Digital Commons Network^™

Nanosilica Supplementation In Tomato Increases Oviposition On Stems And Caterpillar Mortality In The Tomato Pinworm, Kokou R. Fiaboe, Fathiya M. Khamis, Xavier Cheseto, Abdullahi A. Yusuf, Baldwyn Torto

All Peer-Reviewed Publications

Silicon-induced responses play a key role in plant defense against herbivory, though the underlying mechanisms remain underexplored. In this study, we examined how mesoporous silica nanoparticles (MSNs) affect tomato (Solanum lycopersicum) defense against an invasive and highly destructive lepidopteran herbivore, the tomato pinworm (TPW), Phthorimaea absoluta. In tomato plants supplemented with MSN, prior exposure to TPW oviposition shifted subsequent egg-laying from a preference for leaves to an even distribution between stems and leaves. This shift was not observed in nonsilicon-supplemented plants. Prolonged oviposition triggered pigmentation in the basal cells of type I glandular trichomes on the stems of silicon-supplemented plants. …

From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose: The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program. Design/methodology/approach: This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former …

Verification Of Bit-Flip Attacks Against Quantized Neural Networks, Yedi Zhang, Lei Huang, Pengfei Gao, Fu Song, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amount of bits within its parameter storage memory system to induce harmful behavior), has emerged as a relevant area of research. Existing studies suggest that quantization may serve as a viable defense against such attacks. Recognizing the documented susceptibility of real-valued neural networks to such attacks and the comparative robustness of quantized neural networks (QNNs), in this work, we introduce BFAVerifier, the first verification framework designed to formally verify the absence of bit-flip attacks against …

The Lao Penal Code: A Comparative Discourse, Wing Cheong Chan, Wing Cheong Chan, Phanthasak Mingnakhone, Perrine Simon, Stanley Yeo

Research Collection Yong Pung How School Of Law

This book comprises a commentary on the criminal law of Laos dealing with the general principles of criminal responsibility, and describing the main offences against the person and property under the Lao Penal Code 2017.

Ada-Gen: Iterative And Incremental Generation Of Full-Stack Apps For Learning Agile/Devops Software Development Practices, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly …

On Unraveling Student Resilience And Academic Performance In Higher Education, Aldy Gunawan, Ee-Peng Lim, Audrey Tedja Widjaja, William Tov, James Foo, Lieven Lode E. Demeester

Research Collection School Of Computing and Information Systems

The transition period from pre-tertiary to higher education levels is critical. We explore the role of resilience by conducting a survey to investigate students’ resilience and the relationship with overall academic performance, learning experience, and well-being. This effort is part of an initiative to develop strategies for better student engagement in the academic program, enhance their resilience, and prepare them for a competitive job market. We conclude that (i) high-resilience students are associated with better life satisfaction and are likely to perform well academically, (ii) a favorable learning environment supports students to study and perform well in the university, and …

Capo: Cooperative Plan Optimization For Efficient Embodied Multi-Agent Cooperation, Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees Snoek, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where discussion and cooperative plan are crucial. To solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance the cooperation efficiency of LLM-based embodied agents. Inspired by human cooperation schemes, CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and …

Neural Multi-Objective Combinatorial Optimization Via Graph-Image Multimodal Fusion, Jinbiao Chen, Jiahai Wang, Zhiguang Cao, Yaoxin Wu

Research Collection School Of Computing and Information Systems

Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve …

Llm-Enhanced Multiple Instance Learning For Joint Rumor And Stance Detection With Social Context Information, Ruichao Yang, Jing Ma, Wei Gao, Hongzhan Lin

Research Collection School Of Computing and Information Systems

The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. Rumors can be identified by cross-referencing stances in related posts, and stances are influenced by the nature of the rumor. However, existing stance detection methods often require post-level stance annotations, which are costly to obtain. We propose a novel LLM-enhanced Multiple Instance Learning (MIL) approach to jointly predict post stance and claim class labels, supervised solely by claim labels, using an undirected microblog propagation model. …

Sita: Structurally Imperceptible And Transferable Adversarial Attacks For Stylized Image Generation, Jingdan Kang, Haoxin Yang, Yan Cai, Huaidong Zhang, Xuemiao Xu, Yong Du, Shengfeng He

Research Collection School Of Computing and Information Systems

Image generation technology has brought significant advancements across various fields but has also raised concerns about data misuse and potential rights infringements, particularly with respect to creating visual artworks. Current methods aimed at safeguarding artworks often employ adversarial attacks. However, these methods face challenges such as poor transferability, high computational costs, and the introduction of noticeable noise, which compromises the aesthetic quality of the original artwork. To address these limitations, we propose a Structurally Imperceptible and Transferable Adversarial (SITA) attacks. SITA leverages a CLIP-based destylization loss, which decouples and disrupts the robust style representation of the image. This disruption hinders …

Open-Set Graph Anomaly Detection Via Normal Structure Regularisation, Qizhou Wang, Guansong Pang, Mahsa Salehi, Xiaokun Xia, Christopher Leckie

Research Collection School Of Computing and Information Systems

This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced …

Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …

Scuzer: A Scheduling Optimization Fuzzer For Tvm, Xiangxiang Chen, Xingwei Lin, Jingyi Wang, Jun Sun, Jiashui Wang, Wenhai Wang

Research Collection School Of Computing and Information Systems

The concept of Deep Learning (DL) compiler was proposed to deploy DL models more efficiently on diverse hardware through optimization techniques. As one of the most popular DL compilers, TVM incorporates three levels (high-level, schedule, and low-level) of optimizations, which can inadvertently introduce code logic bugs and build failure bugs. Among these optimizations, scheduling optimization is the core component of DL compilers, which ensures the acceleration of models on all devices. However, the existing works only focus on the testing of high-level and low-level optimizations in TVM, fail to take the most important and challenging intermediate scheduling optimization layer into …

Sdrc Update (Volume 6, Issue 3), Joanna Cayton

SDRC Publications

No abstract provided.

Know Where You Go: Infestation Dynamics And Potential Distribution Of Two Bed Bug Species (Hemiptera: Cimicidae) In Africa, Dennis M. Mbuta, Bonoukpoè M. Sokame, Fathiya M. Khamis, Komivi S. Akutse

All Peer-Reviewed Publications

Bed bugs (Hemiptera: Cimicidae) are blood-feeding insects closely linked with humans and animals, causing discomfort, and posing potential threats as disease vectors. This study focuses on Cimex lectularius (common bed bug) and Cimex hemipterus (tropical bed bug), the two key species identified in Africa. Cimex lectularius infests human habitats, while C. hemipterus, more common in Africa, infests diverse habitats, including bat colonies. In our study, we investigated the infestation dynamics and distribution of bed bugs in Africa, when considering climate, habitat, and host availability using system dynamics and ecological niche modelling techniques. System dynamics modelling analyses in Kenya revealed varied …

Cultivation Of Global Competencies In The Post-Covid-19 Era: A Multinational Survey, Reiko Yamada, Rie Mori, Yumiko Sugitani, Takurya Kimura, Shihwei Sun

Journal of Comparative & International Higher Education

No abstract provided.

Interpretative Phenomenological Analysis Of Wellbeing Of An International Student With Depression And Suicidality From A Coping Perspective, Rumiko Nakano

Journal of Comparative & International Higher Education

No abstract provided.

Students On The Move: Assessing Intercultural Competence Of American Study Abroad Sojourners In Japan And Korea, Jeongi Lee, Kathryn Negrelli

Journal of Comparative & International Higher Education

No abstract provided.

Perspectives Of Support Services Among Non-Traditional International Students: A Phenomenological Exploration, Christian M. Gallie, Robert A. Griffin

Journal of Comparative & International Higher Education

No abstract provided.

Women In Academic Leadership In The East And The West: Navigating Challenges To Attain Key Leadership Positions In Male-Dominated Societies, Ngoc Lan Thi Dang

Journal of Comparative & International Higher Education

No abstract provided.

Comparative And International Higher Education And The Aid Sector Graduate Pipeline, Zeena Zakharia, Deepa Srikantaiah, Shytance Wren, Langan Courtney, Katya Murillo

Journal of Comparative & International Higher Education

No abstract provided.

Transporting Hbcu Students To Morocco Through Films: Action Research And Reflection On A Piloted Course, Roger W. Anderson

Journal of Comparative & International Higher Education

No abstract provided.

Book Review Of Empires Of Ideas: Creating The Modern University From Germany To America To China By William C. Kirby (Harvard Up, 2022), Ryan M. Allen

Journal of Comparative & International Higher Education

No abstract provided.

Development Of Al Ain Flash Flood Risk Maps\ Utilizing High-Resolution And Accurate Remote Sensing Data, Khaled Khamis Al Arfati

Dissertations

Al Ain City has undergone significant land use transformations from 1992 to 2022, marked by a population increase of over 50% and extensive development activities. This period has seen the construction of new residential areas, infrastructure, and commercial establishments, as well as enhancements to green spaces. An analysis of the Digital Terrain Model (DTM) revealed an 83% alteration in the terrain due to urbanization and shifting sand dunes, with most elevation changes under 5 meters.

The primary aim of this dissertation is to develop accurate flash flood risk maps for

Al Ain using high-resolution remote sensing data. This research highlights …

المسؤوليَّة الجِنَائِيَّة عن الاتجار بالأعضاء والأنسجة البشريَّة في ضوء المرسوم بقانون اتحادي رقم (25) لسنة 2023 – دراسة مقارنة, مريم سعيد الحمودي

Theses

Criminal Liability for Trafficking in Human Organs and Tissues in Light of Federal Decree Law No. (25) of 2023 – “A Comparative Study”

The crimes of trafficking in human organs and the legal responsibilities they raise are among the most heinous, complex, and multidimensional criminal phenomena, and are considered the most dangerous sources of human rights violations, because they have taken an approach that violates the integrity, dignity, and humanity of the human body

The trade in human organs, as one of the forms of human trafficking crimes, which is an organized cross-border crime, has become a fast …

School Leadership Insights And Practices: Understanding Bullying Prevention Strategies Among Teachers In Private And Public Schools Across The Uae, Wissal Mohsen

Theses

This thesis investigates the detrimental effects of bullying on students, including its impacts on mental health, academic performance, and overall well-being. Addressing this issue necessitates a proactive strategy involving school leaders, teachers, and parents. Data was collected via a questionnaire: selected items employed a 5-point Likert scale, while others used a multiple-choice format. Analysis was conducted in SPSS and included descriptive statistics, one-way ANOVA, t-tests, and binary logistic regression. The t-tests indicated that leadership practices implemented to prevent bullying in both public and private schools were highly effective and statistically significant (p < .05). This research contributes to our understanding of how school leadership and teacher involvement influence the efficacy of bullying-prevention strategies. By using quantitative methods, the study offers a comprehensive examination of school policies, bullying-awareness initiatives, and reporting systems. The findings highlight the importance of adopting context-specific approaches to bolster prevention efforts within schools. Future research that combines qualitative and quantitative analyses may yield deeper insights into the real-world challenges encountered in bullying-intervention strategies, thereby bridging the gap between policy formulation and practical implementation to ensure anti-bullying programs are both effective and culturally relevant.

The Impact Of Rubric-Based Feedback On Efl Writing Proficiency: Exploring Revision Skills And Learning Transfer, Ahmed Abdelraheem Elyan

Theses

This study explores how rubric-based feedback influences the writing development of English as a Foreign Language (EFL) learners, with a particular focus on their revision practices, learning transfer, and the challenges they face with cognitive overload. The research was carried out in a high school context in the United Arab Emirates (UAE), aiming to understand the real impact of structured feedback on students’ writing performance.

Twenty Arabic-speaking students participated in the study, completing four essay writing tasks over eight weeks. These included a pre-test, two feedback-supported drafts, and a post-test. Their essays were assessed using an analytic rubric designed to …

Construction Of Stock Portfolios By Machine Learning Methods, Alfan Gehad Abulehia

Theses

We study the theory and application of two machine learning (ML) algorithms: Long-Short Term Memory Network (LSTM) and Random Forest. The study begins by providing an overview of mathematical foundation, highlighting the significance of mathematics in machine learning. We study and explain the mathematical details involved in these two algorithms. We also study closely related subjects which include but not limited to gradient descent, automatic differentiation, and recurrent neural network. The main ideas behind these topics form the foundation of any ML algorithms.

As an application of the ML algorithms, we focus on the U.S. stock market. We build stock …

A Bayesian Belief Networks Approach To Prioritize Road Safety Mitigation Measures For Eco-Mobility Modes, Fatmah Mohamed Alyammahi

Theses

This study presents an innovative decision-making approach that enables the prioritization of road safety measures for eco-mobility users—pedestrians, cyclists and light-electric micromobility riders—in Abu Dhabi. The analysis uses 2032 eco-mobility crash records (1016 pedestrian, 567 bicycle, 449 e-scooter) from 2020-2023 supplied by Abu Dhabi Police and the Department of Municipalities and Transport. A supervised Bayesian Belief Network (BBN) was constructed around six empirically estimated variables: visibility, predictability, road curvature, obstructions, lighting and speed limit. Maximum-likelihood conditional-probability tables were derived from the cleaned dataset; Beta priors were applied only where cell counts were sparse. Ten conventional machine-learning models—including Random Forest, Gradient …

Assessing Readiness For Transformation From Rulebased To Ai-Based Chatbot In Uae Healthcare: A Case Study Of A Rehabilitation Hospital In Abu Dhabi, Mubarak Alketbi

Theses

This study investigates the readiness for transforming rule-based chatbots to AI-based chatbots in UAE healthcare, examining a rehabilitation hospital in Abu Dhabi through quantitative research involving healthcare professionals (N=96) and technical analysis. Findings revealed positive perceptions of the current system alongside enhancement opportunities through AI capabilities, with perceived usefulness strongly correlating with behavioural intention, high service quality ratings for empathy and responsiveness, midcareer professionals demonstrating the highest AI acceptance levels, and system integration identified as the highest priority implementation area.

The research contributes to healthcare technology transformation knowledge in the UAE by providing a structured implementation framework addressing technical requirements, …