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Mimic: Ai And Ar-Enhanced Multi-Modal, Immersive, Relative Instruction Comprehension, Dhanuja Wanniarachchi, Archan Misra Mar 2025

Mimic: Ai And Ar-Enhanced Multi-Modal, Immersive, Relative Instruction Comprehension, Dhanuja Wanniarachchi, Archan Misra

Research Collection School Of Computing and Information Systems

We present a multimodal instruction comprehension framework, called MImIC, that utilizes visual sensing (including LIDAR and 2D RGB sensing) & AI spatial reasoning capabilities to support more seamless and immersive interaction between humans and AI-driven situated assistive agents. MImIC's key new capability is to support disambiguation of a wider set of relative spatial references that users naturally employ while issuing spatially-situated instructions. To support enhanced visual grounding via a combination of both fully-qualified and relative attribute references, MImIC uses (a) a fine-tuned transformer-based language translation DNN to accurately convert natural verbal commands into a structured set of machine understandable constraints …

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Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu Mar 2025

Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

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Evaluating Software Development Agents: Patch Patterns, Code Quality, And Issue Complexity In Real-World Github Scenarios, Zhi Chen, Lingxiao Jiang Mar 2025

Evaluating Software Development Agents: Patch Patterns, Code Quality, And Issue Complexity In Real-World Github Scenarios, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single …

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Backdoor Token Unlearning: Exposing And Defending Backdoors In Pretrained Language Models, Peihai Jiang, Xixiang Lyu, Yige Li, Jing Ma Mar 2025

Backdoor Token Unlearning: Exposing And Defending Backdoors In Pretrained Language Models, Peihai Jiang, Xixiang Lyu, Yige Li, Jing Ma

Research Collection School Of Computing and Information Systems

Supervised fine-tuning has become the predominant method for adapting large pretrained models to downstream tasks. However, recent studies have revealed that these models are vulnerable to backdoor attacks, where even a small number of malicious samples can successfully embed backdoor triggers into the model. While most existing defense methods focus on post-training backdoor defense, efficiently defending against backdoor attacks during training phase remains largely unexplored. To address this gap, we propose a novel defense method called Backdoor Token Unlearning (BTU), which proactively detects and neutralizes trigger tokens during the training stage. Our work is based on two key findings: 1) …

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Sticky Information Technology Investment: Theory And Empirical Evidence, Peng Liang, Hasan Cavusoglu, Nan Hu Mar 2025

Sticky Information Technology Investment: Theory And Empirical Evidence, Peng Liang, Hasan Cavusoglu, Nan Hu

Research Collection School Of Computing and Information Systems

This article provides a new way of thinking about managerial discretion in information technology (IT) investment decisions. We delve into the existence, antecedents, and consequences of sticky IT investment behavior, an understudied managerial deliberate resource commitment decision in response to changes in sales. Guided by downsizing theory, we initially theorize and find that IT investments exhibit stickiness: IT investments move downward less for sales decreases than they move upward for equivalent increases. Then drawing upon agency theory, adjustment costs theory, and managerial expectations theory—which influence managers’ motivation for downsizing—we predict and demonstrate that managers’ empire-building incentives, their avoidance of adjustment …

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Intellectual Humility Without Limits: Magnanimous Humility, Disagreement And The Epistemology Of Resistance, Brandon Yip Mar 2025

Intellectual Humility Without Limits: Magnanimous Humility, Disagreement And The Epistemology Of Resistance, Brandon Yip

Research Collection School of Social Sciences

In this paper, I provide a characterisation of a neglected form of humility: magnanimous humility. Unlike most contemporary analyses of humility, magnanimous humility is not about limitations but instead presupposes that one possesses some entitlement in a context. I suggest that magnanimous intellectual humility (IH) consists in a disposition to appropriately refrain from exercising one's legitimate epistemic entitlements because one is appropriately motivated to pursue some epistemic good. I then shown that Magnanimous IH has an important role to play in contexts of disagreement and oppression. It calls on knowing parties to refrain from pressing their epistemic entitlements to facilitate …

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A Call For Clarity In Contractual Accessions To Shareholder And Partnership Agreements, Kwan Ho Lau Mar 2025

A Call For Clarity In Contractual Accessions To Shareholder And Partnership Agreements, Kwan Ho Lau

Research Collection Yong Pung How School Of Law

This paper explores understudied issues surrounding accessions to shareholder and partnership agreements: the process by which such accessions take effect; the survival of equities following an accession; and the enforcement of a condition for incoming shareholders to have to execute and deliver a deed of accession. Accessions happen extremely often in modern commercial life, which renders surprising the dearth of academic and judicial discussion, but more disconcerting is the unsettledness of some of the complex issues implicated. The repurposing of unilateral contracts to explain how deeds of accession operate is not fully tested in English law; the conception of partial …

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Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng Mar 2025

Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng

Research Collection School Of Computing and Information Systems

A temporal bipartite graph is a graph model that incorporates time-related information into its edges, making it suitable for modeling real-world phenomena like disease outbreaks. However, this temporal information is often sensitive. To protect the privacy of graph data, researchers have explored various approaches to preserve privacy in graph queries, with reachability queries being popular and fundamental as they determine the possibility of reaching one node from others in a graph. While privacy-preserving reachability queries have been extensively studied, existing efforts often overlook the valuable attribute information present in both edges and nodes of the graphs. Moreover, reachability queries on …

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Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang Mar 2025

Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang

Research Collection School Of Computing and Information Systems

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android’s own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this …

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Forward-Secure Hierarchical Delegable Signature For Smart Homes, Jianfei Sun, Guowen Xu, Yang Yang, Xuehuan Yang, Xiaoguo Li, Cong Wu, Zhen Liu, Guomin Yang, Robert H. Deng Mar 2025

Forward-Secure Hierarchical Delegable Signature For Smart Homes, Jianfei Sun, Guowen Xu, Yang Yang, Xuehuan Yang, Xiaoguo Li, Cong Wu, Zhen Liu, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Aiming to provide people with great convenience and comfort, smart home systems have been deployed in thousands of homes. In this paper, we focus on handling the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) fine-grained, privacy-preserving authorization for smart home users and integrity protection of communication contents; (2) flexible self-sovereign permission delegation; (3) forward security of previous messages. To our knowledge, no previous system has been designed to consider these three security and privacy requirements simultaneously. To tackle these challenges, we put forward the first-ever …

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Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination, Aanindya Sundar Das, Guansong Pang, Monowar Bhuyan Mar 2025

Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination, Aanindya Sundar Das, Guansong Pang, Monowar Bhuyan

Research Collection School Of Computing and Information Systems

Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the …

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Revisiting Sentiment Analysis For Software Engineering In The Era Of Large Language Models, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, David Lo Mar 2025

Revisiting Sentiment Analysis For Software Engineering In The Era Of Large Language Models, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, David Lo

Research Collection School Of Computing and Information Systems

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging.This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software …

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Understanding Individual Agent Importance In Multi-Agent System Via Counterfactual Reasoning, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Jun Hu, Qing Wang, Fanjiang Xu Mar 2025

Understanding Individual Agent Importance In Multi-Agent System Via Counterfactual Reasoning, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Jun Hu, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has provided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent's importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the …

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How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng Mar 2025

How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng

Research Collection School Of Computing and Information Systems

An attribute-based credential (ABC) system allows a user, obtaining a credential on a set of attributes from an issuer, to anonymously prove a subset of attributes to a service provider. Nowadays, delegation is an important requirement of ABC, which allows a user to delegate his credentials to other users. However, traditional delegatable ABC systems only support delegating a credential with all attributes. In many scenarios, an appropriate delegation is a user can delegate his credential on parts of attributes to others. Another requirement is revocation of credentials in case of unexpected events. In this article, we propose a delegatable and …

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Leveraging Constraint Violation Signals For Action Constrained Reinforcement Learning, Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar Mar 2025

Leveraging Constraint Violation Signals For Action Constrained Reinforcement Learning, Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar

Research Collection School Of Computing and Information Systems

In many RL applications, ensuring an agent’s actions adhere to constraints is crucial for safety. Most previous methods in Action-Constrained Reinforcement Learning (ACRL) employ a projection layer after the policy network to correct the action. However projection-based methods suffer from issues like the zero gradient problem and higher runtime due to the usage of optimization solvers. Recently methods were proposed to train generative models to learn a differentiable mapping between latent variables and feasible actions to address this issue. However, generative models require training using samples from the constrained action space, which itself is challenging. To address such limitations, first, …

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Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin Mar 2025

Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we …

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Individual Liberty, Safety And Police Liabilities Under The Mental Health (Care And Treatment) Act, Gary Kok Yew Chan Mar 2025

Individual Liberty, Safety And Police Liabilities Under The Mental Health (Care And Treatment) Act, Gary Kok Yew Chan

Research Collection Yong Pung How School Of Law

How should a society strike a balance between the objective of ensuring safety from dangers that may be posed by individuals believed to have a mental disorder and the deprivation of their liberty? How should police officers discharge their duties in apprehending such individuals with a view to conveying them to a medical practitioner at a psychiatric institution? These legal issues took centrestage in the Singapore High Court decision of Mah Kiat Seng v Attorney-General in which the apprehended individual brought claims in false imprisonment against a police officer. The decision examined the underlying purposes of the Singapore Mental Health …

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Unpacking Singapore’S Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti Mar 2025

Unpacking Singapore’S Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti

Research Collection School Of Economics

In Singapore, most land is state-owned, with the state generally issuing leasehold estates via state leases of not more than 99 years1, depending on the intended land use. Naturally, the value of a leasehold estate, which erodes over time as the lease approaches the end of its term, is a key component of the premium charged for lease renewals, or the tax imposed for permission given in relation to a development that would increase the value of the land. By law, the state valuation of leasehold land is prescribed by a leasehold relativity table colloquially known as ‘Bala’s Curve’ or …

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Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection, Xin Zhao, Mu Zhang, Xiaopeng Ke, Yu Pan, Yue Duan, Sheng Zhong, Fengyuan Xu Mar 2025

Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection, Xin Zhao, Mu Zhang, Xiaopeng Ke, Yu Pan, Yue Duan, Sheng Zhong, Fengyuan Xu

Research Collection School Of Computing and Information Systems

The emerging virtual machine-based Android packers render existing unpacking techniques ineffective. The state-of-the-art unpacker falls short because it relies on unreliable heuristics and manually crafted semantic models. Hence, it cannot precisely recover app semantics necessary for malware detection. In this paper, we propose DeepVMUnProtect, a deep learning-based approach to automatically and accurately capture the semantics of VM-packed code, so as to facilitate semantic-based Android malware classification. Experiments have shown that DeepVMUnProtect outperforms the state-of-the-art tool on recovering opcode semantics in Qihoo(58.3%), Baidu(47.5%) and NMMP (58.8%) respectively, and can enable semantics-aware malware detection which prior work fails to do.

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Impact Tracing: Identifying The Culprit Of Misinformation In Encrypted Messaging Systems, Zhongming Wang, Tao Xiang, Xiaoguo Li, Biwen Chen, Guomin Yang, Chuan Ma, Robert H. Deng Feb 2025

Impact Tracing: Identifying The Culprit Of Misinformation In Encrypted Messaging Systems, Zhongming Wang, Tao Xiang, Xiaoguo Li, Biwen Chen, Guomin Yang, Chuan Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Encrypted messaging systems obstruct content moderation, although they provide end-to-end security. As a result, misinformation proliferates in these systems, thereby exacerbating online hate and harassment. The paradigm of “Reporting-then-Tracing” shows great potential in mitigating the spread of misinformation. For instance, message traceback (CCS’19) traces all the dissemination paths of a message, while source tracing (CCS’21) traces its originator. However, message traceback lacks privacy preservation for non-influential users (e.g., users who only receive the message once), while source tracing maintains privacy but only provides limited traceability. In this paper, we initiate the study of impact tracing. Intuitively, impact tracing traces influential …

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Comparing Smart City Data Protection Approaches: Digital Consent And The Accountability Framework In Singapore, Wenxi Zhang, Sharanya M Shanmugam, J.G. Allen Feb 2025

Comparing Smart City Data Protection Approaches: Digital Consent And The Accountability Framework In Singapore, Wenxi Zhang, Sharanya M Shanmugam, J.G. Allen

Research Collection Yong Pung How School Of Law

Traditionally, there has been heavy reliance on formal notice and consent models for data protection regimes. However, consent (in particular digital consent) is rarely extended meaningfully in practice, due to behavioural factors including data subjects’ limited cognitive capacity, rational apathy, and dependency on the digital infrastructure around them. Synthesizing legal perspectives with insights drawn from the behavioural and social sciences, this article analyzes the consent problem in the smart city context, where it is exacerbated due to prevailing issues such as economic disincentives for ensuring meaningful consent, since essential urban infrastructure is often provided by private companies. Recognizing the shortcomings …

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Bridging Expert Knowledge With Deep Learning Techniques For Just-In-Time Defect Prediction, Xin Zhou, Donggyun Han, David Lo Feb 2025

Bridging Expert Knowledge With Deep Learning Techniques For Just-In-Time Defect Prediction, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features from commit contents. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may …

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Proactive Conversational Ai: A Comprehensive Survey Of Advancements And Opportunities, Yang Deng, Lizi Liao, Wenqiang Lei, Grace Hui Yang, Wai Lam, Tat-Seng Chua Feb 2025

Proactive Conversational Ai: A Comprehensive Survey Of Advancements And Opportunities, Yang Deng, Lizi Liao, Wenqiang Lei, Grace Hui Yang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system's response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior-a crucial aspect of intelligent conversations-is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general …

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Siniel: Distributed Privacy-Preserving Zksnark, Yunbo Yang, Yuejia Cheng, Kailun Wang, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Guomin Yang, Robert H. Deng Feb 2025

Siniel: Distributed Privacy-Preserving Zksnark, Yunbo Yang, Yuejia Cheng, Kailun Wang, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information. However, existing zkSNARKs suffer from high computation overhead in the proof generation. This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials. Private delegation has become a prominent way to accelerate proof generation. In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS). Our protocol allows a computationally limited …

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Exploring The Trust Paradox: Adoption Dynamics Of Family Trusts Among Chinese High-Net-Worth Individuals, Janet Jing Di Zhang Feb 2025

Exploring The Trust Paradox: Adoption Dynamics Of Family Trusts Among Chinese High-Net-Worth Individuals, Janet Jing Di Zhang

Dissertations and Theses Collection (Open Access)

This research explores the adoption dynamics of family trusts among Chinese High-Net-Worth Individuals (HNWIs), focusing on the key barriers and facilitating factors. Despite the formal recognition of family trusts in China under its Trust Law (2001), the adoption rate remains low. This paradox is addressed through a comparative study between Chinese HNWIs in China and those in Canada, examining the impact of legal, regulatory, and cultural factors on adoption rates. By employing a two-stage quantitative approach, the research develops a Trust Adoption Model that incorporates factors like perceived benefits, professional trust, complexity, and regulatory transparency. The study identifies significant barriers …

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Technology-Enhanced Personalized Learning: Lessons From Online Teaching At Three South-East Asian Universities, Kiran Kaur, Rohaida Mohd Saat, Lieven Demeester, Magdeleine Lew, Wei Leng Neo, Nopphol Pausawasdi, Thasaneeya Ratanaroutai Nopparatjamjomras Feb 2025

Technology-Enhanced Personalized Learning: Lessons From Online Teaching At Three South-East Asian Universities, Kiran Kaur, Rohaida Mohd Saat, Lieven Demeester, Magdeleine Lew, Wei Leng Neo, Nopphol Pausawasdi, Thasaneeya Ratanaroutai Nopparatjamjomras

Research Collection Lee Kong Chian School Of Business

Online teaching during the COVID-19 pandemic compelled many instructors to seek efficient and effective ways to stay connected with their students and improve the learning experience by using a wide range of available technologies. This multiple-case study, in three South-East Asian universities, investigated whether the use of technology in university teaching and learning during that period influenced personalized learning, and if so, how. The study also explored the kinds of institutional support for teachers and learners that led to increased technology-enhanced personalized learning (TEPL). Using a qualitative approach, the study analyzed 23 individual interviews and 3 document analyses (circulars, announcements, …

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Does Felt Obligation Or Gratitude Better Explain The Relationship Between Perceived Organizational Support And Outcomes?, Robert Eisenberger, Xueqi Wen, Dianhan Zheng, Jia Yu, Zihan Liu, Jing Zhang, Lei Wang, Tae-Yeol Kim, Sara Krivacek, Thomas J. Zagenczyk, Min-Kyu Joo, Salar Mesdaghinia, Deog-Ro Lee, Tae-Hyun Kim Feb 2025

Does Felt Obligation Or Gratitude Better Explain The Relationship Between Perceived Organizational Support And Outcomes?, Robert Eisenberger, Xueqi Wen, Dianhan Zheng, Jia Yu, Zihan Liu, Jing Zhang, Lei Wang, Tae-Yeol Kim, Sara Krivacek, Thomas J. Zagenczyk, Min-Kyu Joo, Salar Mesdaghinia, Deog-Ro Lee, Tae-Hyun Kim

Research Collection Lee Kong Chian School Of Business

The employee-organization relationship (EOR) is a key component of organizational behavior. Researchers have consistently shown that employees who perceive that they are supported by their organizations (favorable EORs) tend to have positive attitudes and behave in a manner that helps the organization to reach its goals. For many years, the reciprocity norm (e.g. felt obligation) has been a widely accepted explanation for the EOR. Yet, despite calls to explore additional pathways, little work has examined other mediating mechanisms between favorable EORs and outcomes. We draw on the affect theory of social exchange to argue for the mediating effects of gratitude, …

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Consumption Responses To Income Shocks Through Lottery Winning, Seonghoon Kim, Kanghyock Koh Feb 2025

Consumption Responses To Income Shocks Through Lottery Winning, Seonghoon Kim, Kanghyock Koh

Research Collection School Of Economics

We study the effects of lottery winning on consumption spending using newly available household survey data in Singapore. We find strong consumption responses to a transitory income shock via lottery wins. Lottery winners spend about half of their prizes within 12 months of winning. We show that consumption responses are stronger among households with more binding liquidity constraints and less risk aversion, which is consistent with the standard life-cycle model. The strong consumption response suggests that fiscal stimulus policies or other public transfer programs could be an effective means of boosting consumption spending of the economy in the short run.

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When Leaders Self-Sacrifice In Times Of Crisis: The Roles Of Employee Emotional Suppression And Leader Coping Strategies, Xingyu Wang, Xueqi Wen, Zihan Liu, Yixing Lisa Gao, Juan M. Madera Feb 2025

When Leaders Self-Sacrifice In Times Of Crisis: The Roles Of Employee Emotional Suppression And Leader Coping Strategies, Xingyu Wang, Xueqi Wen, Zihan Liu, Yixing Lisa Gao, Juan M. Madera

Research Collection Lee Kong Chian School Of Business

While literature articulates the relevance of self-sacrificial leadership to crisis situations, little attention has been paid to employees’ attitudinal and behavioral responses to self-sacrificial leadership. This is a particularly salient gap in the scholarship, given the decisions leaders must make to address challenges in the hospitality industry (e.g., the COVID-19 pandemic). Drawing on conservation of resources theory, this pair of field and experimental studies examines how individual differences in employee emotional suppression and leader coping strategy moderate the impacts of self-sacrificial leadership on employee perceptions of leader effectiveness. By sampling U.S. hospitality employees, the studies reveal that leaders who display …

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Qultsf: Long-Term Time Series Forecasting With Quantum Machine Learning, Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson, Mile Gu Feb 2025

Qultsf: Long-Term Time Series Forecasting With Quantum Machine Learning, Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson, Mile Gu

Research Collection School Of Computing and Information Systems

Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models ‘Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)’ showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving …

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