Digital Commons Network^™

Learning To Generate Explainable Stock Predictions Using Self‑Reflective Large Language Models, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important …

Discovering Personalized Characteristic Communities In Attributed Graphs, Yudong Niu, Yuchen Li, Panagiotis Karras, Yanhao Wang, Zhao Li

Research Collection School Of Computing and Information Systems

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of …

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …

A Survey On Searchable Symmetric Encryption, Feng Li, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Jianting Ning, Robert H. Deng

Research Collection School Of Computing and Information Systems

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state-of-the-art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past …

Evaluation Of An End-To-End Radiotherapy Treatment Planning Pipeline For Prostate Cancer, Mohammad Daniel El Basha, Court Laurence, Carlos Eduardo Cardenas, Julianne Pollard-Larkin, Steven Frank, David T. Fuentes, Falk Poenisch, Zhiqian H. Yu

Dissertations and Theses (Open Access)

Radiation treatment planning is a crucial and time-intensive process in radiation therapy. This planning involves carefully designing a treatment regimen tailored to a patient’s specific condition, including the type, location, and size of the tumor with reference to surrounding healthy tissues. For prostate cancer, this tumor may be either local, locally advanced with extracapsular involvement, or extend into the pelvic lymph node chain. Automating essential parts of this process would allow for the rapid development of effective treatment plans and better plan optimization to enhance tumor control for better outcomes.

The first objective of this work, to automate the treatment …

Using Pre-Trained Models For Vision-Language Understanding Tasks, Rui Cao

Dissertations and Theses Collection (Open Access)

In recent years, remarkable progress has been made in Artificial Intelligence (AI), with an increasing focus on integrating AI systems into people’s daily lives. In the context of our diverse world, research attention has shifted towards applying AI to multimodal understanding tasks. This thesis specifically addresses two key modalities, namely, vision and language, and explores Vision-Language Understanding (VLU).

In the past, addressing VLU tasks involved training distinct models from scratch using task-specific data. However, limited by the amount of training data, models may easily overfit the training data and fail to generalize. A recent breakthrough is the development of Pre-trained …

Developing Machine Learning Agency Among Youth: Characterizing Youth Critical Use, Examination, And Production Of Machine Learning Applications, Ibrahim Oluwajoba Adisa

All Dissertations

This study explores how middle school-aged youths at an afterschool center utilized, examined, and produced AI applications for social good with the support of adults and peers. The study employs a qualitative single case study design. It uses thematic analysis and quantitative ethnography methods to analyze data from multiple sources, including field notes, interviews, focus groups, story completions, video recordings, and artifacts. Findings indicated that engaging youths in critical exploration of AI tools enhanced their ability to design interest-based AI applications that provide solutions for healthcare problems, security, and accessibility. Moreover, the computational thinking practices that youth engaged in increased …

The Human Side Of Adaptive Autonomy: Design Considerations For Adaptive Autonomous Teammates, Allyson Hauptman

All Dissertations

Ground-breaking advances in artificial intelligence (AI) have led to the possibility of AI agents operating not just as useful tools for teams, but also as full-fledged team members with unique, interdependent roles. This possibility is fueled by the human desire to create more and more autonomous systems that possess computational powers beyond human capability and the promise of increasing the productivity and efficiency of human teams dramatically. Yet, for all the promise and potential of these human-AI teams, the inclusion of AI teammates presents several challenges and concerns for both teaming and human-centered AI.

An important part of teaming is …

Exploring Graph Neural Networks In Reinforcement Learning: A Comparative Study On Architectures For Locomotion Tasks, Gaukhar Nurbek

Theses and Dissertations

Deep Reinforcement learning (DRL) has gained importance in optimizing control policies, while Graph Neural Networks (GNNs) offer a robust approach for modeling complex relationships within systems represented as graphs. This thesis investigates the integration of DRL and GNNs to optimize control policies for robotic tasks, with a focus on locomotion. It compares static and dynamic GNN architectures for control policy predictions, revealing their strengths and limitations in adapting to locomotion predictions. The study assesses the impact of model structure complexity on GNNs' predictive capabilities, showcasing how intricate model structure can maximize GNNs' potential in capturing spatial and relational dependencies when …

Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas

Graduate Theses and Dissertations (2019 - present)

This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …

Exploring The Relationship Between Anxiety And Virtual Reality Sickness, David Wesley Woolverton

Graduate Theses and Dissertations (2019 - present)

As virtual reality (VR) becomes more commonly used in education, it is important to understand the technology’s weakness and mitigate any potential negative effects on student success. One adverse side-effect of VR use is simulation-induced motion sickness, known in the context of VR as VR sickness. Previous research by Howard and Van Zandt (2021) found that possessing a phobia had a significant positive correlation with VR sickness, but only if the phobia is triggered by the simulation, suggesting that symptoms are actually connected to the anxiety the phobia induces. This study explored the hypothesized correlation between anxiety and VR sickness, …

Examining Outcomes Of Privacy Risk And Brand Trust On The Adoption Of Consumer Smart Devices, Marianne C. Loes

Graduate Theses and Dissertations (2019 - present)

With more connected devices on earth than there are people, Internet of Things (IoT) is arguably just as innovative as the original introduction of the Internet. Though much of the research on technology acceptance and adoption has been conducted in organizational settings, the consumer use of IoT technologies, such as smart devices, is becoming a fertile field of research. The merger of these research streams is especially relevant from a societal perspective as smart devices become more embedded in consumer’s daily lives, particularly with the introduction of the “meta verse.” While original technology acceptance research is limited to two system-specific …

Cloud Computing Integration Into Mixed-Reality: Physical To Abstraction, Yassine Chahid, Patrick Slattery

Publications and Research

This research evaluates the progression of cloud computing and mixed-reality technologies, and to identify how these technologies influence advancements in the latter. Both cloud computing and mixed reality have significantly impacted the IT field and the services available to the public and various institutions. Cloud computing provides a valuable way to process information or allocate computational resources on otherwise limited hardware. Augmented or virtual reality hardware would greatly benefit from this by offloading resource-intensive tasks to other machines. The research methodology involves analyzing essential components of both innovations, divided into multiple categories. These components range from physical, hardware-based elements to …

Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso

Electronic Theses, Projects, and Dissertations

Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and …

Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa

Electronic Theses, Projects, and Dissertations

A novel technique for remote sensing image scene classification is employed using the Compact Vision Transformer (CVT) architecture. This model strengthens the power of deep learning and self-attention algorithms to significantly intensify the accuracy and efficiency of scene classification in remote sensing imagery. Through extensive training and evaluation of the RSSCNN7 dataset, our CVT-based model has achieved an impressive accuracy rate of 87.46% on the original dataset. This remarkable result underscores the prospect of CVT models in the domain of remote sensing and underscores their applicability in real-world scenarios. Our report furnishes an elaborate account of the model's architecture, training …

An Empirical Study On The Efficacy Of Llm-Powered Chatbots In Basic Information Retrieval Tasks, Naja Faysal

Electronic Theses, Projects, and Dissertations

The rise of conversational user interfaces (CUIs) powered by large language models (LLMs) is transforming human-computer interaction. This study evaluates the efficacy of LLM-powered chatbots, trained on website data, compared to browsing websites for finding information about organizations across diverse sectors. A within-subjects experiment with 165 participants was conducted, involving similar information retrieval (IR) tasks using both websites (GUIs) and chatbots (CUIs). The research questions are: (Q1) Which interface helps users find information faster: LLM chatbots or websites? (Q2) Which interface helps users find more accurate information: LLM chatbots or websites?. The findings are: (Q1) Participants found information significantly faster …

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …

Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam

Electronic Theses, Projects, and Dissertations

Accidents pose a significant risk to both individual and property safety, requiring effective detection and response systems. This work introduces an accident detection system using a convolutional neural network (CNN), which provides an impressive accuracy of 86.40%. Trained on diverse data sets of images and videos from various online sources, the model exhibits complex accident detection and classification and is known for its prowess in image classification and visualization.

CNN ensures better accident detection in various scenarios and road conditions. This example shows its adaptability to a real-world accident scenario and enhances its effectiveness in detecting early events. A key …

Modeling The Spatiotemporal Variations Of The Magnetic Field In Active Regions On The Sun Using Deep Neural Networks, Godwill Asare Mensah Mensah

Open Access Theses & Dissertations

Solar active regions are areas on the Sun's surface that have especially strong magnetic fields. Active regions are usually linked to a number of phenomena that can have serious detrimental consequences on technology and, in turn, human life. Examples of these phenomena include solar flares and coronal mass ejections, or CMEs. The precise predictionof solar flares and coronal mass ejections is still an open problem since the fundamental processes underpinning the formation and development of active regions are still not well understood. One key area of research at the intersection of solar physics and artificial intelligence is deriving insights from …

In-Between Frame Generation For 2d Animation Using Generative Adversarial Networks, Francisco Arriaga Pazos

Open Access Theses & Dissertations

Traditional 2D animation remains a largely manual process where each frame in a video is hand-drawn, as no robust algorithmic solutions exist to assist in this process. This project introduces a system that generates intermediate frames in an uncolored 2D animated video sequence using Generative Adversarial Networks (GAN), a deep learning approach widely used for tasks within the creative realm. We treat the task as a frame interpolation problem, and show that adding a GAN dynamic to a system significantly improves the perceptual fidelity of the generated images, as measured by perceptual oriented metrics that aim to capture human judgment …

Dynamic Storytelling Algorithms Using Contextual Aspects Of A Large Language Model, Alireza Pasha Nouri

Open Access Theses & Dissertations

Storytelling is a set of algorithms used to create narratives by connecting documents in a sequencethat accurately reflects the evolution of events and entities within a particular topic or theme. Early storytelling algorithms face challenges in encoding the progression and interconnections of information between consecutive texts, given that the conventional approaches rely primarily on connecting document pairs based on content overlap. They often neglect critical linguistic features, such as word contexts, semantics, the roles words play across different documents, and attention to the historical contexts of the underlying documents. Many existing storytelling models frequently produce story chains that, while connected …

Automated Composition Of Multivariable Scientific Workflows Considering Scientific Assumptions, Raul Alejandro Vargas Acosta

Open Access Theses & Dissertations

Many ground-breaking scientific experiments require the execution of multiple complex scientific computations. Thus, scientific workflows (i.e., a sequence of scientific computations) have received significant attention, more specifically, the automated composition of scientific workflows. Scientific workflows that repurpose data may have unique scientific assumptions that need to be considered when composing a workflow. Workflow composition tools have enabled a wider range of stakeholders (e.g., policymakers, the general public, and researchers) to create and execute workflows; however, domain expertise is still required for these tasks. The overarching goal of this work is to further improve the automatic composition of scientific workflows by …

Formalization Of A Security Framework Design For A Health Prescription Assistant In An Internet Of Things System, Thomas Rolando Mellema

Electronic Theses and Dissertations

Security system design flaws will create greater risks and repercussions as the systems being secured further integrate into our daily life. One such application example is incorporating the powerful potential of the concept of the Internet of Things (IoT) into software services engineered for improving the practices of monitoring and prescribing effective healthcare to patients. A study was performed in this application area in order to specify a security system design for a Health Prescription Assistant (HPA) that operated with medical IoT (mIoT) devices in a healthcare environment. Although the efficiency of this system was measured, little was presented to …

Sports Science: An Entrepreneurial Venture, Nicole J. Jones

Senior Honors Projects

In sports science, ensuring maximum athlete safety and optimizing data utilization are pivotal yet leave room for further work. My project, Unbeaten SafeWare, addresses these critical issues by focusing on two primary concerns: preventing heat-related and cardiac illnesses, which are significant causes of athlete fatalities, and enhancing the transparency and utility of sports data. This initiative involves developing a shirt integrated with sensors to monitor vital signs and an athlete management system to handle data input, storage, analysis, and accessibility for athletes.

The project has advanced through the efforts of a multidisciplinary team, which includes biomedical engineering undergraduates, two faculty …

Online Learning For Acid-Fast Bacilli Detection In Histopathological Images, Shizhao Wang

UNLV Theses, Dissertations, Professional Papers, and Capstones

The acid-fast stain is frequently used for laboratory diagnosis of tuberculosis. It is a labor intensive task requiring thorough examination of extremely high-resolution images to pinpoint the presence of the mycobacteria. This paper presents a machine learning assisted slide image analysis tool with the aim of aiding histopathology professionals in the accurate diagnosis of tuberculosis in patients through the analysis of microscopic imagery. The proposed tool combines a digital whole slide image viewer with an online learning framework. We also conducted a survey of different state-of-the-art online learning methods, and found that MIR with pre-training has the best performance on …

Sliding Markov Decision Processes For Dynamic Task Planning On Uncrewed Aerial Vehicles, Trent Wiens

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision …

The Kruger Collection Reimagined: A Case Study In 3d Scanning And Interactive Exhibit Design, Annissa Davis

Department of Anthropology: Theses and Student Research

This thesis examines the use of 3D modeling in museum exhibition to create exploratory exhibits that facilitate unique relationships between the visitors and the collection beyond what is provided by the collection’s in person counterparts. Typical use of 3D modeling in museums is currently often representative rather than exploratory. By employing a Digital Humanities lens to approach the development of a digital exhibition utilizing 3D technology and interactive elements created in a video game engine (Unity), this thesis project evaluates these potential new relationships. Using the Eloise Kruger Collection of Miniatures as a case study, the following text details the …

Identifying Temporomandibular Disorder Morphological Risk Factors Via Explainable Deep Learning And Multiscale Biomechanical Modeling, Shuchun Sun

All Dissertations

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and …

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko

Research Collection School Of Computing and Information Systems

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …