Other Computer Engineering Commons^™

Game Based Learning: Engaging Students And Measuring Their Progress, Jonathan Stover, Siegwart Mayr

Campus Research Month

Digital natives are constantly surrounded by technology. Therefore, traditional methods of teaching are becoming obsolete and increasingly creative solutions are required to keep students engaged. Among these solutions is a concept called game based learning. Game based learning is a unique educational experience that incorporates the engagement factors of video games with education. Even though it is still in an early stage of adoption, game based learning has been proven to be a surprisingly effective tool for providing students with a valuable educational experience. Even with this evidence in mind, current game based learning programs still hold the potential to …

Machine Learning Based Software Fault Prediction Models, Gurmeet Kaur, Jyoti Pruthi, Parul Gandhi

Karbala International Journal of Modern Science

The study aims to identify soft-computing-based software fault prediction models that assist in resolving issues related to the quality, reliability, and cost of the software projects. It proposes models for implementation of software fault prediction using decision-tree regression and the K-nearest neighbor technique of machine learning. The proposed models have been designed and implemented in Python using designed metric suites as input, and the predicted-faults as output, for the real-time, wider dataset from the Promise repository. By comparing the prediction and validation results of the proposed models for the same dataset, it has been concluded that the decision-tree regression-based fault …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

The Fashion Visual Search Using Deep Learning Approach, Smita V. Bhoir, Sunita R. Patil

Library Philosophy and Practice (e-journal)

In recent years, the World Wide Web (WWW) has established itself as a popular source of information. Using an effective approach to investigate the vast amount of information available on the internet is essential if we are to make the most of the resources available. Visual data cannot be indexed using text-based indexing algorithms because it is significantly larger and more complex than text. Content-Based Image Retrieval, as a result, has gained widespread attention among the scientific community (CBIR). Input into a CBIR system that is dependent on visible features of the user's input image at a low level is …

Enhancing Cyberspace Monitoring In The United States Aviation Industry: A Multi-Layered Approach For Addressing Emerging Threats, Matthew Janson

Doctoral Dissertations and Master's Theses

This research project examined the cyberspace domain in the United States (U.S.) aviation industry from many different angles. The research involved learning about the U.S. aviation cyberspace environment, the landscape of cyber threats, new technologies like 5G and smart airports, cybersecurity frameworks and best practices, and the use of aviation cyberspace monitoring capabilities. The research looked at how vulnerable the aviation industry is from cyber-attacks, analyzed the possible effects of cyber-attacks on the industry, and suggests ways to improve the industry's cybersecurity posture. The project's main goal was to protect against possible cyber-attacks and make sure that the aviation industry …

The Exigency And How To Improve And Implement International Humanitarian Legislations More Advantageously In Times Of Both Cyber-Warfare And Cyberspace, Shawn J. Lalman

Doctoral Dissertations and Master's Theses

This study provides a synopsis of the following topics: the prospective limiters levied on cyber-warfare by present–day international legislation; significant complexities and contentions brought up in the rendering & utilization of International Humanitarian Legislation against cyber-warfare; feasible repercussions of cyber-warfare on humanitarian causes. It is also to be contended and outlined in this research study that non–state actors can be held accountable for breaches of international humanitarian legislation committed using cyber–ordnance if sufficient resources and skill are made available. It details the factors that prosecutors and investigators must take into account when organizing investigations into major breaches of humanitarian legislation …

Para Cima Y Pa’ Abajo: Building Bridges Between Hci Research In Latin America And In The Global North, Pedro Reynolds-Cuéllar, Marisol Wong-Villacres, Karla A. Badillo-Urquiola, Mayra Donaji Barrera-Machuca, Franceli L. Cibrian, Marianela Ciolfi Felice, Carolina Fuentes, Laura Sanely Gaytán-Lugo, Vivian Genaro Motti, Monica Perusquía-Hernández, Oscar A. Lemus

Engineering Faculty Articles and Research

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Low-Power Redundant-Transition-Free Tspc Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer, Zisong Wang, Peiyi Zhao, Tom Springer, Congyi Zhu, Jaccob Mau, Andrew Wells, Yinshui Xia, Lingli Wang

Engineering Faculty Articles and Research

In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% …

Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He

Electronic Theses and Dissertations

Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus …

Procedural Model Of The Monitoring System, Fayzullajon Botirov

Chemical Technology, Control and Management

This article discusses the process of monitoring information security incidents and factors affecting the effectiveness of monitoring. A procedural model of the functioning of the information security incident monitoring system is constructed, based on the study of its structure, the principles of operation of individual components and literature data, the analysis of individual stages.

Comprehensive Wind Speed Forecasting-Based Analysis Of Stacked Stateful & Stateless Models, Swayamjit Saha, Amogu Uduka, Hunter Walt, James Lucore

Graduate Student Research Symposium

Wind speed is a powerful source of renewable energy, which can be used as an alternative to the non-renewable resources for production of electricity. Renewable sources are clean, infinite and do not impact the environment negatively during production of electrical energy. However, while eliciting electrical energy from renewable resources viz. solar irradiance, wind speed, hydro should require special planning failing which may result in huge loss of labour and money for setting up the system. In this poster, we discuss four deep recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM and Statcked Stateful GRU which …

Author Gender Identification Considering Gender Bias, Manuela N. Jeyaraj, Sarah Jane Delany

Conference papers

Writing style and choice of words used in textual content can vary between men and women both in terms of who the text is talking about and who is writing the text. The focus of this paper is on author gender prediction, identifying the gender of who is writing the text. We compare closed and open vocabulary approaches on different types of textual content including more traditional writing styles such as in books, and more recent writing styles used in user generated content on digital platforms such as blogs and social media messaging. As supervised machine learning approaches can reflect …

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 …