Responding To Some Challenges Posed By The Re-Identification Of Anonymized Personal Data,
2019
Rivier University
Responding To Some Challenges Posed By The Re-Identification Of Anonymized Personal Data, Herman T. Tavani, Frances S. Grodzinsky
Computer Ethics - Philosophical Enquiry (CEPE) Proceedings
In this paper, we examine a cluster of ethical controversies generated by the re-identification of anonymized personal data in the context of big data analytics, with particular attention to the implications for personal privacy. Our paper is organized into two main parts. Part One examines some ethical problems involving re-identification of personally identifiable information (PII) in large data sets. Part Two begins with a brief description of Moor and Weckert’s Dynamic Ethics (DE) and Nissenbaum’s Contextual Integrity (CI) Frameworks. We then investigate whether these frameworks, used together, can provide us with a more robust scheme for analyzing privacy concerns that …
Market Research On Student Concert Attendance At Bgsu's College Of Musical Arts,
2019
Bowling Green State University
Market Research On Student Concert Attendance At Bgsu's College Of Musical Arts, Mary Solomon
Honors Projects
Bowling Green State University boasts a well established College of Musical Arts which holds concerts performed by esteemed faculty, prestigious guest artists, and students. The school hosts these events in Kobacker Hall and Bryan Recital Hall which can accommodate up to 800 and 250 audience members, respectively. However, performances in Kobacker hall only fill one- fourth of the 800 seats, on average. Why is this so? This project aims to investigate the factors that influence students’ decisions to attend concerts at the College of Musical Arts (CMA). By methodology of survey research and statistical analysis, this project will look into …
Black Swamp Pub And Bistro Analysis,
2019
Bowling Green State University
Black Swamp Pub And Bistro Analysis, Sara Aniol
Honors Projects
The Black Swamp Pub and Bistro is a full-service restaurant located in the Union on the Bowling Green State University Campus. We mainly do sit-down service, but we also do take-out orders and have a full bar with draft beers as well as mixed drinks. Our menu tends to change a lot, with new additions as well as some of the items being deleted. My goal of this project is to try to give some insight on the patterns that are too big to see with day-to-day operations as well as give some recommendations for the future that is backed …
Examining The Relationship Between Pre-Collegiate Educational Experiences And Religious Affiliation,
2019
Bowling Green State University
Examining The Relationship Between Pre-Collegiate Educational Experiences And Religious Affiliation, Erica Augustyniak
Honors Projects
This paper explores the relationship between the type of school students experienced before college and how that schooling affected the students’ religious affiliation. The specific types of schools examined are public and private schools with private schools being further divided into religious and non-religious private schools. I explore the differences in religious importance among several groups including students who attended Catholic schools and those who did not, students who attended religious schools for varying lengths of time (low, medium, high, and no involvement), and students who had a choice in the schools they attended and those who did not. I …
Twitter And Disasters: A Social Resilience Fingerprint,
2019
Purdue University
Twitter And Disasters: A Social Resilience Fingerprint, Benjamin A. Rachunok, Jackson B. Bennett, Roshanak Nateghi
Purdue University Libraries Open Access Publishing Fund
Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that …
Kadafrica: Survey Analysis To Support Research For Smallholder Farmers,
2019
Southern Methodist University
Kadafrica: Survey Analysis To Support Research For Smallholder Farmers, Gregory Asamoah, Robert Gill, Frank Sclafani, Bivin Sadler
SMU Data Science Review
In this paper, we present an analysis of survey data with the goal of determining if the KadAfrica training program, a social organization in Uganda, has a significant effect on the lives of the girls who participate in the program. This is done through an observational study of girl’s responses to several pre-program and post-program questions. These questions include topics such as the girl’s access to hygiene materials and their personal views on family finances. In addition to providing an analysis of historical data, we established a data platform in which future data can be stored and analyzed in an …
Machine Learning Vs Conventional Analysis Techniques For The Earth’S Magnetic Field Study,
2019
Southern Methodist University
Machine Learning Vs Conventional Analysis Techniques For The Earth’S Magnetic Field Study, Sheri Loftin, Sarah J. Fite, Laura V. Bishop, Stavros Kotsiaros
SMU Data Science Review
Abstract. Current techniques for calculating and generating models used for analyzing the Earth’s magnetic field are laborious and time-consuming. We assert that machine learning can have a significant impact on building magnetic field models more quickly and on various levels of complexity, specifically as it pertains to data cleansing and sorting. Our approach to this problem uses a reverse iterative multi-phase process for data cleansing, in which, initially, the CHAOS-6 model data is examined to determine if machine learning can be used to differentiate between useful data components for spherical harmonics, versus data noise. During this phase, six different machine …
The Housing Bubble,
2019
Louisiana Tech University
The Housing Bubble, Shelby Brown
Mathematics Senior Capstone Papers
The housing market is constantly changing. Fluctuating housing prices and a flooded market have home buyers hesitant to commit and sellers on edge. What if the prospective buyer or seller could take this financial step already knowing the state of the market? The purpose of this project is to attempt to predict the next housing bubble. A multivariable regression analysis is conducted using relevant data including variables such as average property prices, number of foreclosures, etc. in the United States beginning in the year 2009. The trends, patterns, and models created from the regression analysis are compared against data models …
Angry Birds Fly High Again With Data Analytics,
2019
Singapore Management University
Angry Birds Fly High Again With Data Analytics, Singapore Management University
Perspectives@SMU
User feedback has transformed Rovio’s culture and game design
The Evolution Of Data Science: A New Mode Of Knowledge Production,
2019
Kennesaw State University
The Evolution Of Data Science: A New Mode Of Knowledge Production, Jennifer Lewis Priestley, Robert J. Mcgrath
Faculty Articles
Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production …
Enhancing Smes’ Data Analytics Capability Through University Tie-Ups,
2019
Singapore Management University
Enhancing Smes’ Data Analytics Capability Through University Tie-Ups, Gary Pan, Poh Sun Seow, Benjamin Huan Zhou Lee
Research Collection School Of Accountancy
Harnessing the power of data analytics, SMEs can now generate visualisations of the company's historical data to date, and predictions for the future - something which is nearly impossible before the era of big data.
Newsworthy Migrants: Sentiment And Text Analysis Of Dutch Newspapers,
2019
SIT Study Abroad
Newsworthy Migrants: Sentiment And Text Analysis Of Dutch Newspapers, Nicholas J. Schmitz
Independent Study Project (ISP) Collection
Understanding how a migrant population is viewed and displayed by the host country has been a struggle for a long period of time for anyone studying migration. Traditional methods of collecting information were tedious, time intensive and expensive. However, Big data has been providing unique solutions to gaps in information in many different fields across the world. Media plays an important part in developing and assessing the public opinion on a topic. With the availability of a large number of online articles from historical time periods, it is possible to use quantitative analysis, such as text analytics, to can see …
Data Analytics Pipeline For Rna Structure Analysis Via Shape,
2019
University of Nebraska at Omaha
Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson
UNO Student Research and Creative Activity Fair
Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses that provides confidence …
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning,
2019
SDSMT
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
SDSU Data Science Symposium
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …
An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks,
2019
Southern Methodist University
An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine
SMU Data Science Review
In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the neural …
Comparisons Of Performance Between Quantum And Classical Machine Learning,
2019
Southern Methodist University
Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran
SMU Data Science Review
In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher accuracy …
Comparative Analysis Of Students’ Performance Between Online And On Campus In An Introductory Statistics Course,
2019
Georgia College and State University
Comparative Analysis Of Students’ Performance Between Online And On Campus In An Introductory Statistics Course, Kendal Mcdonald
The Corinthian
In this research, we compare students’ performance in an online and on-campus introductory statistics and probability course at Georgia College. MyStatLab is the learning management system used in both the online and on-campus courses for homework and quizzes. The online data is produced by five summer courses between Summer 2014 to Summer 2017 and the on-campus data is produced from nine on-campus courses from Spring 2014, Spring 2016, and Spring 2017. For homework, the research compares the scores made between online and on-campus. For quizzes, we test if there is a difference between the scores and the number of attempts …
Using Neural Networks To Classify Discrete Circular Probability Distributions,
2019
Claremont Colleges
Using Neural Networks To Classify Discrete Circular Probability Distributions, Madelyn Gaumer
HMC Senior Theses
Given the rise in the application of neural networks to all sorts of interesting problems, it seems natural to apply them to statistical tests. This senior thesis studies whether neural networks built to classify discrete circular probability distributions can outperform a class of well-known statistical tests for uniformity for discrete circular data that includes the Rayleigh Test1, the Watson Test2, and the Ajne Test3. Each neural network used is relatively small with no more than 3 layers: an input layer taking in discrete data sets on a circle, a hidden layer, and an output …
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data,
2019
University of Central Florida
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang
Honors Undergraduate Theses
This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past …
Fixed Choice Design And Augmented Fixed Choice Design For Network Data With Missing Observations,
2019
Smith College
Fixed Choice Design And Augmented Fixed Choice Design For Network Data With Missing Observations, Miles Q. Ott, Matthew T. Harrison, Krista J. Gile, Nancy P. Barnett, Joseph W. Hogan
Statistical and Data Sciences: Faculty Publications
The statistical analysis of social networks is increasingly used to understand social processes and patterns. The association between social relationships and individual behaviors is of particular interest to sociologists, psychologists, and public health researchers. Several recent network studies make use of the fixed choice design (FCD), which induces missing edges in the network data. Because of the complex dependence structure inherent in networks, missing data can pose very difficult problems for valid statistical inference. In this article, we introduce novel methods for accounting for the FCD censoring and introduce a new survey design, which we call the augmented fixed choice …
