Digital Commons Network^™

Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen

Research Collection School Of Computing and Information Systems

With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …

Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude

Research Collection School Of Computing and Information Systems

The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.

Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring …

Quantum Machine Learning For Credit Scoring, Nikolaos Schetakis, Davit Aghamalyan, Micheael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs …

Design Of Off-Grid Lighting Business Models To Serve The Poor: Field Experiments And Structural Analysis, Bhavani Shanker Uppari, Serguei Netessine, Ioana Popescu, Rowan P. Clarke

Research Collection Lee Kong Chian School Of Business

A significant proportion of the world’s population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of travelling to recharge the lamp) along with the impact of liquidity constraints (due to poverty) on lamp usage, and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers’ recharge …

Untangling Influence: The Effect Of Follower-Followee Comparison On Social Media Engagement, Yi Peng, Liling Lu

Research Collection Lee Kong Chian School Of Business

Social media platforms and marketers are keen on identifying truly influential creators. Number of followers (i.e., those who follow creators) and number of followees (i.e., those who are followed by creators) serve as cues to infer creators' influence. However, a cue of creators' actual influence is under-addressed and its effect on social media engagement remains to be explored. This research fills the gap in the literature by investigating how the cue of creators' actual influence (i.e., follower-followee comparison) may affect followers' engagement behavior. The study further examines the moderation effects of media-, topic-, and creator-related factors. The present work leverages …

Do Firms With Technological Capabilities Rush In? Evidence From The Timing Of Licensing Of Stanford Inventions, Young-Choon Kim, Reddi Kotha, Mooweon Rhee

Research Collection Lee Kong Chian School Of Business

This study investigates the influence of licensees’ technological capabilities on the timing of technology licensing in university technology commercialization. Drawing on the appropriation-collaboration tension from the literature on university technology licensing and intellectual property management, we propose that while the licensee’s technological capabilities drive early licensing by averting technological obsolescence, this effect diminishes significantly with an overlap in the technological domain of the focal invention due to expropriation concerns. Cox regression analysis of Stanford University’s invention dataset confirmed our hypotheses. This research reveals that technology licensing experiences delays with the most suitable licensees, namely, those with strong technological capabilities in …

Anatomical Depiction: How Showing A Product's Inner Structure Shapes Product Valuations, Seo Yoon Kang, Junghan Kim, Arun Lakshmanan

Research Collection Lee Kong Chian School Of Business

Anatomical depiction is a technique where the product is decomposed into components that are spatially arranged in a layer-by-layer manner to visually explicate its inner structure. The authors demonstrate that anatomical depiction, compared to non-anatomical depiction, enhances product valuation. This effect occurs because anatomical depiction elicits a ‘coming together’ of the inner components in consumers’ minds thereby evoking a gestalt image of the product – a process labeled simulated assemblage. The elicitation of simulated assemblage in turn boosts their confidence in the product’s performance. Two field experiments first demonstrate that anatomical depiction leads to greater engagement in online settings such …

Cultural Tightness In Organizations: Investigating The Impact Of Formal And Informal Cultural Tightness On Employee Creativity, Roy Y. J. Chua, Na Zhao, Meng Han

Research Collection Lee Kong Chian School Of Business

This paper delineates cultural tightness into formal versus informal aspects to depict the strength of norms and the extent of sanctions emanating from both formal and informal norms. Organizations with high formal cultural tightness regulate behaviors through explicit written norms and official sanctions, whereas those with high informal cultural tightness regulate behaviors through uncodified norms, collective beliefs, and informal social sanctions. Through a field study across 14 diverse companies in two countries (Malaysia and the Philippines) and two experiments involving participants from the United States, we found that perceived informal cultural tightness consistently exerts a more significant impact on stifling …

Housing Markets Since Shapley And Scarf, Mustafa Oguz Afacan, Gaoji Hu, Jiangtao Li

Research Collection School Of Economics

Shapley and Scarf (1974) appeared in the first issue of the Journal of Mathematical Economics, and is one of the journal’s most impactful publications. As we approach the remarkable milestone of the journal’s 50th anniversary (1974–2024), this article serves as a commemorative exploration of Shapley and Scarf (1974) and the extensive body of literature that follows it.

Teaching Software Development For Real-World Problems Using A Microservice-Based Collaborative Problem-Solving Approach, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Experienced and skillful software developers are needed in organizations to develop software products effective for their business with shortened time-to-market. Such developers will not only need to code but also be able to work in teams and collaboratively solve real-world problems that organizations arefacing. It is challenging for educators to nurture students to become such developers with strong technical, social, and cognitive skills. Towards addressing the challenge, this study presents a Collaborative Software Development Project Framework for a course that focuses on learning microservices architectures anddeveloping a software application for a real-world business. Students get to work in teams to …

Smu Libraries – An Enabling Partner In Ai Information Literacy, Samantha Seah, Zhe Benedict Yeo, Lukas Tschopp

Research Collection Library

SMU Libraries plays a pivotal role in advancing AI information literacy within the larger need for digital literacy skills in the SMU community. In this presentation, participants will get an overview of SMU Libraries' engagement and partnerships with the academic community and will showcase initiatives and resources supporting AI literacy. This includes a discussion of insights from the scholarly literature, research findings and critical perspectives to inform teaching and learning practices related to AI. Speakers will share SMU Libraries’ contributions towards awareness and adoption of AI through a portfolio of successful collaborations and initiatives with partners and stakeholders within and …

Green Transition And Financial Stability: The Role Of Green Monetary And Macroprudential Policies And Vouchers, Ying Tung Chan, Maria Teresa Punzi, Hong Zhao

Sim Kee Boon Institute for Financial Economics

This paper analyzes a mix of alternative policies in supporting the green transition and the phase-out of fossil fuels, without compromising financial stability. An environmental dynamic stochastic general equilibrium (E-DSGE) model with two sectors (green and brown) and endogenous default is developed to assess potential climate-induced financial stability threats that can be mainly generated through physical and transition risks mechanism. Those risks are evaluated through a compound capital depreciation shock and a carbon tax shock. The paper offers several findings. First of all, a too stringent carbon tax would increase the medium-term default rate in both sectors, harming financial stability …

Wild Bootstrap Inference For Instrumental Variables Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang

Research Collection School Of Economics

We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives. We further develop a wild bootstrap Anderson–Rubin test for the full-vector …

Covid-19 And Management Scholarship: Lessons For Conducting Impactful Research, Gerard George, Gokhan Ertug, Jonathan P. Doh, Johanna Mair, Ajnesh Prasad

Research Collection Lee Kong Chian School Of Business

The COVID-19 pandemic provided an opportunity for management scholars to address large-scale and complex societal problems and strive for greater practical and policy impact. A brief overview of the most-cited work on COVID-19 reveals that, compared with their counterparts in other disciplines, leading management journals and professional associations lagged in providing a platform for high-impact research on COVID-19. To help management research play a more active role in responding to similar global challenges in the future, we propose an integrative framework that emphasizes a phenomenon’s impact, the conditions that the phenomenon creates at multiple levels, and the responses of actors …

The Status Of Status Research: A Review Of The Types, Functions, Levels And Audiences, Matteo Prato, Gokhan Ertug, Fabrizio Castellucci, Tengjian Zou

Research Collection Lee Kong Chian School Of Business

Our review of 154 articles published over the last decade portrays an evolution of status research. This body of literature has transitioned from viewing status as a monolithic construct to appreciating its inherently multidimensional nature, characterized by diverse types, functions, levels, and audience structures. Although this shift has expanded our knowledge, it has also introduced increased complexity and fragmentation. To systematize this scattered work on a multifaceted view of status, we develop a comprehensive framework that integrates the diverse research findings. For each constituent part of this framework, we review key themes and insights in the literature and outline future …

Designing Pareto-Optimal Selection Systems For Multiple Minority Subgroups And Multiple Criteria, Wilfried De Corte, Paul R. Sackett, Filip Lievens

Research Collection Lee Kong Chian School Of Business

Currently used Pareto-optimal (PO) approaches for balancing diversity and validity goals in selection can deal only with one minority group and one criterion. These are key limitations because the workplace and society at large are getting increasingly diverse and because selection system designers often have interest in multiple criteria. Therefore, the article extends existing methods for designing PO selection systems to situations involving multiple criteria and multiple minority groups (i.e., multiobjective PO selection systems). We first present a hybrid multiobjective PO approach for computing selection systems that are PO with respect to (a) a set of quality objectives (i.e., criteria) …

Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia

Research Collection School Of Computing and Information Systems

The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …

Public Cleanliness Satisfaction Survey 2023, Paulin Straughan, Mathews Mathew

Research Collection School of Social Sciences

The Singapore Management University undertook the sixth wave of the Public Cleanliness Satisfaction Survey (PCSS) with 2,010 Singapore residents providing responses to the survey from November 2023 to January 2024.

Similar to the findings from the 2022 wave of PCSS, the 2023 wave of the PCSS continued to reflect an overall satisfaction with public cleanliness in Singapore. Majority of survey respondents (94%) were satisfied with the cleanliness of public spaces that they had recently visited, which was an increase of 2% from 2022. Satisfaction with the cleanliness of food outlets saw the largest increase (by 3%) among all location types, …

Application Of Singapore's New Rules On Service Out Of Jurisdiction: Three Arrows Capital And Nw Corp, Adeline Chong

Research Collection Yong Pung How School Of Law

No abstract provided.

Hiv Estimation Using Population Based Surveys With Non-Response: A Partial Identification Approach, Oyelola A Adegboye, Tomoki Fujii, Denis H. Y. Leung, Siyu Li

Research Collection School Of Economics

HIV estimation using data from the Demographic and Health Surveys (DHS) is lim-ited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instru-ment. Using Manski’s partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and …

Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language …

Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Tristan Lim

Research Collection School Of Computing and Information Systems

The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …

Marco: A Stochastic Asynchronous Concolic Explorer, Jie Hu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Concolic execution is a powerful program analysis technique for code path exploration. Despite recent advances that greatly improved the efficiency of concolic execution engines, path constraint solving remains a major bottleneck of concolic testing. An intelligent scheduler for inputs/branches becomes even more crucial. Our studies show that the previously under-studied branch-flipping policy adopted by state-of-the-art concolic execution engines has several limitations. We propose to assess each branch by its potential for new code coverage from a global view, concerning the path divergence probability at each branch. To validate this idea, we implemented a prototype Marco and evaluated it against the …

Redriver: Runtime Enforcement For Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Xiaodong Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous driving systems (ADSs) integrate sensing, perception, drive control, and several other critical tasks in autonomous vehicles, motivating research into techniques for assessing their safety. While there are several approaches for testing and analysing them in high-fidelity simulators, ADSs may still encounter additional critical scenarios beyond those covered once they are deployed on real roads. An additional level of confidence can be established by monitoring and enforcing critical properties when the ADS is running. Existing work, however, is only able to monitor simple safety properties (e.g., avoidance of collisions) and is limited to blunt enforcement mechanisms such as hitting the …

Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The rapid progress of autonomous vehicles (AVs) has brought the prospect of a driverless future closer than ever. Recent fatalities, however, have emphasized the importance of safety validation through large-scale testing. Multiple approaches achieve this fully automatically using high-fidelity simulators, i.e., by generating diverse driving scenarios and evaluating autonomous driving systems (ADSs) against different test oracles. While effective at finding violations, these approaches do not identify the decisions and actions that caused them -- information that is critical for improving the safety of ADSs. To address this challenge, we propose ACAV, an automated framework designed to conduct causality analysis for …

Towards Low-Resource Rumor Detection: Unified Contrastive Transfer With Propagation Structure, Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng

Research Collection School Of Computing and Information Systems

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a simple yet effective framework with unified contrastive transfer learning, to detect rumors by adapting the features learned from well-resourced rumor data to that …

Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen

Research Collection School Of Computing and Information Systems

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …

Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …

W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …