Digital Commons Network^™

Hello, Jarvis, Archan Misra

Asian Management Insights

How AI-enabled interactive agents will reshape our workforce of today and tomorrow.

From Emergency Rooms To Boardrooms, Tow Keang Lim

Asian Management Insights

How doctors think can improve business decision-making.

Study On The Influence Of Confucianism And Corporate Governance Mechanism On Performance, Yujie Wu

Dissertations and Theses Collection (Open Access)

As the cornerstone of traditional Chinese culture, Confucianism profoundly influences Chinese enterprises' behaviour. This study empirically examines the impact of Confucianism and corporate governance mechanisms on companies' financial and social performance, yielding the following principal conclusions utilizing data from Chinese-listed companies:

Firstly, Confucianism significantly and positively influences corporate performance. Enterprises deeply rooted in Confucian culture often exhibit superior financial and social performance. This finding indicates the positive role of Confucianism in promoting responsible corporate behaviours and enhancing performance.

Secondly, the impact of corporate governance mechanisms on corporate performance is complex. An increase in institutional ownership correlates positively with corporate performance, …

The Effect Of Internet Firms’ Data Analytics Capability On Their Innovation Speed And Innovation Quality: A Dynamic Capability Perspective, Yeyu Hua

Dissertations and Theses Collection (Open Access)

With the advent of big data era, data plays a pivotal role in sustainingfirms’ competitive advantages. Although a few studies have shown that data analytics capability contributes to firms’ innovative performance, these studies either focus on general innovative performance or specific types of innovation, such as incremental innovation, radical innovation, and supply chaininnovation. In this thesis, I enrich this stream of literature by conducting twostudies to further examine the relationship between data analytics capabilityand innovation speed as well as innovation quality. This thesis consists of twostudies. Study 1 is a survey study, in which I investigate the relationshipbetween data analytics …

Impact Of Hr Slack On Firm Performance: An Empirical Study On Chinese High-Tech Firms, Weixiong Feng

Dissertations and Theses Collection (Open Access)

As part of organizational slack, HR slack plays a role in managing external risks and opportunities when the global business environment is in turmoil. In recent years, Chinese high-tech companies have faced external shocks such as reverse globalization, the decoupling of economic and trade relations, supply chain disruptions, and epidemics. These have posed new challenges to their strategy formulation, business operations, and talent acquisition. High-tech Chinese companies must know whether they must prepare adequate human resources as buffers to address these challenges and opportunities. For high-tech companies, this study proposes, for the first time, that HR slack must be analyzed …

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way …

Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; …

Self-Admitted Technical Debts Identification: How Far Are We?, Hao Gu, Shichao Zhang, Qiao Huang, Zhifang Liao, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged by the developers and needs additional work or resources to address in the future. In recent years, though many methods have been proposed to detect SATDs, these methods have mainly focused on Java-type code comments published by Maldonado et al. It is unclear whether these methods trained on Maldonado's code comments dataset can find SATD in other programming languages or other software artifacts, such as issue trackers, pull requests, and commit messages effectively. In order to answer the above confusion and investigate how far our community …

Sustainability Forecasting For Deep Learning Packages, Junxiao Han, Yunkun Wang, Zhongxin Liu, Lingfeng Bao, Jiakun Liu, David Lo, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, …

Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Pseudocode diffing precisely locates similar parts and captures differences between the decompiled pseudocode of two given binaries. It is particularly useful in many security scenarios such as code plagiarism detection, lineage analysis, patch, vulnerability analysis, etc. However, existing pseudocode diffing and binary diffing tools suffer from low accuracy and poor scalability, since they either rely on manually-designed heuristics (e.g., Diaphora) or heavy computations like matrix factorization (e.g., DeepBinDiff). To address the limitations, in this paper, we propose a semantics-aware, deep neural network-based model called SIGMADIFF. SIGMADIFF first constructs IR (Intermediate Representation) level interprocedural program dependency graphs (IPDGs). Then it uses …

Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma

Research Collection School Of Computing and Information Systems

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selectorclassifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the …

Win: Weight-Decay-Integrated Nesterov Acceleration For Faster Network Training, Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Training deep networks on large-scale datasets is computationally challenging. This work explores the problem of “how to accelerate adaptive gradient algorithms in a general manner", and proposes an effective Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptive algorithms. Taking AdamW and Adam as examples, per iteration, we construct a dynamical loss that combines the vanilla training loss and a dynamic regularizer inspired by proximal point method, and respectively minimize the first- and second-order Taylor approximations of dynamical loss to update variable. This yields our Win acceleration that uses a conservative step and an aggressive step to update, and linearly combines these …

Stability Verification In Stochastic Control Systems Via Neural Network Supermartingales, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking …

Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz , a machine learning guided smart contract fuzzing framework. …

Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We present the hardware design and software pipeline for an ultra-low power device, in the form factor of a wearable badge, that supports energy efficient sensing, processing and wireless transfer of human voice commands and interactions. The proposed system, called PA2BLO, is envisioned to support both: (a) real-time, scalable, authorized voice based interaction and control of devices and appliances, and (b) longitudinal, low-power logging of natural voice interactions. PA2BLO in-troduces two key novel capabilities. First, it includes a low power, low-complexity voice authentication module that is able to reliably authenticate an authorized user only using low sampling rate (500 Hz) …

Demystifying Faulty Code: Step-By-Step Reasoning For Explainable Fault Localization, Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo

Research Collection School Of Computing and Information Systems

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers …

Gsword: Gpu-Accelerated Sampling For Subgraph Counting, Chang Ye, Yuchen Li, Shixuan Sun, Wentian Guo

Research Collection School Of Computing and Information Systems

Subgraph counting is a fundamental component for many downstream applications such as graph representation learning and query optimization. Since obtaining the exact count is often intractable, there have been a plethora of approximation methods on graph sampling techniques. Nonetheless, the state-of-the-art sampling methods still require massive samples to produce accurate approximations on large data graphs. We propose gSWORD, a GPU framework that leverages the massive parallelism of GPUs to accelerate iterative sampling algorithms for subgraph counting. Despite the embarrassingly parallel nature of the samples, there are unique challenges in accelerating subgraph counting due to its irregular computation logic. To address …

Analysts' Earnings Per Share Forecasts: The Effects Of Forecast Uncertainty And Forecast Precision On Investor Judgements, Clarence Goh

Research Collection School Of Accountancy

This study uses controlled experiments to investigate the joint effects of forecast uncertainty and forecast precision on investor judgements. It finds that forecast precision moderates the effects of forecast uncertainty on investors' forecast reliability judgements such that the effects of forecast uncertainty on investors' judgements of forecast reliability are more negative when an analyst's point earnings per share (EPS) forecast is rounded than when it is precise. In addition, the relationship between forecast precision and investors' judgements of forecast reliability is mediated by investors' perceptions of forecast attributes. The evidence also suggests that while forecast uncertainty exerts a negative effect …

Segment Disaggregation And Equity-Based Pay Contracts, Young Jun Cho, Hojun Seo

Research Collection School Of Accountancy

We study the role of segment disaggregation in equity-based pay contracts in diversified firms. Disaggregated segment disclosures can improve the observability of managerial actions in internal capital markets and thus increase implicit incentives for managers to allocate resources as desired by shareholders, substituting for explicit incentives provided to CEOs. We use the adoption of Statement of Financial Accounting Standards No. 131 as an identification strategy and find that firms affected by this segment reporting mandate significantly decreased the provision of equity-based incentives in the post-adoption period, especially for firms with higher operating volatilities. This effect is also more pronounced for …

State Institutions In Northeast Thailand: Lao Ethnics And The Thai Identity, Jacob Ricks

Research Collection School of Social Sciences

In this last chapter on state representation, we focus on a case where there has been an absence of demands. In Northeast Thailand, the large ethnic Lao population has not demanded cultural concessions from the state. In fact, not only have the demands been absent, but most people in the region see themselves as Thai (the broader national identity) or Isan (a moniker meaning “northeast”)—as opposed to ethnically Lao. The absence of the Lao identity has less to do with the absence of civic associations from the bottom up than with the absence of political representation from the top down. …

Negative Work-To-Family Spillover Stress And Heightened Cardiovascular Risk Biomarkers In Midlife And Older Adults, Andree Hartanto, K.T.A.Sandeeshwara Kasturiratna, Meilan Hu, Shu Fen Diong, Verity Y. Q. Lua

Research Collection School of Social Sciences

Objectives: The current study aimed to investigate the health implications of negative work-to-family spillover on cardiovascular risk biomarkers. Methods: In a large-scale cross-sectional dataset of working or self-employed midlife and older adults in the United States (N = 1179), we examined five biomarkers linked to cardiovascular risk, including high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, triglyceride, interleukin-6, and C-reactive protein. Negative work-to-family spillover, measured using a four-item self-reported questionnaire, was included into our model to study its association with these cardiovascular risk biomarkers. Results: Our findings indicate a significant association between negative work-to-family spillover and cardiovascular risk biomarkers – higher …

Grievance Politics And Technocracy In A Developmental State, Ijlal Naqvi, Federico M. Rossi, Rayner Kay Jin Tan

Research Collection School of Social Sciences

This article uses a process-tracing approach to understand changes in Singapore’s health sector from the start of self-rule in 1959 to the end of the COVID-19 pandemic in 2022. Singapore is a developmental state recognized for its effective management of healthcare costs and its lack of political freedom. In both respects, the ‘Singapore model’ is of interest to other cities and nations. The standard narrative is one of technocratic proficiency in a context in which civic freedoms are heavily constrained, but this article identifies the surprisingly important role of social voices at key moments. It finds episodes in which effective …

Reward Penalties On Augmented States For Solving Richly Constrained Rl Effectively, Jiang Hao, Tien Mai, Pradeep Varakanthan, Minh Huy Hoang

Research Collection School Of Computing and Information Systems

Constrained Reinforcement Learning employs trajectory-based cost constraints (such as expected cost, Value at Risk, or Conditional VaR cost) to compute safe policies. The challenge lies in handling these constraints effectively while optimizing expected reward. Existing methods convert such trajectory-based constraints into local cost constraints, but they rely on cost estimates, leading to either aggressive or conservative solutions with regards to cost. We propose an unconstrained formulation that employs reward penalties over states augmented with costs to compute safe policies. Unlike standard primal-dual methods, our approach penalizes only infeasible trajectories through state augmentation. This ensures that increasing the penalty parameter always …

From Asset Flow To Status, Action And Intention Discovery: Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain …

Bootstrap Inference For Quantile Treatment Effects In Randomized Experiments With Matched Pairs, Liang Jiang, Xiaobin Liu, Peter C B Phillips, Yichong Zhang

Research Collection School Of Economics

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren

Research Collection School Of Economics

Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The "local Gaussian" property of the generic continuous-time benchmark model enables optimal "finite-sample" inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.

Panel Data Models With Time-Varying Latent Group Structures, Yiren Wang, Peter C. B. Phillips, Liangjun Su

Research Collection School Of Economics

This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is …

Who Watched Pre/Post-Lecture Tutorial Videos? Does Flipped Learning Help Beginners In Economics?, Bei Hong

Research Collection School Of Economics

The modern-day classroom is characterized by academic diversity, with students from varied backgrounds and with different levels of prior knowledge. To cater to the diverse abilities of students, this paper explored the use of flipped learning as a teaching approach in an introductory economics course. We investigated the effectiveness of 40 pre-lecture videos covering basic concepts and 27 post-lecture tutorial videos focusing on practice in improving students' exam outcomes, especially for beginners in economics who may require more support from instructors than other students. We collected data about video engagement and students' grades in progress assessments. Surveys were conducted to …