Digital Commons Network^™

Implicit Neural Representation For Image Reconstruction, Canyu Zhang

Theses and Dissertations

Image reconstruction seeks to restore corrupted images and recover visual content that has been lost or degraded. Such degradation may result from low resolution, occlusion, masking, or shadow interference. This problem has become an increasingly significant research topic, as visual information plays a central role in almost every aspect of modern life. Neural network based approaches have recently emerged as highly effective solutions for this task. In particular, convolutional neural networks and transformer based architectures have demonstrated remarkable success in producing visually convincing reconstructions. However, these models remain constrained in several important ways, one of the most critical being that …

Computational Analogies In The Era Of Large Language Models, Amarakoon Mudiyanselage Thilini Wijesiriwardene

Theses and Dissertations

Analogical reasoning is an important part of human cognition requiring the integration of abstract reasoning, pattern recognition, and background knowledge. Despite significant advances in language modeling, the capacity of current methods to accurately identify, model, and evaluate analogies remains fundamentally underexplored.

Analogies enable individuals to perceive deep similarities between superficially different situations. Effective analogy-making requires integrating knowledge about the external world with abstract reasoning and pattern recognition capabilities. While current language models (LMs), trained on massive textual corpora using autoregressive or masked objectives, achieve impressive performance across Natural Language Processing (NLP) tasks such as text generation, summarization, and classification, their …

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

Boosting Chart-To-Code Generation In Mllm Via Dual Preference-Guided Refinement, Zhihan Zhang, Yixin Cao, Lizi Liao

Research Collection School Of Computing and Information Systems

Translating chart images into executable plotting scripts-referred to as the chart-to-code generation task-requires Multimodal Large Language Models (MLLMs) to perform fine-grained visual parsing, precise code synthesis, and robust cross-modal reasoning. However, this task is inherently under-constrained: multiple valid code implementations can produce the same visual chart, and evaluation must consider both code correctness and visual fidelity across diverse dimensions. This makes it difficult to learn accurate and generalizable mappings through standard supervised fine-tuning. To address these challenges, we propose a dual preference-guided refinement framework that combines a feedback-driven, dual-modality reward mechanism with iterative preference learning. Our approach introduces a structured …

Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improving Accuracy, Reliability, And Latency, Nazmus Ashrafi

Theses

The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi agent collaboration and (2) runtime execution of information-based …

Lens: Lightweight And Explainable Llm-Based Apt Detection At The Edge For 6g Security, Suhib Bani Melhem, Muhammed Golec, Abdulmalik Alwarafy, Yaser Khamayseh

All Works

Expected to be deployed in the early 2030s, sixth-generation (6G) wireless networks, with their high speed and integration with cutting-edge technology such as intelligent edge computing, expand the attack surface and face serious cyber threat risks such as Advanced Persistent Threats (APTs). This type of cyber attack can imitate benign network traffic and operate for long periods of time without being detected by traditional detection systems. This paper introduces LENS, a lightweight and explainable LLM-based network security framework designed to address this cybersecurity threat for 6G environments. LENS uses a fine-tuned DistilBERT model to convert raw network streams into natural …

Application Of Dematel Based On Bipolar Neutrosophic Sets For Sustainable Agriculture Practices, Lazim Abdullah, Nor Liyana Amalini Binti Mohd Kamal

Neutrosophic Systems with Applications

The development of natural capital is a fundamental objective within sustainable agricultural systems, where the optimization of both crop and livestock production is vital to addressing global food demands. Despite this imperative, major agricultural sectors such as paddy and rubber production, often fall short of satisfying consumption needs. This study aims to identify and prioritize the most influential criteria for sustainable agriculture using the Bipolar Neutrosophic Set-based Decision-Making Trial and Evaluation Laboratory (BNS-DEMATEL) method. Expert evaluations were elicited from five agricultural specialists using linguistic assessments to analyze the performance and interdependencies among sustainability criteria. Computational analyses were conducted using MATLAB …

Evaluating And Ranking Genai Chatbots Under Uncertainty: A Type-2 Neutrosophic Rancom–Marcos Mcdm Framework, Hend Ahmed, Abduallah Gamal

Neutrosophic Systems with Applications

Owing to integrate GenAI chatbots to enhance productivity across various tasks, this research presents T2NN-RANCOM-MARCOS multi-attribute decision-making model, which employs Type-2 Neutrosophic Number (T2NN) to handle uncertain data, the RANCOM method, distinguished by its easy, highly repeatable, less time consuming, more appropriate to deal with problems exceeds 5 criteria with expert errors to assign subjective weights to criteria and MARCOS method to evaluate and rank eight GenAI chatbots against six main criteria are included 23 sub-criteria: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, Trust and Confidence and Economic are primarily derived from QUEST evaluation …

Facilitating The Move Towards A Wellbeing Economy Through Supporting Infrastructure: A Review Of Frontrunner Countries, Henry Bartelet, Vincent De Gooyert

Quantitative Methods and Information Technology Faculty Publications

As more governments seek to shift beyond gross domestic product (GDP) as the dominant measure of progress, the concept of a “wellbeing economy” has gained traction. This approach prioritizes human and environmental wellbeing through broader, more holistic policy frameworks. A central question in this transition is what the implications of shifts to a wellbeing economy are for infrastructure investments. In this study, we identify four frontrunner governments, Bhutan, Scotland, New Zealand, and Wales, that have formally adopted wellbeing economy principles. We analyse how each operationalizes the concept and assess the resulting implications for infrastructure planning and investment. Despite shared goals, …

Dilemmas Encountered While Planning Social And/Or Political Elementary Mathematics Tasks, Amanda Sugimoto, Simon Byeonguk Han, Courtney Koestler, Eva Thanheiser, Mathew Felton-Koestler, Molly Robinson

Mathematics and Statistics Faculty Publications and Presentations

A mathematical task rooted in social and/or political contexts has the potential to engage students in exploring their own real-ities and learning about the realities of others while building their mathematical knowledge. While there are planning and in-structional practices recommended for these types of tasks, no single practice can fully capture the nuances and challenges thatteachers might encounter. Therefore, this study identified dilemmas encountered by a team of mathematics education research-ers while planning a mathematical task rooted in social and/or political contexts. Using qualitative analysis, the researchersidentified three planning dilemmas: (a) what social and/or political goal to establish, (b) how …

Retracted: Idea Density And Grammatical Complexity As Neurocognitive Markers, Diego Iacono, Gloria Feltis

Department of Neurology Faculty Papers

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

In Memoriam: Holmes Rolston Iii, Editorial Board

The International Journal of Ecopsychology (IJE)

No abstract provided.

فعالية برنامج رياضي لتنمية المهارات الاجتماعية لدى الصم, Nashwan Abdullah Nashwan, Hani Sulaiman Alkhaledi, Ayat J. Nashwan

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

هدفت الدراسة إلى الكشف عن فعالية برنامج رياضي لتنمية المهارات الاجتماعية لدى الصم، وقد تألف مجتمع الدراسة من (60) أصماً في محافظة الزرقاء، وتكونت عينة الدراسة من (20) أصماً من طلبة الصف التاسع الأساسي ضمن الفئة العمرية 14 عاماً، وتم اختيار عينة الدراسة بطريقة عشوائية تم تقسيمها الى مجموعة تجريبية تتكون من عشرة أصماء، وأخرى ضابطة تتكون من عشرة أصماء، واستخدم الباحثون المنهج شبه التجريبي، وتم تصميم مقياس المهارات الاجتماعية لدى الصم، وحصل المقياس على درجات جيدة من الصدق والثبات، وأظهرت النتائج بعد تطبيق البرنامج فعالية البرنامج لتنمية المهارات الاجتماعية لدى الصم، وتم تقديم عدد من المقترحات لمساعدة هذه الفئة …

Script-Based Inferences In An Image Schema Story Understander, Jamie C. Macbeth, Boming Zhang, Sharmin Badhan

Computer Science: Faculty Publications

Recent studies of large language models (LLMs) have revealed that they lack human-like cognitive models of reasoning and understanding. An important thread of research merges image schemas into symbolic artificial intelligence systems where their use as conceptual building blocks and primitives shows promise for the study of human cognition through intelligent systems that perform neurosymbolically. The work presented in this paper demonstrates image schema primitives being used in structures of a representation system called conceptual dependency (CD) and in broader commonsense knowledge structures called scripts. We present a story under- standing system that uses image schemas as primitives in scripts …

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Vibemus: Proactive Agentic System For Music Personalization, Zhiliang Guo, Teng Tu, Yunshan Ma, Xun Yang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) enable diverse forms of AI-assisted creation, yet they often struggle to bridge the preference-articulation gap: users may provide incomplete or vague intentions or lack the vocabulary to specify what they want, yielding outputs misaligned with true preferences. To address this gap and facilitate music creation in a vibe-centric environment, we introduce VibeMus, a proactive agentic system built on open-source components. The system engages in multi-turn dialogue to progressively determine the music’s emotion, genre, lyrics, and other aspects before generation. Simulated evaluations show that proactive clarification improves alignment with users’ intended nuances. Our approach is training-free, leveraging …

Scientific Multimodal Summarization : Integrating Knowledge Across Textual, Visual And Auditory Content, Zusheng Tan

Lingnan Theses (MPhil & PhD)

As scientific publications increasingly incorporate multimodal content, ranging from textual descriptions to figures, tables, presentation videos, and audio, there is a growing need for summarization systems that can effectively process and integrate information across these diverse modalities.

This work presents a comprehensive exploration of Scientific Multimodal Summarization, introducing a series of novel architectures and datasets aimed at advancing this emerging field. 1): We begin by introducing CMT-Sum, which integrates multimodal scientific source content (i.e., primarily paper text and figures) to generate high-quality textual summaries and identify representative graphical abstracts. We refer to this task as Scientific Multimodal Summarization with …

A Dynamic Hierarchical Attention Framework For Multimodal Malware Detection, Tamanna Nazmin

Graduate Theses and Dissertations

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller(DFC). Our methodology consistently classifies …

Lighttransfer: Your Long-Context Llm Is Secretly A Hybrid Model With Effortless Adaptation, Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In this work, we propose LightTransfer, a lightweight method that transforms models such as LLaMA into hybrid variants. Our approach identifies lazy layers -- those focusing on recent or initial tokens -- and replaces their full attention with streaming attention. This transformation can be performed without any training for long-context …

Towards Multimodal Emotional Support Conversation Systems, Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems’ ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, …

Guiding Multiple Remote Users In Physical Tasks With Language-Driven Robotic Telepresence, Ruyi Li, Jingfei Guo, Xinyi Zhang, Xuji Zhang, Zeqing Li, Jiannan Li, Jiangtao Gong

Research Collection School Of Computing and Information Systems

Remote assistance through robotic telepresence could involve both control and memory challenges, particularly in one expert to multiple workers situation. In this work, we proposed a novelty language-driven interface to facilitate remote collaboration through telepresence robots. Through operations and maintenance expert interviews and a scenario simulation study, we identified key pain points in executing one-expert-multiple-workers remote guidance using the telepresence robot and proposed two design goals, which together consist of five sub-design goals with corresponding features. These features were integrated into a standard telepresence robot, resulting in the development of a Collaborative LLM-based Embodied Assistant Robot, named CLEAR Robot. A …

Harnessing Graphs For Knowledge Representation In Natural Language Processing, Uras Varolgunes

Dissertations

This work proposes innovative methods for integrating domain-specific knowledge into natural language processing tasks through the use of graphs, aiming to enhance the performance of models across various domains, including finance and healthcare. Several novel approaches are proposed that fuse graph structures with modern deep learning techniques, addressing the challenges of missing word embeddings, label prediction, and graph representation learning for large language models.

First, a powerful embedding method built on top of the recent advances in latent graph learning is introduced to address the critical problem of word embedding imputation. Second, a graph-enhanced label attention model designed for medical …

The Effect Of Group Counseling On Hope With Diverse Undergraduates During Sustainability-Focused Internship, Hailey Spinks, Michael D. Hannon, Matthew Murray, Nina M. Goodey, Lora Billings, Amy Tuininga

PSEG Institute for Sustainability Studies Publications

Many adolescents report feeling overwhelmed, depressed, anxious, and hopeless in the face of global climate change. These feelings are both valid and antithetical to the necessary hope and courage it takes to address real-world environmental crises and contribute to meaningful societal impact. The Green Teams Program, an internship-based experiential learning initiative, prepares young professionals with skills and knowledge needed to address these challenges and provides an intentionally inclusive and safe environment for students from all backgrounds to do so. This structured environment includes psychoeducational group counseling among other supports. Intern self-assessments revealed significant gains in measures of Hope, Agency, …

Indifferentiability Analysis Of Symmetric Key Ciphers, Sayantan Paul

Doctoral Theses

The thesis presented here analyses the security of certain selected symmetric key ciphers - The ciphers analyzed are the 2 and 3-round Confusion-Diffusion Network, the 3-round Cascade Cipher with two independent keys, and the Feistel Construction with 7 and 8 rounds. Substitution Permutation Networks (SPNs) are widely used in the design of modern symmetric cryptographic building blocks. Attacks against the 2-round Confusion-Diffusion Network construction have been exhibited by Dodis et al. (2016a) in their Eurocrypt 2016 paper titled ‘Indifferentiability of Confusion-Diffusion Networks’, and by Da, Xu and Guo (2021b) in their paper ‘Sequential Indifferentiability of Confusion-Diffusion Networks’. Both attacks mentioned …

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …

Impact Of Retrieval Augmented Generation And Large Language Model Complexity On Undergraduate Exams Created And Taken By Ai Agents, Erick S. Tyndall, Colleen Gayheart, Alexandre Some, Joseph Genz, Torrey J. Wagner, Brent T. Langhals

Faculty Publications

The capabilities of large language models (LLMs) have advanced to the point where entire textbooks can be queried using retrieval-augmented generation (RAG), enabling AI to integrate external, up-to-date information into its responses. This study evaluates the ability of two OpenAI models, GPT-3.5 Turbo and GPT-4 Turbo, to create and answer exam questions based on an undergraduate textbook. 14 exams were created with four true-false, four multiple-choice, and two short-answer questions derived from an open-source Pacific Studies textbook. Model performance was evaluated with and without access to the source material using text-similarity metrics such as ROUGE-1, cosine similarity, and word embeddings. …

Modeling The Importance Of Life Exposure Factors On Memory Performance In Diverse Older Adults: A Machine Learning Approach, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Ruijia Chen, Omonigho M. Bubu, Rachel Whitmer, Paola Gilsanz, Zvinka Z. Zlatar

Moss-Magee Rehabilitation Papers

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and …

The Future Of The Arab Media Industry In Utilizing Artificial Intelligence Technologies: A Foresight Study (2024 To 2034), Mohammedwesam Amer, Abdullah Mohammed Atbiqa

Middle East Journal of Communication Studies

This study explores the future of the Arab media industry from 2024 to 2034 in light of the increasing integration of artificial intelligence (AI) technologies. It identifies key media sectors likely to be affected by AI—such as content creation, news production, editing, and audience engagement—and proposes strategies for effective implementation. The study employs a descriptive survey methodology grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected via an electronic questionnaire from a purposive sample of 264 respondents across 18 Arab countries, including academic elites and media professionals working with Arab media institutions. Findings reveal …

Towards Scalable Schema Mapping Using Large Language Models, Christopher Buss, Mahdis Safari, Arash Termehchy, David Maier, Stefan Lee

Computer Science Faculty Publications and Presentations

The growing need to integrate information from many diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex and costly to maintain. While recent advances suggest that large language models (LLMs) can assist in automating schema mapping, key challenges remain. We motivate future research in schema mapping generation by highlighting key challenges, presenting a competitive bidirectional schema matching pipeline, and exploring the limitations of current methods for generating more complex mappings.