Digital Commons Network^™

Managerial Trustworthiness And Buybacks, Sterling Huang, Kaisa Snellman, Theo Vermaelen

Research Collection School Of Accountancy

CEO trustworthiness is positively related to long-term excess returns after buyback announcements. When the CEO is trustworthy, statements that the stock is undervalued are more credible. CEO trustworthiness is initially measured by the extent to which people in the county where the company headquarters is located trust each other. Further, the positive impact of trustworthiness on excess returns is higher when the CEO has been a long-term resident of a high-trust county, and correspondingly, trustworthy CEOs are less likely to be accused of financial misreporting. Our conclusions are confirmed when we use alternative measures of trustworthiness such as employee trust …

Dimensions Of Social Networks: A Taxonomy And Operationalization, Michael Genkin, Nicholas Harrigan, Rajalakshmi Kanagavel, Janice Yap

Research Collection School of Social Sciences

What are the basic types of social network ties captured by name generators? While there have been several classifications proposed, and a large proliferation of name generators capturing various tie content has emerged, there is no clear way to map a given name generator to a particular tie type. Building on previous research, this paper proposes a framework for doing so in a principled way based on two studies. Study 1 is a dimension reduction of 24 common name generators. We find two dimensions (Valence and Social Distance), three positive tie types (Admiration, Closeness, Socialize), and three negative tie types …

A Puzzle About Meaning And Luck, Matthew Hammerton

Research Collection School of Social Sciences

This article raises a puzzle about luck and meaning in life. The puzzle shows that, in certain cases involving luck, standard intuitions about the meaningfulness of various lives conflict with basic theoretical assumptions about the nature of meaning. After setting out the puzzle, several options for resolving it are developed and evaluated.

The Fundamental Divisions In Ethics, Matthew Hammerton

Research Collection School of Social Sciences

What are the fundamental divisions in ethics? Which divisions capture the most important and basic options in moral theorizing? In this article, I reject the ‘Textbook View’ which takes the tripartite division between consequentialism, deontology, and virtue ethics to be fundamental. Instead, I suggest that moral theories are fundamentally divided into three independent divisions, which I call the neutral/relative division, the normative priority division, and the maximizing division. I argue that this account of the fundamental divisions of ethics better captures the main concerns that normative ethicists have when assessing moral theories. It also helps us make progress in comparative …

Building A Supply Chain For Emergency: A Case Study Of Kingfa's Mask Supply Chain, Nanjing Li

Dissertations and Theses Collection (Open Access)

In case of emergency such as pandemic or earthquake, there are usually surge demands for some necessary products, which are usually far more than available production capacities thus it is necessary to quickly build more production capacities. However, building production capacities is not just building production lines but to constructing whole supply chains, from raw materials and equipment sourcing, manufacturing flow lines to demand management.

To quickly construct a supply chain for fulfilling such emergency demands is very challenging both theoretically and practically. This thesis aims to develop a model of supply chain construction for emergency demand and identify the …

Spatial Panel Data Models: Unbalanced Panel, Threshold Effect And Network Structure, Xiaoyu Meng

Dissertations and Theses Collection (Open Access)

This thesis studies the estimation and inference problems for spatial panel data models when the panels are unbalanced, when the panels contain threshold effects, or when the panels contain time-varying network structures. These three scenarios divide the thesis naturally into three chapters.

The first chapter considers estimation and inferences for fixed effects spatial panel data models based on unbalanced panels that result from randomly missing spatial units. The unbalanced nature of the panel data renders the standard method of estimation inapplicable. In this chapter, we proposed an M-estimation method where the estimating functions are obtained by adjusting the concentrated quasi …

Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root. We develop a data-driven semiparametric and identification-robust approach to inference that reveals such ambiguities and documents the prevalence of weak identification in many realized volatility and trading volume series. The identification-robust empirical evidence generally favors long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.

Blocklens: Visual Analytics Of Student Coding Behaviors In Block-Based Programming Environments., Sean Tung, Huan Wei, Haotian Li, Yong Wang, Meng Xia, Huamin. Qu

Research Collection School Of Computing and Information Systems

Block-based programming environments have been widely used to introduce K-12 students to coding. To guide students effectively, instructors and platform owners often need to understand behaviors like how students solve certain questions or where they get stuck and why. However, it is challenging for them to effectively analyze students’ coding data. To this end, we propose BlockLens, a novel visual analytics system to assist instructors and platform owners in analyzing students’ block-based coding behaviors, mistakes, and problem-solving patterns. BlockLens enables the grouping of students by question progress and performance, identification of common problem-solving strategies and pitfalls, and presentation of insights …

Codem: Conditional Domain Embeddings For Scalable Human Activity Recognition, Abu Zaher Md Faridee, Avijoy Chakma, Zahid Hasan, Nirmalya Roy, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the effect of auxiliary labels in improving the classification accuracy of wearable sensor-based human activity recognition (HAR) systems, which are primarily trained with the supervision of the activity labels (e.g. running, walking, jumping). Supplemental meta-data are often available during the data collection process such as body positions of the wearable sensors, subjects' demographic information (e.g. gender, age), and the type of wearable used (e.g. smartphone, smart-watch). This information, while not directly related to the activity classification task, can nonetheless provide auxiliary supervision and has the potential to significantly improve the HAR accuracy by providing extra guidance on how …

Climate Change And Lord Of The Rings?, Winston T. L. Chow

Research Collection College of Integrative Studies

Where did we make the ‘wrong turn’? Are we at the end of the road? Winston Chow, one of the lead authors of the 6th Assessment Report published by the Intergovernmental Panel on Climate Change (IPCC), shares his perspectives on how far down the road the world is on climate change.

Communicative Strategies For Building Public Confidence In Data Governance: Analyzing Singapore's Covid-19 Contact-Tracing Initiatives, Gordon Kuo Siong Tan, Sun Sun Lim

Research Collection College of Integrative Studies

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program …

Information Sources, Perceived Personal Experience, And Climate Change Beliefs, Sonny Rosenthal

Research Collection College of Integrative Studies

This study proposes and tests a model of serial mediation based on the norm activation model and value-belief-norm theory. It argues that beliefs about climate change are related to perceived personal experience, which is related to the use of different information sources. Structural equation modeling of survey data from 1084 adult residents of Singapore found mixed support for three hypotheses. Results showed that perceived personal experience of climate change was related to the use of traditional media (β = 0.20), social media (β = 0.16), and interpersonal sources (β = 0.13), but not institutional sources. Perceived personal experience of climate …

Targeting Occupant Feedback Using Digital Twins: Adaptive Spatial-Temporal Thermal Preference Sampling To Optimize Personal Comfort Models, Mahmoud M. Abdelrahman, Clayton Miller

Research Collection College of Integrative Studies

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant’s preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method …

Gauging The Acceptance Of Contact Tracing Technology: An Empirical Study Of Singapore Residents’ Concerns With Sharing Their Information And Willingness To Trust, Ee-Ing Ong, Wee Ling Loo

Research Collection Yong Pung How School Of Law

In response to the COVID-19 pandemic, governments began implementing various forms of contact tracing technology. Singapore’s implementation of its contact tracing technology, TraceTogether, however, was met with significant concern by its population, with regard to privacy and data security. This concern did not fit with the general perception that Singaporeans have a high level of trust in its government. We explore this disconnect, using responses to our survey (conducted pre-COVID-19) in which we asked participants about their level of concern with the government and business collecting certain categories of personal data. The results show that respondents had less concern with …

Empowering The Courts To Order The Use Of Amicable Dispute Resolution: The Singapore Rules Of Court 2021, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

The civil justice regime in Singapore entered a new phase of radical reforms with effect from 1 April 2022. The reforms have substantially expanded the role of amicable dispute resolution (ADR). Parties have a duty to consider ADR prior to and during civil proceedings. More significantly, the courts have been empowered to order parties to attempt ADR, taking into account the ideals of the Rules of Court and all relevant circumstances. This note analyses the key reforms relating to the use of ADR with reference to comparable English developments. It discusses the broad yet ambivalent scope of ADR that could …

Adaptive Temporal Grouping For Black-Box Adversarial Attacks On Videos, Zhipeng Wei, Jingjing Chen, Hao Zhang, Linxi Jiang, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Deep-learning based video models, which have remarkable performance on action recognition tasks, are recently proved to be vulnerable to adversarial samples, even those generated in the black-box setting. However, these black-box attack methods are insufficient to attack videos models in real-world applications due to the requirement of lots of queries. To this end, we propose to boost the efficiency of black-box attacks on video recognition models. Although videos carry rich temporal information, they include redundant spatial information from adjacent frames. This motivates us to introduce the adaptive temporal grouping (ATG) method, which groups video frames by the similarity of their …

Multimodal Zero-Shot Hateful Meme Detection, Jiawen Zhu, Roy Ka-Wei Lee, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme detection. While the proposed methods have yielded promising results, these classification methods are mostly supervised and heavily rely on labeled data that are not always available in the real-world setting. Therefore, this paper explores and aims to perform hateful meme detection in a zero-shot setting. Working towards this goal, we propose Target-Aware Multimodal Enhancement (TAME), which is a novel deep generative framework that can improve existing hateful meme classification …

Co-Advise: Cross Inductive Bias Distillation, Sucheng Ren, Zhengqi Gao, Tiany Hua, Zihui Xue, Yonglong Tian, Shengfeng He, Hang Zhao

Research Collection School Of Computing and Information Systems

The inductive bias of vision transformers is more relaxed that cannot work well with insufficient data. Knowledge distillation is thus introduced to assist the training of transformers. Unlike previous works, where merely heavy convolution-based teachers are provided, in this paper, we delve into the influence of models inductive biases in knowledge distillation (e.g., convolution and involution). Our key observation is that the teacher accuracy is not the dominant reason for the student accuracy, but the teacher inductive bias is more important. We demonstrate that lightweight teachers with different architectural inductive biases can be used to co-advise the student transformer with …

Deep One-Class Classification Via Interpolated Gaussian Descriptor, Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their …

A Practical Comparison Of Quantum And Classical Leaderless Consensus, Paul Robert Griffin, Dimple Mevada

Research Collection School Of Computing and Information Systems

Quantum computing is coming of age and being explored in many business areas for either solving difficult problems or improving business processes. Distributed ledger technology (DLT) is now embedded in many businesses and continues to mature. Consensus, at the heart of DLTs, has practical scaling issues and, as we move into needing bigger datasets, bigger networks and more security, the problem is ever increasing. Consensus agreement is a non-deterministic problem which is a good match to quantum computers due to the probabilistic nature of quantum phenomena. In this paper, we show that quantum nodes entangled in a variety of network …

Freed: An Efficient Privacy-Preserving Solution For Person Re-Identification, Bowen Zhao, Yingjiu Li, Ximeng Liu, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on …

Learnings From A Pilot Hybrid Question Answering System: Cqas: Case Study Based On A Singapore Government Agency's Customer Service Centre, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

The Singapore Government first released their digital government blueprint in 2018 with the key message for all their agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services, especially for individual citizens. During COVID-19, Singapore government agencies introduced many COVID-19 digital initiatives resulting in more incoming inquiries from citizens to respective agencies. This surge in inquiries created the challenge on the agencies' end to meet service level agreements. One widely adopted solution is the use of chatbot technology that directly interfaces with the customer. However, several organisations have faced …

Open Access Enables New Tools And Features, Aaron Tay

Research Collection Library

Open Access levels are now at a tipping point. According to Digital Science’s Dimensions for the year 2020, Open Access surpassed subscription publication globally for the first time (Hook, 2021). Similarly in Dec 2020, with Elsevier agreeing to deposit open references to Crossref (the last major publisher to do), over 90% of Crossref references are now open (Hutchin, 2021). The combination of these two trends, both Open Access to full text and Open Scholarly metadata as well as Machine learning techniques has led to a rise of new tools in areas such as literature mapping , new indexes and new …

The Vigilante Identity And Organizations, Fan Xuan Chen, Maja Graso, Karl Aquino, Lily Lin, Joey T. Cheng, Katherine Decelles, Abhijeet K. Vadera

Research Collection Lee Kong Chian School Of Business

We test the theoretical and practical utility of the vigilante identity, a self-perception of being the kind of person who monitors their environment for signs of norm violations, and who punishes the perceived norm violator, without formal authority. We develop and validate a measure of the vigilante identity scale (VIS) and demonstrate the scale’s incremental predictive validity above and beyond seemingly related constructs (Studies 1 – 2e). We show that the VIS predicts hypervigilance towards organizational wrongdoing (Studies 2 and 4), punishment intentions and behavior in and of organizations (Studies 3 and 4) as well as in the wider community …

Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi

Research Collection School Of Computing and Information Systems

Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose …

Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag

Research Collection School Of Computing and Information Systems

Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples. In this work, we propose Task Agnostic Transfer Learning (TATL), a novel framework motivated by dermatologists’ behaviors in the skincare context. TATL learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL’s attribute-agnostic segmenter only detects skin attribute regions, it enjoys …

Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger

Research Collection School Of Computing and Information Systems

Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were …

Tourgether360: Exploring 360° Tour Videos With Others, Kartikaeya Kumar, Lev Poretski, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Contemporary 360° video players do not provide ways to let people explore the videos together. Tourgether360 addresses this problem for 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead “context” view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators appear as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. In this work, we describe the intellectual precedents for this work, our design goals, and our implementation approach of Tourgether360. Finally, we discuss future work based …

Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Present studies have discovered that state-of-the-art deep learning models can be attacked by small but well-designed perturbations. Existing attack algorithms for the image captioning task is time-consuming, and their generated adversarial examples cannot transfer well to other models. To generate adversarial examples faster and stronger, we propose to learn the perturbations by a generative model that is governed by three novel loss functions. Image feature distortion loss is designed to maximize the encoded image feature distance between original images and the corresponding adversarial examples at the image domain, and local-global mismatching loss is introduced to separate the mapping encoding representation …

Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang

Research Collection School Of Computing and Information Systems

Multiple-Choice Question Answering (MCQA) is one of the challenging tasks in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In OpenbookQA dataset [1], the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options …