Is Open Science Possible In Healthcare And Medical Research?, Wilson Goh

Singapore Open Research Conference 2024

Open science, which promotes transparency, accessibility, and collaboration in research, has revolutionized many scientific fields. However, the unique complexities of healthcare research, including patient privacy, data security, and regulatory requirements, present significant challenges to its full implementation. Despite these challenges, strategic national efforts are paving the way toward greater openness. In this presentation, I will highlight some of these initiatives, including the Singapore MOH Trust system, international consortia efforts to share national data such as the UK BioBank, and MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Centre. Additionally, I will …

Beyond Borders, Beyond Boundaries: How Globalization Affects Real Estate Prices, Ritu Mehlawat

Dissertations and Theses Collection (Open Access)

In this comprehensive study, we aim to investigate the impact of Globalisation on house prices globally. Our analysis spans annual data from 42 countries, including OECD and some Non-OECD countries, from 1991 to 2021. Our research underscores the role of Globalisation in shaping house prices, with distinct impacts contingent on the type of Globalisation. Financial globalisation, which primarily encompasses capital flow coupled with enabling regulatory environment, is one of the drivers of house price increases. Conversely, Interpersonal, and Informational globalisation, which mainly involves the physical or virtual proximity of people supported by favourable conditions, has a negative effect on house …

Elevating Automated Software Maintenance Tasks With Large Language Models, Xin Zhou

Dissertations and Theses Collection (Open Access)

Software engineering involves many tasks across different phases such as requirements, design, implementation, testing, and maintenance. Among them, software maintenance is a crucial phase, typically accounting for more than half of the software life cycle's duration.
To boost developer productivity, in recent years, numerous research endeavors in software engineering have sought to automate certain software maintenance tasks through the application of machine learning techniques.
Since 2020, the emergence of advanced Large Language Models (LLMs) of code has opened new avenues for enhancing automated solutions in software maintenance.
This dissertation presents a series of works aimed at advancing automated solutions for …

Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …

Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang

Research Collection School Of Computing and Information Systems

In the realm of dialogue-to-image retrieval, the primary challenge is to fetch images from a pre-compiled database that accurately reflect the intent embedded within the dialogue history. Existing methods often overemphasize inter-modal alignment, neglecting the nuanced nature of conversational context. Dialogue histories are frequently cluttered with redundant information and often lack direct image descriptions, leading to a substantial disconnect between conversational content and visual representation. This study introduces VCU, a novel framework designed to enhance the comprehension of dialogue history and improve cross-modal matching for image retrieval. VCU leverages large language models (LLMs) to perform a two-step extraction process. It …

Padlet As A Tool For Collaborative And Personalized Learning For University Courses, Yi Meng Lau, Wee Ling Loo, Bei Hong, Yuanto Kusnadi

Research Collection School Of Computing and Information Systems

This study investigates the effectiveness of Padlet, a technology-enhanced tool, that supports collaborative and personalized learning, and its success in meeting specific learning outcomes and course objectives. We developed a framework that synthesizes the learning characteristics of technology-enabled collaborative and personalized learning, integrating the strengths of both learning modes. Unlike other studies which typically focus on a single discipline, our framework is evaluated with the use of Padlet across four university courses, spanning three disciplines (Information Systems, Economics and Law). Survey results showed that Padlet enhanced both learning types and achieved the desired outcomes. Linear regression analysis underscored Padlet's effectiveness …

Empowering Ageing-In-Place, Zidane Tiew, Wei Tin Hiah, Rachel Wen Yi Ngu, Wensi Lim

ROSA Research Briefs

In the past year, three governmental ministries – the Ministry of Health, Ministry of National Development, and Ministry of Transport – have jointly led Age Well SG, a national programme that seeks to support older Singaporeans in ageing well in their homes and their communities. This programme reflects the government’s priority in enabling ageing-in-place, a trend that has been increasingly welcomed by older Singaporeans. Current policies that seek to enable ageing-in-place are centred on three forms of enhancements in the environments in which older Singaporeans live, such as the implementation of Active Ageing Centres, strengthening of support for care needs, …

Power Shift: Steering The Energy Sector Towards A Net Zero Future, Tengku Muhammad Taufik

Asian Management Insights

Tengku Muhammad Taufik, President and Group CEO of PETRONAS, discusses how the global energy company navigates an increasingly volatile energy market while advancing its commitment to a Net Zero future. Balancing traditional operations with the demands of an accelerated energy transition, PETRONAS focuses on delivering energy to its stakeholders while meeting sustainability expectations. The company adopts frontier technologies and artificial intelligence (AI) as key enablers in its strategy to achieve a sustainable and resilient energy future.

Does The Monitoring Effectiveness Improve After Busy Independent Directors Become Less Busy? — A Quasi-Experiment From China, Hui Gao

Dissertations and Theses Collection (Open Access)

The importance of independent directors is well-recognized both in academic literature and among regulators. This dissertation leverages the introduction of the Management Measures for Independent Directors of Listed Companies (hereafter the Measures) in China, which reduces the maximum allowable concurrent directorships from five to three, to investigate whether fewer directorships allow independent directors to devote more time and attention to enhancing their monitoring effectiveness.

Prior literature offers mixed findings on the impact of busy independent directors on corporate governance and firm value, with no clear consensus on whether multiple directorships affect their oversight capabilities. Analyzing data on independent directors’ votes …

Food Computing: Domain Adaptation And Causal Inference, Qing Wang

Dissertations and Theses Collection (Open Access)

This dissertation addresses two challenges in food computing: food recognition and food image-to-recipe retrieval. The main research ideas are: (1) leveraging Large Language Models (LLMs) to augment food image representations to mitigate the combined challenges of domain gaps and data imbalance in fine-grained food recognition; (2) proposing a causal-theory inspired cross-modal representation learning formulation for reducing the bias caused by the emphasis on certain ingredients for cross-modal recipe retrieval; and (3) extending the framework to incorporate multiple confounding factors, particularly ingredients and cooking actions, allows for more comprehensive modeling of the food image-torecipe retrieval problem.

We first explore the challenges …

Class Name Guided Out-Of-Scope Intent Classification, Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu, Xiaoli Li

Research Collection School Of Computing and Information Systems

The paper introduces Semantics of Class Labelbased Unsupervised Out of Scope Intent Detection (SCOOS), a novel method aimed at enhancing out-of-scope (OOS) intent classification in task-oriented dialogue systems. Unlike prior approaches that rely solely on indomain (ID) data features, SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. The method entails forming a compact feature space centered around the semantics of class labels by minimizing losses between ID features and class names. SCOOS achieves this by creating a compact feature space centered around class label semantics, achieved through minimizing losses between in-domain (ID) features and class names. …

Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma

Research Collection School Of Computing and Information Systems

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …

Hi3d: Pursuing High-Resolution Image-To-3d Generation With Video Diffusion Models, Haibo Yang, Yang Chen, Yingwei Pan, Ting Yao, Zhineng Chen, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D awareness. In this work, we present High-resolution Image-to-3D model (Hi3D), a new video diffusion based paradigm that redefines a single image to multi-view images as 3D-aware sequential image generation (i.e., orbital video generation). This methodology delves into the underlying temporal consistency knowledge in video diffusion model that generalizes well to geometry consistency across multiple views in 3D generation. Technically, Hi3D first empowers the pre-trained video diffusion model with 3D-aware …

Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this article, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage, we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then …

Large Language Models For Software Engineering: A Systematic Literature Review, Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs …

Badfl: Backdoor Attack Defense In Federated Learning From Local Model Perspective, Haiyan Zhang, Xinghua Li, Mengfan Xu, Ximeng Liu, Tong Wu, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions incorrect. Existing defenses based on identifying and eliminating malicious weights ignore the similarity variation of the local weights during iterations in the malicious model detection and the presence of benign weights in the malicious model during the malicious local weight elimination, resulting in a poor defense and a degradation of global model …

Thoughts To Target: Enhance Planning For Target-Driven Conversation, Zhonghua Zheng, Lizi Liao, Yang Deng, Ee-Peng Lim, Minlie Huang, Liqiang Nie

Research Collection School Of Computing and Information Systems

In conversational AI, large-scale models excel in various tasks but struggle with target-driven conversation planning. Current methods, such as chain-of-thought reasoning and tree-search policy learning techniques, either neglect plan rationality or require extensive human simulation procedures. Addressing this, we propose a novel two-stage framework, named EnPL, to improve the LLMs’ capability in planning conversations towards designated targets, including (1) distilling natural language plans from target-driven conversation corpus and (2) generating new plans with demonstration-guided in-context learning. Specifically, we first propose a filter approach to distill a high-quality plan dataset, ConvPlan1. With the aid of corresponding conversational data and support from …

Don’T Just Say “I Don’T Know”! Self-Aligning Large Language Models For Responding To Unknown Questions With Explanations, Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ …

Selective Annotation Via Data Allocation: These Data Should Be Triaged To Experts For Annotation Rather Than The Model, Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Ido Dagan

Research Collection School Of Computing and Information Systems

To obtain high-quality annotations under limited budget, semi-automatic annotation methods are commonly used, where a portion of the data is annotated by experts and a model is then trained to complete the annotations for the remaining data. However, these methods mainly focus on selecting informative data for expert annotations to improve the model predictive ability (i.e., triage-to-human data), while the rest of the data is indiscriminately assigned to model annotation (i.e., triage-to-model data). This may lead to inefficiencies in budget allocation for annotations, as easy data that the model could accurately annotate may be unnecessarily assigned to the expert, and …

Beyond Persuasion : Towards Conversational Recommender System With Credible Explanations, Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs are highly persuasive, they can mislead users by incorporating incredible information in their explanations, ultimately damaging the long-term trust between users and the CRS. To address this, we propose a simple yet effective method, called PC-CRS, to enhance the credibility of CRS’s explanations during persuasion. It guides the explanation generation through our proposed credibility-aware persuasive strategies and then gradually refines explanations via post-hoc self-reflection. Experimental results demonstrate the efficacy of PC-CRS in promoting persuasive …

A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …

Consecutive Batch Model Editing With Hook Layers, Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam

Research Collection School Of Computing and Information Systems

As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive and batch scenarios to edit the model behavior directly. Despite all these practical expectations, existing model editing methods fail to realize all of them. Furthermore, the memory demands for such sequential model editing approaches tend to be prohibitive, frequently necessitating an external memory that grows incrementally over time. To cope with these challenges, we propose CoachHooK, a model editing method that simultaneously supports sequential and batch editing. CoachHooK is memory-friendly as it only needs a …

Law-Aware Autonomous Driving, Yang Sun

Dissertations and Theses Collection (Open Access)

Autonomous driving systems (ADSs) necessitate comprehensive testing prior to deployment in Autonomous Vehicles (AVs). High-fidelity simulators are crucial for this testing, as they can replicate a wide range of scenarios, including those that are difficult or dangerous to recreate in real-world conditions. While previous approaches have demonstrated that test cases can be generated automatically, they often focus on weak oracles (e.g., reaching the destination without collisions) and fail to assess whether the journey was conducted safely and in compliance with some complex property specifications such as traffic laws. In this dissertation, beyond assessing basic properties like energy consumption and proximity …

Finding Purpose In Higher Education: Insights From The Distinctive Career Of Professor Rajani Naidoo, Howard Thomas

Research Collection Lee Kong Chian School Of Business

The author explores Professor Rajani Naidoo’s inspiring journey in higher education, marked by advocacy, leadership, and a commitment to social justice. Born in apartheid South Africa, Rajani’s formative experiences with inequality shaped her lifelong dedication to equity in education. Her academic path took her to the University of Bath, where she directed the International Centre for Higher Education Management, emphasising interdisciplinary research and ethical leadership. Now Deputy Vice-Chancellor at the University of Exeter, she drives cultural change and inclusivity. Rajani’s work challenges competitive norms, advocates global collaboration, and envisions universities as agents of societal good. Her story exemplifies purpose-driven academic …

Review Forum : Intervention Before Interventionism : A Global Genealogy. By Patrick Quinton-Brown. Oxford: Oxford University Press. 2024., Robbie Shilliam, Lina Benabdallah, Heloise Weber, Craig N. Murphy, Samuel Moyn, Jennifer M. Welsh, Patrick Quinton-Brown

Research Collection School of Social Sciences

What if core concepts in International Relations (IR) yielded multiple interpretations that were connected to various political projects, pursued with differing consequences for global governance? What would be the harm of teaching one interpretation as the only interpretation, connected to the only project, with a singular consequence? How might our analysis of the present and our expectations of the future shift if we pluralized our understandings of the political life of core concepts? Patrick Quinton-Brown's book challenges us to do so with one of the core concepts of IR: intervention.Intervention before interventionism joins recent critiques of the Responsibility to Protect …

Celebrating Professor Rita Mcgrath: Winner Of The Ck Prahalad Outstanding Scholar / Practitioner Award, Strategic Management Society, Us 2022: A Conversation With Howard Thomas, Rita Mcgrath, Howard Thomas

Research Collection Lee Kong Chian School Of Business

This article highlights Rita McGrath’s extraordinary career and contributions to management thinking, particularly at the intersection of strategy and innovation. Raised in a science-oriented family, McGrath’s academic journey began with a PhD at Wharton and continued at Columbia Business School. She pioneered concepts like “Discovery-Driven Planning” and the idea of “transient competitive advantage,” reshaping how organisations navigate uncertainty and innovation. Her works, including The End of Competitive Advantage and Seeing Around Corners, blend rigorous research with practical insights. A tireless advocate for diversity in academia, McGrath’s achievements bridge academic excellence and real-world impact, serving as an inspiration for future leaders.

Validity Of Social Media Assessments In Personnel Selection: A Systematic Review Of The Initial Evidence, Franz W. Mönke, Nicolas Roulin, Filip Lievens, Marie Therese Bartossek, Philipp Schäpers

Research Collection Lee Kong Chian School Of Business

Approximately two out of three recruiters report screening candidates’ KSAOs (knowledge, skills, abilities, and other characteristics like personality) or hireability based on social media platforms (e.g., Facebook, LinkedIn), often referred to as cybervetting. However, various researchers cautioned against engaging in this emerging practice due to questions about the validity of social media assessments. Therefore, we conducted a systematic review to summarize initial research on the psychometric properties of social media assessments: Reliability, construct-related validity, and criterion-related validity. Our literature search yielded 12 studies with 536 raters and 2,019 ratees, and most of these studies addressed personality traits. We found that …

Catching The Digital Wave : Jumbo Group Shows How Digital Transformation And Its Seafood Business Can Go Together, Xuesong Geng, Andrew Chin, Thomas Lim

Research Collection Lee Kong Chian School Of Business

JUMBO Group shows how digital transformation and its seafood business can go together.

Knowledge “Hiding And Seeking” During The Pandemic: Who Really Wins In The New Normal?, Kim-Lim Tan, Ivy S. H. Hii, Chuen Kong Kevin Cheong

Research Collection Lee Kong Chian School Of Business

PurposeThe recent COVID-19 pandemic caused a severe economic downturn. Employees working in these organisations face employment uncertainty. The pandemic disrupted their daily routines, and it added a layer of complexity to the already resource-constrained environment. During these times, employees would conserve their resources to maintain competitiveness, one of which is knowledge hiding. While economic activities are resuming, the appearance of new variants could mean the transition towards endemicity could be put on hold. Hence, there is a need to rethink the behaviour of employees as they would have elevated levels of anxiety towards resuming daily work activities. Therefore, this study …

Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …