Digital Commons Network^™

D2sr: Decentralized Detection, De-Synchronization, And Recovery Of Lidar Interference, Darshana Rathnayake, Hemanth Sabbella, Meera Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR’s active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple …

"Asian" Principles For The Recognition And Enforcement Of Foreign Judgments? Singapore As A Case Study, Adeline Chong

Research Collection Yong Pung How School Of Law

This paper considers if there can be said to be an “Asian” body of principles for the recognition and enforcement of foreign judgments. Tapping on the results of a research project which was conducted from 2016 to 2020, it is submitted that the answer to this query is in the negative. However, it is suggested that what marks out the “Asian” approach to private international law is the willingness of Asian countries to look outwards for reform and development and to balance the adoption of international norms against important local norms and objectives. Singapore’s approach to the recognition and enforcement …

Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the …

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 …

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 …

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 …

Essays On Corporate Finance, Su Hee Yun

Dissertations and Theses Collection (Open Access)

Chapter 1. The impact of ESG disasters on Green and Brown firms

I investigate the effect of a firm’s prior ESG reputation on the market impact of ESG incidents. I find that firms with a better ESG reputation, i.e., higher ESG ratings, experience less negative stock-market reactions and analysts' forecast revisions compared to firms with a poorer ESG reputation. But managers of Greener firms, when producing earnings guidance, do not forecast a lower impact of these incidents on future earnings. Similarly, actual decreases in future earnings following these incidents are not significantly different between Green and Brown firms. Altogether, the …

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 …

Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti

Research Collection School Of Computing and Information Systems

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in terms of time complexity but also has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation costs scale economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering …

Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test …

Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - …

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 …

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 …

Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …

Probing Effects Of Contextual Bias On Number Magnitude Estimation, Xuehao Du, Ping Ji, Wei Qin, Lei Wang, Yunshi Lan

Research Collection School Of Computing and Information Systems

The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network's accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of- distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods …

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 …

Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen

Research Collection School Of Computing and Information Systems

Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …

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 …

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 …

Does Voice Endorsement By Supervisors Enhance Or Constrain Voicer's Personal Initiative? Countervailing Effects Via Feeling Pride And Feeling Envied, Mingyun Huai, Xueqi Wen, Zihan Liu, Xingyu Wang, Wen-Dong Li, Mo Wang

Research Collection Lee Kong Chian School Of Business

While the previous research has examined antecedents of supervisors’ voice endorsement, it has generally overlooked its effects on voicers’ affective and behavioral reactions, probably because of the underlying assumption that supervisors’ voice endorsement is inherently beneficial and likely to encourage more proactive behaviors in the future. In this research, we offer a theoretical model of the double-edged effects of supervisors’ voice endorsement on voicers’ subsequent personal initiative. Drawing on cognitive appraisal theory and related research, we proposed that supervisors’ voice endorsement prompts two different cognitive appraisal processes in voicers that evoke two distinct emotional experiences—feeling pride and feeling envied—with countervailing …

The Sacred Dimensions Of The Bri's Infrastructural Commons, Orlando Woods, David A. Palmer

Research Collection College of Integrative Studies

Whilst the idea of infrastructure has animated scholarship for the past twenty years at least, there remains a need for more expansive understandings of what infrastructure is, and what it can be. The speed, scale and material disruptiveness with which many of the infrastructural megaprojects that constitute China’s Belt and Road Initiative (BRI) have been developed both underpin and validate this need. Whilst the effects of the BRI are both manifold and diffuse, all of them expose situations of infrastructural complexity. Rather than considering the BRI simply as the imposition of new material infrastructures on a landscape, we posit the …

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, …

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, …

Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang

Research Collection School Of Computing and Information Systems

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious …

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 …

Gain-Loss Domain And Social Value Orientation As Determinants Of Risk Allocation Decisions, Ming-Hong Tsai, Verlin B. Hinsz

Research Collection School of Social Sciences

People often make less risky decisions for themselves than others. We examined how people allocated risks (i.e., determining the ratio of uncertain outcomes to certain outcomes) between themselves and others. We also investigated gain (vs. loss) domain and social value orientation as predictors of risk allocations. The results of three experiments demonstrated that participants were more likely to share their risks equally between themselves and others than distribute risk unequally. In the gain (vs. loss) domain, participants allocated fewer risks to themselves and more risks to the other person for unequal risk allocations. Compared to proselfs, prosocials were more likely …

Adavis: Adaptive And Explainable Visualization Recommendation For Tabular Data, Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang

Research Collection School Of Computing and Information Systems

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable …

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 …

(Trade) War And Peace: How To Impose International Trade Sanctions, Gustavo De Souza, Naiyuan Hu, Haishi Li, Yuan Mei

Research Collection School Of Economics

What is the most cost-efficient way to impose trade sanctions against Russia? We build a quantitative model of international trade with input–output connections. Sanctioning countries choose import tariffs to simultaneously maximize their income and minimize Russia's income, with different weights placed on these objectives. We find, first, that for countries with low willingness to pay for sanctions against Russia, the most cost-efficient sanction is an approximately 20% tariff on all Russian products. Second, if countries are willing to pay at least US$0.70 for each US$1 drop in Russian welfare, an embargo on Russia's mining and energy products is the most …