Digital Commons Network^™

Politics Speak Louder Than Skills: Political Similarity Effects In Hireability Judgments In Multiparty Contexts And The Role Of Political Interest, Franz W. Moenke, Filip Lievens, Ursula Hess, Philipp Schaepers

Research Collection Lee Kong Chian School Of Business

Recruiters increasingly cybervet job applicants by checking their social media profiles. Theory (i.e., the political affiliation model, PAM) and research show that during cybervetting, recruiters are exposed to job-unrelated information such as political affiliation, which might trigger similarity-attraction effects and bias hireability judgments. However, as the PAM was developed in a more polarized two-party political system, it is pivotal to test and refine the PAM in a multiparty context. Therefore, we asked working professionals from the United States (two-party context, N = 266) and Germany (multiparty context, N = 747) to rate an applicant’s hireability after cybervetting a LinkedIn profile …

Fairness Perceptions Of Artificial Intelligence: A Review And Path Forward, Devesh Narayanan, Mahak Nagpal, Jack Mcguire, Shane Schweitzer, David De Cremer

Research Collection Lee Kong Chian School Of Business

A key insight from research on organizational justice is that fairness is in the eye of the beholder. With increasing discussions–especially among computer scientists and policymakers–about the potential biases and unfairness of decisions made by Artificial Intelligence (AI) systems, there is a critical need to consider how decision-subjects perceive the fairness of AI-led decision-making. Drawing upon theoretical and empirical perspectives on perceived fairness in organizational justice scholarship, this review categorizes and analyzes perceptions of AI fairness as they impact the effective implementation of AI in workplaces and beyond. Specifically, we review existing empirical research on AI fairness according to distinct …

The Dark Side Of Category Expansion: Will Existing Ones Pay The Price?, Qi Yu, Ron Berman, Eric Bradlow

Research Collection Lee Kong Chian School Of Business

Many retailers that manage product assortments believe that “more is better”. We challenge this conventional wisdom by demonstrating that a retailer may face more price-sensitive demand for existing categories after a category expansion. While introducing a new category to a store may increase the demand for the existing ones, this increase sometimes suggests a movement towards the steeper part of the demand curve and an increase in price sensitivity. We build a model of multi-category purchases with transportation costs and generate a series of theoretical predictions of the changes in price sensitivity after category expansion. We then estimate the model …

Provably Secure Decisions Based On Potentially Malicious Information, Dongxia Wang, Tim Muller, Jun Sun

Research Collection School Of Computing and Information Systems

There are various security-critical decisions routinely made, on the basis of information provided by peers: routing messages, user reports, sensor data, navigational information, blockchain updates, etc. Jury theorems were proposed in sociology to make decisions based on information from peers, which assume peers may be mistaken with some probability. We focus on attackers in a system, which manifest as peers that strategically report fake information to manipulate decision making. We define the property of robustness: a lower bound probability of deciding correctly, regardless of what information attackers provide. When peers are independently selected, we propose an optimal, robust decision mechanism …

Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …

Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang

Research Collection School Of Computing and Information Systems

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread , a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, …

Towards Explainable Neural Network Fairness, Mengdi Zhang

Dissertations and Theses Collection (Open Access)

Neural networks are widely applied in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug, non-transparent and subject to fairness issues. Discrimination has been observed in various machine learning models, including Large Language Models (LLMs), which calls for systematic fairness evaluation (i.e., testing, verification or even certification) before their deployment in ethic-relevant domains. If a model is found to be discriminating, we must apply systematic measure to improve its fairness. In the literature, multiple categories of fairness improving methods have been discussed, including pre-processing, in-processing and post-processing.
In this dissertation, …

Key Success Factors Of Strategic Transformation For Sino-French Cooperation In The Development Of Third-Party Markets In Africa, Bin Wang

Dissertations and Theses Collection (Open Access)

The most popular form of third-party market cooperation is an innovative international economic and trade cooperation model initiated by China under the context of the “Belt and Road” initiative nowadays. Although China and France, as non-state actors, have actively promoted trilateral cooperation and improved various agreements, the trilateral countries have not achieved effective resource complementarity due to differences in political and economic systems and goals, resulting in high complexity in the cooperation process.

Therefore, the research topic of this study is dedicated to addressing this pain point and assisting Chinese enterprises in leveraging third-party markets to expand overseas business under …

Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used …

The Effects Of Leader Member Exchange Differentiation On Task Performance And Organizational Citizenship Behaviors, Junhui Gan

Dissertations and Theses Collection (Open Access)

This study delves into the intricate dynamics of Leader-Member Exchange (LMX) within working teams, with a specific focus on Leader-Member Exchange Differentiation (LMXD) and its subsequent impact on task performance and Organizational Citizenship Behaviors (OCB). Utilizing a comprehensive, multisource dataset comprising 394 employees across 120 store teams in three furnishing shopping malls in Zhejiang Province, China, this research employs a two-wave data collection methodology to examine the proposed theoretical model.

Contrary to prevailing assumptions in the LMX literature, this study reveals that LMXD does not significantly influence anticipated team conflict, nor does it exert indirect effects on task performance and …

Research On The Industrial Chain Network-Driven Innovation Model Of China's Audio-Visual Industry Park, Yang Chen

Dissertations and Theses Collection (Open Access)

This dissertation explores the model through which Chinese audio-visual industry parks drive innovation leveraging industrial chain networks, with Xing Guang Movie and Television Park as the core case study. It also integrates analyses of several other representative Chinese audio-visual industry parks. Based on these case analyses, it identifies critical elements essential for successful innovation within audio-visual industry parks driven by industrial chain networks, thereby providing a realistic foundation for the theoretical framework of the thesis.

Drawing on innovation theory, network theory, industrial chain network theory, and collaborative capability theory, this study abstracts the model driving innovation through industrial chain networks …

Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao

Research Collection School Of Computing and Information Systems

Traditional kernel updates such as perfective maintenance and vulnerability patching requires shutting the system down, disrupting continuous execution of applications. Enterprises and researchers have proposed various live updating techniques to patch the kernel with lower downtime to reduce the loss of useful uptime. However, existing kernel live update techniques either rely on specific support from the target OS, or are deployed in virtualized environments (i.e., systems running in virtual machines). In this article we present KShot , a hardware-assisted live and secure kernel function update mechanism for native operating systems. By leveraging x86 SMM and Intel SGX, KShot runs in …

Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yui Lim Lui, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …

High-Dimensional Iv Cointegration Estimation And Inference, Peter C. B. Phillips, Igor L. Kheifets

Research Collection School Of Economics

A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those …

Heterogeneous Adaptability: Learning, Cash Resources, And The Fine-Grained Adjustment Of Misaligned Governance, Xavier Martin, Ilya Cuypers

Research Collection Lee Kong Chian School Of Business

Research Summary: When can a firm make fine-grained adjustments to misaligned subsidiary governance? We examine whether and under what conditions a firm will adapt the equity stake it owns in a subsidiary, enabling improved alignment of the stake with the uncertainty in the local environment. We predict that the rate of adaptation of misaligned equity stakes depends on the experiential and vicarious learning from which the firm can draw, and that these learning effects are contingent on possessing fungible slack resources, specifically cash. Using a sample of 726 Japanese-foreign subsidiaries established in 38 host countries over a 21-year period, we …

Do Real-Time Strategy Video Gamers Have Better Attentional Control?, Mengxin He, Lin-Xuan Xu, Chiang-Shan R. Li, Zihan Liu, Jiaqi Hu, Xiangyi Guo, Hongyun Liu, Jin-Tao Zhang

Research Collection Lee Kong Chian School Of Business

Objective: Do real-time strategy (RTS) video gamers have better attentional control? To examine this issue, we tested experienced versus inexperienced RTS video gamers on multi-object tracking tasks (MOT) and dual-MOT tasks with visual or auditory secondary tasks (dMOT). We employed a street-crossing task with a visual working memory task as a secondary task in a virtual reality (VR) environment to examine any generalized attentional advantage. Background: Similar to action video games, RTS video games require players to switch attention between multiple visual objects and views. However, whether the attentional control advantage is limited by sensory modalities or generalizes to real-life …

Efficient Privacy-Preserving Spatial Data Query In Cloud Computing, Yinbin Miao, Yutao Yang, Xinghua Li, Linfeng Wei, Zhiquan Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes …

A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Enabling Ai And Robotic Coaches For Physical Rehabilitation Therapy: Iterative Design And Evaluation With Therapists And Post-Stroke Survivors, Min Hun Lee, Daniel Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient’s exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative …

Investor Reaction To Spacs' Voluntary Disclosures, Vincent Castellani, Karl A. Muller, K.J. Park

Research Collection School Of Accountancy

SPACs are formed to combine with and provide a private firm public trading status and a capital infusion. Firms that enter the public market through a SPAC combination are believed to possess greater voluntary disclosure discretion than traditional IPOs as they obtain their public trading status through a merger. Consistent with regulators’ concerns, recent research finds that SPACs use this discretion opportunistically by issuing optimistic guidance. This study examines how investors respond to these disclosures. We find that optimistic projections increase retail purchasing, which is higher than that of institutional purchasing. Additionally, we find that investors partially see through the …

Internal And Social Sources Shape Judgments About The Mental And Physical Nature Of An Experience, Jacinth Jia Xin Tan, Alexander Karan, Dolores Albarracin

Research Collection School of Social Sciences

Although humans are intuitive dualists, little is known about whether they hold lay beliefs about the origins or sources of their intuitive perceptions of what is physical and what is mental. Drawing on theories of the sources of phenomenological experiences, we examined if people hold beliefs about the internal and social origins of judments that their experiences are physical or mental. In Study 1, participants provided physical or mental judgments about a range of personal experiences, and reported relying on both internal (i.e., examining own body and thoughts) and social (i.e., observing others) sources as information for their judgments. To …

Leveraging Instagram To Enhance Self-Esteem: A Self-Affirmative Intervention Study And Multilevel Mediation Analysis, Shuna Shiann Khoo, Hwajin Yang, Wei Xing Toh

Research Collection School of Social Sciences

Although studies have consistently indicated that heavier use of social networking sites (SNS) perpetuates poorer self-esteem outcomes, no study has examined potential intervention methods that can yield positive effects from SNS use. We hypothesized that viewing one's Instagram profile would have self-affirmative effects on self-perception because the profile typically showcases curated instrumental positive aspects of self. Furthermore, these self-affirmative effects would indirectly improve state self-esteem via enhanced clarity of self-concept. To test our hypothesis, we designed an experimental intervention study where one group viewed their Instagram profile regularly, while another group viewed a neutral abstract art profile. Using multilevel latent …

The Effect Of Mere Presence Of Smartphone On Cognitive Functions: A Four-Level Meta-Analysis, Andree Hartanto, Verity Y. Q. Lua, K. T. A. Sandeeshwara Kasturiratna, Paye Shin Koh, Germaine Y. Q. Tng, Manmeet Kaur, Frosch Y. X. Quek, Jonathan L. Chia, Nadyanna M. Majeed

Research Collection School of Social Sciences

As smartphones have become portable and immersive devices that afford social, informational, and recreational conveniences unbounded by physical restrictions, most daily activities have become closely intertwined with the presence of smartphones. This constant presence of smartphones in daily activities, however, may be concerning as some studies have suggested that smartphones—even their mere presence—can be distracting and can impair cognitive outcomes. However, such findings have not been consistently observed. To reconcile mixed findings, the current meta-analysis synthesized 166 effect sizes drawn from 53 samples and 33 studies including 4,368 participants on the effect of mere presence of smartphone on cognitive functions. …

Active Code Learning: Benchmarking Sample-Efficient Training Of Code Models, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), especially for those with limited budgets. Therefore, efficiently training models of code with less human effort has become an emergent problem. Active learning is such a technique to address this issue that allows developers to train a model with reduced data while producing models with desired performance, which has been well studied in computer vision and natural language processing domains. Unfortunately, there is no such work that explores the effectiveness of active learning for code …

Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng

Research Collection School Of Computing and Information Systems

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) [1] toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2,2)-threshold Paillier cryptosystem with fast …

A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang

Research Collection School Of Economics

We consider a linear combination of jackknife Anderson-Rubin (AR) and orthogonalized Lagrangian multiplier (LM) tests for inference in IV regressions with many weak instruments and heteroskedasticity. We choose the weight in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed linear combination test controls asymptotic size and is admissible. Under strong identification, we further show that our linear combination test is the uniformly most powerful test against local alternatives among all tests that are constructed based on the jackknife AR and LM tests only and invariant …

Equal Predictive Ability Tests Based On Panel Data With Applications To Oecd And Imf Forecasts, Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang

Research Collection School Of Economics

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, …

A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain …

Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le

Research Collection School Of Computing and Information Systems

Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …

Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …