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Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi Mar 2026

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

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The Impact Of Corporate Social Responsibility On Employees' Work Attitudes And Behaviors, Qian Yuan Mar 2026

The Impact Of Corporate Social Responsibility On Employees' Work Attitudes And Behaviors, Qian Yuan

Dissertations and Theses Collection (Open Access)

Why do some new employees voluntarily leave within a short period after joining a corporation, while others do not? Drawing on information asymmetry theory and adverse selection theory, this dissertation argues that information asymmetry and adverse selection between job seekers and firms before organizational entry, or during the job search process, are important reasons for new employees’ short-term voluntary turnover. Because corporate social responsibility (CSR) can provide people with information about a firm’s capabilities, willingness, and intentions, this dissertation proposes that firms’ CSR activities can provide job seekers with information about various aspects of the firm. In doing so, CSR …

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The Impact Of Job Demands On Work And Family Outcomes Among Healthcare Workers In China: The Moderating Effect Of Gender And Professional Identity Integration (G-Pii) And Communal Orientation, Wei Zhou Mar 2026

The Impact Of Job Demands On Work And Family Outcomes Among Healthcare Workers In China: The Moderating Effect Of Gender And Professional Identity Integration (G-Pii) And Communal Orientation, Wei Zhou

Dissertations and Theses Collection (Open Access)

Long work hours, emotional strain, and stress are just a few of the high demands placed on healthcare workers in China, which have led to occupational burnout, tension between job and family, and worse health. This research investigates the relationship between job demands and burnout via the lens of the Job Demands-Resources model and social identity theory, work-family balance, subjective well-being, and career commitment among Chinese healthcare workers. We focus on the moderating roles of gender and professional identity integration (G-PII) and communal orientation. In a two-wave investigation, 317 medical professionals from different Chinese hospitals took part. Hierarchical regression studies …

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How Product Innovation And Process Innovation Impact Product Performance, Wei Wang Mar 2026

How Product Innovation And Process Innovation Impact Product Performance, Wei Wang

Dissertations and Theses Collection (Open Access)

In an increasingly competitive global market characterized by accelerating technological iteration and rapidly evolving consumer demands, innovation has become a critical means for firms to adapt to dynamic changes and achieve competitive advantage. Grounded in dynamic capabilities theory and the resource-based view, this study empirically investigates the mechanisms through which product innovation and process innovation affect product performance, drawing on survey data from 215 products. It further examines the mediating role of product flexibility and the moderating effect of product positioning. The results indicate that both product innovation and process innovation have a significant positive impact on product performance. Product …

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A Study On The Relationship Between Traffic Spillover And Rental Rates Of Shopping Centre Stores Based On Consumer Movement Data, Hongjun Zhang Mar 2026

A Study On The Relationship Between Traffic Spillover And Rental Rates Of Shopping Centre Stores Based On Consumer Movement Data, Hongjun Zhang

Dissertations and Theses Collection (Open Access)

This study is based on consumer movement trajectory data and examines the relationship between traffic spillover among stores within shopping centres and their rent ratios, responding to the practical limitation that traditional rental pricing mechanisms often overlook the dynamic structure of customer flows. Using micro-level consumer movement data and store rental data from three shopping centres in Shenzhen, Nanjing, and Chongqing, this paper constructs a store transition network reflecting customer movements between stores. Social network analysis is employed to measure stores’ network influence indicators (such as degree centrality, betweenness centrality, and structural holes) as well as traffic spillover indicators derived …

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Securing Ai Models Against Backdoors, Jailbreaks, And Prompt Injections, Myat Min Nay Mar 2026

Securing Ai Models Against Backdoors, Jailbreaks, And Prompt Injections, Myat Min Nay

Dissertations and Theses Collection (Open Access)

Deep neural networks (DNNs) and large language models (LLMs) in safety-critical settings face a family of related threats: an adversary can corrupt training so a hidden trigger flips a model’s behavior (backdoor), craft inference-time inputs that override safety alignment (jailbreaks) or smuggle in task-overriding instructions (prompt injections), or steer the model’s stance on whole concepts (semantic inconsistencies). Current defenses address each of these in isolation. This dissertation argues they admit a unified treatment through a shared diagnostic design principle: a matched perturbation, probing, or operating condition exposes a method-specific diagnostic signal in the model’s internal computation or observable behavior, and …

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Expanding Public Health Insurance To Parents: Effect On Parents' And Children’S Well-Being, Michelle Marcus, Xuan Zhang Mar 2026

Expanding Public Health Insurance To Parents: Effect On Parents' And Children’S Well-Being, Michelle Marcus, Xuan Zhang

Research Collection School Of Economics

Expanding public health insurance to parents may not only improve parental outcomes but also have spillover effects on their children. This paper leverages the natural experiment of the Affordable Care Act (ACA) to estimate the causal effects of expanding public health insurance to low-income parents on their well-being and that of their children. Using a difference-in-differences model and data from the 2010–2017 National Health Interview Surveys, we observe significant improvements in health care access, increased health care utilization, reduced financial burdens, and slight improvements in health status among low-income parents. For children in Medicaid expansion states, we find reductions in …

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Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong Mar 2026

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

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Compensation For Victims Of Crimes: Should Victims’ Financial Means And Insurance Coverage Matter? — Criminal Procedure Code 2010, S 359(1); Public Prosecutor V Ong Eng Siew [2025] Sghc 55, Benjamin Joshua Ong Mar 2026

Compensation For Victims Of Crimes: Should Victims’ Financial Means And Insurance Coverage Matter? — Criminal Procedure Code 2010, S 359(1); Public Prosecutor V Ong Eng Siew [2025] Sghc 55, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

Under s 359(1) of the Criminal Procedure Code 2010, the court can order an offender to compensate the victim with a sum which the victim would have been able to recover in a civil claim in tort against the offender. The courts have used this useful power extensively, though problems remain. One such problem is seen in Ong Eng Siew, where the court declined to make a compensation order. Given the purpose of the compensation system, the court was not correct to hold – in effect – that the purpose of s 359(1) is to benefit only impecunious victims, and …

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Entangled Transitions: How Covid-19 Widened Well-Being Gaps Among Youth In Singapore (2017–2022), Vincent Chua, Han Ei Chew, Isabelle Tan, Pamela Lee Mar 2026

Entangled Transitions: How Covid-19 Widened Well-Being Gaps Among Youth In Singapore (2017–2022), Vincent Chua, Han Ei Chew, Isabelle Tan, Pamela Lee

Research Collection Office of Impact

COVID-19 was not merely a biomedical crisis but a deeply social phenomenon entangled with existing inequalities. Drawing on Peter Kelly's notion of entanglement, we conceptualise the pandemic as an intersectional crisis − where time, structural disadvantage and socio-emotional well-being became interwoven. Using six waves of longitudinal data (2017–2022) from Youth STEPS, a panel study of young people in Singapore, we track within-person changes in life satisfaction across key youth transitions before, during and after the pandemic. Our findings show that the negative effects of unemployment and insecure work on well-being were markedly more severe during the pandemic than in earlier …

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Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai Mar 2026

Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …

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Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang Mar 2026

Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do …

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Interpretable Machine Learning For Personalized Profiling Of Mild Cognitive Impairment From Daily Activities, Budhitama Subagdja, Ah-Hwee Tan, Kenneth Kwok, Iris Rawtaer Mar 2026

Interpretable Machine Learning For Personalized Profiling Of Mild Cognitive Impairment From Daily Activities, Budhitama Subagdja, Ah-Hwee Tan, Kenneth Kwok, Iris Rawtaer

Research Collection School Of Computing and Information Systems

Continuous monitoring of individual daily activities is essential to detect mild cognitive impairment (MCI) wherein timely intervention can still be applied to prevent more severe mental decline. Recent approaches in predicting MCI are mostly considering digital biomarkers across individuals but often neglecting specific indicators from a single person over a long period of time. Making this personalized, dynamic, and highly noisy prediction model with irregular distribution of missing information to be explainable and actionable for clinical use, remains a challenge. This paper presents a study on a personalized MCI prediction and profiling from an in-home and mobile cognitive health monitoring …

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Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin Mar 2026

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

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A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao Mar 2026

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

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Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu Mar 2026

Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu

Research Collection School Of Computing and Information Systems

In this study, we propose a reinforcement learning-based adaptive variable neighborhood search (RL-AVNS) method designed for effectively solving the Vehicle Routing Problem with Multiple Time Windows (VRPMTW). Unlike traditional adaptive approaches that rely solely on historical operator performance, our method integrates a reinforcement learning framework to dynamically select neighborhood operators based on real-time solution states and learned experience. We introduce a fitness metric that quantifies customers’ temporal flexibility to improve the shaking phase, and employ a transformer-based neural policy network to intelligently guide operator selection during the local search. Extensive computational experiments are conducted on realistic scenarios derived from the …

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Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua Mar 2026

Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) face two key challenges, heterogeneity and heterophily, which often degrade performance. Existing approaches either focus narrowly on specific meta-paths, limiting their expressiveness, or are expressive but cannot effectively leverage higher-order neighbors. In this paper, we propose the Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which combines local independent filtering to adaptively handle meta-path subgraphs with varying homophily ratios, and global hybrid filtering to capture high-order neighbor interactions with linear computational complexity. On five heterogeneous graph benchmarks—DBLP, ACM, IMDB, AMiner, and Yelp—H2SGNN consistently outperforms strong baselines, for example, achieving +1.0% Macro-F1 and +1.3% Micro-F1 on IMDB. It …

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Edgeclip: Injecting Edge-Awareness Into Visual-Language Models For Zero-Shot Semantic Segmentation, Jiaxiang Fang, Shiqiang Ma, Guihua Duan, Fei Guo, Shengfeng He Mar 2026

Edgeclip: Injecting Edge-Awareness Into Visual-Language Models For Zero-Shot Semantic Segmentation, Jiaxiang Fang, Shiqiang Ma, Guihua Duan, Fei Guo, Shengfeng He

Research Collection School Of Computing and Information Systems

Effective segmentation of unseen categories in zero-shot semantic segmentation is hindered by models’ limited ability to interpret edges in unfamiliar contexts. In this paper, we propose EdgeCLIP, which addresses this by integrating CLIP with explicit edge-awareness. Based on the premise that edge variation patterns are similar across both seen and unseen class objects, EdgeCLIP introduces the Contextual Edge Sensing module. This module accurately discerns and utilizes edge information, which is crucial in complex border areas where conventional models struggle. Further, our Text-Guided Dense Feature Matching strategy precisely aligns text encodings with corresponding visual edge features, effectively distinguishing them from background …

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Global Value Chains And Economic Inequality, Vivek Soundararajan, Ari Van Assche, Hari Bapuji, Gokhan Ertug Mar 2026

Global Value Chains And Economic Inequality, Vivek Soundararajan, Ari Van Assche, Hari Bapuji, Gokhan Ertug

Research Collection Lee Kong Chian School Of Business

In their inaugural editorial, Van Assche and De Marchi (2024) argued that meaningful progress in international business policy requires a more fine-grained understanding of how private international business practices intersect with public policy objectives. This editorial builds on that insight by applying it to a critical yet still underexplored issue – one on which we hope to see more research in this journal: the role of multinational enterprises (MNEs) and global value chains (GVCs) in shaping economic inequality within developing economies. Decisions related to sourcing, contracting, and GVC governance are inherently distributional rather than neutral, with important implications for how …

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Corrigendum To “Interim Rationalizable Implementation Of Functions” (Kunimoto T, Saran R, Serrano R (2024) Mathematics Of Operations Research 49(3):1791–1824), Takashi Kunimoto, Rene Saran, Roberto Serrano Feb 2026

Corrigendum To “Interim Rationalizable Implementation Of Functions” (Kunimoto T, Saran R, Serrano R (2024) Mathematics Of Operations Research 49(3):1791–1824), Takashi Kunimoto, Rene Saran, Roberto Serrano

Research Collection School Of Economics

This is the brief corrigendum to “Interim rationalizable implementation of functions” [Kunimoto T, Saran R, Serrano R (2024) Interim rationalizable implementation of functions. Math. Oper Res. 49(3):1791–1824].

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The Feelit System: Application Content-Aware Perspectives And Challenges On Understanding User Likes In Social Network Posts, Konstantinos Theocharidis, Hady W. Lauw, Panagiotis Karras Feb 2026

The Feelit System: Application Content-Aware Perspectives And Challenges On Understanding User Likes In Social Network Posts, Konstantinos Theocharidis, Hady W. Lauw, Panagiotis Karras

Research Collection School Of Computing and Information Systems

In a series of our prior works, we study influence and subscription maximization problems in social networks that are based on posts having influential content; as content we consider a set of features where each feature corresponds to a specific social network page, whereas influence and subscription relate to gaining the postlike and subscription-to-brand page of targeted users, respectively; subscription is conceptually achieved as repetitive influence on users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is a complex problem that has not been adequately …

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Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu Feb 2026

Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu

Research Collection School Of Computing and Information Systems

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective …

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Prisrv+: Privacy And Usability-Enhanced Wireless Service Discovery With Fast And Expressive Matchmaking Encryption, Yang Yang, Guomin Yang, Yingjiu Li, Pengfei Wu, Rui Shi, Minming Huang, Jian Weng, Hwee Hwa Pang, Deng, Robert H. Feb 2026

Prisrv+: Privacy And Usability-Enhanced Wireless Service Discovery With Fast And Expressive Matchmaking Encryption, Yang Yang, Guomin Yang, Yingjiu Li, Pengfei Wu, Rui Shi, Minming Huang, Jian Weng, Hwee Hwa Pang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

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Prompt Tuning Without Labeled Samples For Zero-Shot Node Classification In Text-Attributed Graphs, Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang Feb 2026

Prompt Tuning Without Labeled Samples For Zero-Shot Node Classification In Text-Attributed Graphs, Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang

Research Collection School Of Computing and Information Systems

Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-commerce. Zero-shot node classification in text-attributed graphs (TAGs) presents a significant challenge, particularly due to the absence of labeled data. In this paper, we propose a novel Zero-shot Prompt Tuning (ZPT) framework to address this problem by leveraging a Universal Bimodal Conditional Generator (UBCG). Our approach begins with pre-training a graph-language model to capture both the graph structure and the associated textual descriptions of each node. Following this, a conditional generative model …

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Fortifying The Seams Between C/C++ And Rust: Characterizing Bugs In Interop Tools, Xuemeng Cai, Jiakun Liu, Cunyang Liu, Lingfeng Bao, Yijun Yu, Lingxiao Jiang Feb 2026

Fortifying The Seams Between C/C++ And Rust: Characterizing Bugs In Interop Tools, Xuemeng Cai, Jiakun Liu, Cunyang Liu, Lingfeng Bao, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Rust has become increasingly popular in recent years due to its safety and high performance. Despite these advantages, Rust projects rarely start from scratch in practice, and many Rust-based systems instead use hybrid programming, where Rust interoperates with existing C/C++ code. To reduce the manual effort involved in this interoperation (interop) process, several interop tools have been proposed to facilitate hybrid programming between Rust and C/C++. However, the challenges and limitations of these tools remain largely unexplored, leaving developers unclear about the future directions and users unclear about the appropriate usage scenarios. To fill the gap, we mined 320 bugs …

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Mm-Attackg: A Multimodal Approach To Attack Graph Construction With Large Language Models, Yongheng Zhang, Xinyun Zhao, Yunshan Ma, Haokai Ma, Yingxiao Guan, Guozheng Yang, Yuliang Lu, Xiang Wang Feb 2026

Mm-Attackg: A Multimodal Approach To Attack Graph Construction With Large Language Models, Yongheng Zhang, Xinyun Zhao, Yunshan Ma, Haokai Ma, Yingxiao Guan, Guozheng Yang, Yuliang Lu, Xiang Wang

Research Collection School Of Computing and Information Systems

Cyber Threat Intelligence (CTI) parsing aims to extract key threat information from massive data, transform it into actionable intelligence, enhance threat detection and defense efficiency, including attack graph construction, intelligence fusion, and indicator extraction. Among these research topics, Attack Graph Construction (AGC) is essential for visualizing and understanding the potential attack paths of threat events from CTI reports. Existing approaches primarily construct the attack graphs purely from the textual data to reveal the logical threat relationships between entities within the attack behavioral sequence. However, they typically overlook the specific threat information inherent in visual modalities, which preserves key threat details …

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Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai Feb 2026

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

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The Case For Ai Authorship In Copyright Law, Cheng Lim Saw, Duncan Lim Feb 2026

The Case For Ai Authorship In Copyright Law, Cheng Lim Saw, Duncan Lim

Research Collection Yong Pung How School Of Law

Today, with generative AI, literary and artistic works can be created almost effortlessly. There is at present intense debate as to whether works generated by AI – broadly categorised as “AI-assisted” and “AI-generated” works – ought to attract copyright protection. AI-assisted works are those that involve some degree of human intervention. Where AI-generated works are concerned, however, such works are created autonomously by the AI itself with minimal (de minimis) input from an identifiable human being. Presently, it is generally accepted that AI-generated works do not attract copyright protection for want of a human author. This article examines whether it …

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Defending Code Language Models Against Backdoor Attacks With Deceptive Cross-Entropy Loss, Guang Yang, Yu Zhou, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, David Lo, Taolue Chen Feb 2026

Defending Code Language Models Against Backdoor Attacks With Deceptive Cross-Entropy Loss, Guang Yang, Yu Zhou, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, David Lo, Taolue Chen

Research Collection School Of Computing and Information Systems

Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularly backdoor attacks, is often overlooked in this process. The previous research has focused on designing backdoor attacks for CLMs, but effective defenses have not been adequately addressed. In particular, existing defense methods from natural language processing, when directly applied to CLMs, are not effective enough and lack generality, working well in some models and scenarios but failing in others, thus fall short in consistently mitigating backdoor attacks. To bridge this gap, we first confirm the phenomenon of …

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Derivation Of An Updated Brief Multivariable Prediction Model To Detect Panic-Related Anxiety In Emergency Department Patients With Cardiopulmonary Complaints, Sharon C. Sung, Felicia J. L. Ang, Arul Earnest, Leslie E. C. Lim, Shreshtha Jolly, Gilaine Rui Ng, A. John Rush, Marcus E. H. Ong Feb 2026

Derivation Of An Updated Brief Multivariable Prediction Model To Detect Panic-Related Anxiety In Emergency Department Patients With Cardiopulmonary Complaints, Sharon C. Sung, Felicia J. L. Ang, Arul Earnest, Leslie E. C. Lim, Shreshtha Jolly, Gilaine Rui Ng, A. John Rush, Marcus E. H. Ong

Research Collection School of Social Sciences

Background Patients with panic related-anxiety (i.e., panic attacks or panic disorder) frequently present to emergency departments (EDs) with cardiopulmonary complaints but are often undiagnosed, which can lead to recurrent visits and prolonged distress. This study aimed to derive a new symptom-based multivariable diagnostic prediction model to detect panic-related anxiety in ED patients with cardiopulmonary symptoms.Methods We conducted a single-blind prospective derivation study over 15 months in the ED of a major tertiary hospital in Singapore. Patients presenting with symptoms of palpitations, chest pain, dizziness, or difficulty breathing were assessed using the Structured Clinical Interview for DSM Disorders (SCID) to diagnose …

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