Digital Commons Network^™

Enhancing Student Engagement In Introductory Economics With Interactive Videos And Synthesized Readings In Flipped Learning, Bei Hong, Duan Ning, Magdeleine Lew

Research Collection School Of Economics

A major challenge faced by university economics instructors is to find more time to incorporate active learning into the classroom, which encourages student participation. As a result, more instructors choose to flip their classrooms, in which students are encouraged to perform preparatory reading or watch videos to participate more meaningfully and learn more effectively. This study aims to examine how pre-lecture materials enhance student participation by examining the effectiveness of two types of materials: interactive videos and synthesized readings. The synthesized readings contain essential information extracted from the textbook and is presented in a structured manner with highlighted key points. …

Clonality And Epidemiological Studies Of E. Coli Strain Collected From Abu Dhabi Emirate Using Ngs And Bioinformatic Tools, Ahmed Abdelrahman Mohamed

Theses

In the current landscape, where antibiotic resistance in bacteria is steadily rising, the role of bacterial genomics has assumed increasing significance. Therefore, it is necessary for microbiologists to acquire proficiency in the utilization of these methods and tools to harness their potential in gaining valuable insights into bacterial genomes. This can facilitate the development of innovative strategies for managing and treating bacterial infections. Here we present the power of the whole-genome sequencing technology in characterization of multidrug resistant Escherichia coli isolates. Fourteen Escherichia coli isolates were selected for this study. Following the standard microbiological techniques to confirm the sample identification, …

Relationship Between Parents’ Feeding Styles, Nutrition Practices And Risk Of Obesity Among 3-5 Years Old Children In The United Arab Emirates, Reem Mohamed Alkarbi

Theses

Previous studies suggest that parental feeding practices shape preschool eating and physical activity habits, ultimately influencing children's weight status. Despite the high obesity rate in UAE youth, preschoolers’ nutrition habits and their links to parental feeding styles are explored. This study aims to describe parental feeding styles: Emotional (E), Instrumental (I), Prompting and Encouragement (PE), Control Over Eating (COE)), and their associations with nutrition practices, socio-demographic factors, and obesity in UAE preschoolers. Using an online questionnaire, a cross-sectional study surveyed Emirati families with 3-5-year-old children. Data was analyzed using SPSS v.29. 70 preschool Emirati children living in Abu Dhabi participated. …

Technology-Enhanced Mathematics Teaching And Learning: Teachers’ Perspectives On Digital And Generative Ai–Enabled Tool Integration Through A Tpack Lens In Abu Dhabi Schools, Abdalrahman A. Adlouni

Theses

Digital learning is an active approach to education in daily life, and it has been implemented in all the schools in the United Arab Emirates (UAE). Many strategies, such as platforms, online educational websites, and other applications, were used by teachers to support blended learning and assist students in achieving their goals and accomplishing what is required from their curriculum. The main objective of this study is to examine teachers’ perceptions toward teaching mathematics using digital learning tools in Cycle 2 and Cycle 3. Data was collected from (n=114) mathematics teachers in Abu Dhabi City, UAE, during the spring semester …

Effects Of A Taekwondo-Inspired Intervention Program On Children With Adhd, Zubaida Fares Shraim

Dissertations

Exercise and physical activity are beneficial for individuals with Attention Deficit Hyperactivity Disorder (ADHD). The main aim of this study is to investigate the effect of a Taekwondo-inspired intervention program on children with ADHD in the UAE context. The research utilized a mixed methods/quasi-experimental design approach, consisting of two phases. Within the first phase, a promising intensive intervention for children with ADHD has been implemented for five weeks, four sessions weekly. The sample of the first phase consisted of four children aged 8-10 years diagnosed with ADHD. Three assessment instruments were used to collect data in phase one and explore …

Predictive Capacity Of Immune-Related Adverse Events And Cytokine Profiling In Neoadjuvant Immune Checkpoint Inhibitor Trials For Head And Neck Squamous Cell Carcinoma, Angela Alnemri, Sruti Tekumalla, Annie Moroco, Ioannis Vathiotis, Madalina Tuluc, Stacey Gargano, Tingting Zhan, David Cognetti, Joseph Curry, Athanassios Argiris, Alban Linnenbach, Andrew South, Larry Harshyne, Jennifer Johnson, Adam Luginbuhl

Department of Otolaryngology - Head and Neck Surgery Faculty Papers

OBJECTIVES: Certain low-level immune-related adverse events (irAEs) have been associated with survival benefits in patients with various solid tumors on immune checkpoint inhibitors (ICIs). We aimed to investigate the association between irAEs and response to neoadjuvant ICIs in patients with head and neck squamous cell carcinoma (HNSCC) and to identify differences in circulating cytokine levels based on irAE status.

METHODS: This was a retrospective cohort study including three neoadjuvant clinical trials from July 2017 to January 2022: NCT03238365 (nivolumab ± tadalafil), NCT03854032 (nivolumab ± BMS986205), NCT03618654 (durvalumab ± metformin). The presence and type of irAEs, pathologic treatment response, and survival …

Imitating Cost-Constrained Behaviors In Reinforcement Learning, Qian Shao, Pradeep Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert. Existing work in imitation learning and inverse reinforcement learning has focused on imitation primarily in unconstrained settings (e.g., no limit on fuel consumed by the vehicle). However, in many real-world domains, the behavior of an expert …

Pkt-Sin: A Secure Communication Protocol For Space Information Networks With Periodic K-Time Anonymous Authentication, Yang Yang, Wenyi Xue, Jianfei Sun, Guomin Yang, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Space Information Network (SIN) enables universal Internet connectivity for any object, even in remote and extreme environments where deploying a cellular network is difficult. Access authentication is crucial for ensuring user access control in SIN and preventing unauthorized entities from gaining access to network services. However, due to the complex communication environment in SIN, including exposed links and higher signal delay, designing a secure and efficient authentication scheme presents a significant challenge. In this paper, we propose a secure communication protocol for SIN with periodic k-time anonymous authentication (named PkT-SIN) that allows satellite users to anonymously authenticate to ground stations …

Sgsh : Stimulate Large Language Models With Skeleton Heuristics For Knowledge Base Question Generation, Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen

Research Collection School Of Computing and Information Systems

Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework …

Generalized Graph Prompt: Toward A Unification Of Pre-Training And Downstream Tasks On Graphs, Xingtong Yu, Zhenghao Liu, Yuan Fang, Et Al.

Research Collection School Of Computing and Information Systems

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the 'pre-train, fine-tune' and 'pre-train, prompt' paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of …

D3still : Decoupled Differential Distillation For Asymmetric Image Retrieval, Yi Xie, Yihong Lin, Wenjie Cai, Xuemiao Xu, Huaidong Zhang, Yong Du, Shengfeng He

Research Collection School Of Computing and Information Systems

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these oneto-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is …

The Whole Is Better Than The Sum : Using Aggregated Demonstrations In In-Context Learning For Sequential Recommendation, Wang Lei, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation datasets show that LLMSRec-Syn outperforms state-of-the-art LLM-based sequential recommendation methods. In some cases, LLMSRec-Syn can perform on par with …

Factors Affecting Online Consumers' Cultural Presence And Cultural Immersion Experiences In Live Streaming Shopping, Lifu Li, Kyeong Kang, Yafei Feng, Anqi Zhao

Research Collection School Of Computing and Information Systems

This paper conducts research on the interaction between ethnic minority group (EMG) live streamers and online consumers, aiming to present how EMG live streamers’ appearance and interaction factors affect online consumers’ cultural experience during live streaming shopping. Based on the social presence theory and the immersion theory, this paper explores how EMG live streamers impact online consumers’ cultural experience, and it designs personal EMG knowledge as a moderating variable under specific social and cultural backgrounds. Through analysing 325 online questionnaires based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the study results support that both …

On Efficient Object-Detection Nas For Adas On Edge Devices, Diksha Gupta, Rhui Dih Lee, Laura Wynter

Research Collection School Of Computing and Information Systems

Object detection is a crucial building block for Advanced Driving Assistance Systems (ADAS). These systems require real-time accurate detection on resource-constrained edge devices. Deep learning models are emerging as popular techniques over traditional methods with superior performance. A hurdle in deploying these models is the inference time and computational cost of these models, in addition to training challenges for specialized tasks.We address this using supernet training-based neural architecture search (NAS) to obtain a variety of object detection models at a scale specific to the ADAS application. To this end, we consider a convolutional neural network-based object detection model. We produce …

Network-Based Representations And Dynamic Discrete Choice Models For Multiple Discrete Choice Analysis, Huy Hung Tran, Tien Mai

Research Collection School Of Computing and Information Systems

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application …

Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li

Research Collection School Of Computing and Information Systems

Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and timely preventive actions. To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging masked graph representation, supplements traditional classification methodology, empowering analysts with critical insights for rapid responses. Through the empirical study, which took three person-months, we identify and summarize four fine-grained malicious behaviors during the lurking stage, constructing an annotated dataset. Our evaluation …

Inceptionnext: When Inception Meets Convnext, Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7×7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs, it largely harms the model efficiency on powerful computing devices due to the high memory access costs. For example, ConvNeXtT has similar FLOPs with ResNet-50 but only achieves ∼ 60% throughputs when trained on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt can improve speed, it results in significant performance degradation, which …

Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Radio Frequency fingerprinting, based on WiFi or cellular signals, has been a popular approach for localization. However, adoptions in real-world applications have confronted with challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ~1-2 meter accuracy in both 2D and 3D environments [13]. This method relies on the …

How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …

Criticality Aware Canvas-Based Visual Perception At The Edge, Ila Gokarn

Research Collection School Of Computing and Information Systems

Efficient and effective machine perception remains a formidable challenge in sustaining high fidelity and high throughput of perception tasks on affordable edge devices. This is especially due to the continuing increase in resolution of sensor streams (e.g., video input streams generated by 4K/8K cameras and neuromorphic event cameras that produce ≥ 10 MEvents/second) and computational complexity of Deep Neural Network (DNN) models, which overwhelms edge platforms, adversely impacting machine perception efficiency. Given the insufficiency of the available computation resources, a question then arises on whether selected regions/components of the perception task can be prioritized (and executed preferentially) to achieve highest …

Refining Chatgpt-Generated Code: Characterizing And Mitigating Code Quality Issues, Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Kla Tantihamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo

Research Collection School Of Computing and Information Systems

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation.In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, …

Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …

Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …

Re-Calibration Of Curial Intervention In Public Policy Challenges Against Arbitral Awards, Darius Chan, Elias Ngai Hum Khong

Research Collection Yong Pung How School Of Law

When an award debtor challenges an award on public policy grounds, usually the principle of finality prevails, and courts will consider the award debtor bound by the decision of the tribunal. However, because public policy has implications beyond the disputing parties themselves, some courts consider themselves justified in reviewing the award. There is therefore a tension between finality versus the court’s duty to stand as the guardian of public policy. Whether a review of an award should be allowed under this ground, and if so, the extent of permissible review, differs across various jurisdictions. For instance, common law authorities have …

Sustainability And The Sunlight Of Disclosure: Esg Disclosure In Three Asian Financial Centres, Rachel Phang, Yaru Chia

Research Collection Yong Pung How School Of Law

The prevailing paradigm of sustainable finance places great importance on environmental, social and governance (ESG) information and, therefore, on the mechanism of reporting and disclosure. This article presents, analyses and compares the approaches to ESG disclosure in three Asian financial centres: Singapore, Hong Kong and Shanghai. The article uncovers commonalities and divergences in the drivers, dynamics, content and trends of ESG disclosure in the selected jurisdictions. In doing so, it looks forwards, seeking to anticipate and shape the development of ESG disclosure in Asia. At the same time, it looks beyond ESG disclosure, remaining mindful that the mechanism itself is …

Fitting The Communication Forum To The Mediation Fuss: Choosing The Appropriate Communication Mode For Mediation In The Post-Pandemic World, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

Since the COVID-19 pandemic, mediations have shifted dramatically from face-to-face settings to the virtual realm, resulting in the widespread acceptance of using virtual communication channels, including videoconferencing, audio calls, and text messaging. With the waning of the pandemic, mediators and parties presently face a plethora of choices in fitting their mediation to the appropriate communication channel. Thus, having an accurate, evidence-based understanding of different communication modes’ impact on mediation is necessary to design an optimal mediation process.Some decades ago, Sander and Goldberg formulated the phrase “fitting the forum to the fuss” to describe the process of choosing the most appropriate …

Ai Employment Decision-Making: Integrating The Equal Opportunity Merit Principle And Explainable Ai, Gary Kok Yew Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of reasonable users (employing organisations) and affected persons (employees and job candidates) as to the AI output. Both the …

Fintech: Finance, Technology And Regulation, Nydia Remolina

Research Collection Yong Pung How School Of Law

Fintech: finance, technology and regulation, by Ross P. Buckley, Douglas W. Arner,and Dirk A. Zetzsche, Cambridge, Cambridge University Press, 2024, 330 pp, £29.99(paperback), ISBN 978-1-009-07821-4

Technology Selection In A Rural School District: A Case Study, Stephnie J. Helton

Electronic Theses and Dissertations

This study examined the steps taken to select technology devices, applications, and websites to be used in the classroom during the Covid-19 pandemic. As schools shut down around the world and transitioned to alternative methods of educational delivery, there was an increased need to analyze the selection process educators used to select technology devices, applications, and websites. Literature indicates that teachers could be overwhelmed with the many technology hardware and software choices available and end up making less-than-sufficient selections. This case study investigated what the technology director, administrators, and teachers deemed important and necessary in selecting educational applications and devices …

The Impact Of Soft Skills Training On Guest Satisfaction In The Hospitality Industry, Lawrie Colaco Mr

Masters Projects

The hospitality industry has become immensely fast-paced, and prioritizing guest satisfaction levels has been one of the main objectives. While hard skills such as technical expertise and operational proficiency are important aspects which are required by the hospitality staff, soft skills also play a pretty significant role. Soft skills enable the staff to be adaptive and communicative, it raises their empathy levels and teaches them to control their emotions and perform under stressful working environments. The importance of soft skills in this sector cannot be overemphasized, not only does this help with harbouring a positive attitude among the employees but …