Other Computer Sciences Commons^™

Modeling Of Radio Array Distribution, Yahya Emran Abdelhadi

Thesis/ Dissertation Defenses

In the quest to understand the mysteries of the universe through radio astronomy, selecting the right tools and methods is crucial. Just as radio astronomy provides a unique lens through which to study the universe, the adoption of open-source software, such as Python, similarly offers a distinct advantage in scientific research. Today most research is done on open-source code which reduces the limitation to have access to the science and research resources to improve the quality and the contributed researcher. For some people, access to software and tools is very expensive and limited. This work focuses on converting the IDL …

Bibliography For "3-D Printing Display", Isabella Piechota, Arianna Tillman, Annikah Carpio

Library Displays and Bibliographies

A bibliography created to support a display about 3D printing at the Leatherby Libraries during November 2024-February 2025 at the Leatherby Libraries at Chapman University.

Weed Seed Wizard Case Study - Don't Stop Harvest Weed Management Because It’S A Dry Year, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This case …

Targeting Federated Learning: A Study Of Membership Inference Attacks On Healthcare Data, Brett W. Hillyard, Aditi S. Lappathi

College of Engineering Summer Undergraduate Research Program

This study investigates the vulnerabilities of federated learning models in the healthcare domain, specifically focusing on membership inference attacks (MIA). Federated learning allows local models to train on sensitive healthcare data without sharing the data itself, making it an attractive method for protecting privacy. However, even in this decentralized framework, models remain vulnerable to MIAs, where attackers can infer whether certain data points were used to train a model by analyzing model updates. Using the Texas100 dataset, this study demonstrates that as the number of local models increases, the attack accuracy of MIAs also increases due to higher bias …

Enhancing Place-Based Interaction With Emotion Ai And Augmented Reality, Jake Maier, Ivan Martinez

College of Engineering Summer Undergraduate Research Program

This project explores the integration of augmented reality (AR) and Emotion AI technologies to enhance user experiences in physical environments. By seamlessly merging virtual elements with real-world contexts, we aim to deepen individuals’ interactions and perceptions of their surroundings. Leveraging AR technology enables users to access contextual information, engage with interactive content, and navigate spaces with heightened immersion and understanding. Additionally, Emotion AI enhances these experiences by detecting and responding to users’ emotional states, fostering personalized and emotionally resonant interactions. We aim to integrate digital content within physical environments using mixed-reality headsets equipped with eye-tracking capabilities and consumer-grade wireless EEG …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk

Computer Science Faculty Research & Creative Works

We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its …

Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a planned space-based observatory designed to localize MeV to TeV transients such as gamma-ray bursts in real time using onboard computational hardware. The Antarctic Demonstrator for APT (ADAPT) is a prototype high-altitude balloon mission scheduled to fly during the 2025–26 season. Gamma-ray-induced scintillations in CsI tiles will be captured by perpendicular arrays of optical fibers running across both tile surfaces, as well as SiPM-based edge detectors to improve light collection and calorimetry. Signal samples are captured by analog waveform digitizer ASICs then sent to the front end of the computational pipeline, which is designed …

The Use Of Social Media For Organisational Learning: A Systematic Review Of Literature, Harry Moongela, Marie Hattingh

African Conference on Information Systems and Technology

Social media (categorised as Web 2.0 platforms) has been reported to significantly impact and improve organisational learning (OL), but there seems to be a lack of systematic literature reviews (SLR) covering the use of social media to facilitate OL. Thus, this article conducted a SLR for both social media and OL by presenting the significant concepts, models and frameworks from literature. A total number of 48 articles were found for the analysis method and the findings reveal important concepts such as types of OL, levels of OL and frameworks incorporating both social media and OL. The results also show that …

Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez

SMU Data Science Review

This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …

Pulling Up Stakes: Migrating Digital Collections From Contentdm To Digital Commons, Adam C. Northam

Velma K. Waters Library Faculty Publications

No abstract provided.

Further Evaluations Of A Didactic Cpu Visual Simulator (Cpuvsim), Renato Cortinovis, Tamer Mohamed Abdellatif, Devender Goyal, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

This paper discusses further evaluations of the educational effectiveness of an existing CPU visual simulator (CPUVSIM). The CPUVSIM, as an Open Educational Resource, has been iteratively improved over a number of years following an Open Pedagogy approach, and was designed to enhance novices’ understanding of computer operation and mapping from high-level code to assembly language. The literature reports previous evaluations of the simulator, at K12 and undergraduate level, conducted from the perspectives of both developers and students, albeit with a limited sample size and primarily through qualitative methods. This paper describes additional evaluation activities designed to provide a more comprehensive …

Advancing Affective Computing: Emotion Recognition And Tracking Across Diverse Contexts (Varied Environments), Shao Liu

Dissertations, Theses, and Capstone Projects

Affective Computing (AC) is an interdisciplinary field that recognizes, interprets, and processes human emotions. Emotions are complex, involving consciousness, physical sensations, and behavioral expressions, and are significant in various domains like mental health, human-computer interaction, and social security. Real-world applications of AC include monitoring drivers’ emotional states to improve road safety and understanding the emotions expressed by artists in visual arts. Traditional methods relying on facial expressions often fall short due to the nuanced nature of emotions, which vary across individuals, cultures, and contexts. Accurate AC systems require sophisticated, multimodal models to handle these variations and external factors like noise …

Advancing Endovascular Neurosurgery Training With Extended Reality: Opportunities And Obstacles For The Next Decade, Shray Patel, Michael Covell, Saarang Patel, Sandeep Kandregula, Sai Krishna Palepu, Avi Gajjar, Oleg Shekhtman, Georgios Sioutas, Ali Dhanaliwala, Terence Gade, Jan-Karl Burkhardt, Visish Srinivasan

SKMC Student Presentations and Publications

Background: Extended reality (XR) includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). Endovascular neurosurgery is uniquely positioned to benefit from XR due to the complexity of cerebrovascular imaging. Given the different XR modalities available, as well as unclear clinical utility and technical capabilities, we clarify opportunities and obstacles for XR in training vascular neurosurgeons. Methods: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. Studies were critically appraised using ROBINS-I. Results: 19 studies were identified. 13 studies used VR, while 3 studies used MR, and 3 studies used AR. …

Integrating Authentication Schemes In Augmented And Virtual Reality Classrooms, Naheem Noah

Electronic Theses and Dissertations

Augmented Reality and Virtual Reality (AR/VR) technologies are revolutionizing educational experiences, but their widespread adoption hinges on addressing critical security and usability challenges, particularly in the domain of user authentication. This research presents an investigation into the security landscape of AR/VR and explores a graphical authentication scheme called “Things” that enhances both security and usability in immersive learning environments. Through a systematic evaluation of popular AR/VR devices and applications, potential vulnerabilities and limitations were identified, such as high usage of pin/passwords which are susceptible to shoulder-surfing attacks, lack of multi-factor authentication, and unclear data-sharing practices. A review of existing knowledge-based …

Open-Source Forensics Tools Are Great Tools For Critical Used Machines, Erik Herrera

Electronic Theses and Dissertations

Open-Source software exists on everything from operating systems to daily productivity applications. In digital forensics, a very popular tool that is used to learn on and expand is Autopsy. Autopsy is known in the digital world due to its potential and wide usage. It is in many built packages of software inside the open-source world of applications. It is built into premade operating systems that are involved in Digital Forensics and Penetration Testing. Prebuilt OS includes Kali Linux and Computer Aided Investigative Environment (CAINE).

In the application to defend Open-Source software being just as good as closed-source software, I will …

Analyzing The Usability, Performance, And Cost-Efficiency Of Deploying Ml Models On Various Cloud Computing Platforms, Hongyu Wang

Masters Theses (Archived)

With the enhanced computing capabilities and accessibility to cloud resources, major cloud computing providers such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure offer Machine Learning (ML) and AI services. Their primary purpose is to provide efficiency, scalability, and adaptability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers of the services often question which ML-AI service will best suit their organizational and business needs. This study compares and analyzes the usability, performance, and cost-efficiency of deploying Machine Learning (ML) models across three cloud platforms: GCP, AWS, and …

Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott

Electronic Theses and Dissertations

The Internet of Vehicles (IoV) holds immense potential for revolutionizing transporta- tion systems by facilitating seamless vehicle-to-vehicle and vehicle-to-infrastructure communication. However, challenges such as congestion, pollution, and security per- sist, particularly in rural areas with limited infrastructure. Existing centralized solu- tions are impractical in such environments due to latency and privacy concerns. To address these challenges, we propose a decentralized clustering algorithm enhanced with Federated Deep Reinforcement Learning (FDRL). Our approach enables low- latency communication, competitive packet delivery ratios, and cluster stability while preserving data privacy. Additionally, we introduce a trust-based security framework for IoV environments, integrating a central authority …

Interpretation Models For Prostate Lesion: Detecting, Explaining, And Understanding., Mehmet Akif Gulum

Electronic Theses and Dissertations

Prostate cancer is a major public health concern, affecting millions of men worldwide. While early detection and treatment of prostate cancer is critical for improving patient outcomes, the detection of prostate lesions is even more important for timely intervention and management of the disease. Prostate lesions are abnormal growths or lumps within the prostate gland, which may or may not be cancerous. The timely detection and accurate diagnosis of prostate lesions is crucial for effective treatment and management of the disease. In recent years, deep learning models have shown promise in accurately detecting and characterizing prostate lesions using advanced imaging …

Offensive Content Detection In Online Social Platforms, Ebuka Okpala

All Dissertations

Online social platforms enable users to connect with large, diverse audiences and the ability for a message or content to flow from one user to another user, user to followers, followers to user, and followers to followers. Of course, the advantages of this are apparent, and the dangers are also clearly obvious. The user-generated content could be abusive, offensive, or hateful to other users, possibly leading to adverse health effects or offline harm. As more of society's public discourse and interaction move online and these platforms grow and increase their reach, it is inherently important to protect the safety of …