount of profit, mostly from retail. Manipulation is largely absent in the fifteen-minute contracts: lengthening the contract horizon removes it, providing the market-design remedy our model and evidence support.

Queuing Uncertainty Of Limit Orders, Bart Yueshen Zhou

Research Collection Lee Kong Chian School Of Business

Limit orders submitted around the same time are subject to random latencies and will be queued accordingly. In equilibrium, end-of-queue limit orders always lose money—the liquidity supply appears excessive. The model generates empirical predictions regarding such “overshooting” liquidity: (i) new limit orders appear fleeting—clustered submissions are followed by immediate cancellations, (ii) the resulting cancel-to-add count ratio reflects adverse selection, and (iii) the cancel-to-add size ratio measures high-frequency market-making activity. Welfare can be hurt by the overshooting liquidity if it induces excessive speculation. Overall, the model contributes to a more comprehensive understanding and better utilization of order book data.

Cycles Of Inequality In The Marketplace: Insights From Macro, Marketer, And Consumer Perspectives, Debora V. Thompson, Amna Kirmani, Rebecca Hamilton, Andy Li, Christilene Du Plessis, Daniel Fernandes, Guillaume Johnson, Brent Mcferren, Jian Ni, Vladmir Pavlov, Francine Petersen, Lisa Scheer, Yan Vieites, Keith Wilcox

Research Collection Lee Kong Chian School Of Business

Seeking inequality via differentiation is a fundamental theme in the marketing literature: consumers derive utility from products that convey socially valued attributes, and marketers target consumers by giving them opportunities to differentiate on socially valued attributes. However, as a large body of evidence shows, inequality can reduce consumer well-being and limit economic growth. In this paper, we take a systemic view of marketplace inequality, examining the interdependence among consumers, marketers, and macro forces in shaping inequality in markets for goods and services. Our broad review of the marketing literature across ten marketing journals and a variety of subdomains within the …