Computer Sciences Commons^™

Information Security Ambassadors’ Perceptions Of Peer-Led Motivation In Phishing Detection, Kingkane Malmquist

Walden Dissertations and Doctoral Studies

Phishing rates are increasing yearly and continue to compromise data integrity. The need to guard business information is vital for organizations to meet their business objectives and legal obligations. The purpose of this phenomenological study was to explore security ambassadors’ perceptions of motivating their peers to adopt safe internet behaviors in a large medical campus in Minnesota. Hackman and Oldham’s job characteristic motivation theory was used to frame the study. Data were collected from semistructured interviews with 20 security ambassadors. Data coding and analysis yielded 7 themes: rewarding, value, personal interest, limited information security knowledge, increased interest, communication, and topics …

Security Strategies To Prevent Data Breaches In Infrastructure As A Service Cloud Computing, Alberta Amanda Pratt-Sensie

Walden Dissertations and Doctoral Studies

Due to the ever-growing threat of security breaches that information technology (IT) organizations continually face, protecting customer information stored within the cloud is critical to ensuring data integrity. Research shows that new categories of data breaches constantly emerge; thus, security strategies that build trust in consumers and improve system performance are a must. The purpose of this qualitative multiple case study was to explore and analyze the strategies used by database administrators (DBAs) to secure data in a private infrastructure as a service (IaaS) cloud computing. The participants comprised of 6 DBAs from 2 IT companies in Baltimore, Maryland, with …

Remark On Artificial Intelligence, Humanoid And Terminator Scenario: A Neutrosophic Way To Futurology, Victor Christianto, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This article is an update of our previous article in this SGJ journal, titled: On Gödel's Incompleteness Theorem, Artificial Intelligence & Human Mind [7]. We provide some commentary on the latest developments around AI, humanoid robotics, and future scenario. Basically, we argue that a more thoughtful approach to the future is "technorealism."

The Role Of Latency And Task Complexity In Predicting Visual Search Behavior, Leilani Battle, R. Jordan Crouser, Audace Nakeshimana, Ananda Montoly, Remco Chang, Michael Stonebraker

Computer Science: Faculty Publications

Latency in a visualization system is widely believed to affect user behavior in measurable ways, such as requiring the user to wait for the visualization system to respond, leading to interruption of the analytic flow. While this effect is frequently observed and widely accepted, precisely how latency affects different analysis scenarios is less well understood. In this paper, we examine the role of latency in the context of visual search, an essential task in data foraging and exploration using visualization. We conduct a series of studies on Amazon Mechanical Turk and find that under certain conditions, latency is a statistically …

A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal

Theses and Dissertations--Electrical and Computer Engineering

Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement …

Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes

Browse all Theses and Dissertations

Sentence embeddings are frequently generated by using complex, pretrained models that were trained on a very general corpus of data. This thesis explores a potential alternative method for generating high-quality sentence embeddings for highly specialized corpora in an efficient manner. A framework for visualizing and analyzing sentence embeddings is developed to help assess the quality of sentence embeddings for a highly specialized corpus of documents related to the 2019 coronavirus epidemic. A Topological Data Analysis (TDA) technique is explored as an alternative method for grouping embeddings for document clustering and topic modeling tasks and is compared to a simple clustering …

Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman

Browse all Theses and Dissertations

As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of …

Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak

Graduate Theses, Dissertations, and Problem Reports (ETD)

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, …

Representation Learning With Adversarial Latent Autoencoders, Stanislav Pidhorskyi M.S.

Graduate Theses, Dissertations, and Problem Reports (ETD)

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.
Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wise
similarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon …

A Content Analysis-Based Approach To Explore Simulation Verification And Identify Its Current Challenges, Christopher J. Lynch, Saikou Y. Diallo, Hamdi Kavak, Jose J. Padilla

VMASC Publications

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison …

Shipbuilding Supply Chain Framework And Digital Transformation: A Project Portfolios Risk Evaluation, Rafael Diaz, Katherine Smith, Rafael Landaeta, Antonio Padovano

VMASC Publications

Program portfolio managers in digital transformation programs have a need for knowledge that can guide decisions related to the alignment of program investments with the sustainability and strategic objectives of the organization. The purpose of this research is to illustrate the utility of a framework capable of clarifying the cost-benefit tradeoffs stemming from assessing digitalization program investment risks in the military shipbuilding sector. Our approach uses Artificial Neural Network to quantify benefits and risks per project while employing scenario analysis to quantify the effects of operational constraints. A Monte Carlo model is used to generate data samples that support the …

Certified Functions For Mesh Generation, Andrey N. Chernikov

Chemistry & Biochemistry Faculty Publications

Formal methods allow for building correct-by-construction software with provable guarantees. The formal development presented here resulted in certified executable functions for mesh generation. The term certified means that their correctness is established via an artifact, or certificate, which is a statement of these functions in a formal language along with the proofs of their correctness. The term is meaningful only when qualified by a specific set of properties that are proven. This manuscript elaborates on the precise statements of the properties being proven and their role in an implementation of a version of the Isosurface Stuffing algorithm by Labelle and …

Cybersecurity Education Through Technological And Engineering Literacy Standards, Philip A. Reed, Steven A. Barbato

STEMPS Faculty Publications

No abstract provided.

A Tutorial And Future Research For Building A Blockchain-Based Secure Communication Scheme For Internet Of Intelligent Things, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Minho Jo

Computational Modeling & Simulation Engineering Faculty Publications

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of …

Remembering The City: An Augmented Reality Reconstruction Of Memory, Power, And Identity In Ho Chi Minh City Through Cartography & Architecture, Thuy Dinh

Senior Independent Study Theses

Cartography and architecture are official channels that facilitate remembrance in Ho Chi Minh City. Maps and buildings serve as sites for actors of memory to manipulate the city's narratives and shape its collective identity. Power enables the production of space and knowledge through sites of memory. The ruling regimes of Ho Chi Minh City have leveraged control over the natural environment and the local population to create new forms of materials that propagate their ideologies and ideals for the city. Alterations to the natural and built environments in the city legitimize the authorities' official narratives for its history and future …

The Picture Fuzzy Distance Measure In Controlling Network Power Consumption, Florentin Smarandache, Ngan Thi Roan, Salvador Coll Arnau, Marina Alonso Diaz, Juan Miguel Martinez Rubio, Pedro Lopez, Fran Andujar, Son Hoang Lee, Manh Van Vu

Branch Mathematics and Statistics Faculty and Staff Publications

In order to solve the complex decision making problems, there are many approaches and systems based on fuzzy theory were proposed.

Multiplicative Noise Removal: Nonlocal Low-Rank Model And It's Proximal Alternating Reweighted Minimization Algorithm, Xiaoxia Liu, Jian Lu, Lixin Shen, Chen Xu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal. The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank. By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex …

An Assessment Of Pet Dose Reduction With Penalized Likelihood Image Reconstruction Using A Computationally Efficient Model Observer, Howard C. Gifford, C. Ross Schmidtlein, Andrzej Krol, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost- effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target …

Where Did The Time Go?, John C. Viaud, Bilal Abdulmajid, Vitali Surmach, Jia Yanxia

Capstone Showcase

Study shows that most people spend a full quarter of their active hours on their mobile device which can take a serious toll on our productivity as well as our mental and physical well-being. We created an Android app that is able to track and visualize phone usage patterns to help user establish awareness of how much and in what ways they use their Android devices. Unlike currently existing phone time management apps, such as Apple’s ScreenTime and Google’s new Digital Wellbeing, our app provides functionalities to encourage off-phone time and personal goal management.

Transfer Equivalency Site For The Registrar, Janelle N. Rohrbach, Alissa C. Cielecki, Jules A. Turner

Capstone Showcase

The website, arcadia-transfer-equiv.com, was drafted and created for Bill Enick, Registrar at Arcadia University. This was an expansion of a preliminary version of the website created by Caleigh Diefenthaler, Christian Charney, and Dylan Power. The site includes two transfer equivalency tools which can be used to search for course equivalencies by academic institution or by a given Arcadia course. The tools, which pull from a database maintained by the Registrar, will display courses that have transferred to the University successfully. The website also hosts two interactive forms. These forms allow Arcadia students to request approval for courses that have or …

Modeling Gene Expression With Differential Equations, Madison Kuduk

Capstone Showcase

Gene expression is the process by which the information stored in DNA is convertedinto a functional gene product, such as protein. The two main functions that makeup the process of gene expression are transcription and translation. Transcriptionand translation are controlled by the number of mRNA and protein in the cell. Geneexpression can be represented as a system of first order differential equations for the rateof change of mRNA and proteins. These equations involve transcription, translation,degradation and feedback loops. In this paper, I investigate a system of first orderdifferential equations to model gene expression proposed by Hunt, Laplace, Miller andPham in …

Exploration And Implementation Of Neural Ordinary Differential Equations, Long Huu Nguyen, Andy Malinsky

Capstone Showcase

Neural ordinary differential equations (ODEs) have recently emerged as a novel ap- proach to deep learning, leveraging the knowledge of two previously separate domains, neural networks and differential equations. In this paper, we first examine the back- ground and lay the foundation for traditional artificial neural networks. We then present neural ODEs from a rigorous mathematical perspective, and explore their advantages and trade-offs compared to traditional neural nets.

Complex Ciliary Flows Around Stentor Polymorphus In Solutions Of 2% Buttermilk And Chlamydomonas Reinhardtii, Eliana B. Smithstein

Scripps Senior Theses

Stentor are large, unicellular ciliates of the Heterotricha order. They live in both freshwater and marine habitats and are mostly found in ponds. I studied Stentor polymorphus, which is a species of Stentor only recently discovered to be lab culturable. They range from 0.5-1.5mm in length and are unusual because they live with endosymbiotic algae and are much more likely than other, more widely studied, species of Stentor to form aggregates while they are eating. There are three main components to this thesis: First, I established protocols for keeping a viable S. polymorphus culture, since no protocols had been …

Developing A Uas-Deployable Methane Sensor Using Low-Cost Modular Open-Source Components, Gavin Demali

Williams Honors College, Honors Research Projects

This project aimed to develop a methane sensor for deployment on an unmanned aerial system (UAS), or drone, platform. This design is centered around low cost, commercially available modular hardware components and open source software libraries. Once successfully developed, this system was deployed at the Bath Nature Preserve in Bath Township, Summit County Ohio in order to detect any potential on site fugitive methane emissions in the vicinity of the oil and gas infrastructure present. The deliverables of this project (i.e. the data collected at BNP) will be given to the land managers there to better inform future management and …

Understanding The Digital Lives Of Transnational Students: A Case Study, Chowaing Chagra Belekeh

Open Access Theses & Dissertations

The proliferation and the fast-paced evolution of digital information communication technologies (ICTs) in contemporary times have arguably raised concern for us to comprehend what we do with these technologies and what these technologies do for us. The experience of engaging these technologies may not necessarily be the same for everyone—especially students who come from around the world to attain post-graduate degrees in the United States. This research focused on understanding the digital lives, choices, and experiences of transnational students who navigate and negotiate geopolitical borders and boundaries (physical)– in their quest for education. Using a case study analysis and collecting …

A Comparative Study Of The Impact Of Depth In Deep Learning Architectures, Kirsten Byers

Open Access Theses & Dissertations

Machine Learning continues to evolve as applications become more complex. Neural Networks, or Deep Networks, are integral to machine learning and the entire taxonomy of Artificial Intelligence [Sze17]. Intelligent structures and algorithms continue to advance, keeping pace with the complexi-ty of data. Changes in architecture, algorithms, and parameters are necessary to keep up with com-putational complexity and data available. This study focuses on how changes in depth of the archi-tecture affect performance on three distinct datasets, including one on Heart Disease. An adaptable network is created in original code, trained, and tested on these datasets. Its performance parameters are observed …

Compound Vision Approach For Autonomous Vehicles Navigation, Michael Mikhael

Open Access Theses & Dissertations

An analogy can be made between the sensing that occurs in simple robots and drones and that in insects and crustaceans, especially in basic navigation requirements. Thus, an approach in robots/drones based on compound eye vision could be useful. In this research, several image processing algorithms were used to detect and track moving objects starting with images upon which a grid (compound eye image) was superimposed, including contours detection, the second moments of those contours along with the grid applied to the original image, and Fourier Transforms and inverse Fourier Transforms. The latter also provide information about scene or camera …

Deep Learning For Overhead Imagery: Algorithms And Applications, Anthony Manuel Ortiz Cepeda

Open Access Theses & Dissertations

Remote sensing using overhead imagery has critical impact to the way we understand our environment and offers crucial information for scene understanding, climate change research, disaster response, urban planning, forest management, and many other applications. At present, deep learning is increasingly used in remote sensing, but mostly borrowing algorithms developed for natural images in the computer vision community. Specific challenges arise while applying deep learning to remote sensing. These challenges include issues related to the high dimensionality and limited labeled data, security and robustness to adversarial attacks, and model generalization. In this Thesis we focus on tackling these key challenges. …

Comparing Predictive Performance Of Statistical Learning Models On Medical Data, Francis Biney

Open Access Theses & Dissertations

This work investigates the predictive performance of 10 Machine learning models on three medical data including Breast cancer, Heart disease and Prostate cancer. Furthermore, we use the models to identify risk factors that contribute significantly to these diseases.

The models considered include; Logistic regression with L1 and L_2 penalties, Principal component logistic regression(PCR-LR), Partial least squares logistic regression(PLS-LR), Multivariate adaptive regression splines(MARS), Support vector machine with Radial Basis Kernel (SVM-RBK), Random Forest(RF), Gradient Boosting Machines(GBM), Elastic Net (Enet) and Feedforward Neural Network(FFNN). The models were grouped according to their similarities and learning style; i) Linear regularized models: LR-Lasso, LR-Ridge and …

Finalcache: Eviction Based On Implicit Entry Reachability, Adrian Veliz

Open Access Theses & Dissertations

Software caches for garbage collected systems are unaware which cache entries are referenced by a client program. Lacking this key information, software caches cannot determine the cache’s contribution to the program’s memory footprint. Furthermore, their eviction heuristics must rely on access history as a proxy for usage. Divergence between usage and access history can undermine the intention of eviction thereby resulting in problematic cache behavior.

This dissertation proposes designs for a novel family of “usage-based” software cache informed of entry reachability by the automatic memory management system. Unlike extant software caches, usage-based caches can accurately determine their memory footprint because …