Digital Commons Network^™

Creating And Delivering Audio Descriptions For Videos, Rosiana Natalie

Dissertations and Theses Collection (Open Access)

Despite anti-discrimination regulations mandating the provision of audio descriptions (ADs), the majority of online video content remains inaccessible to blind and low-vision (BLV) individuals. This is because these ADs are either absent or fail to adequately address the diverse and unique needs of the audience. Traditionally, content creators have relied on professionals to author ADs. However, this gold standard may not be accessible for some content creators because this method is still costly and has a long turnaround time. Moreover, when ADs are available, they tend to be static and unalterable, failing to cater to the unique preferences of BLV …

Executive Function And Feedback In Regulatory Monitoring: Towards An Understanding Of Flexible And Adaptive Emotion Regulation, Gilaine Rui Ng

Dissertations and Theses Collection (Open Access)

Monitoring is a process which involves attending to feedback regarding the efficacy of ongoing regulatory strategies and determining whether to switch or maintain strategies (i.e., strategy switching or maintenance) in order to maximize adaptive outcomes. While conceptual accounts underscore the critical role of monitoring in flexible and adaptive emotion regulation, empirical research remains scarce, and the cognitive factors which subserve the monitoring process remain unclear and poorly understood. To bridge this gap, we investigate how components of executive function (EF)—a set of cognitive control processes necessary for flexible, goal-directed behavior—would differentially support the monitoring process. Additionally, we examine the extent …

Examining A Regulatory Flexibility Framework Of Psychological Distress: Integrating Distress Tolerance, Emotion Differentiation, And Emotion Regulation Flexibility, Germaine Yue Qi Tng

Dissertations and Theses Collection (Open Access)

Several theoretical accounts identify emotion regulation (ER) difficulties, including poor abilities to withstand or distinguish negative emotions, as a transdiagnostic risk factor for symptoms of psychological distress. Considering this, nascent evidence hints that ER flexibility may be a mediating mechanism that explains the relationship between specific ER abilities (i.e., distress tolerance, negative emotion differentiation) and distress symptoms. Across time-lagged (Study 1) and three-wave longitudinal (Study 2) investigations of college-aged adults, a regulatory flexibility framework of distress was examined in which ER flexibility mediates the respective pathways from distress tolerance and negative emotion differentiation to psychological distress symptoms. Furthermore, we explored …

Decentralized Consensus And Governance For Collaborative Intelligence, Huiwen Liu

Dissertations and Theses Collection (Open Access)

The data economy today is becoming increasingly collaborative in nature. Take business intelligence, for example. To unleash the full potential of big data, it is essential to integrate multi-source data depicting entities from a multi-faceted and multi-modal perspective, which, not surprisingly, is not achievable by any company alone. In collaborative intelligence, there are two core issues, namely "trust" and "incentive". The core mechanisms to solve these two problems are consensus and tokenization separately.

To solve the trust problem more effectively, we propose a systematic consensus evaluation framework to investigate whether existing consensus algorithms can do so. After a lot of …

On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open-source software (OSS) projects. In this regard, we first investigate …

The Role Of Executive Function In Cognitive Reappraisal: A Meta-Analytic Review, Wei Xing Toh, Jun Sheng Keh, James J. Gross, Laura L. Carstensen

Research Collection School of Social Sciences

Cognitive reappraisal refers to the reinterpretation of a situation to alter its emotional meaning. Theoretically, executive functions (EFs), such as inhibition, updating, and shifting, are core elements of reappraisal processes. However, empirical studies have yielded inconsistent evidence as to whether and to what extent EFs are associated with reappraisal. To address this issue, we conducted a meta-analysis of the literature in which 179 effect sizes from 59 independent samples (N = 4,703) were included. Using random-effects metaregression with robust-variance estimates and small-sample corrections, we also examined whether variation in effect sizes could be accounted for by potential moderators, such as …

How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Generative AI tools can provide people with the ability to create virtual environments and scenes with natural language prompts. Yet, how people will formulate such prompts is unclear---particularly when they inhabit the environment that they are designing. For instance, it is likely that a person might say, "Put a chair here,'' while pointing at a location. If such linguistic and embodied features are common to people's prompts, we need to tune models to accommodate them. In this work, we present a Wizard of Oz elicitation study with 22 participants, where we studied people's implicit expectations when verbally prompting such programming …

Towards Human-Centered Proactive Conversational Agents, Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …

Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …

Certified Robust Accuracy Of Neural Networks Are Bounded Due To Bayes Errors, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achieving robustness whilst maintaining accuracy. In this work, we offer a novel perspective based on Bayes errors. By adopting Bayes error to robustness analysis, we investigate the limit of certified robust accuracy, taking into account data distribution uncertainties. We first show that the accuracy inevitably decreases in the pursuit of …

Augmenting Decision With Hypothesis In Reinforcement Learning, Minh Quang Nguyen, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling efficiency. However, our study shows it suffers from low exploitation in early training period and bias sensitiveness. To address these issues, we propose to augment the decision-making process with hypothesis, a weak form of environment description. Our approach relies on prompting the learning agent with accurate hypotheses, and designing a ready-to-adapt policy through incremental learning. We propose the ALH algorithm, showing detailed analyses on a typical learning scheme and a diverse set of Mujoco benchmarks. Our algorithm produces a significant improvement over value-based learning algorithms and other strong baselines. …

Transformer Models With Explainability For It Telemetry And Business Events, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

Temporal event data are commonly encountered in software applications across a wide range of domains, from IT telemetry and system logs to business process automation. Temporal event data in software applications carry information in various forms: both structured and unstructured, and with both regular and irregular occurrence frequency, making the representation of temporal event data challenging. Further chal-lenges come from the diversity in terms of the scale and volume of events that need to be summarized by the representation. We propose a general and unified approach to handle temporal event data for the purpose of learning a predictive transformer model. …

Mtdscout : Complementing The Identification Of Insecure Methods In Android Apps Via Source-To-Bytecode Signature Generation And Tree-Based Layered Search, Zicheng Zhang, Haoyu Ma, Daoyuan Wu, Debin Gao, Xiao Yi, Yufan Chen, Yan Wu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Modern Android apps consist of both host app code and third-party libraries. Traditional static analysis tools conduct taint analysis for API misuses on the entire app code, while third-party library (TPL) detection tools focus solely on library code. Both approaches, however, are prone to some inherent false negatives: taint analysis tools may neglect third-party libraries or face timeouts/errors in whole app-based analysis, and TPL detection tools are not designed for pinpointing specific vulnerable methods. These challenges underscore the need for enhanced identification of insecure methods in Android apps, particularly for app markets addressing open-source security incidents. In this paper, we …

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 …

Cds Channels Of Influence On Discretionary Accruals, Hao Cheng, Kian Guan Lim

Research Collection Lee Kong Chian School Of Business

Existing studies indicated that firm debt holders can use the credit default swap (CDS) market to hedge their credit risk, and thus they would reduce their monitoring of the firms, leading to largely distressed firms shirking and increasing positive abnormal earnings accruals. Besides providing insurance, however, the CDS spreads also perform price discovery of credit risk information sought by trade creditors and potential lenders who are not protected. High absolute abnormal discretionary accruals or bad earnings quality, especially negative abnormal accruals, would lead adverse CDS price signals that are very costly to the firm. This compels the firm under nondistressed …

Business Model Choice For Heavy Equipment Manufacturer, Philippe Blaettchen, Niyazi Taneri, Sameer Hasija

Research Collection Lee Kong Chian School Of Business

Technological advances enable new business models for heavy equipment manufacturers wherein customers access equipment without ownership. We seek to understand the profitability and environmental performance of different emerging business models in light of salient economic and operational factors. We develop a game-theoretic model to identify the optimal choice between a traditional ownership-based business model and two access-based models: servicization and peer-to-peer sharing. After-sales services, equipment characteristics, usage environments, and fuel prices affect this choice. We also provide a novel framework to analyze business models’ environmental impact, which incorporates trade-offs between economic value and environmental costs and shows that all models …

Relationship Commitment Regulation: Influencing A Partner's Commitment To Achieve One's Commitment Goals, Laura V. Machia, Kenneth Tan, Christopher R. Agnew

Research Collection School of Social Sciences

We introduce a model of relationship commitment regulation, which describes how romantic partners might use empirically-validated antecedents of commitment level strategically to influence a partner's commitment toward their relationship based on certain commitment goals they have for the relationship. People are generally aware of and relatively accurate with respect to knowing their partner's level of commitment and also have preferences regarding their desired level. We summarize key antecedents of commitment level (i.e., satisfaction level, alternative quality, investment size, future plans, and subjective norms) and then describe how each can be manipulated via relationship maintenance activities to move a partner's commitment …

Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi Zhang

Research Collection School Of Economics

We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman's recent congressional testimony.

Rank-Guaranteed Auctions, Wei He, Jiangtao Li, Weijie Zhong

Research Collection School Of Economics

We propose a combinatorial ascending auction that is “approximately” optimal, requiring minimal rationality to achieve this level of optimality, and is robust to strategic and distributional uncertainties. Specifically, the auction is rankguaranteed, meaning that for any menu M and any valuation profile, the ex-post revenue is guaranteed to be at least as high as the highest revenue achievable from feasible allocations, taking the (|M| + 1)th-highest valuation for each bundle as the price. Our analysis highlights a crucial aspect of combinatorial auction design, namely, the design of menus. We provide simple and sufficient menus in various settings.

Secure And Flexible Wildcard Queries, Qing Wang, Donghui Hu, Meng Li, Guomin Yang

Research Collection School Of Computing and Information Systems

Wildcard Keyword Searchable Encryption (WKSE) enables users to search desired encrypted files with wildcard queries. Previous schemes only enabled single-character wildcard queries or restricted multi-character wildcard queries. Even if the two types of queries are supported by several schemes, they are vulnerable to correlation attacks and composition attacks. In this paper, we propose a WKSE scheme Secure Flexible Wildcard Queries (SFWQ) that supports highly flexible wildcard queries and resists correlation and composition attacks. Specifically, we adopt the interval matching method instead of traditional position matching, so that SFWQ supports a variety of queries, including single-character wildcard queries, multi-character wildcard queries, …

Double Issuer-Hiding Attribute-Based Credentials From Tag-Based Aggregatable Mercurial Signatures, Rui Shi, Yang Yang, Yingjiu Li, Huamin Feng, Guozhen Shi, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Attribute-based anonymous credentials offer users fine-grained access control in a privacy-preserving manner. However, in such schemes obtaining a user's credentials requires knowledge of the issuer's public key, which obviously reveals the issuer's identity that must be hidden from users in certain scenarios. Moreover, verifying a user's credentials also requires the knowledge of issuer's public key, which may infer the user's private information from their choice of issuer. In this article, we introduce the notion of double issuer-hiding attribute-based credentials ( DIHAC ) to tackle these two problems. In our model, a central authority can issue public-key credentials for a group …

Evilscreen Attack: Smart Tv Hijacking Via Multi-Channel Remote Control Mimicry, Yiwei Zhang, Siqi Ma, Tiancheng Chen, Juanru Li, Robert H. Deng, Elisa Bertino

Research Collection School Of Computing and Information Systems

Modern smart TVs often communicate with their remote controls (including the smartphone simulated ones) using multiple wireless channels (e.g., Infrared, Bluetooth, and Wi-Fi). However, this multi-channel remote control communication introduces a new attack surface. An inherent security flaw is that remote controls of most smart TVs are designed to work in a benign environment rather than an adversarial one, and thus wireless communications between a smart TV and its remote controls are not strongly protected. Attackers can leverage such a flaw to abuse the remote control communication and compromise smart TV systems. In this paper, we propose EvilScreen, a novel …

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 …

Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …

Reinforcement Learning For Strategic Airport Slot Scheduling: Analysis Of State Observations And Reward Designs, Anh Nguyen-Duy, Duc-Thinh Pham, Jian-Yi Lye, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to the NP-hard nature, the strategic airport slot scheduling problem is calling for exploring sub-optimal approaches, such as heuristics and learning-based approaches. Moreover, the continuous increase in air traffic demand requires approaches that can work well in new scenarios. While heuristics rely on a fixed set of rules, which limits the ability to explore new solutions, Reinforcement Learning offers a versatile framework to automate the search and generalize to unseen scenarios. Finding a suitable state observation and reward structure design is essential in using Reinforcement Learning. In this paper, we investigate the impact of providing the Reinforcement Learning agent …

Legal Governance Of Artificial Intelligence In Healthcare Devices And Services In Singapore, Warren B. Chik, Haran Sugumaran

Research Collection Yong Pung How School Of Law

The use of artificial intelligence (AI) in healthcare applies across a spectrum of goods and services that support the industry. The main benefits of developing and applying AI to healthcare are: (a) the potential for it to perform functions beyond what can be done manually by the natural person; (b) the greater efficiency and accuracy that it can give to detection, diagnosis and medication/recovery; (c) the minimisation of risk and the possible lowering of cost (in the long term); and (d) in appropriate cases, minimal human intervention or involvement, especially in situations or conditions that may present a risk of …

Retail Investors' Activity And Climate Disasters, Marinela Adriana Finta

Sim Kee Boon Institute for Financial Economics

We analyze the effects of climate disasters on retail investors’ trading activity. Results show that retail investors trade significantly less during and around climate disasters, and retail buyers exhibit higher returns than sellers. Climate disasters weaken the positive return predictability of the past month’s order imbalances while strengthening it for the past six month’s order imbalances. In the short run, firms within climate disaster counties with retail net buying underperform those with negative imbalances. Instead, in the long run, firms within and outside climate disaster counties with positive order flows outperform those with negative order flows. Finally, the estimates on …

Nonstandard Errors, Albert J. Menkvelt, Anna Dreber, Et Al., Shihao Yu, Bart Zhou Yueshen, Emiliano Sebastian Pagnotta

Research Collection Lee Kong Chian School Of Business

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

The Effect Of Carbon Pricing On Firm Performance: Worldwide Evidence, Tinghua Duan, Frank Weikai Li, Hong Zhang

Research Collection Lee Kong Chian School Of Business

Economists recommend combating climate change with carbon pricing; however, a major block to pricing emissions is concerns about economic costs. This paper examines the impacts of carbon pricing initiatives on the operating performance and market value of publicly listed firms around the world. Using the staggered enactment of carbon pricing initiatives across jurisdictions and a triple difference approach, we find a significant reduction in the profitability and value of carbon-intensive firms relative to low-emission firms after the enactment of carbon pricing policies. The reduction in firm profits is driven by both a decrease in sales growth and an increase in …