Digital Commons Network^™

The Influence Of Digitization Degree On The Internationalization Performance Of Family Firms, Hao Zhang

Dissertations and Theses Collection (Open Access)

In the report of the 20th National Congress of the Communist Party of China, it was clearly stated that efforts should be made to accelerate the development of thedigital economy, deepen the integration of the digital economy with the real economy, and strive to build a digital industrial cluster with international competitiveness. This grand blueprint not only highlights China's high regard for the future development of the digital economy but also points out the direction for China's economictransformation and upgrading. Meanwhile, the "Overall Layout Plan for Digital ChinaConstruction" further elaborates on this strategy and sets the ambitious goal of basically …

The Relationship Between Industry Regulation And The Quality Of Micro-Dramas, Shuoyu Yao

Dissertations and Theses Collection (Open Access)

This paper is a study on the micro-drama industry, exploring the reasons for a large number of low-quality micro-dramas. Specifically, there are two potential problems: (1) the industry value allocation model is unreasonable, so producing high-quality micro-dramas does not receive sufficient returns; (2) regulatory oversight of the industry is insufficient. To investigate this recent phenomenon and the underlying problems, we collected a dataset of 165 micro-dramas and use the newly introduced regulation on the micro-drama industry in June 2024 as a policy shock. The findings show that, before the new regulations were introduced, micro-dramas with higher quality in expression received …

Adversarial Attacks On Event-Based Pedestrian Detectors: A Physical Approach, Guixu Lin, Muyao Niu, Qingtian Zhu, Zhengwei Yin, Zhuoxiao Li, Shengfeng He, Yinqiang Zheng

Research Collection School Of Computing and Information Systems

Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused on improving detection accuracy, the robustness of event-based visual models against physical adversarial attacks has received limited attention. For example, adversarial physical objects, such as specific clothing patterns or accessories, can exploit inherent vulnerabilities in these systems, leading to misdetections or misclassifications. This study is the first to explore physical adversarial attacks on event-driven pedestrian detectors, specifically investigating whether certain clothing patterns worn by pedestrians can cause these detectors to fail, effectively rendering them unable …

Subway, Collaborative Matching, And Innovation, Yumi Koh, Li Jing, Jianhuan Xu

Research Collection School Of Economics

Using rapid expansion of the Beijing subway from 2000 to 2018, we analyze its impact on collaborative matches in innovations. We find that an hour reduction in travel time between a pair of locations in Beijing brought 14.85% to 37.69% increase in collaborated patents. Far-apart location pairs were more affected, and the local average causal response is approximately 34.92% to 82.29%. The effect is mainly driven by increased matches among highly productive innovators. The entry of new innovators, relocation of existing innovators, and collaborations among low-productive innovators also contribute to the increase in collaborative matches, especially in the long run.

Unexpected Defaults: The Role Of Information Opacity, Aytekin Ertan, Yun Lee, Regina Wittenberg-Moerman

Research Collection School Of Accountancy

Bond defaults are undesirable yet natural outcomes of risky investments. What is also crucial but hitherto underexplored is the unexpectedness of defaults. We develop a parsimonious measure of default unexpectedness and highlight its economic importance by demonstrating that unexpected defaults are associated with unfavorable recovery outcomes and adverse price changes in peer firm bonds. We then examine how default unexpectedness relates to information opacity. We find that firms with opaque financial reporting and weak voluntary disclosure experience more unexpected defaults. Defaults also occur more unexpectedly when the external information environment is opaque—when rating agencies disagree on a firm’s credit risk …

The Spillover Effects Of Environmental Lawsuits On Industry Peers, Xia Chen, Qiang Cheng, Xuefan Peng, Ziliang Zhan

Research Collection School Of Accountancy

We examine the spillover effects of high-profile environmental lawsuits on industry peers. We find that compared with control firms, industry peers experience a decrease in chemical releases and an increase in pollution-related disclosures after the lawsuits. These results are more pronounced for the industry peers who have higher levels of chemical releases in the pre-lawsuit period and who are the peers of firms sued for damage to human health. Lastly, we provide confirmatory evidence that industry peers increase abatement activities after the lawsuits and experience a decline in financial performance, likely due to the increase in abatement costs.

The Secret Ingredients Of Strategy Implementation, Robin Speculand

Asian Management Insights

Knowing what to do and actually doing it are two very different things. Leaders need to develop the same discipline, passion, and tenacity for implementing strategy that they had when crafting it. There are seven key principles that can guide an organisation to succeed in strategy implementation. Implementation is about taking the right actions, so leaders need to support and guide employees to work differently.

Reimagining Education: Keeping The Human In The Loop, Pradeep Varakantham, Sidney Tio

Asian Management Insights

How educators can work with generative artificial intelligence models to improve learning. Artificial intelligence (AI) helps break the mould of one-size-fits-all education by creating personalised learning paths that adapt to each student’s pace and style. By combining human expertise with AI capabilities, educators can create learning experiences that are both structured and flexible, thus getting the best of both worlds. While promising, AI used in educational contexts must carefully navigate privacy concerns, ensure fairness across all student groups, and support appropriate learning progression.

Law, Politics, And The Academy In Asia: Navigating Constraints As Public Law Scholars, De Maartje Visser, Qian Liu, Victor V. Ramraj

Research Collection Yong Pung How School Of Law

How do academics interested in the study of legal topics that implicate the state relate to and deal with pressures that shape the space available to conduct research? This article examines the nature and impact of such pressures on Asia-focused public law scholars who must contend with a more diverse socio-political environment than the liberal democratic setting in which questions of academic freedom are typically explored. We find that the Asia-centric academy is affected by a wide range of constraints that notably extends beyond intra-institutional demands to those put in place by the state. This article also highlights how the …

An Analysis Of Unstructured Marketing Data To Aid Online Platforms, Qingli Zeng

Dissertations and Theses Collection (Open Access)

As digital platforms increasingly rely on unstructured data such as images, audio, and video to engage users, understanding how these sensory elements influence consumer behavior has become essential. This dissertation investigates the role of visual and auditory cues in shaping user engagement and decision-making in digital environments. Specifically, it examines how image distinctiveness and vocal tone characteristics affect consumer attention, interaction, and commitment across different online contexts.

The first essay explores the impact of image distinctiveness on consumer search behavior within a Southeast Asian real estate platform. Grounded in the Theory of Visual Attention (TVA), this study develops a novel …

Research On The Mechanism Of China's Stock Market And The Efficiency Of Factor-Based Stock Selection Strategies, Huaixiang Xing

Dissertations and Theses Collection (Open Access)

Based on the data of 4280 firms listed on China's A-share market from 2010 to 2023, this paper constructs 20 market factors and 4 financial factors to systematically study multi factor stock selection strategies in China's stock market. Furthermore, this article analyzes the impact of China's capital market mechanisms on stock selection strategies, which include margin trading and securities lending mechanism, Mainland-Hong Kong Stock Connect mechanism, analyst coverage mechanism, and information disclosure mechanism. The main conclusions are as follows:

Firstly, this paper reveals that 12 market factors, including size, price, and liquidity are all effective pricing factors, and the strategy …

Exploring Intelligent Manufacturing: How Artificial Intelligence Affects Productivity And Labor Demand At The Enterprise Level, Jie Gu

Dissertations and Theses Collection (Open Access)

Manufacturing is a cornerstone of national economic health and social stability, yet it faces challenges such as declining profits, rising labor costs, and an aging workforce. In China, the manufacturing sector is undergoing a critical transformation, driven by technological advancements like artificial intelligence (AI) and the push for intelligent manufacturing. This study explores how AI revitalizes the manufacturing sector by enhancing enterprise productivity and reshaping labor demand, with a focus on quality inspection processes. Using a leading bearing factory as a case study, the research employs econometric models, A/B testing, and interviews to quantify AI’s impact on production efficiency, costs, …

Rome Was Not Built In A Day — Impact Of Market Agility And External Awareness On Iterative Innovation In Online Gaming Industry, Li Ming Gu

Dissertations and Theses Collection (Open Access)

Despite the prosperity of online gaming industry in China, online gaming firms cannot ignore the survival crisis brought by fierce competition. It is quite common for game developers to face issues like low game survival rates and poor performance, with some games even failing upon launch. To address these challenges and gain competitive advantages, game developers accelerate iteration speed and continuously provide users with new content or services. Since most companies found they cannot meet their development needs through traditional innovation models, understanding how to achieve high performance through iterative innovation has become crucial for game developers. Innovation starts with …

Knowledge Judgment, Managerial Decision-Making, And Firm Performance: From The Perspective Of Entrepreneurial Spirit Reconfiguration In Second-Generation Family Businesses, Xinhua Li

Dissertations and Theses Collection (Open Access)

Since the reform and opening up, Chinese family businesses, as the main body of the private economy, have played a significant role in the development of the national economy. As the first - generation entrepreneurs are entering their later years, inter - generational succession has become an important issue. This doctoral thesis focuses on the dynamic reshaping process of the entrepreneurial spirit of the second - generation entrepreneurs in family businesses and explores how it affects the improvement of corporate performance. Based on the cutting - edge theories of Austrian economics, this study constructs a research framework of "knowledge judgment …

Retrieval Augmented Recipe Generation, Guoshan Liu, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu- cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal …

Imageinthat: Manipulating Images To Convey User Instructions To Robots, Karthik Mahadevan, Blaine Lewis, Jiannan Li, Bilge Mutlu, Anthony Tang, Tovi Grossman

Research Collection School Of Computing and Information Systems

Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods---natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer-horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which …

Neurovig: Integrating Event Cameras For Resource-Efficient Video Grounding, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra

Research Collection School Of Computing and Information Systems

Spatio-Temporal Video Grounding (STVG) - the task of identifying the target object in the field-of-view that the language instruction refers to - is a fundamental vision-language task. Current STVG approaches typically utilize feeds from an RGB camera that is assumed to be always-on and process the video frames using complex neural network pipelines. As a result they often impose prohibitive system overheads (energy latency) on pervasive devices. To address this we propose NeuroViG with two key innovations: (a) leveraging on event streams from a low-power neuromorphic event camera sensor to perform selective triggering of the more energy-hungry RGB camera for …

Explainable Neural Networks With Guarantees: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

Multisfl: Towards Accurate Split Federated Learning Via Multi-Model Aggregation And Knowledge Replay, Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Split Federated Learning (SFL) effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multimodel aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem. MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models for local training and an aggregated master model for knowledge sharing among branch …

Ragg: Retrieval-Augmented Grasp Generation Model, Zhenhua Tang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

Intent-based grasp generation inherently involves challenges such as manipulation ambiguity and modality gaps. To address these, we propose a novel Retrieval-Augmented Grasp Generation model (RAGG). Our key insight is that when humans manipulate new objects, they initially mimic the interaction patterns observed in similar objects, then progressively adjust hand-object contact. Consequently, we develop RAGG as a two-stage approach, encompassing retrieval-guided generation and structurally stable grasp refinement. In the first stage, we propose a Retrieval-Augmented Diffusion Model (ReDim), which identifies the most relevant interaction instance from a knowledge base to explicitly guide grasp generation, thereby mitigating ambiguity and bridging modality gaps …

Hand1000: Generating Realistic Hands From Text With Only 1,000 Images, Haozhuo Zhang, Bin Zhu, Yu Cao, Yanbin Hao

Research Collection School Of Computing and Information Systems

Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using …

Lightprof: A Lightweight Reasoning Framework For Large Language Model On Knowledge Graph, Tu Ao, Yanhua Yu, Yuling Wang, Yang Deng, Zirui Guo, Liang Pang, Pinghui Wang, Tat-Seng Chua, Xiao Zhang, Zhen Cai

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly or produce harmful results. Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs by structurally organizing and connecting a wide range of entities and relations. Existing KG-based LLM reasoning methods only inject KGs’ knowledge into prompts in a textual form, ignoring its structural information. Moreover, they mostly rely on close-source models or open-source models with large parameters, which poses challenges to high resource consumption. To address this, we propose a …

Aligning Large Language Models For Faithful Integrity Against Opposing Argument, Yong Zhao, Yang Deng, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. To this end, we investigate the problem of maintaining faithful integrity in LLMs. This involves ensuring that LLMs adhere to their faithful statements in the face of opposing arguments and are able to correct their incorrect statements when presented with faithful arguments. In this work, we propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation (AFICE), which aims to align the LLM responses with faithful integrity. Specifically, …

Learning To Identify Seen, Unseen And Unknown In The Open World: A Practical Setting For Zero-Shot Learning, Sethupathy Parameswaran, Yuan Fang, Chandan Gautam, Savitha Ramasamy, Xiaoli Li

Research Collection School Of Computing and Information Systems

As vision-language models advance, addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically, a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training, unseen classes with only the semantic information in training, and unknown samples with no prior information from training. Existing methods either handle seen and unseen classes together (ZSL) or seen and unknown classes (known as Open-Set Recognition, OSR). However, none addresses the simultaneous handling of all three, which we term Open-Set Zero-Shot Learning (OZSL). To address this problem, we propose a …

Enhanced Sample Selection With Confidence Tracking: Identifying Correctly Labeled Yet Hard-To-Learn Samples In Noisy Data, Weiran Pan, Wei Wei, Feida Zhu, Yong Deng

Research Collection School Of Computing and Information Systems

We propose a novel sample selection method for image classification in the presence of noisy labels. Existing methods typically consider small-loss samples as correctly labeled. However, some correctly labeled samples are inherently difficult for the model to learn and can exhibit high loss similar to mislabeled samples in the early stages of training. Consequently, setting a threshold on per-sample loss to select correct labels results in a trade-off between precision and recall in sample selection: a lower threshold may miss many correctly labeled hard-to-learn samples (low recall), while a higher threshold may include many mislabeled samples (low precision). To address …

Every Voice Has Its Bright And Dark Sides: Understanding Observers’ Reactions To Coworkers’ Voice Behaviors, Szu-Han Lin, Shereen Fatimah, Emily C. Poulton, Cony M. Ho, D. Lance Ferris, Russell E. Johnson

Research Collection Lee Kong Chian School Of Business

The majority of research on voice has focused on how employee voice influences voicers and targets of voice (e.g. supervisors and organizations). We advance theory on voice by examining how third-party observers react to expressions of voice behavior by coworkers. Drawing from affective events theory (AET), we examine the potential benefits and detriments of coworker voice behaviours. Results from an experience sampling study and an experiment revealed that coworker voice was associated with an increase in third-party observers' inspiration, prompting third-party observers to engage in their own voice behaviours. Although coworker voice did not have a significant main effect on …

Csr As Hedging Against Institutional Transition Risk: Corporate Philanthropy After The Sunflower Movement In Taiwan, Yishu Cai, Lori Yue, Fangwen Lin, Shipeng Yan, Haibin Yang

Research Collection Lee Kong Chian School Of Business

Firms with political connections to a regime with an authoritarian history face a dilemma when the regime undergoes a democratic transition. Such connections provide an essential competitive advantage when the regime is in power but become a liability when an institutional transition brings democratic change. This study theorizes that when mass protests expose a regime’s distorted policies favoring elites over others and signal a high probability of regime turnover, firms may hedge against the risks associated with their political connections by engaging in philanthropy. We further contend that this effect is stronger for firms located in regions characterized by the …

Offline Safe Reinforcement Learning Using Trajectory Classification, Ze Gong, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most existing methods in offline safe RL rely on cost constraints at each time step (derived from global cost constraints) and this can result in either overly conservative policies or violation of safety constraints. In this paper, we propose to learn a policy that generates desirable trajectories and avoids undesirable trajectories. To be specific, we first partition the pre-collected dataset of state-action trajectories into desirable and undesirable subsets. Intuitively, the desirable set contains high reward and …

Occlusion-Insensitive Talking Head Video Generation Via Facelet Compensation, Yuhui Deng, Yuqin Lu, Yangyang Xu, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

Talking head video generation involves animating a still face image using facial motion cues derived from a driving video to replicate target poses and expressions. Traditional methods often rely on the assumption that the relative positions of facial keypoints remain unchanged. However, this assumption fails when keypoints are occluded or when the head is in a profile pose, leading to inconsistencies in identity and blurring in certain facial regions. In this paper, we introduce Occlusion-Insensitive Talking Head Video Generation, a novel approach that eliminates the reliance on spatial correlation of keypoints and instead leverages semantic correlation. Our method transforms facial …

Adapting Knowledge Prompt Tuning For Enhanced Automated Program Repair, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to enhance software reliability by automatically generating bug-fixing patches. Recent work has improved the state-of-the-art of APR by fine-tuning pre-trained large language models (LLMs), such as CodeT5, for APR. However, the effectiveness of fine-tuning be-comes weakened in data scarcity scenarios, and data scarcity can be a common issue in practice, limiting fine-tuning performance. To alleviate this limitation, this paper adapts prompt tuning for enhanced APR and conducts a comprehensive study to evaluate its effectiveness in data scarcity scenarios, using three LLMs of different sizes and six diverse datasets across four programming languages. Prompt tuning rewrites …