Computer Sciences Commons^™

Kennesaw State University Research Computing Facilities And Resources, Tom Boyle, Ramazan Aygun

Training Materials, User Guides, and Documentation

The Kennesaw State University High Performance Computing (HPC) resources represent the University’s commitment to research computing. This resource contains verbiage for users of Kennesaw State University's HPC resources to include in their grants and publications. Please use the recommended citation rather than including the listed authors in the your citations.

The current version was published Fall 2023. Previous versions can be found below.

Predicting Bus Travel Times In Washington, Dc Using Artificial Neural Networks (Anns), Stephen Arhin, Babin Manandhar, Hamdiat Baba Adam, Adam Gatiba

Mineta Transportation Institute

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Public Discourse Against Masks In The Covid-19 Era: Infodemiology Study Of Twitter Data, Mohammad A. Al-Ramahi, Ahmed El Noshokaty, Omar El-Gayar, Tareq Nasralah, Abdullah Wahbeh

Computer Information Systems Faculty Publications (Archived)

Background:

Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.

Objective:

This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.

Methods:

We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data …

A Comprehensive Mapping And Real-World Evaluation Of Multi-Object Tracking On Automated Vehicles, Alexander Bassett

Doctoral Dissertations and Master's Theses

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field.

This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of …

Somewhere Over The Rainbow: Exploring The Sense For Relevance In Children, Monica Landoni, Theo Huibers, Emiliana Murgia, Mohammad Aliannejadi, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

We explore the facets of relevance that guide children when assessing materials retrieved by search engines when looking for information in the classroom. We involved children in a collaborative exercise and asked them to design innovative icons to point their peers towards useful results. We also asked them to complete a survey meant to capture explicit motivators guiding their design. This resulted in a rich set of metaphors. Analysis of the emerging metaphors is what allowed us to identify and discuss the many interpretations of relevance children naturally assign to resources they find in response to school-related information discovery …

Estimation Of Fair Ranking Metrics With Incomplete Judgments, Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yilmaz

Computer Science Faculty Publications and Presentations

There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of items to particular groups, often identified using protected attributes such as gender or ethnicity. To date, these metrics typically assume the availability and completeness of protected attribute labels of items. However, the protected attributes of individuals are rarely present, limiting the application of fair ranking metrics in large scale systems. In order to address this problem, we propose a sampling strategy and estimation technique for four fair ranking metrics. We formulate a robust and unbiased estimator which can operate even …

Spellchecking For Children In Web Search: A Natural Language Interface Case-Study, Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude …

Dijkstra’S Pathfinder, Taylor F. Malamut

Honors Theses

Dijkstra’s algorithm has been widely studied and applied since it was first published in 1959. This research shows that Dijkstra’s algorithm can be used to find the shortest path between two stations on the Washington D.C. Metro. After exploring different types of research and applying Dijkstra’s algorithm, it was found that the algorithm will always yield the shortest path, even if visually a shorter path was initially expected.

Ai Use In Claims Processing And Utilization Review, Robert Rosenthal Dds

The Journal of the Michigan Dental Association

This paper investigates the use of artificial intelligence (AI) in claims processing and utilization review in the dental industry. This article aims to explore the potential benefits of AI in this area, such as increased efficiency, accuracy, and fraud detection. The paper begins by providing an overview of the current state of claims processing and utilization review in the dental industry. It then discusses the potential applications of AI in this area, such as automated claims adjudication, predictive analytics, and image recognition. The paper then presents a case study of P&R Dental Strategies, LLC, a leading business intelligence solutions provider …

The Emergence Of Artificial Intelligence In Dental Care Delivery, Robert A. Faiella D.M.D., M.M.Sc., M.B.A., Shaju Puthussery M.S.

The Journal of the Michigan Dental Association

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in the evolution of dental care delivery. As oral health specialists, dentists continually seek to enhance their ability to prevent, diagnose, and manage oral diseases while maintaining and improving patient oral health. The integration of AI offers unprecedented opportunities to revolutionize dental practice and patient care.

AI is rapidly advancing in healthcare, including dental care, with a projected global healthcare AI market value of $45.2 billion by 2026. This technology can potentially revolutionize prevention, diagnosis, treatment planning, and treatment outcomes.

Aspects of AI in dentistry include:

· Diagnostic Accuracy …

Learning How To Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection, Hussein Khalid Almulla

Theses and Dissertations

Search-based test generation is guided by feedback from one or more fitness functions— scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage—do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current …

An Analysis Of The Interpretability Of Neural Networks Trained On Magnetic Resonance Imaging For Stroke Outcome Prediction, Esra Zihni, John D. Kelleher, Bryony Mcgarry

Conference papers

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

Auto-Grading Oct Images Diagnostic Tool For Retinal Disease Classification, Shiyu Tian

Master's Theses (2009 -)

Retinal eye disease is the most common reason for visual deterioration. Long-term management and follow-up are critical to detect the changes in symptoms. Optical Coherence Tomography (OCT) is a non-invasive diagnostic tool for diagnosing and managing various retinal eye diseases. With the increasing desire for OCT image, the clinicians are suffered from the burden of time on diagnoses and treatment. This thesis proposes an auto-grading diagnostic tool to divide the OCT image for retinal disease classification. In the tool, the classification model implements convolutional neural networks (CNNs), and the model training is based on denoised OCT images. The tool can …

Using Grids As Password Entry Devices, Karol Lejmbach

Master's Theses (2009 -)

The classic text-based password has been around for a very long time. A lot of security research has been conducted on it. A set of best practices has been available for many years stressing the use of longer and more complex passwords. The issue with this approach is that humans have a hard time recalling long complex sequences of characters. Worse, the more complex the string of characters the more prone it is to being written down which is the most detrimental security threat. The goal of this paper is to introduce and provide an introductory analysis of a grid-based …

Cybersecurity Legislation And Ransomware Attacks In The United States, 2015-2019, Joseph Skertic

Graduate Program in International Studies Theses & Dissertations

Ransomware has rapidly emerged as a cyber threat which costs the global economy billions of dollars a year. Since 2015, ransomware criminals have increasingly targeted state and local government institutions. These institutions provide critical infrastructure – e.g., emergency services, water, and tax collection – yet they often operate using outdated technology due to limited budgets. This vulnerability makes state and local institutions prime targets for ransomware attacks. Many states have begun to realize the growing threat from ransomware and other cyber threats and have responded through legislative action. When and how is this legislation effective in preventing ransomware attacks? This …

Hierarchical And Adaptive Filter And Refinement Algorithms For Geometric Intersection Computations On Gpu, Yiming Liu

Dissertations (1934 -)

Geometric intersection algorithms are fundamental in spatial analysis in Geographic Information System (GIS). This dissertation explores high performance computing solution for geometric intersection on a huge amount of spatial data using Graphics Processing Unit (GPU). We have developed a hierarchical filter and refinement system for parallel geometric intersection operations involving large polygons and polylines by extending the classical filter and refine algorithm using efficient filters that leverage GPU computing. The inputs are two layers of large polygonal datasets and the computations are spatial intersection on pairs of cross-layer polygons. These intersections are the compute-intensive spatial data analytic kernels in spatial …

Predictive Analysis On Knee X-Ray Image And Mosquito Spectral Data, Manzur Rahman Farazi

Dissertations (1934 -)

The aims of this dissertation are to develop predictive algorithms for twopractical applications: classification of knee osteoarthritis (OA) based on knee x-ray image and age prediction of mosquitoes based on near infrared spectra (NIRS) data. For the OA classification problem, we develop an automated algorithm that reads the pixel-wise color intensities for x-ray images and performs an OA severity classification. Identification of the region of interest (ROI) is a primary step for successful automated classification process. We develop an efficient algorithm to detect ROI and from the detected ROI, we extracted width-based features using pixel intensity difference (PID). The PID …

Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung

Research Collection School Of Computing and Information Systems

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …

Robust And Universal Seamless Handover Authentication In 5g Hetnets, Yinghui Zhang, Robert H. Deng, Elisa Bertino, Dong Zheng

Research Collection School Of Computing and Information Systems

The evolving fifth generation (5G) cellular networks will be a collection of heterogeneous and backward-compatible networks. With the increased heterogeneity and densification of 5G heterogeneous networks (HetNets), it is important to ensure security and efficiency of frequent handovers in 5G wireless roaming environments. However, existing handover authentication mechanisms still have challenging issues, such as anonymity, robust traceability and universality. In this paper, we address these issues by introducing RUSH, a Robust and Universal Seamless Handover authentication protocol for 5G HetNets. In RUSH, anonymous mutual authentication with key agreement is enabled for handovers by exploiting the trapdoor collision property of chameleon …

Tour: Dynamic Topic And Sentiment Analysis Of User Reviews For Assisting App Release, Tianyi Yang, Cuiyun Gao, Jingya Zang, David Lo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues …

Machine Learning Based Approaches Towards Robust Android Malware Detection, Jiayun Xu

Dissertations and Theses Collection (Open Access)

The Android platform is becoming increasingly popular and numerous applications (apps) have been developed by organizations to meet the ever increasing market demand over years. Naturally, security and privacy concerns on Android apps have grabbed considerable attention from both academic and industrial
communities. Many approaches have been proposed to detect Android malware in different ways so far, and most of them produce satisfactory performance under the given Android environment settings and labelled samples. However, existing approaches suffer the following robustness problems:

In many Android malware detection approaches, specific API calls are used to build the feature sets, and their feature …

J Mich Dent Assoc April 2021

The Journal of the Michigan Dental Association

In the April 2021 issue of the Journal of the Michigan Dental Association, we offer a comprehensive range of original feature content showcasing the latest developments in dental practice and knowledge, including:

1. AI in Dental Care Delivery: Explore the groundbreaking role of Artificial Intelligence (AI) and Machine Learning in dental care, revolutionizing efficiency, safety, care outcomes, and treatment planning consistency.
2. AI in Dental Claims Processing: Discover how AI is employed by third-party payers to streamline dental claims processing, resulting in cost containment and the proactive identification of potential fraud, waste, and abuse.
3. Evidence-Based Dentistry: As part of …

10-Minute Ebd: Artificial Intelligence In Orthodontics, Jayne Kessel Dds

The Journal of the Michigan Dental Association

This Ten-Minute Evidence-Based Dentistry Article provides an example of the implementation of the EBD search process with trusted search engines for the identification of the best literature through critical appraisal to answer a clinical question. "For patients receiving orthodontic care, is an AI-generated treatment plan as likely to achieve acceptable outcomes?" Orthodontic treatment planning is a complex and time-consuming process that requires a high degree of expertise. Artificial intelligence (AI) has the potential to assist orthodontists in this process by automating some of the tasks involved, such as cephalometric analysis, surgery decisions, extraction decisions, and anchorage decisions.

A recent systematic …

Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, Erick Serrano

Undergraduate Research Symposium Posters

With a surge in popularity of machine learning as a whole, many researchers have sought optimization methods to reduce the complexity of neural networks; however, only recent attempts have been made to optimize neural networks via quantum computing methods. In this paper, we describe the training process of a feed forward neural network (FFNN) and the time complexity of the training process. We highlight the inefficiencies of the FFNN training process, particularly when implemented with gradient descent, and introduce a call to action for optimization of a FFNN. Afterward, we discuss the strides made in quantum computing to improve the …

Integration And Analysis Of Gaze Behavior In Augmented Reality, George Villaume

Honors Capstones

No abstract provided.

Static Analysis Of Haskell For Suitable Metrics For Grading, Christian Fontenot

Honors Capstones

No abstract provided.

Looking Back! Using Early Versions Of Android Apps As Attack Vectors, Yue Zhang, Jian Weng, Jia-Si Wneg, Lin Hou, Anjia Yang, Ming Li, Yang Xiang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Android platform is gaining explosive popularity. This leads developers to invest resources to maintain the upward trajectory of the demand. Unfortunately, as the profit potential grows higher, the chances of these Apps getting attacked also get higher. Therefore, developers improved the security of their Apps, which limits attackers ability to compromise upgraded versions of the Apps. However, developers cannot enhance the security of earlier versions that have been released on the Play Store. The earlier versions of the App can be subject to reverse engineering and other attacks. In this paper, we find that attackers can use these earlier versions …

Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen

Research Collection School Of Computing and Information Systems

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier …

Determining The Number Of Communities In Degree-Corrected Stochastic Block Models, Shujie Ma, Liangjun Su, Yichong Zhang

Research Collection School Of Economics

We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio. For estimation, we consider a spectral clustering together with binary segmentation method. This approach guarantees an upper bound for the pseudo likelihood ratio statistic when the model is over-fitted. We also derive its limiting distribution when the model is under-fitted. Based on these properties, we establish the consistency of our estimator for the true number of communities. Developing these theoretical properties require a mild condition on the average degree: growing at a rate faster than log(n), where n is the number …