Digital Commons Network^™

Anger And Disgust Shape Judgments Of Social Sanctions Across Cultures, Especially In High Individual Autonomy Societies, Per A. Andersson, Andree Hartanto, Et Al

Research Collection School of Social Sciences

When someone violates a social norm, others may think that some sanction would be appropriate. We examine how the experience of emotions like anger and disgust relate to the judged appropriateness of sanctions, in a pre-registered analysis of data from a large-scale study in 56 societies. Across the world, we find that individuals who experience anger and disgust over a norm violation are more likely to endorse confrontation, ostracism and, to a smaller extent, gossip. Moreover, we find that the experience of anger is consistently the strongest predictor of judgments of confrontation, compared to other emotions. Although the link between …

Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …

Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …

Perceived Context Typicality And Beliefs In The Generalizability Of Management Research Findings, Przemyslaw Hensel, Adam Tatarynowicz

Research Collection Lee Kong Chian School Of Business

Despite growing calls for a greater internationalization of management research, the discipline still struggles with the challenge of integrating diverse national contexts. While recent decades have seen a change toward a more equitable treatment of all national contexts, the belief that research conducted outside the United States is less generalizable remains strong. In this research note, we explore the general perceptions of what is considered a “typical” study context by associating them with authors' variable tendencies to report threats to external validity. Using a sample of 400 papers from seven top-tier management journals, we find that research based on non-US …

Cross-Exchange Crypto Risk: A High-Frequency Dynamic Network Perspective, Yifu Wang, Wanbo Lu, Min-Bin Liu, Rui Ren, Wolfgang Karl Hardle

Sim Kee Boon Institute for Financial Economics

Cross-exchange crypto trading presents inherent risks, particularly for centralized exchanges. Investors observe exacerbating crypto volatility and counterparty risk and would like to quantify these elements of crypto trades. The multiple exchanges require a multivariate view on the structures of risk spillover across exchanges. Here, a Multivariate Heterogeneous AutoRegression (MHAR) model is designed and analyzed, accommodating the stylized facts of crypto markets, including 24/7 trading and the long-memory effect on return variations. The proposed MHAR approach clearly reveals the intensity of interconnectedness among exchanges during extreme events, e.g., the Bitcoin market. Additionally, one observes extremely volatile eigenvector centralities of Futures Exchange …

Bubbly Booms And Welfare, Feng Dong, Yang Jiao, Haoning Sun

Research Collection School Of Economics

We show the competing effects of a housing bubble on the real economy by developing a multi-sector dynamic model with housing production. On the one hand, firms can sell or collateralize their housing, so a housing bubble helps firms obtain credit to finance their investment and expand production. On the other hand, a boom in the housing sector crowds out labor in the non-housing sector. We show that housing booms can reduce social welfare both in the steady state and in the transitional dynamics only when the production externalities in the non-housing sector are sufficiently large. We quantitatively evaluate our …

Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.

On-Site Sensory Experience Boosts Acceptance Of Cultivated Chicken, Mark Chong, Angela K. Y. Leung, Tricia Marjorie Fernandez

Research Collection Lee Kong Chian School Of Business

This study set out to assess if presenting cultivated chicken in the context of a familiar meal, in a familiar dining setting, would motivate repeat consumption and recommendation. A survey of 107 diners was conducted at Huber's Butchery and Bistro in Singapore – the world's first butchery to serve cultivated meat – from April to June 2023. The findings showed that eating cultivated chicken significantly boosted post-consumption acceptance levels. In addition, cultivated chicken's tastiness may be a more important factor than its integration into a familiar meal or dish in fostering repeat consumption. Implications for the cultivated meat industry, limitations, …

Assessing Impact Of Urban Densification On Outdoor Microclimate And Thermal Comfort Using Envi-Met Simulations For Combined Spatial-Climatic Design (Cscd) Approach, Shreya Banerjee, Rachel X.Y. Pek, Sin Kang Yik, Graces N. Ching, Xiang Tian Ho, Dzyuban Yuliya, Peter J. Crank, Juan A. Acero, Winston T. L. Chow

Research Collection College of Integrative Studies

Future urban planning requires context-specific integration of spatial design and microclimate especially for tropical cities with extreme weather conditions. Thus, we propose a Combined Spatial-Climatic Design approach to assess impact of urban densification on annual outdoor thermal comfort performance employing ENVI-met simulations for Singapore. We first consider building bylaws and residential site guidelines to develop eight urban-density site options for a target population range. We further classify annual weather data into seven weather-types and use them as boundary conditions for the simulations. Comparing such fifty-six combined spatial-climatic simulation outputs by analyzing Outdoor Thermal Comfort Autonomy, we report the influence of …

Silver Screen Reversals Of The Domino Theory: American Cold War Movies And The Re-Imagination Of British Experiences In Southeast Asia, Wen-Qing (Wei Wenqing) Ngoei

Research Collection School of Social Sciences

This essay examines how Hollywood was affected by the successful anticommunism of Britain and its local allies in Malaya and Singapore, victories that unfolded alongside Vietnam’s mounting crisis in the early 1960s. It shows that American movies of this era which portrayed the intertwining of US and British experiences in 1950s Malaya and 1940s Singapore conveyed an uneasy yet clear optimism about U.S. involvement in Southeast Asia.

On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao

Research Collection School Of Computing and Information Systems

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …

An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko

Research Collection School Of Computing and Information Systems

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …

Academic Literature Review In Age Of Ai And Large Language Models​, Aaron Tay

Research Collection Library

Explore the evolving landscape of academic research with a focus on open data and AI advancements, particularly in natural language processing. Join us for a practical presentation on leveraging emerging tools for literature review. Discover platforms like Connected Papers, ResearchRabbit, and Litmaps, offering paper exploration and recommendations based on initial 'seed papers.' Dive into AI-enhanced search engines like Elicit, Scispace, Semantic Scholar, and Scite.ai, powered by Large Language Models such as BERT and GPT. Learn about the latest developments, strengths, and weaknesses of these tools, and how they reshape literature review methods, from tool selection to query input techniques.

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 …

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.

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 …

Streamlined Workflow For Qualitative Data Analysis With Whisper And Atlas.Ti, Bella Ratmelia, Bryan Leow, Danping Dong

Research Collection Library

Qualitative research, often reliant on interviews and focus groups, stands to benefit significantly from the latest advancements and innovations in AI. This workshop presents a practical and efficient research workflow with two powerful tools—Whisper and ATLAS.ti—to streamline the process of qualitative data analysis.

Whisper is an open-source machine learning model for speech recognition and transcription by OpenAI. With the ability to achieve human-level accuracy in speech recognition, it significantly reduces the time and effort required for transcribing, saving your time for more valuable work.

Moving beyond transcription, the workshop presents ATLAS.ti, a well-established qualitative data analysis tool. Learn how to …

Allocating Vehicle Registration Permits, Massimiliano Landi, Domenico Menicucci

Research Collection School Of Economics

We compare social welfare, consumer surplus and profits in two different institutional settings in which an item whose quantity is fixed and controlled (vehicle registration permit) is allocated to the buyers of a complementary good (car). In the first setting, which resembles the way in which vehicle registration permits are allocated in Singapore, the central planner runs a uniform price auction for permits in which the consumers who bid the highest receive the permits and pay the highest losing bid. Then each winning consumer purchases a car from a seller. In the alternative setting, the central planner first allocates the …

Specifying And Estimating Vector Autoregressions Using Their Eigensystem Representation, Leo Krippner

Sim Kee Boon Institute for Financial Economics

This article introduces the principles and mechanics of the eigensystem vector autoregression (EVAR) framework, where a VAR may be specified and estimated directly via its eigenvalue and eigenvector parameters. Using explicit constraints on the eigensystem permits control of a VAR ís allowable dynamics, which is illustrated empirically with standard and time-varying VAR estimations specified to be always non-explosive.

Getting Published And Raising Research Visibility, Pin Pin Yeo

Research Collection Library

Embarking on publishing your research can be exciting yet daunting. This seminar will guide you through crafting a compelling paper, navigating the submission process, and enhancing your paper's visibility. Learn what editors and reviewers seek, how to select the right journal, and how to publicise your research effectively. Whether you're a seasoned researcher or new to publishing, join us to amplify the impact of your work and share your discoveries with the world.

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 …

Nationalist Sentiments And The Multinational Enterprise: Insights From Organizational Sociology, Jesper Edman, Ilya R. P. Cuypers, Gokhan Ertug, Ruth V. Aguilera

Research Collection Lee Kong Chian School Of Business

International business scholars have recognized the impact of political and economic nationalism on the multinational enterprise (MNE). We complement these approaches by highlighting the sociological manifestations of nationalism and their implications for the MNE. We argue that nationalist sentiments, i.e. widely-shared assumptions of superiority over other nations and cultures, constitute an under-researched but critical element in international business (IB). Drawing insights from organizational sociology, we elucidate how nationalist sentiments manifest in the MNE’s external and internal environment. Specifically, we suggest that nationalist sentiments accentuate national institutional logics, generate status-based categorizations of foreign and domestic firms, and heighten emphasis on national …

Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning

Research Collection School Of Computing and Information Systems

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …

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 …

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …

An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …

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 …

Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Vision-Language Pre-Training (VLP) has demonstrated remarkable potential in aligning image and text pairs, paving the way for a wide range of cross-modal learning tasks. Nevertheless, we have observed that VLP models often fall short in terms of visual grounding and localization capabilities, which are crucial for many downstream tasks, such as visual reasoning. In response, we introduce a novel Position-guided Text Prompt ( PTP ) paradigm to bolster the visual grounding abilities of cross-modal models trained with VLP. In the VLP phase, PTP divides an image into N x N blocks and employs a widely-used object detector to identify objects …

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 …