Digital Commons Network^™

Spatio-Temporal Patterns Of Heat Index And Heat-Related Emergency Medical Services (Ems), Kijin Seong, Junfeng Jiao, Akhil Mandalapu, Dev Niyogi

Research Collection College of Integrative Studies

Despite growing concerns about heat waves due to climate change and their health impacts, there has been limited research on patterns of extreme heat during summertime and their association with heat-related Emergency Medical Services (EMS) incidents. This study examines spatiotemporal patterns of the heat index (HI) and its relationship to heat-related EMS incidents in Austin-Travis County, Texas, focusing on the summers of 2020 and 2021. Collecting 47,838 heat-related EMS incidence cases and aggregating them at the tract level (N = 290), the research employs spatiotemporal analysis, spatial autocorrelation, K-means clustering, and geographically weighted Poisson regression to identify disparities in heat-related …

Research On The Influence Mechanism Of Value Co-Creation On Enterprise Performance: Empirical Evidence From China's Motorsport Culture Industry, Xiaodong Weng

Dissertations and Theses Collection (Open Access)

We are in an era of digital economy and enterprise change management. The application of digital technologies such as Artificial Intelligence (AI), Blockchain, Cloud Computing, and Big Data (collectively known as ABCD technologies) has increased the competition in the value chain and value network between customers and enterprises. Users can now intervene in product design, transaction, and feedback before, during, and after the process, breaking through the limitations of time and space. They leverage their low-cost advantage to draw attention, shifting from a traditional, unidirectional supply logic to a value co-creation logic. This influences both enterprises and customers to produce …

Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical …

Efficient And Secure Federated Learning Against Backdoor Attacks, Yinbin Miao, Rongpeng Xie, Xinghua Li, Zhiquan Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, …

Big Data Analytics And Management Forecasting Behavior, Beng Wee Goh, Na Li, Tharindra Ranasinghe

Research Collection School Of Accountancy

This paper investigates whether the use of Big Data analytics by firms has a spillover effect on management forecasting behavior. Insights provided by Big Data could potentially improve firms’ ability to forecast earnings (supply channel) and investor demand for earnings information is likely higher for firms engaging in data analytics (demand channel). Using a text-based measure of firms’ commitments to and usage of Big Data analytics, we find that Big Data analytics usage is positively associated with the propensity to issue management earnings forecasts. Consistent with the “supply channel” explanation, we find that Big Data analytics usage is positively associated …

The Impact Of Managerial Myopia On Cybersecurity: Evidence From Data Breaches, Wen Chen, Xing Li, Haibin Wu, Liandong Zhang

Research Collection School Of Accountancy

Using a sample of U.S. firms for the period 2005–2017, we provide evidence that managerial myopic actions contribute to corporate cybersecurity risk. Specifically, we show that abnormal cuts in discretionary expenditures, our proxy for managerial myopia, are positively associated with the likelihood of data breaches. The association is largely driven by firms that appear to cut discretionary expenditures to meet short-term earnings targets. In addition, the association is stronger for firms with greater short-term equity incentives, higher earnings response coefficients, low levels of institutional block ownership, or large market shares. Finally, firms appear to increase discretionary expenditures upon the announcement …

How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of research. Unlike traditional program analysis-based generators, this text-based approach is more generalized and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its e ectiveness and potential challenges. To …

Text-Driven Video Prediction, Xue Song, Jingjing Chen, Bin Zhu, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption …

Associations Of Household And Neighborhood Contexts And Hair Cortisol Among Mexican-Origin Adolescents From Low-Income Immigrant Families, Ka I. Ip, Wen Wen, Wei Xiang Sim, Shanting Chen, Su Yeong Kim

Research Collection School of Social Sciences

Although neighborhood contexts serve as upstream determinants of health, it remains unclear how these contexts “get under the skin” of Mexican-origin youth, who are disproportionately concentrated in highly disadvantaged yet co-ethnic neighborhoods. The current study examines the associations between household and neighborhood socioeconomic status (SES), neighborhood racial–ethnic and immigrant composition, and hair cortisol concentration (HCC)—a physiological index of chronic stress response—among Mexican-origin adolescents from low-income immigrant families in the United States. A total of 297 (54.20% female; mage = 17.61, SD = 0.93) Mexican-origin adolescents had their hair cortisol collected, and their residential addresses were geocoded and merged with the …

Pias: Privacy-Preserving Incentive Announcement System Based On Blockchain For Internet Of Vehicles, Yonghua Zhan, Yang Yang, Hongju Cheng, Xiangyang Luo, Zhuangshuang Guan, Robert H. Deng

Research Collection School Of Computing and Information Systems

More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. Firstly, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Secondly, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a …

Sound And Complete Witnesses For Template-Based Verification Of Ltl Properties On Polynomial Programs, Krishnendu Chatterjee, Amir Goharshady, Ehsan Goharshady, Mehrdad Karrabi, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both …

Enhancing Multi-Agent System Testing With Diversity-Guided Exploration And Adaptive Critical State Exploitation, Xuyan Ma, Yawen Wang, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this …

Ft2ra: A Fine-Tuning-Inspired Approach To Retrieval-Augmented Code Completion, Qi Guo, Shangqing Liu, Xiaofei Xie, Ze Tang Tang

Research Collection School Of Computing and Information Systems

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for …

Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause …

An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …

Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo

Research Collection School Of Computing and Information Systems

The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as “ghost commits”. The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the …

Self-Supervised Spatial-Temporal Normality Learning For Time Series Anomaly Detection, Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang

Research Collection School Of Computing and Information Systems

Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning …

Fdi : Attack Neural Code Generation Systems Through User Feedback Channel, Zhensu Sun, Xiaoning Du, Xiapu Luo, Fu Song, David Lo, Li Li

Research Collection School Of Computing and Information Systems

Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a pre-trained neural model and make it available to general users as a service (e.g., through remote APIs) and incorporate a feedback mechanism to extensively collect and utilize the users' reaction to the generated code, i.e., user feedback. However, the security implications of such feedback have not yet been explored. With a systematic study of current feedback mechanisms, we find that feedback makes these systems vulnerable to feedback data injection (FDI) attacks. We discuss the methodology of FDI …

Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …

Multi-Modal Alignment Via Hyperbolic Geometry, Suyu Liu

Dissertations and Theses Collection (Open Access)

Strong capabilities of generalization to unseen domains are vital for deep neural networks. While existing methods have shown promising results without source domain access, they mostly rely on models that are extensively pre-trained on source domains or overlook the intricate hierarchical structures inherent in visual and textual features. These limitations may have bad impacts on performances, especially on datasets with many classes. To overcome this, in this paper we propose a novel approach that projects the model onto hyperbolic geometry and employs geometric optimal transport to align cross-modal features in an unsupervised manner. Unlike Euclidean geometry, hyperbolic geometry is characterized …

Quality Assurance In Software Engineering: A Journey Towards Explainable Automated Solutions, Ratnadira Widyasari

Dissertations and Theses Collection (Open Access)

In today's digital era, the pervasive influence of software on daily life underscores the necessity for high-quality and reliable systems. Software failures can result in substantial harm and financial losses, highlighting the pivotal role of Software Quality Assurance (SQA). While automated SQA techniques have evolved to aid developers in ensuring software quality, the necessity for explainability in these automated solutions has become equally important. For example, in automated fault localization, only identifying suspicious locations is insufficient; it is essential to provide reasoning on why these locations are suspicious. This dissertation presents a series of interconnected studies aimed at developing explainable …

Team Development Through Leader Mindfulness And Authentic Leadership: Leader Mindfulness As An Antecedent To Authentic Leadership And Its Relationship With Team Development As Seen In Actual Turnover, Turnover Intention, Performance, Internal Mobility, And Promotions, Stefan Juerg Wermelinger

Dissertations and Theses Collection (Open Access)

The following research examines leader mindfulness as an antecedent to authentic leadership and its relationship with team development seen in actual turnover, turnover intention, performance, internal mobility, and promotions. The study has a significant advantage as team leaders and team members could be surveyed in a real company, and data from the human resource department could be linked to reconstruct the team settings. In addition, insights into internal mobility, actual turnover, and promotions could be calculated.

The study uses a theoretical framework that builds on leader mindfulness as an antecedent of authentic leadership and authentic leadership as a root concept …

To Whom Thou Art Bound: Bicultural Identity Integration Moderates The Influence Of Conspiracy Beliefs On Chinese Americans’ Ingroup Bias, Edison Tan, Chi-Ying Cheng, Angela K. Y. Leung, Sheila X. R. Wee

Research Collection School of Social Sciences

Endorsing conspiracy beliefs about an outgroup typically fosters ingroup bias. However, the response of bicultural individuals to conspiracy theories about one of their ingroups remains understudied. We posited that bicultural individuals’ display of ingroup bias in such situations hinges on their levels of bicultural identity integration (BII). Two studies involving Chinese American participants revealed that conspiracy beliefs about China were associated with lower Chinese ingroup bias among those with higher BII levels. In Study 1, high BII Chinese Americans who endorsed conspiracy theories about China reported less favorable perceptions of the Chinese ingroup, but not among low BII Chinese Americans. …

Do Pedestrian Safety Improvements Affect Older Adults' Health And Social Outcomes Equitably? A Quasi Experiment In Singapore, Shin Bin Tan, William Tov, Paulin Tay Straughan

Research Collection School of Social Sciences

Pedestrian-friendly neighborhoods are believed to encourage greater social participation, community engagement, and sense of social inclusion, which are important to older individuals at higher risk of being socially isolated. However, most studies on neighborhood walkability, social participation and social inclusion are cross-sectional, making it difficult to robustly establish causal links. Much research on neighborhood walkability is also based in North America and Europe, leaving a knowledge gap on the impact of walkability within other geographic contexts. Furthermore, there is a lack of empirical evidence about whether benefits from traffic calming schemes are distributed equitably. To reduce these empirical gaps, our …

The Social Evaluation Of Accents And Perceived Social Influence In Singapore: A Comparison Of American And Singaporean English Accents, Matthew H. S. Ng, Chi-Ying Cheng

Research Collection School of Social Sciences

Accents are an important differentiator between groups which influence social perception and interaction, especially in a diverse country like Singapore. Social identity theory suggests that individuals would exhibit favoritism towards their own accents. However, the accent prestige theory demonstrates instances whereby foreign accents are perceived as more prestigious than one's own accent and are associated with more positive characteristics. This paper sought to explore which of these two theories is more prevalent in Singapore by comparing the perceptions of American English accents and local Singaporean English accents along the competence-warmth paradigm of the Stereotype Content Model. Further, the current research …

The Link Between People's Social Perceptions Of Cultivated Meat Eaters And Their Acceptance Of Cultivated Meat, Xiaoyu Dai, Angela K. Y. Leung, Mark Chong

Research Collection School of Social Sciences

Low consumer acceptance emerges as one important barrier to the introduction of cultivated meat, a novel food which offers an opportunity for more sustainable and ethical meat production. Due to the motives for impression management and self-esteem, one factor that could contribute to people's acceptance of cultivated meat is their perceptions of other individuals who consume cultivated meat. In the current research, two online survey studies with 393 Singaporean undergraduate students and 401 American adults were conducted to explore the perceptions of cultivated meat eaters. In both studies, participants were randomly assigned to read one of three profiles that described …

Are Corporations Responding To Civil Society Pressure?: A Multilevel Analysis Of Corporate Emissions, Annika Rieger

Research Collection School of Social Sciences

Previous research in the world-society tradition associates improvements in nation-level environmental outcomes with greater civil society integration. However, research in the world-systems tradition indicates these improvements depend on a nation’s position in the global political-economic hierarchy. To test whether these patterns are present at the organizational level, I estimate a multilevel model using corporate emissions data from the Carbon Disclosure Project and include interactions between world-system position and three measures of civil society integration: number of NGOs, proportion of corporations with climate-management incentives, and number of corporate UN Global Compact signatories. I find that the relationship between civil society pressure …

Appointment Scheduling With Delay Tolerance Heterogeneity, Shuming Wang, Jun Li, Marcus Ang, Tsan Sheng Ng

Research Collection Lee Kong Chian School Of Business

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service-time uncertainty. We aim to capture the delay-tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user-tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance Sensitivity, Monotonicity, Convexity and Positive Homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the …

From The Guest Editors: The Human Side Of The Future Of Work: Understanding The Role People Play In Shaping A Changing World, Jochen I. Menges, Lauren C. Howe, Erika Hall, Jon M. Jachimowicz, Sharon K. Parker, Riki Takeuchi, Abhijeet K. Vadera, Ashley Whillans, Susan K. Cohen

Research Collection Lee Kong Chian School Of Business

For as long as there has been work, there has been a “future of work,” through humans’ ingenuity and drive to get things done easier, faster, and better. With the industrial revolution, efforts to shape a better future of work were dominated by improvements in machinery that soon led to fears that these machines might take away jobs from workers, enriching the rich and leaving the poor poorer (Allen, 2017). Later, John Maynard Keynes (1930) optimistically predicted technological unemployment (a net loss of jobs due to shifts in the nature of work outpacing the economy’s ability to redeploy talent) would …

The Importance Of Sampling Frequency For Estimates Of Well-Being Dynamics, Stephen James Hoskins, David W. Johnston, Johannes S. Kunz, Michael A. Shields, Kevin E. Staub

ROSA Journal Articles and Publications

Using a high-frequency panel survey, we examine the sensitivity of estimated self-reported well-being (SWB) dynamics to using monthly, quarterly, and yearly data. This is an important issue if SWB is to be used to evaluate policy. Results from autoregressive models that account for individual-level heterogeneity indicate that the estimated persistence using yearly data is near zero. However, estimated persistence from monthly and quarterly data is substantial. We estimate that persistence to shocks typically lasts around six months and has a net present value of 75–80 per cent of the contemporaneous effect. Estimates are similar for different domains of SWB.