Other Computer Engineering Commons^™

Stand-Up Comedy Visualized, Berna Yenidogan

Dissertations, Theses, and Capstone Projects

Stand-up comedy has become an increasingly popular form of comedy in the recent years and comedians reach audiences beyond the halls they are performing through streaming services, podcasts and social media. While comedic performances are typically judged by how 'funny' they are, which could be proxied by the frequency and intensity of laughs through the performance, comedians also explore untapped social issues and provoke conversation, especially in this age where interaction with artists goes beyond their act. It is easy to see commonalities in the topics addressed in comedians’ work such as relationships, race and politics.This project provides an interactive …

Completeness Of Nominal Props, Samuel Balco, Alexander Kurz

Engineering Faculty Articles and Research

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams.

Data Integration Based Human Activity Recognition Using Deep Learning Models, Basamma Umesh Patil, D V Ashoka, Ajay Prakash B. V

Karbala International Journal of Modern Science

Regular monitoring of physical activities such as walking, jogging, sitting, and standing will help reduce the risk of many diseases like cardiovascular complications, obesity, and diabetes. Recently, much research showed that the effective development of Human Activity Recognition (HAR) will help in monitoring the physical activities of people and aid in human healthcare. In this concern, deep learning models with a novel automated hyperparameter generator are proposed and implemented to predict human activities such as walking, jogging, walking upstairs, walking downstairs, sitting, and standing more precisely and robustly. Conventional HAR systems are unable to manage real-time changes in the surrounding …

A Literature Review On Agile Methodologies Quality, Extreme Programming And Scrum, Naglaa A. Eldanasory, Engy Yehia, Amira M. Idrees

Future Computing and Informatics Journal

most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners.

Understanding And Quantifying Human Factors In Programming From Demonstration: A User Study Proposal, Shakra Mehak, Aayush Jain, John D. Kelleher, Philip Long, Michael Guilfoyle, Maria Chiara Leva

Conference papers

Programming by demonstration (PbD) is a promising method for robots to learn from direct, non-expert human interaction. This approach enables the interactive transfer of human skills to the robot. As the non-expert user is at the center of PbD, the efficacy of the learned skill is largely dependent on the demonstrations provided. Although PbD methods have been extensively developed and validated in the field of robotics, there has been inadequate confirmation of their effectiveness from the perspective of human teachability. To address this gap, we propose to experimentally investigate the impact of communicating robot learning process on the efficacy of …

Automation, Ai, And Future Skills Needs: An Irish Perspective, Raimunda Bukartaite, Daire Hooper

Articles

This study explores insights from key stakeholders into the skills they believe will be necessary for the future of work as we become more reliant on artificial intelligence (AI) and technology. The study also seeks to understand what human resource policies and educational interventions are needed to support and take advantage of these changes.

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

Virtual Plc Platform For Security And Forensics Of Industrial Control Systems, Syed Ali Qasim

Theses and Dissertations

Industrial Control Systems (ICS) are vital in managing critical infrastructures, including nuclear power plants and electric grids. With the advent of the Industrial Internet of Things (IIoT), these systems have been integrated into broader networks, enhancing efficiency but also becoming targets for cyberattacks. Central to ICS are Programmable Logic Controllers (PLCs), which bridge the physical and cyber worlds and are often exploited by attackers. There's a critical need for tools to analyze cyberattacks on PLCs, uncover vulnerabilities, and improve ICS security. Existing tools are hindered by the proprietary nature of PLC software, limiting scalability and efficiency.

To overcome these challenges, …

Implicit Racial Bias: A Human Computer Interaction Study Using Eye Tracker, Wenfan Zhang

Master's Projects

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater …

Enhancing Driver Distraction Detection Through The Synergy Of Deep And Traditional Machine Learning, Gowtham Chandrasekaran

Master's Projects

Distracted driving is a major contributor to motor vehicle accidents, causing injury and loss of life. It is one of the major factors that affect the overall driving behavior of a person. Insurance companies take into consideration factors like gender, age, etc. to set insurance premiums for their customers. Today, machine learning and artificial intelligence can eradicate this bias. A machine learning model can analyze driving behavior, such as the frequency and severity of accidents, the speed at which they drive, and their habits such as distracted driving. Based on this information, the model can then determine the risk of …

Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil

Master's Projects

Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …

Enhancing The Queueing Process For Yioop's Scheduler, Gargi Sheguri

Master's Projects

Indexing in search engines is the process of storing information related to crawled pages to facilitate searches. A crucial determinant of the success of a search engine is the efficiency of the indexing process utilized, which greatly affects both the speed and relevancy of search results. Yioop is an open-source web search engine that employs an inverted index strategy, wherein each term is mapped to a list of the documents it appeared in while crawling.

The primary aim of this project is to better the indexing system used by Yioop, and thus improve the quality of the Search Engine Results …

Temporal Dilation In Video Resnet For Sign Language Translation, Xiaoqian Yang

Master's Projects

Sign languages, vital for communication among the deaf and hard-of-hearing (DHH) people, face a significant linguistic diversity challenge with over 200 distinct sign languages worldwide. Bridging this communication gap is a priority. Traditional tools like interpreters and costly translation devices have limitations. This project aims to use deep learning techniques to develop a model capable of recognizing sign language from short videos. Our model not only recognizes the sign from a single video clip, but is also capable of making prediction of consecutive pairs of signs. To achieve zero-short gesture sequence recognition, we propose a novel temporal dilation strategy, converting …

Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan

Master's Projects

The Capri system is an evidential reasoning system based on the belief function calculus to support automated reasoning and decision making in uncertain environments. Example domains of application include, medical diagnosis, as well as identifying biological biomarkers. The purpose of this project is to build a Python web-based and app-based Graphical User Interface (GUI), called PyGrapher, that facilitates building graphical evidential reasoning models. The graphical models built using PyGrapher will then be converted to a form that is suitable for input to the Capri system. The PyGrapher system provides an intuitive means to build and manipulate evidential reasoning models as …

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

Multi-Agent Learning For Game-Theoretical Problems, Kshitija Taywade

Theses and Dissertations--Computer Science

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Bifurcation Levels Of The Integral Manifolds Of The Newtonian N-Body Problem, Hannah G. Havel

CURE Proceedings

The N-body problem, first proposed by Isaac Newton, is a field of study in mathematics and physics that involves predicting the motion of particles moving under their mutual gravitational attraction. It has significance to many areas of science, including physics and computer science, and is crucial in understanding how the universe works. In fact, it was a primary motivation for Newton's development of calculus. An important application of the N-body problem is within celestial mechanics and involves how planets and other celestial bodies move with mutual gravitational attraction. It is important in developing how satellites behave in space using complicated …

Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose

Theses and Dissertations

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …

Graph Based System For Evidential Reasoning, Divyarajsinh Chauhan

Master's Projects

In the modern data driven world, graph editing tools have become very essential as they provide means to understand, visualize and manipulate complex relationships between various datasets. They have especially played a crucial role in the space of evidential reasoning, where it has made a significant impact in the decision making process by developers, analysts and researchers to understand and represent the connection in the data. Existing tools fail to handle huge amounts of data efficiently and also don’t have the features required to handle tasks related to evidential reasoning.To address these gaps, we developed Pygrapher Web UI tool. We …