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Analyzing Racial Disparities In The United States Homeownership: A Socio-Demographic Study Using Machine Learning, Arya Farahi, Junfeng Jiao Sep 2024

Analyzing Racial Disparities In The United States Homeownership: A Socio-Demographic Study Using Machine Learning, Arya Farahi, Junfeng Jiao

Research Collection College of Integrative Studies

While increasing homeownership has been a focal point for policymakers in the United States, the distribution of access to homeownership opportunities across all families within the country has not been equitable. The complex relation between social factors and economic conditions has significantly shaped the journey toward homeownership. Of particular concern is the persistent disparity in homeownership rates among Black households in the United States. Effectively addressing these disparities within urban communities necessitates comprehensive policy interventions and social initiatives. However, the success of such endeavors hinges upon a comprehensive grasp of the underlying causes of these inequalities. Drawing upon machine learning …

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Developer Reactions To Protestware In Open Source Software: The Cases Of Color.Js And Es5.Ext, Youmei Fan, Dong Wang, Supatsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula Sep 2024

Developer Reactions To Protestware In Open Source Software: The Cases Of Color.Js And Es5.Ext, Youmei Fan, Dong Wang, Supatsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

There is growing concern about maintainers self-sabotaging their work in order to take political or economic stances, a practice referred to as “protestware”. Our objective is to understand the discourse around discussions on such an attack, how it is received by the community, and whether developers respond to the attack in a timely manner. We study two notable protestware cases i.e., colors.js and es5-ext. Results indicate that protestware discussions are spread more quickly on the GitHub platform, while security vulnerabilities are faster on social media. By establishing a taxonomy of protestware discussions, we identify posts that express stances and provide …

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Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft Sep 2024

Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the …

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Certified Quantization Strategy Synthesis For Neural Networks, Yedi Zhang, Guangke Chen, Jun Sun, Jun Sun Sep 2024

Certified Quantization Strategy Synthesis For Neural Networks, Yedi Zhang, Guangke Chen, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Quantization plays an important role in deploying neural networks on embedded, real-time systems with limited computing and storage resources (e.g., edge devices). It significantly reduces the model storage cost and improves inference efficiency by using fewer bits to represent the parameters. However, it was recently shown that critical properties may be broken after quantization, such as robustness and backdoor-freeness. In this work, we introduce the first method for synthesizing quantization strategies that verifiably maintain desired properties after quantization, leveraging a key insight that quantization leads to a data distribution shift in each layer. We propose to compute the preimage for …

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Probing Effects Of Contextual Bias On Number Magnitude Estimation, Xuehao Du, Ping Ji, Wei Qin, Lei Wang, Yunshi Lan Sep 2024

Probing Effects Of Contextual Bias On Number Magnitude Estimation, Xuehao Du, Ping Ji, Wei Qin, Lei Wang, Yunshi Lan

Research Collection School Of Computing and Information Systems

The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network's accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of- distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods …

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Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw Sep 2024

Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …

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Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang Sep 2024

Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang

Research Collection School Of Computing and Information Systems

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious …

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Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen Sep 2024

Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen

Research Collection School Of Computing and Information Systems

Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …

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Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng Sep 2024

Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng

Research Collection School Of Computing and Information Systems

Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel Efficient Cascaded Multiscale Adaptive (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a …

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Comparison Of Evolutionary Algorithms: A Case Study On The Multi-Objective Carbon-Aware Mine Planning, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi Sep 2024

Comparison Of Evolutionary Algorithms: A Case Study On The Multi-Objective Carbon-Aware Mine Planning, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The NP-hard precedence-constrained production scheduling problem (PCPSP) for mine planning chooses the ordered removal of materials from the mine pit and the next processing steps based on resource, geological, and geometrical constraints. Traditionally, it prioritizes the net present value (NPV) of profits across the lifespan of the mine. Yet, the growing shift in environmental concerns also requires shifts to more carbon-aware practices. In this paper, we use the enhanced multi-objective version of the generic PCPSP formulation by adding the NPV of carbon costs as another objective. We then compare how the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto …

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Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou Sep 2024

Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction template design, (iii) empowering MLLMs, and (iv) data generation and filtering. Additionally, we outline two modes of data generation: task-agnostic and task-specific, enabling controllable output. We demonstrate that a synthetic VQA-like dataset trained with LLaVA1.5 enhances performance on 10 …

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A Two-Stage Matheuristic For The Home Healthcare Routing And Scheduling Problem With Perishable Products, Aldy Gunawan, Nabila Yuraisyah Salsabila, Vincent F. Yu, Pham Kien Minh Nguyen Sep 2024

A Two-Stage Matheuristic For The Home Healthcare Routing And Scheduling Problem With Perishable Products, Aldy Gunawan, Nabila Yuraisyah Salsabila, Vincent F. Yu, Pham Kien Minh Nguyen

Research Collection School Of Computing and Information Systems

This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients’ treatments. This problem is formulated as a mixed integer linear programming (MILP). A two-stage matheuristic is then developed as the solution approach. The first stage is a local search to solve the nurse routing problem, and the second stage is run as the relaxed MILP to solve the scheduling problem. The matheuristic is tested on newly generated instances and compared with the results of CPLEX. The proposed matheuristic is able to obtain CPLEX solutions within …

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Robust Image Classification System Via Cloud Computing, Aligned Multimodal Embeddings, Centroids And Neighbours, Wei Lun Koh, Boon Yong Koh, Bing Tian Dai Sep 2024

Robust Image Classification System Via Cloud Computing, Aligned Multimodal Embeddings, Centroids And Neighbours, Wei Lun Koh, Boon Yong Koh, Bing Tian Dai

Research Collection School Of Computing and Information Systems

We propose a framework for a cloud-based application of an image classification system that is highly accessible, maintains data confidentiality, and robust to incorrect training labels. The end-to-end system is implemented using Amazon Web Services (AWS), with a detailed guide provided for replication, enhancing the ways which researchers can collaborate with a community of users for mutual benefits. A front-end web application allows users across the world to securely log in, contribute labelled training images conveniently via a drag-and-drop approach, and use that same application to query an up-to-date model that has knowledge of images from the community of users. …

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Cluster-Wide Task Slowdown Detection In Cloud System, Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng Sep 2024

Cluster-Wide Task Slowdown Detection In Cloud System, Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that …

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Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen Sep 2024

Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - …

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Editorial: Dsaa 2023 Journal Track On Theoretical And Practical Data Science And Analytics., Bin Yang, Feida Zhu, Wei Wei Sep 2024

Editorial: Dsaa 2023 Journal Track On Theoretical And Practical Data Science And Analytics., Bin Yang, Feida Zhu, Wei Wei

Research Collection School Of Computing and Information Systems

This special issue of the International Journal of Data Science and Analytics includes the DSAA 2023 Journal Track papers, which cover advances in both theoretical and practical aspects of data science and analytics, with a particular focus on trustworthy data science and analytics. The track contains nine papers, all of which underwent rigorous review by the guest editors and invited reviewers.

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Quantum Relaxation For Solving Multiple Knapsack Problems, Monit Sharma, Jin Yan, Hoong Chuin Lau, Rudy Raymond Sep 2024

Quantum Relaxation For Solving Multiple Knapsack Problems, Monit Sharma, Jin Yan, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Combinatorial problems are a common challenge in business, requiring finding optimal solutions under specified constraints. While significant progress has been made with variational approaches such as QAOA, most problems addressed are unconstrained (such as Max-Cut). In this study, we investigate a hybrid quantum-classical method for constrained optimization problems, particularly those with knapsack constraints that occur frequently in financial and supply chain applications. Our proposed method relies firstly on relaxations to local quantum Hamiltonians, defined through commutative maps. Drawing inspiration from quantum random access code (QRAC) concepts, particularly Quantum Random Access Optimizer (QRAO), we explore QRAO's potential in solving large constrained …

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Fintech Digital Transformation: Generative Ai, Humanoid Robots, Metaverse, Human-Ai Collaboration, And Industry 5.0, Yuxin Liu, Runyu Wang, Keng Siau Sep 2024

Fintech Digital Transformation: Generative Ai, Humanoid Robots, Metaverse, Human-Ai Collaboration, And Industry 5.0, Yuxin Liu, Runyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

This paper discusses the transformative impact of emerging digital technologies on the digital transformation of the financial industry, focusing on integrating Generative AI (GenAI), humanoid robots, and the Metaverse within the framework of Industry 5.0. Industry 5.0 emphasizes a human-centric approach to technology, where human-AI collaboration plays a central role in reshaping financial services. By reviewing both academic research and practical applications, the paper highlights the current advancements in FinTech, particularly in AI technologies and the Metaverse, and their future potential, demonstrating how these innovations are driving growth, efficiency, and resilience in the financial sector. Further, the paper proposes a …

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Adavis: Adaptive And Explainable Visualization Recommendation For Tabular Data, Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang Sep 2024

Adavis: Adaptive And Explainable Visualization Recommendation For Tabular Data, Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang

Research Collection School Of Computing and Information Systems

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable …

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Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu Sep 2024

Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu

Research Collection School Of Computing and Information Systems

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …

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Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti Sep 2024

Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti

Research Collection School Of Computing and Information Systems

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in terms of time complexity but also has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation costs scale economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering …

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Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui Sep 2024

Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test …

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The Effect Of Nationalism On Governance Choices In Cross-Border Collaborations, Gokhan Ertug, Ilya R. P. Cuypers, Douglas Dow, Jesper Edman Sep 2024

The Effect Of Nationalism On Governance Choices In Cross-Border Collaborations, Gokhan Ertug, Ilya R. P. Cuypers, Douglas Dow, Jesper Edman

Research Collection Lee Kong Chian School Of Business

We examine how nationalism influences governance choice in cross-border collaborations. While nationalism has historically been within the purview of political scientists, we demonstrate its relevance to management scholars by theorizing how nationalist attitudes and behaviors among decision-makers might shape strategic decisions about collaborations with foreign partners. Drawing on insights from the social psychology literature, we theorize how two attitudes commonly associated with nationalism, i.e., lower levels of trust and an unwillingness to work with foreigners, may increase decision-makers’ concerns about opportunistic behavior and invasiveness in cross-border collaborations. Integrating these insights into two key theories of governance choice, i.e., transaction cost …

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The Downstream Impact Of Upstream Tariffs: Evidence From Investment Decisions In Supply Chains, Thorsten Martin, Clemens A. Otto Sep 2024

The Downstream Impact Of Upstream Tariffs: Evidence From Investment Decisions In Supply Chains, Thorsten Martin, Clemens A. Otto

Research Collection Lee Kong Chian School Of Business

We study how US manufacturing firms' investment responds to tariff reductions in supplier industries. Our estimates, based on tariff reductions following multinational trade agreements, suggest that a hypothetical 10% reduction of all upstream tariffs would increase downstream investment by 4% to 6%. This estimate is not explained by decreasing uncertainty and stems from tariff reductions for homogeneous and low-R\&D inputs, consistent with the investment response resulting from cost reductions rather than superior foreign technology embodied in imported inputs. Evidence from an instrumental variable estimation using the sudden increase in Chinese import penetration suggests that import competition also increases downstream investment.

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Quantinar: A Blockchain Peer-To-Peer Ecosystem For Modern Data Analytics, Raul Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Hardle Aug 2024

Quantinar: A Blockchain Peer-To-Peer Ecosystem For Modern Data Analytics, Raul Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Hardle

Sim Kee Boon Institute for Financial Economics

The power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, p-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar, to support quantitative analytics knowledge paired with code in the form of Quantlets or software snippets. The integration of blockchain technology allows Quantinar to ensure fully transparent and reproducible scientific research.

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Financing Just Energy Transitions In Southeast Asia: Application Of The Just Transition Transaction To Indonesia, Vietnam, And Philippines, Abhinav Jindal, Gireesh Shrimali, Bharat Gangwani, Rajiv B. Lall Aug 2024

Financing Just Energy Transitions In Southeast Asia: Application Of The Just Transition Transaction To Indonesia, Vietnam, And Philippines, Abhinav Jindal, Gireesh Shrimali, Bharat Gangwani, Rajiv B. Lall

Sim Kee Boon Institute for Financial Economics

This paper investigates the applicability of the Just Transition Transaction (JTT), initially developed as a financial mechanism for South Africa's energy transition, to Southeast Asian (SEA) countries, including Indonesia, Vietnam, and the Philippines, which heavily rely on coal. Utilizing South Africa as a reference case study, we deconstruct the JTT and develop a novel framework of necessary and conducive features for evaluating its suitability for supporting a just energy transition in SEA. Our findings suggest that the JTT is well-suited for Indonesia and Vietnam but not as well suited for the Philippines. Recommendations for specific research avenues in estimating baselines …

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Deconstructing Ai Literacy For Librarians, Samantha Seah Aug 2024

Deconstructing Ai Literacy For Librarians, Samantha Seah

Research Collection Library

In this presentation, Samantha shares ways we can think about AI literacy - how it is similar and dissimilar from traditional information literacy, what we need to know about the mechanics of AI tools, and how SMU Libraries support the different users within the university in navigating the issues around artificial intelligence.

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Ceo Humility And Corporate Social Irresponsibility: Evidence Based On A New Unobtrusive Measure, Amy Y. Ou, Qian Lu, Xina Li, Chi-Nien Chung, Guoli Chen Aug 2024

Ceo Humility And Corporate Social Irresponsibility: Evidence Based On A New Unobtrusive Measure, Amy Y. Ou, Qian Lu, Xina Li, Chi-Nien Chung, Guoli Chen

Research Collection Lee Kong Chian School Of Business

Chief executive officers (CEOs) are expected to guard their firms against corporate social irresponsibility (CSIR) incidents. In this study, we hypothesize that CEO humility relates negatively to CSIR occurrence and positively to correction because of CEO preferences for protecting stakeholder interests and employing systematic information processing. These associations are stronger in industries with a high number of CSIR incidents and when top management teams have a higher ratio of gender and racial minorities. We develop and validate a new unobtrusive measure of CEO humility by using automated, objective behavioral indicators derived from earnings conference call transcripts. Our arguments and hypotheses …

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The Interaction Effects Of Fintech And Bank Lending On Growth: Evidence From The Asean Region, Tan Swee Liang Aug 2024

The Interaction Effects Of Fintech And Bank Lending On Growth: Evidence From The Asean Region, Tan Swee Liang

Research Collection School Of Economics

This study examined the interaction effects of traditional bank lending and emerging fintech credit on economic growth in six ASEAN economies from 2013 to 2020. Using a two-way fixed effects (FE) model and the panel-corrected standard error (PCSE) method, the study found a statistically significant and positive interaction effect between bank lending and fintech credit. Specifically, fintech credit became increasingly important in shaping economic growth as bank lending ratios exceeded a certain threshold. Countries with high bank lending ratios experienced greater GDP per capita growth when accompanied by greater fintech credit. These findings suggest that policies supporting fintech development alongside …

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Contrastive General Graph Matching With Adaptive Augmentation Sampling, Jianyuan Bo, Yuan Fang Aug 2024

Contrastive General Graph Matching With Adaptive Augmentation Sampling, Jianyuan Bo, Yuan Fang

Research Collection School Of Computing and Information Systems

Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can be limited or costly. Meanwhile, self-supervised learning methods for graph matching often require additional side information such as extra categorical information and input features, limiting their application to the general case. Moreover, designing the optimal graph augmentations for self-supervised graph matching presents another challenge to ensure robustness and effcacy. To address these issues, we introduce a novel Graph-centric Contrastive framework for Graph Matching (GCGM), capitalizing on a vast pool of graph augmentations for contrastive learning, yet without needing …

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