Digital Commons Network^™

What You See Is What You Get? Measuring Companies' Projected Employer Image Attributes Via Companies' Employment Webpages, Christian P. Theurer, Philipp Schapers, Andranik Tumasjan, Isabell M. Welpe, Filip Lievens

Research Collection Lee Kong Chian School Of Business

Information on a company's employment webpage sends signals about the employer image the company intends to project to applicants. Nonetheless, we know little about the content of recruitment signals sent via company employment webpages. This study develops a method to measure companies' projected employer image attributes based on their employment webpages. Specifically, we analyze companies' projected employer image attributes by applying computer-aided text analysis (CATA) to the employment webpages of 461 Fortune 500 companies (i.e., more than 11,100 individual pages). Our results show that projected employer image attributes remain relatively stable over time. Moreover, we find relatively low levels of …

Does Social Media Accelerate Product Recalls? Evidence From The Pharmaceutical Industry, Yang Gao, Wenjing Duan, Huaxia Rui

Research Collection School Of Computing and Information Systems

Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely-studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry where social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration (FDA) drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of …

A Traitor-Resistant And Dynamic Anonymous Communication Service For Cloud-Based Vanets, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this article, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs, which supports several advantageous features. In TD-ACF, each vehicle is represented by a set of attributes instead of its real identity, and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved in this way. Meanwhile, TD-ACF supports two practical requirements in …

Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

This article provides an overview of six examples of public sector AI applications in Singapore that illustrate different ways of enhancing engagement with the public. These applications demonstrate ways of enhancing engagement with the public by providing greater accessibility to government services (access anywhere, anytime) and speedier responses to public processes and feedback. Some applications make it substantially easier for members of the public to do things or make choices, while others reduce waiting time, either across an entire public infrastructure, or for an individual transaction. Some provide highly individualized coaching to guide a person through the process of doing …

Deep Learning For Coverage-Guided Fuzzing: How Far Are We?, Siqi Li, Xiaofei Xie, Yun Lin, Yuekang Li, Ruitao Feng, Xiaohong Li, Weimin Ge, Jin Song Dong

Research Collection School Of Computing and Information Systems

Fuzzing is a widely-used software vulnerability discovery technology, many of which are optimized using coverage-feedback. Recently, some techniques propose to train deep learning (DL) models to predict the branch coverage of an arbitrary input owing to its always-available gradients etc. as a guide. Those techniques have proved their success in improving coverage and discovering bugs under different experimental settings. However, DL models, usually as a magic black-box, are notoriously lack of explanation. Moreover, their performance can be sensitive to the collected runtime coverage information for training, indicating potentially unstable performance. In this work, we conduct a systematic empirical study on …

Risk-Aware Procurement Optimization In A Global Technology Supply Chain, Jonathan Chase, Jingfeng Yang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Supply chain disruption, from ‘Black Swan’ events like the COVID-19 pandemic or the Russian invasion of Ukraine, to more ordinary issues such as labour disputes and adverse weather conditions, can result in delays, missed orders, and financial loss for companies that deliver products globally. Developing a risk-tolerant procurement strategy that anticipates the logistical problems incurred by disruption involves both accurate quantification of risk and cost-effective decision-making. We develop a supplier-focused risk evaluation metric that constrains a procurement optimization model for a global technology company. Our solution offers practical risk tolerance and cost-effectiveness, accounting for a range of constraints that realistically …

On The Effectiveness Of Using Graphics Interrupt As A Side Channel For User Behavior Snooping, Haoyu Ma, Jianwen Tian, Debin Gao, Chunfu Jia

Research Collection School Of Computing and Information Systems

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This paper evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We …

Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

Reorganizing bus frequencies to cater for actual travel demands can significantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an …

Strategic Disclosure And Debt Covenant Violation, Thomas Bourveau, Derrald Stice, Rencheng Wang

Research Collection School Of Accountancy

This study examines how managers change their forecasting behavior as a debt covenant violation approaches. Using a sample of firms that disclose a debt covenant violation (DCV) in their financial statements, we find that management forecasts are more optimistic in the period leading up to a DCV, and this result is not driven by managers’ unintentional forecast bias. Additionally, we find that managers who are more optimistic in their forecasts also take on more risk and increase dividend payouts before violations, consistent with managers strategically using earnings forecasts to justify their activities favorable to shareholders but likely to be curtailed …

Insider Trading Restrictions And Real Activities Earnings Management: International Evidence, Yuanto Kusnadi, Yuanto Kusnadi, Jiwei Wang, Yujie Wang

Research Collection School Of Accountancy

We examine the implications of insider trading restrictions on firms’ real activities earnings management in an international setting. Using a sample of 28 countries over the period from 1992 to 2007, we find evidence that is supportive of the substitution hypothesis, in that managers have incentives to substitute accruals earnings management for real activities earnings management. This effect is found to be more pronounced for firms in countries with more restrictive insider trading regulation. Our result is robust to alternative measures of real activities earnings management and insider trading restrictions, alternative sub-samples, alternative regression specifications, and controlling for endogeneity. In …

Val: Volume And Access Pattern Leakage-Abuse Attack With Leaked Documents, Steven Lambregts, Huanhuan Chen, Jianting Ning, Kaitai Liang

Research Collection School Of Computing and Information Systems

Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS ’21) and exploits both the access and volume …

Deep Learning-Based Text Recognition Of Agricultural Regulatory Document, Hua Leong Fwa, Farn Haur Chan

Research Collection School Of Computing and Information Systems

In this study, an OCR system based on deep learning techniques was deployed to digitize scanned agricultural regulatory documents comprising of certificates and labels. Recognition of the certificates and labels is challenging as they are scanned images of the hard copy form and the layout and size of the text as well as the languages vary between the various countries (due to diverse regulatory requirements). We evaluated and compared between various state-of-the-art deep learningbased text detection and recognition model as well as a packaged OCR library – Tesseract. We then adopted a two-stage approach comprising of text detection using Character …

An Attribute-Aware Attentive Gcn Model For Attribute Missing In Recommendation, Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie

Research Collection School Of Computing and Information Systems

As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., "other") to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A(2)-GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph …

Secure Deterministic Wallet And Stealth Address: Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Zhen Liu, Guomin Yang, Duncan S. Wong, Khoa Nguyen, Huaxiong Wang, Xiaorong Ke, Yining Liu

Research Collection School Of Computing and Information Systems

Deterministic Wallet (DW) and Stealth Address (SA) mechanisms have been widely adopted in the cryptocurrency community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that allows an arbitrary number of derived keys to be generated from a master key. However, these algorithms suffer a vulnerability that, when one derived key is compromised somehow, the damage is not limited to the leaked derived key only, but to the master key and in consequence all derived keys are compromised. In this article, we introduce and formalize a new signature variant, called Key-Insulated and Privacy-Preserving …

Choice-Based Crowdshipping: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies the integration of the crowd workforce into a generic last-mile delivery setting in which a set of known delivery requests should be fulfilled at a minimum cost. In this setting, the crowd drivers are able to choose to perform a parcel delivery among the available and displayed requests. We specifically investigate the question: what tasks should be displayed to an individual driver, so as to minimize the overall delivery expenses? In contrast to past approaches, where drivers are either (a) given the choice of a single task chosen so as to optimize the platform’s profit, or (b) …

Nudging Social Online Referrals: Evidence From A Randomized Field Experiment, Qian Zeng

Dissertations and Theses Collection (Open Access)

With the rise of social commerce platforms and customer engagement in online products and services, firms are focusing their attention on effective social online referral program to encourage customers’ online referral behaviors to grow their customer base. Hence, how to influence customers to participate in online referral is a matter of the utmost importance to firms. However, little empirical research has examined the impact of online referral program on customers’ online referral on social commerce platform. To close this research gap, this dissertation investigates the effectiveness of digital nudging for consumers’ social online referral on social commerce platforms.

Working with …

Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng

Dissertations and Theses Collection (Open Access)

Most traditional machine learning or deep learning methods are based on the premise that training data and test data are independent and identical distributed, i.e., IID. However, it is just an ideal situation. In real-world applications, test set and training data often follow different distributions, which we refer to as the out of distribution, i.e., OOD, setting. As a result, models trained with traditional methods always suffer from an undesirable performance drop on the OOD test set. It's necessary to develop techniques to solve this problem for real applications. In this dissertation, we present four pieces of work in the …

Learning Improvement Heuristics For Solving Routing Problems, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim

Research Collection School Of Computing and Information Systems

Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman …

Toward Intention Discovery For Early Malice Detection In Bitcoin, Ling Cheng, Feida Zhu, Yong Wang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Bitcoin has been subject to illicit activities more often than probably any other financial assets, due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all the three properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without satisfying interpretability and are only available for retrospective analysis of a specific illicit type.First, we present asset transfer paths, which aim to describe addresses' early characteristics. Next, with a decision tree based …

Distinctive Features Of Nonverbal Behavior And Mimicry In Application Interviews Through Data Analysis And Machine Learning, Sanne Rogiers, Elias Corneillie, Filip Lievens, Frederik Anseel, Peter Veelaert, Wilfried Philips

Research Collection Lee Kong Chian School Of Business

This paper reveals the characteristics and effects of nonverbal behavior and human mimicry in the context of application interviews. It discloses a novel analyzation method for psychological research by utilizing machine learning. In comparison to traditional manual data analysis, machine learning proves to be able to analyze the data more deeply and to discover connections in the data invisible to the human eye. The paper describes an experiment to measure and analyze the reactions of evaluators to job applicants who adopt specific behaviors: mimicry, suppress, immediacy and natural behavior. First, evaluation of the applicant qualifications by the interviewer reveals …

Corporate Actions And The Manipulation Of Retail Investors In China: An Analysis Of Stock Splits, Sheridan Titman, Chi Shen Wei, Bin Zhao

Research Collection Lee Kong Chian School Of Business

We identify a group of “suspicious” firms that use stock splits, perhaps along with other activities, to artificially inflate their share prices. Following the initiation of suspicious splits, share prices temporarily increase, and subsequently decline below their presplit levels. Using account level data from the Shanghai Stock Exchange, we find that small retail investors acquire shares in firms initiating suspicious splits, while more sophisticated investors accumulate positions before suspicious split announcements and sell in the postsplit period. We also find that insiders sell large blocks of shares and obtain loans using company stock as collateral around the initiation of suspicious …

Fractile Graphical Analysis In Finance: A New Perspective With Applications, Anil K. Bera, Aurobindo Ghosh

Research Collection Lee Kong Chian School Of Business

Fractile Graphical Analysis (FGA) was proposed by Prasanta Chandra Mahalanobis in 1961 as a method for comparing two distributions at two different points (of time or space) controlling for the rank of a covariate through fractile groups. We use bootstrap techniques to formalize the heuristic method used by Mahalanobis for approximating the standard error of the dependent variable using fractile graphs from two independently selected “interpenetrating network of subsamples.” We highlight the potential and revisit this underutilized technique of FGA with a historical perspective. We explore a new non-parametric regression method called Fractile Regression where we condition on the ranks …

Fine-Grained And Controllably Editable Data Sharing With Accountability In Cloud Storage, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increasing cloud storage service, users can enjoy non-interactive data sharing. Nonetheless, the data owner cannot timely update the shared data all the while. To ensure the timeliness and the authoritative source of the data, some users should be allowed to update the data on behalf of an authoritative data owner without changing data source. However, this allows harmful information to be injected into the data unnoticeably. How to efficiently realize editable cloud-based data sharing supporting malicious user tracing has not been fully explored. To address the problem, we propose a fine-grained and controllably editable cloud-based data sharing scheme …

Fundamental Social Motives Measured Across Forty-Two Cultures In Two Waves, C. M. Pick, ..., Norman P. Li

Research Collection School of Social Sciences

How does psychology vary across human societies? The fundamental social motives framework adopts an evolutionary approach to capture the broad range of human social goals within a taxonomy of ancestrally recurring threats and opportunities. These motives—self-protection, disease avoidance, affiliation, status, mate acquisition, mate retention, and kin care—are high in fitness relevance and everyday salience, yet understudied cross-culturally. Here, we gathered data on these motives in 42 countries (N = 15,915) in two cross-sectional waves, including 19 countries (N = 10,907) for which data were gathered in both waves. Wave 1 was collected from mid-2016 through late 2019 (32 countries, N …

In Covid-19 Health Messaging, Loss Framing Increases Anxiety With Little-To-No Concomitant Benefits: Experimental Evidence From 84 Countries, Charles A. Dorison, Et. Al., Andree Hartanto

Research Collection School of Social Sciences

The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., “If you do not practice these steps, you can endanger yourself and others”) or potential gains (e.g., “If you practice these steps, you can protect yourself and others”)? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message …

Novation And Advance Consent, Kwan Ho Lau

Research Collection Yong Pung How School Of Law

Professor Goode once observed that “Novation need not be left to ad hoc agreement; it is open to the parties to provide for it in advance and in particular to establish a contractual mechanism by which novation takes place automatically on the occurrence of a designated act or event”. This deceptively straightforward proposition is examined in the present article. It explores the legal footing for, and the risks in adopting a pristine version of, the proposition, and considers possible safeguards that may be incorporated within the process of scrutiny, if in any case there arises concern over the effectiveness of …

Posterior-Based Wald-Type Statistic For Hypothesis Testing, Xiaobin Liu, Yong Li, Jun Yu, Tao Zeng

Research Collection School Of Economics

A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as MCMC output) is available, …

Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated …

Tax Challenges In Debt Financing Involving Digital Tokens, Vincent Ooi

Research Collection Yong Pung How School Of Law

When digital tokens are used in debt finance, one cannot assume that the same orthodox tax treatment will apply. The highly specific nature of tax provisions means that they may apply very differently once digital tokens rather than fiat currency are involved. Through a case study of Singapore law, this article shows that if debt finance transactions involving digital tokens are not carefully structured, there may be severe tax consequences, including the inability to deduct borrowing costs or benefit from common tax incentives, and the possible incurrence of additional tax liabilities. This article submits that, under Singapore tax law, it …

Skbi Big 5 Survey 2022 August, Singapore Management University

Sim Kee Boon Institute for Financial Economics

The latest survey results on the largest five economies (Big5) were revised markedly relative to the prior release (pre-Russia-Ukraine conflict), generally indicating weaker growth and higher inflation coupled with incremental ambiguity on the policy front.