Digital Commons Network^™

The Impact Of Corporate Venture Capital On Financing And Innovation In Academic Entrepreneurial Firms, Tingting Luo

Dissertations and Theses Collection (Open Access)

Amid the ongoing advancement of China’s innovation-driven development strategy, the effective transformation of scientific research into industrial impact has become a central concern. Corporate Venture Capital (CVC), as a strategic investment mechanism connecting industrial capital with entrepreneurial ventures, is increasingly viewed as a critical external resource for high-technology start-ups. At the same time, Academic Entrepreneurship Firms (AEFs), characterized by their deep research capabilities and original technological trajectories, play a distinctive role as knowledge suppliers in uncertain, emerging industries. However, whether AEFs can effectively attract CVC support and convert it into innovation performance remains an open empirical and theoretical question.

This …

The Impact Of De-Familization Of Governance Structures On Corporate Performance During The Intergenerational Succession Of Family Businesses, Jing Qiao

Dissertations and Theses Collection (Open Access)

Intergenerational succession poses a vital challenge for family firms, with only 10% of global family businesses surviving to the third generation. Chinese family firms, growing alongside reform and opening-up, now face a critical succession period as founders average over 60 years old.

Based on 1932 A-share listed family firms from 2007 to 2024, this study theoretically and empirically examines how intergenerational succession affects corporate performance, investigates the mediating role of governance de-familization (management, ownership, control rights), and tests the moderating effect of intergenerational differences.

First, succession significantly harms financial and market performance: ROE falls by 1.330% and Tobin’s Q drops …

A Study On The Capital Market Valuation Effects Of Corporate Ai Adoption: An Empirical Analysis Of Manufacturing Firms In China's A-Share Market, Libin Ma

Dissertations and Theses Collection (Open Access)

This dissertation examines whether and how corporate adoption of artificial intelligence (AI) affects capital market valuation in manufacturing firms. As AI is increasingly embedded in R&D design, production organization, supply chain coordination, and operational management, its value implications depend not only on whether firms adopt AI, but also on how AI is deployed within business processes. Existing research has mainly focused on productivity, innovation, labor substitution, and digital transformation, while the enterprise value consequences of differentiated AI application strategies remain insufficiently examined.

Using Chinese A-share listed manufacturing firms from 2015 to 2024 as the sample, this study identifies firm-level AI …

From Ancient Harmony To Modern Sustainability: Assessing The Coupling Of Taoist “Harmony Between Humanity And Nature” With Esg And Its Impact On Firm Innovation And Performance, Biyi Wang

Dissertations and Theses Collection (Open Access)

Against the backdrop of intensifying global ecological crises, the ESG evaluation system has become a core metric for corporate sustainable development worldwide. However, the Western-dominated ESG system lacks crucial cultural dimensions in its application within Chinese corporate practices. The ecological wisdom embedded in the Taoist concept of “Harmony between Humanity and Nature” offers a significant philosophical foundation for the ESG localization. Extant research has focused on the influence of Confucian culture on ESG, with insufficient exploration of the modern managerial value of “Harmony between Humanity and Nature”. A scientific coupling framework linking this concept with ESG indicators has yet to …

Compliance Supervision And Firm Performance: An Empirical Study On Digital Transformation Of The Pharmaceutical Manufacturing Industry, Jun Ma

Dissertations and Theses Collection (Open Access)

The biopharmaceutical industry, characterized by stringent global regulatory oversight, faces a pivotal theoretical tension regarding the nexus between regulatory compliance and corporate performance. The traditional perspectives often frame compliance as an “operational burden”, while the emerging viewpoints posit it as a “strategic asset”. Grounded in an integrated framework of the Resource-Based View (RBV) and Dynamic Capability (DC), this study investigates the moderating role of digital transformation in the relationship between Good Manufacturing Practice (GMP) compliance and corporate performance.

Employing a mixed-method design, this paper conducted two complementary studies. Study 1 adopted a grounded theory based on interviews with nine industry …

Rethinking Artifact Evaluation For Software Engineering In The Age Of Generative Ai, Christoph Treude, Christopher M. Poskitt, Rashina Hoda

Research Collection School Of Computing and Information Systems

Peer review in software engineering research operates under tight time constraints, while generative AI has substantially reduced the human effort required to produce polished research narratives. Reviewer attention is often spent on aspects of submissions such as writing quality or literature positioning that have become relatively less effort-intensive to address, rather than on evaluating the scientific substance of a paper. At the same time, assessing whether methods are implemented correctly, analyses are sound, and claims are supported by evidence remains effort-intensive and dependent on human expertise. In software engineering research, this substance is frequently embodied in artifacts, including code, data, …

Trustgen: A Platform Of Dynamic Benchmarking On The Trustworthiness Of Generative Foundation Models, Yue Huang, Et. Al

Research Collection School Of Computing and Information Systems

Generative foundation models (GenFMs), such as large language models and text-to-image systems, have demonstrated remarkable capabilities in various downstream applications. As they are increasingly deployed in high-stakes applications, assessing their trustworthiness has become both a critical necessity and a substantial challenge. Existing evaluation efforts are fragmented, rapidly outdated, and often lack extensibility across modalities. This raises a fundamental question: how can we systematically, reliably, and continuously assess the trustworthiness of rapidly advancing GenFMs across diverse modalities and use cases? To address these gaps, we introduce TrustGen, a dynamic and modular benchmarking system designed to systematically evaluate the trustworthiness of GenFMs …

Who Said Cve? How Vulnerability Identifiers Are Mentioned By Humans, Bots, And Agents In Pull Requests, Pien Rooijendijk, Christoph Treude, Mairieli Wessel

Research Collection School Of Computing and Information Systems

Vulnerability identifiers such as CVE, CWE, and GHSA are standardised references to known software security issues, yet their use in practice is not well understood. This paper compares vulnerability ID use in GitHub pull requests authored by autonomous agents, bots, and human developers. Using the AIDev pop dataset and an augmented set of pull requests from the same repositories, we analyse who mentions vulnerability identifiers and where they appear. Bots account for around 69.1% of all mentions, usually adding few identifiers in pull request descriptions, while human and agent mentions are rarer but span more locations. Qualitative analysis shows that …

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques predominantly focus on the repair phase, often overlooking the importance of improving the preceding bug localization phase. As a foundational step in issue fixing, bug localization plays a pivotal role in determining the overall effectiveness of the entire process. To enhance the precision of issue fixing by accurately identifying bug locations in large-scale projects, this paper presents BugCerberus, the first hierarchical bug localization framework powered by three customized large language models. First, BugCerberus analyzes intermediate representations …

Weakly Supervised Video Anomaly Detection And Localization With Spatio-Temporal Prompts, Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings …

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Smart contract vulnerabilities have led to billions of dollars in economic losses. Among these, improper Access Control, which allows unauthorized users to execute restricted functions, is particularly prevalent and has caused significant financial damage. Smart contract repositories contain source code, documentation, configuration files, and other artifacts necessary for building and deploying smart contracts. GitHub hosts numerous open-source repositories of this kind, which serve as intermediate artifacts in development and require compilation and packaging to produce deployable contracts. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …

Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose SHAREDLLM, a novel framework based on multi-grained context compression and query-aware information acquisition. SHAREDLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Managing Reproducibility Debt In Scientific Software: A Practical Framework, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Scientific software includes end-user applications, modelling tools, research software for publications, and production systems for real users. It plays a key role across various scientific disciplines by enabling large-scale computation, simulation, and data analysis. Unlike commercial software, scientific software is often developed in dynamic research environments with limited engineering practices, documentation, or testing. This makes it fragile and difficult to reproduce results, even when code and data are available, conditions in which Reproducibility Debt (RpD) accumulates. This paper presents the Reproducibility Debt Management Framework (RpD-MF), which is grounded in evidence from a systematic literature review, practitioner interviews, and a global …

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

Be Responsible In Your Answers! Monitoring Out-Of-Domain Behaviors In Domain-Specific Llms, Boquan Li, Chenzhe Lou, Zhe Ren, Peixin Zhang, Zirui Fu, Jun Sun, Yaowen Zheng

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have accelerated the rapid development of chatbot web applications in various domains, such as coding, biomedicine and psychology. Compared to general LLMs like ChatGPT, domain-specific LLMs require a greater sense of responsibility. For instance, if a programming LLM casually answers medical or psychological questions, it not only misleads the public but also poses legal risks. This highlights new demands for monitoring and preventing such irresponsible behaviors. Existing efforts attempt to monitor LLMs from multiple aspects, such as lying, jailbreaks, and toxic content, while overlooking out-of-domain behaviors. In this work, we propose an innovative LLM domain monitoring …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

Challenges In Synchronous And Remote Collaboration Around Visualization, Matthew Brehmer, Maxime Cordeil, Christophe Hurter, Takayuki Itoh, Wolfgang Büschel, Mahmood Jasim, Arnaud Prouzeau, David Saffo, Lyn Bartram, Sheelagh Carpendale, Chen Zhu-Tian, Andrew Cunningham, Anthony Tang, Samuel Huron, Masahiko Itoh, Arpit Joshi, Kiyoshi Kiyokawa, Hideaki Kuzuoka, Bongshin Lee, Guillermo Molina León

Research Collection School Of Computing and Information Systems

We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence …

Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Software development remains predominantly text-centric, despite decades of evidence showing that developers think and communicate visually. While sketches and diagrams externalize developers’ mental models, they remain disconnected from source code and quickly become outdated. Recent advances in foundation models, capable of both code and visual reasoning, create an opportunity to unify these representations. In this vision paper, we introduce Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. Our prototype connects a code editor with a tablet-based visualization workspace, allowing developers to explore, annotate, and modify systems through freehand sketches interpreted by multimodal …

Understanding Codebase Like A Professional! Human-Ai Collaboration For Code Comprehension, Jie Gao, Yue Xue, Xiaofei Xie, Junming Cao, Soemin Thant, Erika Lee, Bowen Xu

Research Collection School Of Computing and Information Systems

Understanding an unfamiliar codebase is an essential task for developers in various scenarios, such as during the onboarding process. Especially when the codebase is large and time is limited, achieving a decent level of comprehension remains challenging for both experienced and novice developers, even with the assistance of large language models (LLMs). Existing studies have shown that LLMs often fail to support users in understanding code structures or to provide user-centered, adaptive, and dynamic assistance in real-world settings.To address this, we propose learning from the perspective of a unique role, code auditors, whose work often requires them to quickly familiarize …

Autobackdoor: Automating Backdoor Attacks Via Llm Agents, Yige Li, Zhe Li, Wei Zhao, Nay Myat Min, Hanxun Huang, Xingjun Ma, Jun Sun

Research Collection School Of Computing and Information Systems

Backdoor attacks pose a serious threat to the secure deployment of large language models (LLMs), enabling adversaries to implant hidden behaviors triggered by specific inputs. However, existing methods often rely on manually crafted triggers and static data pipelines, which are rigid, labor-intensive, and inadequate for systematically evaluating modern defense robustness. As AI agents become increasingly capable, there is a growing need for more rigorous, diverse, and scalable \textit{red-teaming frameworks} that can realistically simulate backdoor threats and assess model resilience under adversarial conditions. In this work, we introduce \textsc{AutoBackdoor}, a general framework for automating backdoor injection, encompassing trigger generation, poisoned data …

Can’T Get Work Off My Mind: The Effect Of Nonwork Goal Reflection On After-Work Rumination And Well-Being, Trevor A. Foulk, Min-Hsuan Tu, Michael Schaerer, Amber Johnson

Research Collection Lee Kong Chian School Of Business

Many employees lament that their inability to stop ruminating about work during nonwork time is one of their most significant work-related challenges, yet limited research has articulated actionable strategies to address this issue on a day-to-day basis. Integrating goal shielding theory with rumination theory, our model introduces nonwork goal reflection as a deliberate behavioral intervention employees can engage in to curtail work-related rumination, in a way that facilitates well-being during nonwork time. Specifically, we argue that engaging in nonwork goal reflection—where employees intentionally reflect on nonwork goals to be completed during their nonwork time—can curtail work-related rumination and is associated …

Joint Capacity Allocation And Job Assignment Under Uncertainty, Peng Wang, Yun Fong Lim, Gar Goei Loke

Research Collection Lee Kong Chian School Of Business

We study a multi-period joint capacity allocation and job assignment problem. The goal is to simultaneously allocate resources across J different supply nodes and assign jobs from I different demand origins to these J supply nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We consider three features: (i) supply is replenishable after some random time, (ii) demand is random, and (iii) demand can wait and needs not be fully fulfilled immediately. Such problems emerge in many service management settings such as fleet re-positioning for car-sharing, and patient management in healthcare. We …

The Impact Of Employee Equity Incentives On Enterprise Innovation Efficiency: From The Perspective Of Granting Restricted Stocks To Employees, Zongjie Ji

Dissertations and Theses Collection (Open Access)

Against the dual backdrop of the in-depth implementation of the innovation-driven development strategy and the continuous deepening of SOE reforms, restricted equity has emerged a mainstream instrument for medium- and long-term employee incentives in Chinese SOEs. However, the underlying mechanisms, effect characteristics, and boundary conditions through which employee restricted equity incentives influence innovation efficiency in SOEs remain to be systematically examined. Drawing on panel data of 4,880 firm-year observation from Chinese A-share SOMEs over the period of 2015-2024, this study develops a direct effect model of “Equity Incentives-Innovation Efficiency” from the perspective of employee restricted equity grants. It further introduces …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …