Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis,
2024
Georgia Southern University
Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis, Daniel E. Ukene
College of Graduate Studies: Theses & Dissertations
This thesis comprises three distinct, yet interconnected studies addressing critical aspects of web infrastructure management. We begin by studying containerization via Docker and its impact on web server performance, focusing on Apache and Nginx hosted on virtualized environments. Through meticulous load testing and analysis, we provide insights into the comparative performance of these servers, adding users of this technology know which webservers to leverage when hosting their webservice along alongside the infrastructure to host it on. Next, we expand our focus to examine the performance of caching systems, namely Redis and Memcached, across traditional VMs and Docker containers. By comparing …
A Complexity Aware Overlay Architecture For High Integrity Fpga Based Systems,
2024
Virginia Commonwealth University
A Complexity Aware Overlay Architecture For High Integrity Fpga Based Systems, Richard Dwight Hite Jr
Theses and Dissertations
Cyber-Physical systems are becoming more and more prevalent in our society and are simultaneously becoming more complex due to evolving technological capabilities in both hardware and software. This complexity exacerbates verification and validation activities thereby negatively impacting important system attributes like design assurance, system reliability, development costs and trust. These facts necessitate the need for computing architectures that constrain complexity for the sake of assurance. Traditional software and hardware development for safety-critical systems have been demonstrated in previous safety-critical systems and the established development methodologies are well understood. However, both technologies have their strengths and limitations. Processor-based technology (SW based …
Artificial Intelligence In Education: Mathematics Teachers’ Perspectives, Practices And Challenges,
2024
Sohar University, Al-Balqa Applied University
Artificial Intelligence In Education: Mathematics Teachers’ Perspectives, Practices And Challenges, Mohammad A. Tashtoush, Yousef Wardat, Rommel Al Ali, Shoeb Saleh
Iraqi Journal for Computer Science and Mathematics
Efforts have been made to include artificial intelligence (AI) in teaching and learning; nevertheless, the successful deployment of new instructional technology depends on the attitudes of the teachers who conduct the lesson. Few scholars have researched teachers' perspectives on AI use due to a general lack of expertise on how it can be used in the classroom, as well as a lack of specific knowledge about what AI-adopted tools would be like. This study investigated mathematics teachers’ perceptions of implemented AI systems and applications in Abu Dhabi Emirate schools. The sample study consists of 580 male and female math teachers …
Context-Free Grammar Framework For Automatic Shooting Game Enemy Pattern Generation,
2024
Faculty of Engineering
Context-Free Grammar Framework For Automatic Shooting Game Enemy Pattern Generation, Nitit Kaweeratanakit
Chulalongkorn University Theses and Dissertations (Chula ETD)
This research proposes a framework for generating enemy patterns for SHMUPs game. It is directly based on a grammar derived from the enemy behavior of existing commercial SHMUPs, and implemented using a new description language called "Enemy Pattern Description Language" (EPDL). EPDL contains all information required to construct the enemy, with no requirement of external data content. The language is human-readable and can be connected to any game engine of choice using an EPDL interpreter. The interpreter itself consists of lexer and recursive descent parser. The results shown in this research is implemented in. "rdnh", a private fork of Touhou …
การพยากรณ์การจ่ายยาของโรงพยาบาลโดยการเรียนรู้ของเครื่องและวิธีการวิเคราะห์เชิงสถิติ,
2024
คณะวิศวกรรมศาสตร์
การพยากรณ์การจ่ายยาของโรงพยาบาลโดยการเรียนรู้ของเครื่องและวิธีการวิเคราะห์เชิงสถิติ, วริศ ปุณณะหิตานนท์
Chulalongkorn University Theses and Dissertations (Chula ETD)
ในปัจจุบันการพยากรณ์การจ่ายยาของโรงพยาบาลถือเป็นหัวใจสำคัญต่อการจัดการคลังยาและการสั่งซื้อยาเป็นอย่างมาก เนื่องจากการพยากรณ์ที่น้อยเกินไปทำให้ยาไม่เพียงพอส่งผลให้เกิดความล่าช้าภายในโรงพยาบาล ในขณะที่การพยากรณ์ที่มากเกินไปทำให้เปลืองพื้นที่ใช้สอยและอาจทำให้ยาเสื่อมสภาพหรือหมดอายุซึ่งส่งผลให้โรงพยาบาลสูญเสียรายได้ การมีแบบจำลองที่สามารถพยากรณ์ปริมาณการจ่ายยาให้ใกล้เคียงกับค่าจริงจะสามารถลดปัญหาการขาดแคลนยาในแต่ละห้องจ่ายยาหรือการที่ห้องจ่ายยามีการกักตุนตัวยาเกินความจำเป็น จากปัญหาที่กล่าวมาข้างต้น โครงงานมหาบัณฑิตนี้จึงถูกจัดทำขึ้นเพื่อนำเสนอแบบจำลองที่จะมาแทนค่าเฉลี่ยเคลื่อนที่แบบทั่วไปซึ่งจะช่วยให้โรงพยาบาลสามารถพยากรณ์ปริมาณการจ่ายยาแต่ละวันได้แม่นยำมากขึ้น โดยจะนำเทคนิคสำหรับพยากรณ์ข้อมูลที่อยู่ในรูปแบบของอนุกรมเวลามาประยุกต์ใช้กับข้อมูลการจ่ายยาย้อนหลังและข้อมูลการนัดหมายแพทย์ย้อนหลัง หลังจากนั้นจะนำผลลัพธ์ที่ได้มาคำนวณค่าเคลาดเคลื่อนด้วยค่าเฉลี่ยของเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์และค่าเฉลี่ยสมมาตรของเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์และนำผลที่ได้มาใช้ในการเลือกว่าแบบจำลองไหนให้ค่าคลาดเคลื่อนต่ำที่สุด ผลการทดลองพบว่าแบบจำลองซัพพอร์ตเวกเตอร์รีเกรสชันให้ค่าความคลาดเคลื่อนที่ต่ำกว่าค่าเฉลี่ยเคลื่อนที่แบบทั่วไป แบบจำลองที่ผู้จัดทำโครงงานนำเสนอสามารถนำไปประยุกต์ใช้กับการพยากรณ์การจ่ายยาเพื่อให้แต่ละห้องจ่ายยามียาสำหรับให้บริการในปริมาณที่เพียงพอต่อความต้องการ
Uncertainty Quantification In Large Language Models Through Convex Hull Analysis,
2024
University of Stavanger
Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu
Engineering Technology Faculty Publications
Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …
Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning,
2024
Technological University Dublin, Ireland
Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning, Syed Ibrahim Hassam
Doctoral
Regular inspections of pavements are conducted by civil infrastructure departments to evaluate the surface condition. Pavement surfaces are subject to deterioration caused by several factors such as traffic, weather, and sunlight. This deterioration becomes evident through various distresses, including potholes, rutting, cracking, bleeding, patching, and ravelling, which gradually affects the surface layer over time. It is essential to assess the condition of pavements as it not only ensures their usability but also maximises public safety. Effective pavement maintenance requires substantial resources and capital investment to carry out the most suitable maintenance treatments at the optimal time. Furthermore, the outcomes of …
The Impact Of Ai In Gaming Industry,
2024
Sotheby's Institute of Art
The Impact Of Ai In Gaming Industry, Huang Xiaorong
MA Theses
With the rapid development of artificial intelligence (AI) technology, the application
of AI art in game development is becoming increasingly popular. AI art can not only help game developers speed up the creative process but also improve the visual quality and user experience of games. This paper provides an overview of the application of AI art in games, including character design, scene generation, animation production, and more. It also discusses the challenges and future directions of AI art. Through comprehensive analysis of existing research and practices, we find that AI art has tremendous potential in game development but still faces …
Few-Shot Learning For Ner Using Maml,
2024
Virginia Commonwealth University
Few-Shot Learning For Ner Using Maml, Nourchene Bargaoui
Theses and Dissertations
This thesis investigates the application of Few-Shot Learning (FSL) using Model-Agnostic Meta-Learning (MAML) to enhance Named Entity Recognition (NER) within the domain of Natural Language Processing (NLP), specifically focusing on chemical datasets. The primary challenge addressed is the impracticality of relying on extensive annotated datasets, especially in specialized fields like chemistry. The research primarily explores the concept of Few-Shot Learning, aiming to train models on minimal data while maintaining performance across diverse tasks. It delves into the N-way K-shot methodology, where "N" represents the number of classes and "K" signifies the number of examples per class. This approach is further …
Exploring End-User Environments For The Control And Programming Of Collaborative Robots,
2024
Virginia Commonwealth University
Exploring End-User Environments For The Control And Programming Of Collaborative Robots, Luiz Felipe Fronchetti Dias
Theses and Dissertations
To collaborate with the ongoing development of robotics, this thesis highlights three research contributions to collaborative robot programming. The first study evaluates block-based programming as an alternative for two-armed robots. A commercial solution is put in contrast with a block-based programming language. Both programming solutions are evaluated by 52 participants in an experiment involving a pick-and-place task. This study brings insights into human-robot collaboration, including robot positioning and interaction challenges. The second study discusses using mixed-reality devices as a potential workaround to the manual positioning of industrial and collaborative robots. Five different control interfaces implemented in mixed reality were used …
Explore Security And Machine Learning Applications In Next Generation Wireless Networks,
2024
Virginia Commonwealth University
Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang
Theses and Dissertations
Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …
Evaluating Computational Reproducibility Of Jupyter Notebooks Using Machine Learning And Natural Language Processing,
2024
Northern Illinois University
Evaluating Computational Reproducibility Of Jupyter Notebooks Using Machine Learning And Natural Language Processing, A S M Shahadat Hossain
Graduate Research Theses & Dissertations
In recent years, computational reproducibility, which refers to achieving consistent results upon rerunning an experiment, has become one of the major concerns of various research communities. Jupyter Notebook, as a web-based computational notebook application, offers useful features for running and publishing computational experiments through interactive environments. However, rerunning notebooks does not always reproduce the experimental results. This thesis aims to develop novel methods to evaluate reproducibility by comparing different types of outputs between original and rerun notebooks. It also explores the idea of using machine learning models to predict reproducibility of notebooks automatically without the need of rerunning them. Through …
A One-Wheeled Robot For Exploring Rolling Disk Locomotion,
2024
Northern Illinois University
A One-Wheeled Robot For Exploring Rolling Disk Locomotion, David H. J. Schmidt
Graduate Research Theses & Dissertations
The work proposed in this thesis is motivated by observations of one-wheel vehicles called monocycles. Whereas the unicycle has the rider sitting on a seat above the wheel, the monocycle’s rider sits inside a large circular hoop that serves as the wheel. Due to the position of the rider inside the wheel, it lowers the overall center of mass of the vehicle below the center of the wheel. For this reason, the uncontrolled longitudinal (forward/backward, or drive axis) dynamics are stable. For modest speeds above a certain threshold, the lateral (side-to-side, or lean axis) dynamics of the monocycle are also …
Evaluating The Impact Of Perceptual Loss In Generative Adversarial Models And Diffusion Models For Document Image Enhancement,
2024
South Dakota University State
Evaluating The Impact Of Perceptual Loss In Generative Adversarial Models And Diffusion Models For Document Image Enhancement, Farzaneh Karimpour
Electronic Theses and Dissertations
Documents often suffer from various types of degradation which make them difficult to read and restrict OCR performance. This study investigates the effectiveness of perceptual loss in enhancing document image cleanup by comparing a GAN-based model and a diffusion model. In our experiments, we utilized the DE-GAN model as a GAN-based model and the NAF-DPM model as a diffusion model, both enhanced by incorporating perceptual loss. We then compared the results of both models and evaluated them by using the DIBCO 2013, DIBCO 2017, and H-DIBCO 2018 datasets revealed that our approach consistently outperforms existing state-of-the-art methods. Results showed that …
Comparative Analysis Of Deep Learning-Based Anomaly Detection Models For Gps Spoofing Detection,
2024
South Dakota State University
Comparative Analysis Of Deep Learning-Based Anomaly Detection Models For Gps Spoofing Detection, Hasan Mirzakhaninafchi
Electronic Theses and Dissertations
As autonomous vehicles (AVs) become integral to modern transportation, their susceptibility to cyber-attacks, particularly GPS spoofing, presents a serious security threat. This study addresses these challenges by applying a suite of deep learning models to enhance the detection of anomalous GPS signals. Focusing on autoencoder-based architectures, the proposed models such as long short-term memory-based variational autoencoder (LSTM-VAE), LSTM-based autoencoder (LSTM-AE), multilayer perceptron-based variational autoencoder (MLP-VAE), MLP-based Autoencoder (MLPAE), Stacked LSTM-based variational autoencoder (Stacked-LSTM-VAE), stacked LSTM-based autoencoder (Stacked-LSTM-AE), memory-augmented-LSTM-VAE (Mem-LSTM-VAE), and time-series-anomaly-detection-generative-adversarial-networks (TadGAN) were trained exclusively on authentic GPS data. This unsupervised learning approach which used for the above-mentioned models enables …
An Integrated Hybrid P2p-Dr Networks For A Transactive Energy Market Platform Considering Electricity Network Constraints,
2024
South Dakota State University
An Integrated Hybrid P2p-Dr Networks For A Transactive Energy Market Platform Considering Electricity Network Constraints, Sheroze Liaquat
Electronic Theses and Dissertations
No abstract provided.
What Can We Learn From A Co-Creation Journey For A Quick Scan Digital Transformation Maturity Assessment Tool For Development Ngos?,
2024
HU University of Applied Sciences Utrecht
What Can We Learn From A Co-Creation Journey For A Quick Scan Digital Transformation Maturity Assessment Tool For Development Ngos?, Anand Sheombar
Journal of International Technology and Information Management
This paper describes the approach and lessons learned from a co-creation process with Dutch development NGOs to create a practical and easy-to-use assessment tool for practitioners to assess the organisation's maturity level of digital transformation. For this study, we applied a design science research methodology, specifically a six-step co-creation approach suitable for developing maturity models. The digital maturity assessment tool (quick scan) created is a domain- specific digital transformation maturity tool for development NGOs rather than a generally applicable tool. This artefact was evaluated using an eight-point Requirements framework for the development of digital maturity assessment tools. By developing a …
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach,
2024
University of South Carolina - Columbia
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth
Publications
Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …
Finops-Driven Cloud Optimization Models For Enterprise Applications,
2024
University of South Florida
Finops-Driven Cloud Optimization Models For Enterprise Applications, Manikantha Varaprasad Inakollu
Computer Science and Engineering Faculty Publications
Cloud computing has revolutionized enterprise IT infrastructure, yet escalating costs and resource inefficiencies threaten to undermine these benefits. This research examines FinOps-driven optimization models that enable organizations to balance cloud performance, cost efficiency, and business value. The study addresses the critical challenge enterprises face in managing cloud expenditures while maintaining operational excellence. Through comprehensive analysis of FinOps principles and practical optimization frameworks, we develop models that integrate financial accountability, technical efficiency, and business alignment. Our research demonstrates that organizations implementing structured FinOps practices achieve 25-40% cost reductions without compromising application performance. The study contributes both theoretical frameworks for understanding cloud …
Enhancing Erp Auditability And Compliance Using Permissioned Blockchain, A Framework For Transparent And Immutable Enterprise Resource Planning Systems.,
2024
University of South Florida
Enhancing Erp Auditability And Compliance Using Permissioned Blockchain, A Framework For Transparent And Immutable Enterprise Resource Planning Systems., Manikantha Varaprasad Inakollu
Computer Science and Engineering Faculty Publications
Enterprise Resource Planning systems serve as the backbone of modern organizational operations, yet their centralized architecture creates significant challenges for auditability and regulatory compliance. This research proposes a permissioned blockchain framework to enhance ERP auditability by creating immutable, transparent, and traceable records of all system transactions and modifications. The study addresses critical gaps in current ERP systems where transaction histories can be altered, audit trails prove insufficient, and compliance verification remains cumbersome. Through examination of existing ERP limitations and blockchain capabilities, we develop an integrated architecture that maintains operational efficiency while providing cryptographic assurance of data integrity. Our framework employs …
