Computer Sciences Commons^™

Effect Of Mutation And Vaccination On Spread, Severity, And Mortality Of Covid-19 Disease, Dr Hossam Zawbaa, Hasnaa Osama, Ahmed El‐Gendy, Haitham Saeed, Hadeer S. Harb, Yasmin M. Madney, Mona Abdelrahman, Marwa Mohsen, Ahmed M.A. Ali, Mina Nicola, Marwa O. Elgendy, Ihab A. Ibrahim, Mohamed E.A. Abdelrahim

Articles

Coronavirus disease 2019 (COVID-19) has had different waves within the same country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-data sets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for severe acute respiratory syndrome coronavirus 2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, …

Event-Driven Servers Using Asynchronous, Non-Blocking Network I/O: Performance Evaluation Of Kqueue And Epoll, Lorcan Leonard

Dissertations

This research project evaluates the performance of kqueue and epoll in the context of event-driven servers. The evaluation is done through benchmarking and tracing which are used to measure throughput and execution time respectively. The experiment is repeated for both a virtualised and native server environment. The results from the experiment are statistically analysed and compared. These results show significant differences between kqueue and epoll, and a profound impact of virtualisation as a variable.

A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran

Dissertations

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …

Strategies To Protect Against Security Violations During The Adoption Of The Internet Of Things By Manufacturers, Sixtus Anayochukwu Ekwo

Walden Dissertations and Doctoral Studies

Security violations have been one of the key factors affecting manufacturers in adopting the Internet of Things (IoT). The corporate-level information technology (IT) leaders in the manufacturing industry encounter issues when adopting IoT due to security concerns because they lack strategies to protect against security violations. Grounded in Roger’s diffusion of innovations theory, the purpose of this qualitative multiple case study was to explore strategies corporate-level IT leaders use in protecting against security violations while adopting IoT for manufacturers. The participants were senior IT leaders in the eastern region of the United States. The data collection process included interviews with …

Reliable Data Collection: A Tool For Data Integrity In Nigeria, Stella Tonye Whyte

Walden Dissertations and Doctoral Studies

Unreliable and poor-quality data is a significant threat to governmental institutions because of its devastating impact on nations' social and economic well-being. Managers in government organizations require reliable data to inform economic planning and decision-making. Grounded in the theory of total quality management, the purpose of this qualitative multiple case study was to explore strategies information technology (IT) managers in sub-Saharan African countries use to ensure the reliability of data. The participants were 12 IT managers in three government establishments in Port Harcourt, Rivers State, Nigeria, responsible for ensuring the data reliability for economic planning and decision-making. The data collection …

On Studying Distributed Machine Learning, Simeon Eberz

Senior Honors Theses

The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image recognition. Processing data for DL directly on IoT edge devices reduces latency and increases privacy. To overcome the resource constraints of IoT edge devices, the computation for DL inference is distributed between a cluster of several devices. This paper explores DL, IoT networks, and a novel framework for distributed processing of DL in IoT clusters. The aim is to facilitate and simplify deployment, testing, and study of a distributed DL system, even without physical devices. The contributions of this paper are a deployment …

Efficient Modeling Of Random Sampling-Based Lru Cache, Junyao Yang

Dissertations, Master's Theses and Master's Reports

The Miss Ratio Curve (MRC) is an important metric and effective tool for caching system performance prediction and optimization. Since the Least Recently Used (LRU) replacement policy is the de facto policy for many existing caching systems, most previous studies on efficient MRC construction are predominantly focused on the LRU replacement policy. Recently, the random sampling-based replacement mechanism, as opposed to replacement relying on the rigid LRU data structure, gains more popularity due to its lightweight and flexibility. To approximate LRU, at replacement times, the system randomly selects K objects and replaces the least recently used object among the sample. …

Deep Learning Approach On Symptom Questionnaire And Abdominal Radiography For Diagnosis Of Dyssynergic Defecation, Sornsiri Poovongsaroj

Chulalongkorn University Theses and Dissertations (Chula ETD)

Dyssynergic defecation is one of the most common causes of chronic constipation. It is a behavioral problem in which the pelvic floor muscles are unable to coordinate with the surrounding muscles and nerves to evacuate stool. Patients are required to undergo specialized tests only available at tertiary healthcare centers for diagnosis. The aim of this thesis is to develop deep learning-based models to prescreen potential patients from primary and secondary healthcare centers for further diagnostic tests by using easily obtainable data such as symptom questionnaire and abdominal radiography. First, we developed a model which uses symptom questionnaire as an input …

A Deep Learning Model For Predicting Long Non-Coding Rna And Messenger Rna With Model Interpretation, Rattaphon Lin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Long non-coding RNAs (lncRNAs) play important roles in many biological processes and are found to be associated with several diseases. The development of next-generation sequencing technologies has discovered numerous unannotated transcripts. However, classifying these unannotated transcripts by using biological experiments is very time-consuming and expensive. Thus, a computational approach is considered as an alternative solution which is faster and cheaper. Many existing lncRNA identification tools are available, these tools lack an explanation of which features contributed to their prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) together with a prediction …

Relationships Among Dimensions Of Information System Success And Benefits Of Cloud, William Harold Stanley

Walden Dissertations and Doctoral Studies

Despite the many benefits offered by cloud computing’s design architecture, there are many fundamental performance challenges for IT managers to manage cloud infrastructures to meet business expectations effectively. Grounded in the information systems success model, the purpose of this quantitative correlational study was to evaluate the relationships among the perception of information quality, perception of system quality, perception of service quality, perception of system use, perception of user satisfaction, and net benefits of cloud computing services. The participants (n = 137) were IT cloud services managers in the United States, who completed the DeLone and McLean ISS authors’ validated survey …

Addressing High False Positive Rates Of Ddos Attack Detection Methods, Alireza Zeinalpour

Walden Dissertations and Doctoral Studies

Distributed denial of service (DDoS) attack detection methods based on the clustering method are ineffective in detecting attacks correctly. Service interruptions caused by DDoS attacks impose concerns for IT leaders and their organizations, leading to financial damages. Grounded in the cross industry standard process for data mining framework, the purpose of this ex post facto study was to examine whether adding the filter and wrapper methods prior to the clustering method is effective in terms of lowering false positive rates of DDoS attack detection methods. The population of this study was 225,745 network traffic data records of the CICIDS2017 network …

Classification Of Chess Games: An Exploration Of Classifiers For Anomaly Detection In Chess, Masudul Hoque

All Graduate Theses, Dissertations, and Other Capstone Projects

Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played during January, 2021 on lichess.com. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been used for anomaly detection in different fields and thus it is a natural idea to develop classifiers to detect cheating in chess. However, there are no specific examples of this, and it is difficult to obtain data where cheating has occurred. So, in this paper, we develop 4 …

Create A New Login Authentication And User Authorization Using Ms Sql Server, Safet Jahaj

Open Educational Resources

The document describes the steps on creating a new login authentication using the mixed mode, and adding user authorizations.

Deapsecure Computational Training For Cybersecurity Students: Improvements, Mid-Stage Evaluation, And Lessons Learned, Wirawan Purwanto, Yuming He, Jewel Ossom, Qiao Zhang, Liuwan Zhu, Karina Arcaute, Masha Sosonkina, Hongyi Wu

University Administration Publications

DeapSECURE is a non-degree computational training program that provides a solid high-performance computing (HPC) and big-data foundation for cybersecurity students. DeapSECURE consists of six modules covering a broad spectrum of topics such as HPC platforms, big-data analytics, machine learning, privacy-preserving methods, and parallel programming. In the second year of this program, to improve the learning experience, we implemented a number of changes, such as grouping modules into two broad categories, "big-data" and "HPC"; creating a single cybersecurity storyline across the modules; and introducing post-workshop (optional) "hackshops." Two major goals of these changes are, firstly, to effectively engage students to maintain …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …

Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii

Graduate Student Theses, Dissertations, & Professional Papers

Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …

Detection Of Gaussian Attacks In Power Systems Under A Scalable Kalman Consensus Filter Framework, Arnold Fernandes, Rui Bo, Jonathan W. Kimball, Bruce M. Mcmillin

Electrical and Computer Engineering Faculty Research & Creative Works

The dynamic non-linear state-space model of a power-system consisting of synchronous generators, buses, and static loads has been linearized and a linear measurement function has been considered. A distributed dynamic framework for estimating the state vector of the power system has been designed here. This framework employs a type of distributed Kalman filter (DKF) known as a Kalman consensus filter (KCF) which is located at distributed control centers (DCCs) that fuse locally available noise ridden measurements, state vector estimates of neighboring control centers, and a prediction obtained by the linearized model to obtain a filtered state vector estimate. Further, the …

Sample Mislabeling Detection And Correction In Bioinformatics Experimental Data, Soon Jye Kho

Browse all Theses and Dissertations

Sample mislabeling or incorrect annotation has been a long-standing problem in biomedical research and contributes to irreproducible results and invalid conclusions. These problems are especially prevalent in multi-omics studies in which a large set of biological samples are characterized by multiple types of omics platforms at different times or different labs. While multi-omics studies have demonstrated tremendous value in understanding disease biology and improving patient outcomes, the complexity of these studies may increase opportunities for human error. Fortunately, the interrelated nature of the data collected in multi-omics studies can be exploited to facilitate the identification and, in some cases, correction …

Natural User Interface Based American Sign Language Tutoring Program, Bryce J. Allen

Williams Honors College, Honors Research Projects

The COVID-19 pandemic has exposed a substantial shortcoming in the modern American educational system: there is a sufficient need for our educators to be trained in the practices required to provide an educational experience for their students that is as effective as in-person instruction. There exist already systems of online instruction for various academic subjects, such as math and the sciences. In the subject of linguistic studies, educational programs have been developed to evaluate student proficiency in both the written and spoken forms of the language in which they are studying. However, there exist few programs that can effectively provide …

Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis

Williams Honors College, Honors Research Projects

Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …

Football’S Future: An Analytical Interpretation Of The Premier League, Hunter Witeof

Williams Honors College, Honors Research Projects

This project looks to take the statistics of soccer players and run them through an algorithm to determine how well a player is performing. The system that will be designed in the project will look to accomplish 3 main goals: allow the user to enter new statistics, store the data for all 38 game weeks for all 20 teams, and compute a score for each player’s performance for each game as well as the average of all of the player's scores.

A Rebellion Framework With Learning For Goal-Driven Autonomy, Zahiduddin Mohammad

Browse all Theses and Dissertations

Modeling an autonomous agent that decides for itself what actions to take to achieve its goals is a central objective of artificial intelligence. There are various approaches used to build autonomous agents including neural networks, state machines, utility functions, learning agents, and cognitive architectures. In this thesis, we focus on cognitive architectures. Our approach uses specific knowledge of the world, the goals they pursue, and the actions being performed. Most agents do what they are told (i.e., achieve the goals given to them by a human), but a genuinely autonomous agent does more. It can formulate its own goal or …

Partial Facial Re-Imaging Using Generative Adversarial Networks, Derek Desentz

Browse all Theses and Dissertations

Existing facial recognition software relies heavily on using neural networks to extract key facial features to accurately classify known individuals. Some of these key features include the shape, size, and distance between an individual’s eyes, nose, and mouth. When these key features cannot be extracted due to facial coverings, existing applications become inaccurate and unreliable. The accuracy and reliability of these technologies are growing concerns as the facial recognition market continues to grow at an exponential rate. In this thesis, we have developed a web-based application service that is able to take in a partially covered face image and generate …

Clustering Data To Classify Hearthstone Decks, Tim Inzitari

Williams Honors College, Honors Research Projects

The esports game of "Hearthstone" is a collectible card game with a competitive format that has every team submit 4 decks of 30 cards each. Using K-Means clustering an adaptable way to group data for classifying can be made that works well in every update of the game. This system will take in a list of decks and cluster them to easily classify large amounts of information in a timely fashion. This system will be able to be used by the Universities esports department for years to come to aid the preparation of "Hearthstone" matches. This model uses qualities about …

Interactive Virtual Reality Reading Experience, Nathaniel Shetler

Williams Honors College, Honors Research Projects

The project is an interactive virtual reality reading experience. The user is able to read a book or story in VR. When certain achievements are reached, such as finishing a chapter, the user is given the opportunity to transport to the environment that they are reading about. This gives the user a great opportunity to interact and learn hands-on with the material they are reading about. For example, if the user is reading about World War I, they will be given the opportunity to transport to the battlefields/trenches in Europe.

Online Marketplace, Devin Hopkins

Williams Honors College, Honors Research Projects

The goal of this project was to create a website that helped a common person search for items from online marketplaces, such as eBay, Amazon, or Facebook Marketplace. The intention was to ease the burden of needing to search several different websites for the same product. Currently, if a person wants to look for a specific item on multiple different marketplaces, they must go to each marketplace individually and search for it. They must enter their specifications repeatedly and load virtually the same web page multiple times. This project’s goal was to condense that so the user would only have …

Source Code Comment Classification Artificial Intelligence, Cole Sutyak

Williams Honors College, Honors Research Projects

Source code comment classification is an important problem for future machine learning solutions. In particular, supervised machine learning solutions that have largely subjective data labels but are difficult to obtain the labels for. Machine learning problems are problems largely because of a lack of data. In machine learning solutions, it is better to have a large amount of mediocre data than it is to have a small amount of good data. While the mediocre data might not produce the best accuracy, it produces the best results because there is much more to learn from the problem.

In this project, data …

Versatility Of Low-Power Wide-Area Network Applications, Dali Ismail

Wayne State University Dissertations

Low-Power Wide-Area Network (LPWAN) is regarded as the leading communication technology for wide-area Internet-of-Things (IoT) applications. It offers low-power, long-range, and low-cost communication. With different communication requirements for varying IoT applications, many competing LPWAN technologies operating in both licensed (e.g., NB-IoT, LTE-M, and 5G) and unlicensed (e.g., LoRa and SigFox) bands have emerged. LPWANs are designed to support applications with low-power and low data rate operations. They are not well-designed to host applications that involve high mobility, high traffic, or real-time communication (e.g., volcano monitoring and control applications).With the increasing number of mobile devices in many IoT domains (e.g., agricultural …

Methods For Ad Hoc And Conversational Entity Retrieval From Knowledge Graphs, Fedor Nikolaev

Wayne State University Dissertations

The recent years have witnessed the increase in popularity of knowledge graphs in various applications, such as information extraction and retrieval systems, and intelligent assistants. Traditionally, retrieval from knowledge graphs is performed by submitting queries in SPARQL, a rigid query language based on triple patterns and logical operations. In this work, we propose several approaches to ad hoc and conversational entity retrieval that transcend the limitations of this approach by allowing the user to either submit queries using natural language in an ad hoc retrieval setting or have a conversation with an intelligent retrieval system by asking a series of …

Optimizing Sparse Tensor Computations Via Orderings And Multilayered Data-Structures, Trevor Garnett

Summer Community of Scholars Posters (RCEU and HCR Combined Programs)

No abstract provided.