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What Is Wrong With Workism?, Matthew Hammerton May 2025

What Is Wrong With Workism?, Matthew Hammerton

Research Collection School of Social Sciences

Workism is the phenomenon of people making their work the primary source of meaning and identity in their life. Recent critics of workism have argued that there is a growing trend towards it in many societies and that this is a bad thing. This article brings a philosophical perspective to the debate on workism. It develops a precise account of what workism is and evaluates the main objections raised against it by examining their underlying philosophical assumptions. Ultimately, it is argued that workism, as a way of life, is not as objectionable as its critics suggest.

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Dispositional Religiosity Predicts Increased Incidence Of Mixed Emotions: Evidence Across Five Studies Spanning Two Countries, Vincent Y. S. Oh, Andree Hartanto, Ringo M. H. Ho, Eddie M. W. Tong May 2025

Dispositional Religiosity Predicts Increased Incidence Of Mixed Emotions: Evidence Across Five Studies Spanning Two Countries, Vincent Y. S. Oh, Andree Hartanto, Ringo M. H. Ho, Eddie M. W. Tong

Research Collection School of Social Sciences

Across five studies (total N = 8,414), we examined whether religious individuals experienced more mixed emotions. Studies 1 and 2 examined associations between dispositional religiosity and mixed emotions, while Study 3 provided a pre-registered replication. Study 4 provided a pre-registered exploratory examination of three potential mediators of the relationship between dispositional religiosity and mixed emotions: trait dialecticism, cognitive reappraisal, and fear of God. Finally, Study 5 provided pre-registered prospective analyses testing associations between dispositional religiosity and mixed emotions 1 month later. Across all studies, positive and negative emotions were also examined, and latent variable structural equation modeling was performed, controlling …

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The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed May 2025

The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed

Research Collection School of Social Sciences

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 …

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Do Birds Of A Nest Flock Together? A Study Of Home Provinces And Migration Paths Among Filipina Domestic Workers In Hong Kong, Tim F. Liao, Yasmin Y. Ortiga May 2025

Do Birds Of A Nest Flock Together? A Study Of Home Provinces And Migration Paths Among Filipina Domestic Workers In Hong Kong, Tim F. Liao, Yasmin Y. Ortiga

Research Collection School of Social Sciences

There is strong evidence that migration is networked. However, it is unclear whether having the same province of origin can also lead to similar migratory paths. This study investigates the question of whether a shared home province among Filipina migrant domestic workers in Hong Kong is associated with shared or linked migration paths to Hong Kong. Using a unique data source, the 2017 Survey of Migrant Domestic Workers in Hong Kong, we employed sequence analysis to identify which specific provinces exhibit stronger linkages between migratory trajectories among migrants from the same provinces and if so, whether certain provincial characteristics matter. …

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Voting For Winners And Losers In A Hybrid Regime: How Thailand's 2023 Election Shaped Voter Opinion, Jacob Ricks, Allen Hicken May 2025

Voting For Winners And Losers In A Hybrid Regime: How Thailand's 2023 Election Shaped Voter Opinion, Jacob Ricks, Allen Hicken

Research Collection School of Social Sciences

Research in Western Democracies suggests voting has substantive impacts on how one feels about democracy and state institutions. Voting for winners leads to positive feelings while voting for losers has the opposite effect. How, though, do voters interpret electoral results in a system prone to authoritarian intervention? Thailand's 2023 election offers a unique opportunity to examine how voting in a hybrid regime can impact voter opinion. Using a pre- and postelection survey, we demonstrate that, like elections in democracies, an election in a hybrid regime can have a significant impact on voter opinions. When pro-democracy voters win an election in …

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Park Cool Island Modifications To Assess Radiative Cooling Of A Tropical Urban Park, Graces N. Y. Ching, Sin Kang Yik, Su Li Heng, Beatrice H. Ho, Peter J. Crank, Moshe Eliezer Mandelmilch, Xiang Tian Ho, Winston T. L. Chow May 2025

Park Cool Island Modifications To Assess Radiative Cooling Of A Tropical Urban Park, Graces N. Y. Ching, Sin Kang Yik, Su Li Heng, Beatrice H. Ho, Peter J. Crank, Moshe Eliezer Mandelmilch, Xiang Tian Ho, Winston T. L. Chow

Research Collection College of Integrative Studies

Many cities experience urban overheating from climate change and the urban heat island phenomenon. Previous studies demonstrate that parks are a potential nature-based solution to mitigate urban overheating through the ‘Park Cool Island’ (PCI) effect. PCI intensity can be measured through field measurements (FM) or remote sensing. This FM study used a network of meteorological sensors within a park and in its surrounding urban area to ascertain its PCI intensity in Singapore from January to December 2022. Consistently cooler air temperatures were found throughout a 24-h period in the park area, with mean daytime (nighttime) PCI intensity measured ~ 2.21 …

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Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li May 2025

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

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Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan May 2025

Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art …

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Is Subjective Well-Being Insured Against Income Shocks? Evidence From 20-Year Panel Data In South Korea, Jiyeon An, Taehyun Ahn, Kim May 2025

Is Subjective Well-Being Insured Against Income Shocks? Evidence From 20-Year Panel Data In South Korea, Jiyeon An, Taehyun Ahn, Kim

Research Collection School Of Economics

Using 20 years of nationally representative panel data in South Korea, we estimate how life satisfaction responds to income shocks. We document that unexpected income changes significantly impact an individual's life satisfaction, and the magnitudes depend on the persistence of income shocks. We find that permanent income shocks substantially penetrate life satisfaction, while transitory income shocks have minimal impact. We also find that life satisfaction regarding external factors such as family income and leisure activities is more sensitive to income shocks than life satisfaction related to social relationships. Our findings imply that it is critical for the government to address …

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The Impact Of Nafta On Prices And Competition: Evidence From Mexican Manufacturing Plants, Felipe Brugués, Ayumu Ken Kikkawa, Yuan Mei, Pablo Robles May 2025

The Impact Of Nafta On Prices And Competition: Evidence From Mexican Manufacturing Plants, Felipe Brugués, Ayumu Ken Kikkawa, Yuan Mei, Pablo Robles

Research Collection School Of Economics

This paper assesses the impact of the North American Free Trade Agreement on Mexican manufacturing plants’ output prices and markups. We distinguish between Mexican goods that are exported and those sold domestically, and decompose their prices separately into markups and marginal costs. We then analyze how these components were affected by the reductions in Mexican output tariffs, intermediate input tariffs, and U.S. tariffs on Mexican exports. We find that domestically sold products saw a decline in prices as Mexican plants faced more competition and gained access to cheaper inputs. Prices of exported goods fell only slightly as plants increased their …

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Valuation Of Crypto Assets On Blockchain With Deep Learning Approach, Xi Zhou, Yin Pang, Esther Ying Yang, Jing Rong Goh, Shaun Shuxun Wang May 2025

Valuation Of Crypto Assets On Blockchain With Deep Learning Approach, Xi Zhou, Yin Pang, Esther Ying Yang, Jing Rong Goh, Shaun Shuxun Wang

Research Collection School Of Economics

With the rapid expansion of the blockchain ecosystem, crypto asset valuation has become an essential area of study for investors and institutions. Here, we introduce a deep learning framework that was designed to predict the value index of crypto assets by integrating intrinsic value variables and decomposing market prices into value and sentiment components. The Crypto Asset Value-indexing Model (CAVM) was applied to Ethereum’s cryptocurrency ETH to demonstrate its effectiveness. Four econometric tests were conducted to verify the informativeness, predictiveness, and reasonability of the generated value indices, as well as the efficiency of price decomposition. Our findings suggested that the …

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The Latin American Principles For Out-Of-Court Workouts, Aurelio Gurrea-Martinez, Adolfo Rouillon May 2025

The Latin American Principles For Out-Of-Court Workouts, Aurelio Gurrea-Martinez, Adolfo Rouillon

Research Collection Yong Pung How School Of Law

Out-of-court workouts can save significant costs associated with the initiation of a formal insolvency proceeding. Therefore, they can help minimise the loss of value experienced by an insolvent firm, ultimately contributing to the maximisation of the returns to creditors and the reorganisation of viable but financially distressed businesses. Additionally, an out-of-court agreement can provide a level of flexibility and confidentiality that is usually lacking in formal insolvency proceedings. Thus, an out-of-court workout can serve as a valuable mechanism to deal with a situation of financial distress, especially in the context of countries with inefficient insolvency systems and companies with concentrated …

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Augsso: Secure Threshold Single-Sign-On Authentication With Popular Password Collection, Changsong Jiang, Chunxiang Xu, Guomin Yang May 2025

Augsso: Secure Threshold Single-Sign-On Authentication With Popular Password Collection, Changsong Jiang, Chunxiang Xu, Guomin Yang

Research Collection School Of Computing and Information Systems

Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-offailure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve …

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Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu May 2025

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

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Regulating Decentralised Finance? A Singapore Perspective, Rachel Phang May 2025

Regulating Decentralised Finance? A Singapore Perspective, Rachel Phang

Research Collection Yong Pung How School Of Law

The emergent phenomenon of decentralised finance (“DeFi”) can be understood as the performance of financial functions through applications that are built on distributed ledger technology and operate using cryptoassets and smart contracts, without the need for traditional centralised intermediaries. In what ways does DeFi challenge existing financial regulation, and how should regulation develop in light of the DeFi phenomenon? In addressing this question, this article provides an overview of DeFi, surveys the current regulatory landscape in Singapore, considers key regulatory issues and policy considerations associated with DeFi, and discusses some potential directions for the future development of the regulation of …

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Disambiguart: A Neural-Based Inference Model For Knowledge Graph Disambiguation, Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan May 2025

Disambiguart: A Neural-Based Inference Model For Knowledge Graph Disambiguation, Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

One main challenge in constructing a knowledge graph (KG) is to deal with ambiguity. Specifically, an entity in the graph can be assigned with multiple meanings while two or more entities considered to have different meanings may actually be the same. Assigning an entity with the correct meaning may involve re-evaluation of its relevant contexts. This costly operation typically involves searching for other similar entities within the KG such that the context can be determined. In this paper, a new model called DisambiguART is proposed leveraging multi-channel matching and inference in a self-organizing neural network for sense disambiguation in knowledge …

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Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu May 2025

Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu

Research Collection School Of Computing and Information Systems

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …

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Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua May 2025

Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently …

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Robust Threshold Ecdsa With Online-Friendly Design In Three Rounds, Guofeng Tang, Haiyang Xue May 2025

Robust Threshold Ecdsa With Online-Friendly Design In Three Rounds, Guofeng Tang, Haiyang Xue

Research Collection School Of Computing and Information Systems

Threshold signatures, especially ECDSA, enhance key protection by addressing the single-point-of-failure issue. Threshold signing can be divided into offline and online phases, based on whether the message is required. Schemes with low-cost online phases are referred to as “online-friendly”. Another critical aspect of threshold ECDSA for real-world applications is robustness, which guarantees the successful completion of each signing execution whenever a threshold number t of semi-honest participants is met, even in the presence of misbehaving signatories. The state-of-the-art online-friendly threshold ECDSA with-out robustness was developed by Doerner et al. in S&P'24, requiring only three rounds. Recent work by Wong et …

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Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang May 2025

Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations. Despite promising results, these approaches encounter practical limitations due to lengthy optimizations and significant memory consumption. In this work, we introduce Gamba, an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: (1) Efficient Backbone Design: introducing a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length, thereby accommodating a substantial number …

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Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin May 2025

Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior in the global partition phase, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based …

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Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al May 2025

Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al

Research Collection School Of Computing and Information Systems

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside …

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Exploring The Potential Of Large Language Models For Heterophilic Graphs, Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi May 2025

Exploring The Potential Of Large Language Models For Heterophilic Graphs, Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have presented significant opportunities to enhance various machine learning applications, including graph neural networks (GNNs). By leveraging the vast open-world knowledge within LLMs, we can more effectively interpret and utilize textual data to better characterize heterophilic graphs, where neighboring nodes often have different labels. However, existing approaches for heterophilic graphs overlook the rich textual data associated with nodes, which could unlock deeper insights into their heterophilic contexts. In this work, we explore the potential of LLMs for modeling heterophilic graphs and propose a novel two-stage framework: LLM-enhanced edge discriminator and LLM-guided edge reweighting. In the first …

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On Learning Informative Trajectory Embeddings For Imitation, Classification And Regression, Zichang Ge, Changyu Chen, Arunesh Sinha, Pradeep Varakantham May 2025

On Learning Informative Trajectory Embeddings For Imitation, Classification And Regression, Zichang Ge, Changyu Chen, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

In real-world sequential decision making tasks like autonomousdriving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification,and clustering. For example, self-driving cars must replicate humandriving behaviors, while robots and healthcare systems benefitfrom modeling decision sequences, whether or not they come fromexpert data. Existing trajectory encoding methods often focus onspecific tasks or rely on reward signals, limiting their ability togeneralize across domains and tasks.Inspired by the success of embedding models like CLIP andBERT in static domains, we propose a novel method for embeddingstate-action trajectories into a latent space that captures the skillsand competencies in the …

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Limit Theory For Local Polynomial Estimation Of Functional Coefficient Models With Possibly Integrated Regressors, Ying Wang, Peter C. B. Phillips May 2025

Limit Theory For Local Polynomial Estimation Of Functional Coefficient Models With Possibly Integrated Regressors, Ying Wang, Peter C. B. Phillips

Research Collection School Of Economics

Limit theory for functional coefficient cointegrating regression was recently found to be considerably more complex than earlier understood. The issues were explained and correct limit theory derived for the kernel weighted local level estimator in Phillips and Wang (2023b). The present paper provides complete limit theory for the general kernel weighted local th order polynomial estimators of the functional coefficient and the coefficient derivatives. Both stationary and nonstationary regressors are allowed. Implications for bandwidth selection are discussed. An adaptive procedure to select the fit order  is proposed and found to work well. A robust -ratio is constructed following the new …

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The Prospects Of The High Seas Treaty Decisively Reducing The Negative Biodiversity Impacts Of Distant Water Fishing Operations, Shirley V. Scott, Nengye Liu May 2025

The Prospects Of The High Seas Treaty Decisively Reducing The Negative Biodiversity Impacts Of Distant Water Fishing Operations, Shirley V. Scott, Nengye Liu

Research Collection Yong Pung How School Of Law

International fisheries law has so far proven inadequate to prevent over-fishing on the high seas, a key cause of biodiversity loss in areas of the ocean beyond national jurisdiction (ABNJ). Negotiation of the High Seas Treaty (Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction or BBNJ Agreement) afforded an opportunity to respond decisively to the worsening problem. The Treaty was concluded in 2023. It is yet to enter into force. Preparations and negotiations for the Treaty spanned some 20 years. By …

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Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee May 2025

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

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Grounding Ai Use In Learning Science: A Conversation With Steven Miller, Steven Miller, Lieven Demeester Apr 2025

Grounding Ai Use In Learning Science: A Conversation With Steven Miller, Steven Miller, Lieven Demeester

CASTLe: Collection of Articles on Scholarship for Teaching and Learning

In this insightful interview, SMU Associate Provost (Teaching and Learning Innovation) Lieven Demeester and Professor Emeritus of Information Systems Steven Miller discuss the integration of artificial intelligence (AI) in teaching and learning, emphasising the importance of grounding AI use in the fundamentals of learning science. They explore the evolving role of education in the context of AI advancements, highlighting the need for educators to focus on the cognitive aspects of learning, such as goal-directed practice and feedback. They also address the potential of AI as a collaborative agent in group projects and the importance of maintaining accountability and quality control …

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Covid-19 And Investors' Trading Behavior: Evidence From The New Zealand Equity Market, Finn West Wilkinson, Marinela Adriana Finta, Olena Onishchenko Apr 2025

Covid-19 And Investors' Trading Behavior: Evidence From The New Zealand Equity Market, Finn West Wilkinson, Marinela Adriana Finta, Olena Onishchenko

Research Collection Lee Kong Chian School Of Business

This paper examines the trading behavior of retail and institutional investors during the COVID-19 pandemic in New Zealand. Using transaction-level data, it compares how retail and institutional investors trade over the government announcements, lockdown, and reopening periods. Retail and institutional investors' trading intensifies around government announcements, which facilitates attenuating illiquidity during the lockdown. Nevertheless, while retail trading is substantially more prominent during the lockdown than over the control and reopening periods, institutional investors do not trade significantly more per se or on announcements during the lockdown but trade less during the reopening. Their trading also relates to contemporaneous returns, and …

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Right-Sizing The Smart City In Southeast Asia, Prerona Das, Orlando Woods, Lily Kong Apr 2025

Right-Sizing The Smart City In Southeast Asia, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

The idea of right-sizing, or the process of adjusting the size of a city to maximise the efficient use of resources, has traditionally been used in strategic management and the resizing of shrinking cities to promote efficient urban development. Concurrently, in contemporary discourse, the sizing of smart cities has emerged as a critical topic, as size impacts the implementation of smart initiatives. Smaller cities offer the advantage of serving as cost- effective testing grounds for innovative solutions; however, they also need to be sizeable enough to attract private investments and build a robust smart city ecosystem. In this paper we …

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