Performance Evaluation Of Zabbix And Azure Monitor In Hybrid It Infrastructure,
2025
Technological University Dublin
Performance Evaluation Of Zabbix And Azure Monitor In Hybrid It Infrastructure, Ivan Godoy, Cormac Keogh
Academic Poster Collection
Performance Evaluation of Zabbix and Azure Monitor in Hybrid IT Infrastructure
Nobocap: Unlocking Mdr/Ivdr Regulations For Innovators In Europe,
2025
Technological University Dublin
Nobocap: Unlocking Mdr/Ivdr Regulations For Innovators In Europe, Graham Gavin, Claire Brougham
Conference Papers
The NoBoCap project (nobocap.eu) is aimed at addressing some of the challenges encountered by both SMEs and Notified Bodies across the EU. It is a multi-organizational consortium including universities, a Notified Body, and Bio-health and Innovation Hubs and Clusters. The NoBoCap project has several work packages focussed on:• Design and delivery of funded short-term courses.• Creating a dedicated NB job board.• Design and delivery of funded university accredited modules.• Design and development of e-tools to support manufacturers.• Develop a community platform to act as a voice for start-ups and SMEs
Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning,
2025
Eastern Michigan University
Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz
Master's Theses and Doctoral Dissertations
The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …
Immersive Executive Functions Assessment System (Iexec): Integrating Embodied Cognition And Virtual Reality,
2025
University of Texas at Arlington
Immersive Executive Functions Assessment System (Iexec): Integrating Embodied Cognition And Virtual Reality, Hamza Reza Pavel
Computer Science and Engineering Dissertations - Archive
Executive functions (EFs) are higher-order cognitive processes that include working memory, inhibitory control, and cognitive flexibility. These higher-order processes facilitate the achievement of goal-directed behavior and enable both adaptive decision-making and emotional regulation. Traditional EF assessment tools depend on static pen-and-paper tasks or basic computer-based tasks, which fail to capture real-world cognitive complexity and dynamics. Some of these assessment tools are specifically geared towards children or older adults, while others are more generic and designed to be used for people of all ages. This dissertation addresses these limitations by introducing iExec: The Immersive Executive Functions Assessment System, which functions as …
Enabling Energy And Water Sustainability Through Out-Of-Band Emi Sensing And Infrastructure Modeling,
2025
University of Texas at Arlington
Enabling Energy And Water Sustainability Through Out-Of-Band Emi Sensing And Infrastructure Modeling, Pranjol Sen Gupta
Computer Science and Engineering Dissertations - Archive
As demand for Internet and cloud services surges, data centers have emerged as critical infrastructure—but they are also among theworld’s most energy- andwater-intensive facilities. Effective power management, particularly at the server level, is essential for improving efficiency, reliability, and sustainability. However, server-level power monitoring remains uncommon due to the high cost of hardware instrumentation and the intrusiveness of software-based solutions, especially in shared colocation environments. My research introduces a novel, low-cost, and non-intrusive method for server-level power monitoring using conducted electromagnetic interference (EMI). By analyzing EMI signals captured from higher levels in the power distribution network, this approach estimates individual …
Optimizing Indoor Localization Using Rssi And Iq Data With Machine Learning,
2025
Roger Williams University
Optimizing Indoor Localization Using Rssi And Iq Data With Machine Learning, Gokdeniz Tingur
Computer Science Theses
This paper explores implementing and evaluating a Bluetooth Low Energy (BLE)-based indoor localization system using Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) data via machine learning. A survey of localization technologies (RFID, GPS, ZigBee, and BLE) provides context on capabilities and limitations in indoor positioning. IQ data and phase-based angle estimation show how BLE 5.1’s direction-finding features enable sub-meter accuracy. A multi-phase experiment in a three-story academic building examines model performance with different tag distributions, movement patterns, and environmental constraints. Machine learning models such as Support Vector Machines and Deep Neural Networks are trained and evaluated across …
Movie Genre Classification Using Script Texts,
2025
Florida Atlantic University
Movie Genre Classification Using Script Texts, Michael Roman Cuomo
Electronic Theses and Dissertations 2020 - Present
Genres are used to classify movies so that they can be grouped with others that have similar themes and structures. These classifications are categories created by humans. In the process of creating a movie, a script is often the first creation to write and share ideas about a topic. The script contains large amounts of text that is used to describe the dialog, setting and direction of the film. Although the script contains important information for the film, the amount of text can present a challenge for machine learning algorithms. Often in studies on film classification, if text is used, …
Toward Strategy Identification And Subtask Decomposition In Task Exploration,
2025
San Jose State University
Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem
Master's Projects
This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets …
Comparing Funders' Altruism Versus Self-Interest: Leveraging The Context Of Crisis,
2025
James Madison University
Comparing Funders' Altruism Versus Self-Interest: Leveraging The Context Of Crisis, Dan Liu, Guangzhi Shang, Cynthia Fan Yang
Journal of International Technology and Information Management
While reward-based crowdfunding has widespread popularity, the motivations driving funders, balancing self-interest and altruism, have remained puzzling. Prior research has been constrained by examination methods and produced mixed findings regarding the weight of altruism versus self-interest among funders. Our study takes a fresh perspective, delving into funder behavior amid a major crisis—the tumultuous backdrop of the COVID-19 pandemic. Our findings reveal that funders not only display an increased willingness to contribute but also significantly amplify their contributions, particularly to projects in crisis-affected regions, irrespective of external incentives like rewards. This underscores the prevalence of altruistic motives among funders in challenging …
Understanding The Determinants Of Blockchain Adoption: An Empirical Study,
2025
University of Southern Maine
Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch
Journal of International Technology and Information Management
Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …
Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment,
2025
Gannon University
Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment, Jinhee Yoo, Eugene A. Ohu, Radosław Mącik
Journal of International Technology and Information Management
This study (N = 170), involving participants from Nigeria and the U.S., investigated how different technologies (TV and VR) affect users' empathy (α = .93), engagement (α = .93), enjoyment (α = .93), preferences, and likelihood of technology use. Participants watched an animated documentary titled “Is Anna OK?” at two different time points, utilizing VR (Oculus Rift S) and TV, following which they completed measuring empathy, engagement, enjoyment, device preference, and usage likelihood. Analysis via one-way ANOVA and chi-square tests revealed that VR users reported significantly higher empathy and enjoyment compared to TV viewers, particularly on second viewing. Combining both …
Traceai: Intelligent Distributed Tracing Using Large Language Models,
2025
San Jose State University
Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra
Master's Projects
Distributed systems are difficult to trace using traditional methods due to the scale of data volume and complexity, and they usually require a lot of manual analysis. TraceAI tries to solve these problems by integrating Large Language Models with the tracing tools to automatically enhance the trace data evaluation. The project aims to provide an AI-driven solution for monitoring and understanding the flow of requests across services, anomaly detection, root cause analysis and performance optimization. It can thus automate finding out systems problems using LLMs thereby carrying out large scale trace data analysis. Anticipated results from the effort will be …
Faux Capabilities: A Novel Approach For Code Analysis,
2025
San Jose State University
Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse
Master's Projects
When using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the …
Proxy Cap - La Lua Protector,
2025
San Jose State University
Proxy Cap - La Lua Protector, Swift Sheng
Master's Projects
Inspired by the object capability model and sandbox, this project, Proxy Cap, introduces a new Lua access control model that improves the language’s security without sacrificing usability. Object capability is an unconventional but powerful security model. The security model closely observes the principle of least authority. Ambient authority, the omnipresent global environment, does not exist in the object capability computation world, and no resource is accessible unless explicitly assigned. Only connectivity begets connectivity. Lua is an extensible and high-performing scripting language based on ANSI C. The language is popular in many fields but faces security challenges. Lua has a non-traditional …
Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms,
2025
San Jose State University
Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel
Master's Projects
The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …
Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models,
2025
San Jose State University
Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku
Master's Projects
Coral reefs can be primarily found in tropical and sub-tropical regions of our oceans, providing a thriving habitat for millions of species. Marine sponges, which can be frequently found in coral reefs, play a critical role that contributes to the maintenance of these ecosystems, including the recycling of nutrients through water filtration. However, rising ocean temperatures and acidification due to climate change have resulted in the bleaching and death of coral reefs worldwide. In order to preserve these reefs and the sponges that depend on them, scientists have been performing studies on their biodiversity. This includes collecting numerous images of …
Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan,
2025
San Jose State University
Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan, Crystal Kwong
Master's Projects
Generative adversarial networks (GANs) are known for their ability to generate high quality
images mimicking real life or even particular art styles. Yet for all their capability, casually
training a GAN on an average machine can be infeasible as GANs require an enormous amount
of time and data to train. Even with a trained GAN, model inference demands heavy
computations, making GANs difficult to deploy on applications. To address these limitations,
techniques such as transfer learning and quantization have been leveraged to speed up training of
GANs and lighten computational cost of GAN inference. This project aims to use such …
Disease Diagnosis Using Rag Llm With Smart Prompt Engineering,
2025
San Jose State University
Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed
Master's Projects
Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …
Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms,
2025
San Jose State University
Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms, Kush Patel
Master's Projects
Transformer architectures have emerged as powerful tools for time series forecasting, excelling at capturing complex temporal dependencies across multivariate inputs. However, these models are highly susceptible to adversarial attacks such as the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM), which can significantly degrade predictive performance through small, targeted perturbations. This work integrates dynamic attention mechanisms, adaptive masking modules that introduce controlled variability into attention pathways, into a transformer forecasting model to enhance robustness against such attacks. Using two distinct datasets, we compare the performance of a standard transformer and a dynamic attention-enhanced transformer under both clean and …
An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma,
2025
San Jose State University
An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri
Master's Projects
Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect …
