Digital Commons Network^™

Price Comovement And Market Segmentation Of Chinese A- And H-Shares: Evidence From A Panel Latent-Factor Model, Yingjie Dong, Wenxin Huang, Yiu Kuen Tse

Research Collection School Of Economics

This paper examines the price comovement of cross-listed Chinese A- and H-shares using a panel model with latent factors and a heterogeneous long-run structure. Our model is more flexible than the cointegration system and is estimated using the data-driven Cup–Lasso method. The long-run H-share price discounts are heterogeneous across different groups of stocks. We have identified both stationary and nonstationary latent factors in the price differentials, which are driven by different economic variables. By analyzing the factor loadings of the nonstationary latent factor, we identify some trading-friction and information-friction variables that have effects on the price convergence between the A- …

Hypothesis Testing Via Posterior-Test-Based Bayes Factors, Yong Li, Nianling Wang, Jun Yu, Yonghui Zhang

Research Collection School Of Economics

Hypothesis testing via p-value has been criticized in recent years. Bayes factors (BFs) have been tipped as a possible replacement of p-value for hypothesis testing. However, the standard BFs suffer from some theoretical and practical difficulties. For example, they are not well defined under improper priors and are subject to Jeffreys-Lindley-Bartlett’s paradox under vague priors. Moreover, they are difficult to compute for many models. In this paper, we propose to compare sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based BFs are constructed from the posterior version of the likelihood ratio test and the Wald test, respectively. Under …

High-Dimensional Vars With Common Factors, Ke Miao, Peter C. B. Phillips, Liangjun Su

Research Collection School Of Economics

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ1-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for the estimates. A singular value thresholding procedure is used to determine the correct number of factors with probability approaching one. Both the LASSO estimator and the conservative LASSO estimator are employed to improve estimation precision. The conservative …

On The Welfare Role Of Redundant Assets With Heterogenous Forecasts, Shurojit Chatterji, Atsushi Kajii

Research Collection School Of Economics

We study a multiperiod model with a nominal bond that matures in one period and identify the set of e¢ cient allocations that can be sustained as Walrasian equilibria with heterogeneous forecasts. We next add a long maturity bond, which under perfect foresight would be a redundant asset, and show that it fundamentally expands the set of e¢ cient allocations that can be sustained as Walrasian equilibria. Indeed all wealth transfers compatible with e¢ ciency can arise endogenously. The key feature driving this conclusion are forecasting errors, which lead to ex post arbitrage opportunities that induce these income transfers.

Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meeralakshmi Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …

Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our …

Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain's characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The …

Demystifying Performance Regressions In String Solvers, Yao Zhang, Xiaofei Xie, Yi Li, Yi Lin, Sen Chen, Yang Liu, Xiaohong Li

Research Collection School Of Computing and Information Systems

Over the past few years, SMT string solvers have found their applications in an increasing number of domains, such as program analyses in mobile and Web applications, which require the ability to reason about string values. A series of research has been carried out to find quality issues of string solvers in terms of its correctness and performance. Yet, none of them has considered the performance regressions happening across multiple versions of a string solver. To fill this gap, in this paper, we focus on solver performance regressions (SPRs), i.e., unintended slowdowns introduced during the evolution of string solvers. To …

Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang

Research Collection School Of Computing and Information Systems

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the "no collision" requirement) and lack any coverage criteria measures. In this paper, we propose AVUnit, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, AVUnit consists of two new languages for specifying dynamic …

Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on largescale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The …

Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meera Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …

Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

Too many options can be a problem, which is the case for Application Programming Interfaces (APIs). As there are many such APIs, with many more being introduced periodically, it raises the problem of choosing which API to be recommended. Furthermore, numerous APIs are commonly used together with other complementary third-party APIs. It can be challenging for developers to understand how to use each API and to remember all the complementary APIs for the API they want to use. Therefore, an accurate API recommendation approach can improve developers' efficiency in implementing certain functionality. Several approaches have been developed to automatically recommend …

Service Out Under The New Rules Of Court, Ian Mah, Aaron Yoong

Research Collection Yong Pung How School Of Law

The new Rules of Court 2021 seek to provide a more accessible and efficient justice system. The extensiveness of the overhaul, however, brings with it as much unfamiliarity as excitement. This legislation comment examines the changes in the provisions governing service out of jurisdiction and argues that the textual changes also effect substantive changes to how the law is applied. This comment also explores the related issues on the grant of Mareva injunctions in aid of foreign proceedings under the new Rules of Court 2021.

Constitutional Equality And Executive Action: A Comparative Perspective To The Comparator Problem, Kenny Chng

Research Collection Yong Pung How School Of Law

A general right to equality is a common feature of written constitutions around the world. Interesting questions arise when one seeks to apply such rights to discrete executive acts. The subject of such acts has necessarily been singled out from a multitude of possibilities for the purposes of the act. To determine whether a differentiation has occurred such that like cases have not been treated alike, to what or whom should this subject be compared? The question of how one selects the proper comparator becomes especially significant when one notes that whether the equal protection guarantee is triggered at all …

Administrative Concessions And The Efficient Taxation Of Digital Tokens In Singapore, Vincent Ooi

Research Collection Yong Pung How School Of Law

Tax authorities around the world have stepped-up enforcement activities on the taxation of digital tokens and begun providing more guidance to taxpayers. However, the relative novelty of the field means that there is likely to be considerable uncertainty as to the correct tax treatment, both on the part of taxpayers and tax authorities. This requires both parties to seek tax and legal advice that is often duplicative (in the sense that similar issues tend to keep coming up for different taxpayers) and bear the risk of taking an incorrect legal position. In some cases, the strict tax position under the …

Two Decades After Salini V Morocco: The Case For Retaining The Salini Test With Modifications, Darius Chan, Justin Lai

Research Collection Yong Pung How School Of Law

The definition of an investment under Article 25 of the ICSID Convention continues to attract a divergence of views. Ever since its use in 2001, the Salini Test, in its various forms, has become the predominant method that tribunals use to determine whether there is an investment. However, the Salini Test is hardly free from controversy, and suffers from two significant issues. First, its criteria are often subject to differing interpretations, leading to confusion over how the test should actually be applied. Second, the Salini Test has lost its legal force over time, as it has been relegated to factors …

Forecasting Airport Transfer Passenger Flow Using Realtime Data And Machine Learning, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

Problem definition: In collaboration with Heathrow airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces quantile forecasts for the number of passengers arriving at the immigration and security areas. Academic/Practical relevance: Airports and airlines have been challenged to improve decision-making by producing accurate forecasts in real time. Our work is the first to apply machine learning for predicting real-time quantile forecasts in the airport. We focus on passengers’ connecting journeys, which have only been studied by few researchers. Better forecasts of …

The Study Of Followers In Leadership Research: A Systematic And Critical Review, Burak Oc, Kraivin Chintakananda, Michael R. Bashshur, David V. Day

Research Collection Lee Kong Chian School Of Business

Despite the significant amount of existing research examining the relationship of follower-related factors with leadership outcomes, there is no systematic, critical review that integrates and helps leadership scholars make sense of this rapidly growing body of research. To address this gap in the literature, we first briefly discuss the leading perspectives explaining the role of followers in leadership. Next, we identify and discuss the most frequently studied theoretical narratives explaining the relationship between follower-related predictors and leadership outcomes. Because theoretical arguments generally make causal claims, we identify and examine how methodological concerns including power analysis, multicollinearity, and endogeneity might prevent …

Fully Modified Least Squares Cointegrating Parameter Estimation In Multicointegrated Systems, Igor L. Kheifets, Peter C. B. Phillips

Research Collection School Of Economics

Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The semiparametric framework has the advantage that short run dynamics do not need to be modeled and estimation by standard techniques such as fully modified least squares (FM-OLS) on the original system is straightforward. The paper derives FM-OLS …

Constrained Reinforcement Learning In Hard Exploration Problems, Pankayaraj Pathmanathan, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are imposed on trajectories. Recent works in constrained RL have developed methods that ensure constraints can be enforced even at learning time while maximizing the overall value of the policy. Unfortunately, as demonstrated in our experimental results, such approaches do not perform well on complex multi-level tasks, with longer episode lengths or sparse rewards. To that end, wepropose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels …

An Empirical Study Of Package Management Issues Via Stack Overflow, Syful Islam, Raula Kula, Christoph Treude, Bodin Chinthanet, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative …

Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali Xia, Jianqiang Huang, Shibao Zheng, Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier …

Quantization-Aware Interval Bound Propagation For Training Certifiably Robust Quantized Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, A. Thomas Henzinger, Daniela Rus

Research Collection School Of Computing and Information Systems

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training …

Disentangling The Effects Of Smartphone Screen Time, Checking Frequency, And Problematic Use On Executive Function: A Structural Equation Modelling Analysis, Wei Xing Toh, Wee Qin Ng, Hwajin Yang, Sujin Yang

Research Collection School of Social Sciences

The pervasiveness of smartphone engagement among young adults has attracted growing interest regarding its impact on cognitive processes. However, research on the relation between smartphone use and executive function (EF)—a set of adaptive, goal-directed control processes—remains inconclusive due to imprecise estimation of EF dimensions and inconsistent operationalisation of smartphone use in past studies. Therefore, we examined how two indices of smartphone use—screen time and checking frequency—would predict EF (common EF, shifting-specific-, and working-memory-specific components), using a latent-variable approach based on a comprehensive battery of EF tasks. We also examined the moderating role of problematic smartphone use in the link between …

Legal Dispositionism And Artificially-Intelligent Attributions, Jerrold Soh

Research Collection Yong Pung How School Of Law

It is conventionally argued that because an artificially-intelligent (AI) system acts autonomously, its makers cannot easily be held liable should the system's actions harm. Since the system cannot be liable on its own account either, existing laws expose victims to accountability gaps and need to be reformed. Recent legal instruments have nonetheless established obligations against AI developers and providers. Drawing on attribution theory, this paper examines how these seemingly opposing positions are shaped by the ways in which AI systems are conceptualised. Specifically, folk dispositionism underpins conventional legal discourse on AI liability, personality, publications, and inventions and leads us towards …

Remediating System Neglect In Judgmental Demand Forecasting, Srikant Vinakota

Dissertations and Theses Collection (Open Access)

Prior research has shown that individuals tasked with judgmental forecasting of demand based on time-series data overreact in stable environments and underreact in unstable environments. Kremer et al. (2011) attributed this to the system neglect hypothesis, which claims that forecasters emphasize forecast errors over the system parameters.

The present research investigates interventions that mitigate system neglect and address the causal factors for overreaction and underreaction. Given the desire by organizations to move towards touchless planning and automated decision-making, minimizing human judgment and understanding its drivers is of significant practical importance.

We tested four different interventions on an online subject pool …

Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the …

Generalizing Math Word Problem Solvers Via Solution Diversification, Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang

Research Collection School Of Computing and Information Systems

Current math word problem (MWP) solvers are usually Seq2Seq models trained by the (one-problem; one-solution) pairs, each of which is made of a problem description and a solution showing reasoning flow to get the correct answer. However, one MWP problem naturally has multiple solution equations. The training of an MWP solver with (one-problem; one-solution) pairs excludes other correct solutions, and thus limits the generalizability of the MWP solver. One feasible solution to this limitation is to augment multiple solutions to a given problem. However, it is difficult to collect diverse and accurate augment solutions through human efforts. In this paper, …

Planning And Learning For Non-Markovian Negative Side Effects Using Finite State Controllers, Aishwarya Srivastava, Sandhya Saisubramanian, Praveen Paruchuri, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and severity of associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states …

Pose- And Attribute-Consistent Person Image Synthesis, Cheng Xu, Zejun Chen, Jiajie Mai, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

PersonImageSynthesisaimsattransferringtheappearanceofthesourcepersonimageintoatargetpose. Existingmethods cannot handle largeposevariations and therefore suffer fromtwocritical problems: (1)synthesisdistortionduetotheentanglementofposeandappearanceinformationamongdifferentbody componentsand(2)failureinpreservingoriginalsemantics(e.g.,thesameoutfit).Inthisarticle,weexplicitly addressthesetwoproblemsbyproposingaPose-andAttribute-consistentPersonImageSynthesisNetwork (PAC-GAN).Toreduceposeandappearancematchingambiguity,weproposeacomponent-wisetransferring modelconsistingoftwostages.Theformerstagefocusesonlyonsynthesizingtargetposes,whilethelatter renderstargetappearancesbyexplicitlytransferringtheappearanceinformationfromthesourceimageto thetargetimageinacomponent-wisemanner. Inthisway,source-targetmatchingambiguityiseliminated duetothecomponent-wisedisentanglementofposeandappearancesynthesis.Second,tomaintainattribute consistency,werepresenttheinputimageasanattributevectorandimposeahigh-levelsemanticconstraint usingthisvectortoregularizethetargetsynthesis.ExtensiveexperimentalresultsontheDeepFashiondataset demonstratethesuperiorityofourmethodoverthestateoftheart,especiallyformaintainingposeandattributeconsistenciesunderlargeposevariations.