Digital Commons Network^™

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

Spider Mite Response, Agronomic Performance, And Stability Of A Urochloa Spp. Diversity Panel Under Field Conditions, Adrian Mating’I Kimani, David Kariuki Muruu, Paula Espitia-Buitrago, Sylvia Henga, Catherine Muui, Frank Chidawanyika, Rosa Noemi Jauregui

All Peer-Reviewed Publications

Spider mites (Oligonychus trichardti) are emerging as a major constraint to Urochloa forage productivity in East Africa; however, knowledge of genotypic variation and tolerance remains limited. Herein, 55 Urochloa genotypes were evaluated under field-infested and non-infested conditions across two seasons using an alpha-lattice design. Agronomic and physiological traits, including plant height (PH), tiller number (TN), the Normalized Difference Vegetation Index (NDVI), total dry weight (TDW), and mite damage indices (visual severity index (VSI) and stress tolerance index (STI)) were assessed. Infestation reduced biomass by 22.4% on average, with reductions of up to 45% in susceptible genotypes. Significant genotypic variation was …

Agronomic Practices For Climate-Resilient Okra Production In Agroecological Systems: A Review, Samuel Mathu Ndungu, Lokeshwar Kesamreddy, Mathieu A.T. Ayenan, Stephen Othim, Siyabusa Mkuhlani, Yu Hsiang Liu, Amha Besufkad, Wubetu Bihon Legesse, Eric C. Legba, Judith Honfoga, Lukas Pawera

All Peer-Reviewed Publications

Okra is increasingly recognized as a climate-resilient crop within diversified agroecological systems; however, an integrated synthesis of its biophysical, agronomic, and socioeconomic dimensions is lacking. This review consolidates the current knowledge on okra physiology, production ecology, and value chains, with a focus on climate resilience under heat, drought, salinity, pest, and disease pressures. Evidence across regions demonstrates that agroecological practices such as crop diversification, mulching, conservation tillage, integrated pest management, and the use of organic and biological inputs consistently improve soil health, stabilize yields under climatic stress, and reduce dependence on external inputs, although with trade-offs in labor demand and …

Be Responsible In Your Answers! Monitoring Out-Of-Domain Behaviors In Domain-Specific Llms, Boquan Li, Chenzhe Lou, Zhe Ren, Peixin Zhang, Zirui Fu, Jun Sun, Yaowen Zheng

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have accelerated the rapid development of chatbot web applications in various domains, such as coding, biomedicine and psychology. Compared to general LLMs like ChatGPT, domain-specific LLMs require a greater sense of responsibility. For instance, if a programming LLM casually answers medical or psychological questions, it not only misleads the public but also poses legal risks. This highlights new demands for monitoring and preventing such irresponsible behaviors. Existing efforts attempt to monitor LLMs from multiple aspects, such as lying, jailbreaks, and toxic content, while overlooking out-of-domain behaviors. In this work, we propose an innovative LLM domain monitoring …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

The Woah Global Wildlife Health Collaborating Centre Network (Woah-Wildnet): A Coordinated And Transformative Approach To Global Wildlife Health Challenges, David T.S. Hayman, Steve Unwin, Kelly Bateman, Casey Barton Behravesh, Charlotte Berg, Jemma Bergfeld, Cristina Casalone, Claire Cayol, Erin Davis, Sunday Ekesi, Johan Esterhuizen, Merid Getahun, Federica Giorda, Keith Hamilton, Damien O. Joly, Christa Kühn, Jean Claude Manuguerra, Daniel Masiga, Anita Michel, Paolo Mulatti, Misheck Mulumba, Annah Njui, Richard Paley, Antonio Fernández, Sascha Knauf, David P. Tchouassi, Youming Wang, Nathalie Vachiery, Jandouwe Villinger

All Peer-Reviewed Publications

Introduction Wildlife health is integral to functioning, complex ecosystems [1], directly and indirectly influencing the health of people, animals, plants, and the environment [2–4]. Healthy wildlife populations are essential for ecosystem services and are at the heart of the One Health approach [3,4], which aims to sustainably balance and optimize the health of people, animals, and ecosystems through multisectoral and transdisciplinary collaboration [5]. Despite its importance, wildlife health initiatives often operate in silos, limiting capacity to address transboundary threats such as emerging diseases, pollution, and environmental changes. Anthropogenic changes, including habitat loss, degradation, fragmentation, and unsustainable harvesting, exacerbate wildlife health …

Teamwise: Exploring Virtually Embodied Ai Facilitation For Video-Based Team Onboarding, Venkata Akhila Rani Obilisetty, Mikkeline Elleby, Anthony Tang, April Yi Wang

Research Collection School Of Computing and Information Systems

AI-mediated facilitation has emerged as a scalable approach to supporting onboarding and coordination in newly formed remote teams, yet existing systems are predominantly text-based. To explore how video-based, virtually embodied AI facilitators shape team experiences, we present TeamWise, which joins video-based onboarding meetings as an on-screen avatar. TeamWise guides teams through a structured facilitation flow of low-stakes activities to foster rapport, mutual awareness, and shared identity. While the overall sequence of activities and facilitation goals is predefined, the facilitator’s turn-by-turn utterances are generated dynamically by an LLM in response to participant input. We conducted a formative study of TeamWise to …

Challenges In Synchronous And Remote Collaboration Around Visualization, Matthew Brehmer, Maxime Cordeil, Christophe Hurter, Takayuki Itoh, Wolfgang Büschel, Mahmood Jasim, Arnaud Prouzeau, David Saffo, Lyn Bartram, Sheelagh Carpendale, Chen Zhu-Tian, Andrew Cunningham, Anthony Tang, Samuel Huron, Masahiko Itoh, Arpit Joshi, Kiyoshi Kiyokawa, Hideaki Kuzuoka, Bongshin Lee, Guillermo Molina León

Research Collection School Of Computing and Information Systems

We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence …

Reasoning On Time-Series For Financial Technical Analysis, Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and …

Discrete Diffusion For Bundle Construction, Teng Tu, Ai Li, Yunshan Ma, Shuo Xu, Xiaohao Liu, Haokai Ma, Liang Pang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

As a central task in product bundling, bundle construction aims to select a subset of items from large item catalogs to build an entire bundle or, more practically, complete a partial bundle. Existing methods often rely on the sequential construction paradigm that predicts items one at a time, nevertheless, this paradigm is fundamentally unsuitable for the essentially unordered bundles. In contrast, non-sequential methods model a bundle as a set, but still face two dimensionality curses: the combinatorial space grows exponentially with both bundle length and catalog size. Accordingly, we identify two technical challenges: 1) how to effectively and efficiently model …

Artificial Intelligence For Better Inclusive Higher Education: A Study Of A Private University In Cairo, Shereen Ismail

Theses and Dissertations

This study explores how artificial intelligence (AI) tools relate to accessibility for visually impaired students in Egyptian higher education, focusing on University Y in Cairo. Grounded in the Social Model of Disability and Universal Design for Learning, it examines visually impaired students’ lived experiences of AI mediated access and the institutional conditions that shape these experiences. A qualitative phenomenological design was used, with semi-structured interviews conducted with visually impaired undergraduate students and key staff involved in assistive technology, teaching and learning, and student wellbeing; data were analyzed thematically. The findings indicate that participants experience AI tools (e.g., chatbots, transcription and …

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

Learning Feature Inversion For Multi-Class Anomaly Detection Under General-Purpose Coco-Ad Benchmark, Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, Dacheng Tao

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill these gaps, this work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. This enables fair evaluation and sustainable development for different methods on this challenging benchmark. Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which …

Developing Blockchain-Based Transparent E-Commerce Solutions For Danish Smes To Promote Sustainable Design Products, Somnath Mazumdar, Robert John Kauffman, Thomas Jensen, Raghava Rao Mukkamala, Jan Damsgaard

Research Collection School Of Computing and Information Systems

Typically, a firm's objectives include establishing consumer confidence, preserving its brand image, and developing a profitable business strategy. Consumers now place greater emphasis on the sustainability and transparency of their purchases. Given environmental and economic limitations, firms are often compelled to implement sustainable production methods. This is especially a struggle for small- and medium-sized enterprises (SMEs) with new technology, as it can increase their risk of failure. This has led to a problem for consumers, who must cross-check the sustainability-related claims of the firms they buy from. This is challenging because of limited process trace data and restricted enforcement capabilities. …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …

Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose SHAREDLLM, a novel framework based on multi-grained context compression and query-aware information acquisition. SHAREDLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. …

Distributional Vision-Language Alignment By Cauchy-Schwarz Divergence, Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize mutual information, primarily aligning pairwise samples across modalities while overlooking distributional differences. In addition, InfoNCE has inherent conflict in terms of alignment and uniformity in multimodality, leading to suboptimal alignment with modality gaps. To overcome the limitations, we propose CS-Aligner, a novel framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz (CS) divergence with mutual information. CS-Aligner captures both the global distribution information of each modality and the pairwise semantic relationships. We find that the CS divergence …

Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

We consider spatial panel data models with genuine unbalancedness arising from the non-presence of some spatial units in certain time periods. General M-estimation methods are proposed for model estimation, which take into account the estimation of the incidental fixed effects parameters and allow for spatiotemporal heteroskedasticity and high-order time-varying spatial effects. Corrected plug-in methods are proposed for standard error estimation. The proposed estimation and inference methods are rigorously studied for their asymptotic properties and finite sample performance. An application to China’s provincial FDI inflows shows that properly accounting for genuine unbalancedness uncovers significant positive spatial spillovers that are masked when …

Thinktank-Me: A Multi-Expert Framework For Middle East Event Forecasting, Haoxuan Li, He Chang, Yunshan Ma, Yi Bin, Yang Yang, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East–focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting …

Negotiating At A Distance: The Impact Of Communication Media And Negotiator Traits, Dorcas Quek Anderson, Tra My Ngo

Research Collection Yong Pung How School Of Law

Purpose – Prior research has yet to provide a coherent theoretical framework explaining how communication media hinder or advance negotiation success, and many dated studies are unlikely to be relevant. This study aims to examine the impact of four communication media on negotiation outcomes. It also examines the potential moderating effects of the following negotiator characteristics: conflict management style, personality traits and indirect communication style.Design/methodology/approach – A total of 400 participants formed 200 dyads to negotiate a mixed- motive relational conflict through face-to-face (FTF) interaction, videoconferencing, audio call or synchronous text messaging. Linear mixed regression was used to assess the …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

جريمة تزوير المستند الالكتروني طبقا للمرسوم بقانون اتحادي رقم (46) لسنة 2021 بشأن المعاملات الإلكترونية وخدمات الثقة, كلثم راشد المزروعي

Theses

The Crime of Electronic Document Forgery under UAE Legislation: A Legal and Judicial Analysis in Light of Digital Transformation

This study examines the crime of electronic document forgery in light of rapid digital transformation and the challenges it poses to legal systems, particularly in the United Arab Emirates. The research problem lies in the absence of a comprehensive legal framework, the difficulty of classifying technologically evolving forms of forgery, and the challenges associated with proving such crimes using digital evidence.

The study aims to analyze the legal nature of electronic document forgery, distinguish it from traditional forgery, identify its elements …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Modeling And Autonomous Control Systems For A Delivery Drone Application, Dana Helal Alnuaimi

Theses

This thesis focuses on the development of a multi-drone navigation and control system, aiming to enhance payload capacities beyond the limits of single-drone systems. By integrating multiple drones to work collaboratively as a unit, this study addresses the challenges associated with lifting and transporting heavier payloads.

The primary objective is to design and evaluate a multi-drone system capable of working in tandem to transport larger payloads efficiently. The research aims to develop robust control algorithms, supported by system identification for dynamic modeling, and navigation strategies to enable effective coordination between drones.

The study employs a combination of simulation and real-world …

Analysis Of A Coaxial Transmission Line Filled With An Orthorhombic Dielectric-Magnetic Medium, Fathima Manikunnath Abdul Akbar

Theses

Coaxial transmission lines are fundamental means for Transverse Electromagnetic (TEM) wave propagation in RF, microwave and high-speed electronic systems. The study of transmission lines is often familiar when they are filled with isotropic materials; however modern engineered direction dependent materials reshape field distributions. In this thesis, we consider a coaxial transmission line of an inner radius and outer radius b filled with an orthorhombic dielectric-magnetic material, which is described by two anisotropy parameters αx and αy. The potential and field distributions are studied in relation to the ratio b/a as well as the anisotropy parameters αx and αy. Due to …

Optimizing Duckweeds Harvesting Strategies To Maximize Nutrient Recovery And Its Role As A Phytosensor In Different Water Sources While Maintaining Sustainable Biomass Growth, Basma Ghannam

Theses

This thesis is concerned with determining the optimal water treatment and harvesting strategy for Lemna minor duckweed plants. The main objective of this thesis is to examine how duckweed plants grow in terms of biomass, photosynthetic activity, leaf morphology, chlorophyll and carotenoid content, and their efficiency in nutrient removal from different water sources. The methodology of the study included assessing duckweed in three water samples including tap water, saline water, and greywater and three harvesting strategies, including low, medium, and high harvesting intensities to assess their growth and nutrient removal efficiency. The results of the study showed that L. minor …

الوساطة الجزائية ودورها في إنهاء الدعوى الجزائية (دراسة مقارنة), فاطمة راشد السبوسي

Theses

Criminal mediation, criminal proceedings, restorative justice, UAE Criminal Procedure Law, alternatives to criminal litigation

This thesis addresses the topic of criminal mediation as one of the modern mechanisms adopted by contemporary criminal legislation as an alternative to traditional criminal proceedings. The study focuses on the United Arab Emirates, where the Federal Decree-Law No. (38) of 2022 on Criminal Procedure codified criminal mediation, particularly in Articles 352 and following. This raises questions regarding the adequacy and appropriateness of the legal framework regulating this system.

The research problem lies in assessing the extent to which the UAE legal system effectively organizes criminal …

Efficient Fpga Implementation Of A 1 Million-Point Fft, Jwaher Abdulqader Al Tamimi

Theses

The one-million-point Fast Fourier Transform is implemented using a radix-2 single-path delay feedback pipeline architecture. To minimize the computational overhead, twiddle factors were pre-computed and stored in memory. The design uses a fixed-point representation with two integer bits and seven fractional bits, achieving a measured signal-to-noise ratio of 37.98. Given the substantial memory requirements, a memory partitioning approach was used. It mapped the delay buffers in each stage lookup table memory, block random-access memory, or ultra random-access memory, based on word width and memory depth.

The implementation operates successfully at 100 megahertz on a mid-scale field-programmable gate array. Post-implementation reported …

Transport Of Quantum Walks In Electric Fields, Yousef Mohammad Yousef Salah

Theses

This thesis presents an analysis of transport in one-dimensional discrete-time quantum walks (DTQWs) on the Hilbert space ℓ²(ℤ) ⊗ ℂ². Quantum walks serve as fundamental models of coherent quantum transport and exhibit ballistic spreading driven by superposition and interference. The primary focus of this work is the review and derivation of sharp maximal velocity bounds for several classes of quantum walk step operators, including the shift-coin walk, the split-step walk, and models with constant as well as position-dependent coin operators. We establish general a priori bounds that remain valid beyond the translation-invariant regime. For homogeneous models, Fourier and spectral analysis …