Digital Commons Network^™

The Influence Of Venture Capitalists’ Value-Added Service On Capital Financing Outcome: A Research On Chinese Biotech Startups, Hao-Yu Huang

Dissertations and Theses Collection (Open Access)

China’s biotechnology sector has undergone rapid evolution, driven by government policy, capital market reform, and growing global demand for pharmaceutical innovation. Venture capitalists (VCs) have played an increasingly pivotal role not only as financiers but also as strategic partners offering value-added services (VAS). These interventions include operational mentorship, regulatory navigation, talent pipeline facilitation, and strategic repositioning. However, the extent to which such non-financial support influences financing outcomes across startup development stages remains underexplored—particularly in the context of China's unique institutional and industrial environment.

This dissertation examines how VC post-investment interventions affect the financing success of biotech startups in China. Drawing …

Research On Inheritance And Innovation Of Chinese Time - Honored Brands On Brand Attachment, Jiangbo Yin

Dissertations and Theses Collection (Open Access)

Balancing inheritance and innovation to achieve brand revitalization is a pressing practical issue for both academia and industry. This study adopts a dual perspective of enterprises and consumers, selecting the Cantonese cuisine time-honored brand "Tao Tao Ju" as the research subject, and employs a mixed-methods combining qualitative and quantitative research to explore the mechanism on inheritance and innovation of Chinese time-honored brands on brand attachment.

Study 1 utilizes grounded theory to construct a multidimensional structure of inheritance and innovation of Chinese time-honored brands: inheritance dimensions include product and service inheritance, brand spirit inheritance, and historical and cultural inheritance; innovation dimensions …

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 …

Research On Motivations For Corporate Voluntary Environmental Transformation: An Analysis Based On Wwf, Lunyan Lu

Dissertations and Theses Collection (Open Access)

This study investigates the multidimensional motivations driving corporate voluntary environmental transformation and its subsequent impacts, with a focus on political contexts, industry characteristics, and leadership traits. Situated within the framework of additional transformation requirements proposed by environmental non-profit organizations (e.g., WWF), the research examines how environmental transformation serves as a strategic vehicle for corporations to demonstrate social responsibility practices and enhance market competitiveness through philanthropic engagements. By integrating theoretical construction and empirical analysis, this study advances the explanatory frameworks of corporate social responsibility (CSR) theory, stakeholder theory, and resource dependence theory.

This research first identifies intrinsic motivations for corporations to …

Study On Sustainable Development Of Energy Innovation, Maoli Guo

Dissertations and Theses Collection (Open Access)

Energy innovation and sustainable development play significant roles in the economic development and technological progress of a country. This paper first reviews the measurement methods of energy efficiency in existing literature, selects evaluation indicators based on the influencing factors of energy system performance, and proposes an energy efficiency evaluation method combining the Malmquist index and the Data Envelopment Analysis (DEA) model to evaluate and test the performance of energy sustainable development in each province of China from 2005 to 2021.

The study finds that, firstly, the energy efficiency in China has generally shown an upward trend, but there are significant …

Analysis On Factors For The Success Of China's Industrial Parks -- "Crown Model" And Its Empirical Research, Ming Qiu

Dissertations and Theses Collection (Open Access)

Since the reform and opening-up policy, China has systematically promoted the development and construction of economic and technological development zones, high-tech industrial development zones, and various types of industry-focused functional parks by drawing on successful international models and integrating them with China’s context. This has been carried out from the central government to local governments to foster the clustering of regional industries, facilitate industrial upgrading, drive large-scale employment, increase GDP, generate continuous tax revenue, and accelerate urbanization. These efforts have been pivotal in transforming local economies and enhancing China’s competitive advantage in the global market. The author, with 20 years …

The Impact Of Marriage Status On Career Aspiration And Performance, Qiang Yi

Dissertations and Theses Collection (Open Access)

This study explores the impact of marital status on employee workplace behavior and job performance. Specifically, it finds that compared to unmarried employees, married employees demonstrate higher career aspirations and job performance. Further analysis reveals that this effect is primarily driven by the enhancement of family and career commitments brought about by marriage. These increased commitments significantly contribute to the improvement of employees' career aspirations and job performance. Moreover, this study examines the gender differences in the influence of marital status on family and career commitments.

The results show that marriage has a stronger effect on promoting family commitments among …

A Study On Paths And Effectiveness Of Internationalization Of Chinese Apparel Enterprises, Xiaohong Gao

Dissertations and Theses Collection (Open Access)

As globalization advances, international expansion has become a key strategy for firms to enhance their competitive edge. Nevertheless, current literature predominantly centers on developed countries, often overlooking the distinct obstacles and prospects encountered by companies from emerging markets. This gap is particularly evident in the apparel sector, where the impact of branding strategies on internationalization outcomes remains underexplored. This paper constructs an analytical framework of “internationalization context - internationalization pathway selection - internationalization performance”, combines case studies and empirical analysis to explore this research issue in depth, and applies the results of the research to enterprise practice.

This paper analyzes …

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 …

Research On The Economic Performance And Process Optimization Strategies In Chinese Agricultural Enterprises, Zili Xu

Dissertations and Theses Collection (Open Access)

With the booming digital economy, digital transformation has become a key strategy for global enterprises to enhance competitiveness and optimize economic performance. This dissertation takes Chinese agricultural enterprises as the research subject and deeply explores the impact of digital transformation on corporate economic performance and strategies for business process optimization.

The dissertation first reviews the research progress on the digital transformation of agricultural enterprises. Secondly, it uses qualitative text analysis and machine learning technology to assess the digital level of Chinese agricultural enterprises and analyzes their current status and characteristics. The empirical study employs a fixed-effects model and finds that …

Digital Twin Dynamics: Employee’S Willingness To Adopt Ai Digital-Twin In A Craftsmanship Environment, Seow Hoon Tee

Dissertations and Theses Collection (Open Access)

Singapore’s manufacturing sector, contributing 22% to the nation’s GDP, is undergoing a strategic transformation under the Manufacturing 2030 vision, which aims to increase sector value by 50%. However, a critical challenge emerges in the widening age gap among craftsmen, particularly between 30 and 50 years old, misaligning with succession planning efforts. As senior craftsmen approach retirement, the urgency to transfer specialized expertise significantly increases. In response, initiatives such as the National AI Strategy (NAIS) 2.0 and Industry 5.0 efforts by the Singapore Manufacturing Federation (SMF) and Advanced Remanufacturing and Technology Centre (ARTC) emphasize AI-driven solutions to enhance knowledge retention and …

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 …

Divide-And-Conquer: Confluent Triple-Flow Network For Rgb-T Salient Object Detection, Hao Tang, Zechao Li, Dong Zhang, Shengfeng He, Jinhui Tang

Research Collection School Of Computing and Information Systems

RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a "Divide-and-Conquer"strategy. This framework utilizes a unified encoder with specialized decoders, each addressing different subtasks …

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 …

Improving Multimodal Human Pose Estimation By Adversarial Modality Enhancement, Jiangnan Xia, Qilong Wu, Yanyin Guo, Yi Li, Jianghan Cheng, Junwei Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Human pose estimation in computer vision predominantly focuses on the visible modality, with limited research on the infrared modality. No existing methods demonstrate robust performance across both modalities, missing their complementary strengths. This gap arises from the lack of a multimodal benchmark and the difficulty of developing robust multimodal capabilities. To address this, we introduce MMPD, a novel visible-infrared multimodal pose benchmark with high-quality annotations for both modalities. Leveraging MMPD, we expose the limitations of state-of-the-art methods due to modality variance. To overcome this challenge, we propose a novel method-agnostic scheme called AMMPE. By employing the Modality Adversarial Enhancement Stage …

A Contrastive Framework With User, Item And Review Alignment For Recommendation, Viet Hoang Dong, Yuan Fang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-aware recommendation models often exhibit two key limitations. First, they typically rely on reviews as additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches do not integrate reviews into the useritem space, leading to potential divergence or inconsistency among user, item, and review representations. …

Selecting Comparative Sets Of Reviews Across Multiple Items, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While choosing among several products, users may look up reviews from each product they are considering. Due to the large number of reviews of products, selecting representative reviews from one product alone is already a challenging problem. In this work, we further aim to conduct review selection for multiple products simultaneously for comparative purposes. We formulate objective functions that synchronize the review selection and design efficient algorithms to optimize for the objective functions. To narrow down the potentially long list of comparison items into a shorter list of more similar items, we construct a graph representing items’ similarity and design …

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 …

Multi-Uav Reconnaissance Mission Planning Via Deep Reinforcement Learning With Simulated Annealing, Mingfeng Fan, Huan Liu, Guohua Wu, Aldy Gunawan, Guillaume Sartoretti

Research Collection School Of Computing and Information Systems

Unmanned aerial vehicles (UAVs) are widely used in reconnaissance missions due to their autonomy and flexibility. Efficient mission planning for multiple UAVs is crucial for tasks such as traffic monitoring and data collection. However, existing approaches to multi-UAV reconnaissance mission planning problem (MURMPP) often struggle with high computational demands, leading to suboptimal solutions. To overcome this challenge, we introduce a divide-and-conquer framework that splits the problem into two phases: target allocation and UAV routing, effectively reducing computational complexity. Specifically, we propose a hybrid method, SA-NNO-DRL, which combines the nearest neighbor optima-based deep reinforcement learning (NNO-DRL) approach with simulated annealing (SA). …

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 …

Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index, Yuxiang Guo, Zhonghao Hu, Yuren Mao, Baihua Zheng, Yunjun Gao, Mingwei Zhou

Research Collection School Of Computing and Information Systems

Natural language (NL)-driven table discovery identifies relevant tables from large table repositories based on NL queries. While current deep-learning-based methods using the traditional dense vector search pipeline, i.e., representation-index-search, achieve remarkable accuracy, they face several limitations that impede further performance improvements: (i) the errors accumulated during the table representation and indexing phases affect the subsequent search accuracy; and (ii) insufficient query-table interaction hinders effective semantic alignment, impeding accuracy improvements. In this paper, we propose a novel framework Birdie, using a differentiate search index. It unifies the indexing and search into a single encoder-decoder language model, thus getting rid of error …

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, …

Fully Selective Opening Secure Ibe From Lwe, Dingding Jia, Haiyang Xue, Bao Li

Research Collection School Of Computing and Information Systems

Selective opening security ensures that, when an adversary is given multiple ciphertexts and corrupts a subset of the senders (thereby obtaining the plaintexts and the senders’ randomness), the privacy of the remaining ciphertexts is still preserved. Previous selective opening secure IBE schemes encrypt messages bit-by-bit, or only achieve selective-id security. In this paper, we present the first adaptive-id, selective opening secure identity-based encryption (IBE) tightly from LWE. To achieve this, we introduce a new primitive called delegatable all-but-many lossy trapdoor functions (DABM-LTDF) and provide a generic construction that converts DABM-LTDF into an adaptive-id, selective opening secure IBE through a tight …

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 …

Dr. Tongue: Sign-Oriented Multi-Label Detection For Remote Tongue Diagnosis, Yiliang Chen, Steven S. C. Ho, Cheng Xu, Yao Jie Xie, Wing Fai Yeung, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue attributes. The COVID-19 pandemic has heightened the need for accurate remote medical assessments, emphasizing the importance of precise tongue attribute recognition via telehealth. To address this, we propose a Sign-Oriented multi-label Attributes Detection framework. Our approach begins with an adaptive tongue feature extraction module that standardizes tongue images and mitigates environmental factors. This is followed by a Sign-oriented Network (SignNet) that identifies specific tongue attributes, emulating the diagnostic process of experienced practitioners and enabling comprehensive health evaluations. To …