Data Science Commons^™

Simulated Contact Tracing Of Covid-19 Propagation At Kutztown University For Fall 2020, Dale E. Parson

Computer Science and Information Technology Faculty

From mid-May through August 2020 the author designed, built, revised, and analyzed resulting data from two simulation programs for virtual contact tracing of COVID-19 infection propagation at Kutztown University in the fall 2020 semester. The first was command-line driven and non-graphical, with results distributed to faculty and administrators on May 28. The second was a three-dimensional interactive graphical simulation, distributed to faculty, administrators, and the public as a narrated video via YouTube on July 16. The algorithm is an adaptation of spreading activation as used in theoretical psychology and artificial intelligence research since the 1970s. It propagates discrete, probable infections …

The Agnostic Structure Of Data Science Methods, Domenico Napoletani, Marco Panza, Daniele Struppa

MPP Published Research

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within …

Netsci High: Bringing Agency To Diverse Teens Through The Science Of Connected Systems, Stephen M. Uzzo, Catherine B. Cramer, Hiroki Sayama, Russell Faux

Northeast Journal of Complex Systems (NEJCS)

This paper follows NetSci High, a decade-long initiative to inspire teams of teenage researchers to develop, execute and disseminate original research in network science. The project introduced high school students to the computer-based analysis of networks, and instilled in the participants the habits of mind to deepen inquiry in connected systems and statistics, and to sustain interest in continuing to study and pursue careers in fields involving network analysis. Goals of NetSci High ranged from proximal learning outcomes (e.g., increasing high school student competencies in computing and improving student attitudes toward computing) to highly distal (e.g., preparing students for 21st …

Predicting The Outcome Of Nba Games, Matthew Houde

Honors Projects in Data Science

The aim of the project is to create a machine learning model to predict NBA games. The purpose is to build upon and improve existing models. Research into other predictive sports models and machine learning techniques was conducted to understand what is currently being done to predict NBA games and how effective it is in doing so. After a thorough literary review, the model was created using Python and a variety of machine learning techniques. The dataset used had an array of team statistics for both the home and away team for each corresponding matchup and two supporting features were …

Data-Limited Domain Adaptation And Transfer Learning For Learning Latent Expression Labels Of Child Facial Expression Images, Megan Witherow, Winston Shields, Manar Samad, Khan Iftekharuddin

College of Engineering & Technology (Batten) Posters

While state-of-the-art deep learning models have demonstrated success in adult facial expression classification by leveraging large, labeled datasets, labeled data for child facial expression classification is limited. Due to differences in facial morphology and development in child and adult faces, deep learning models trained on adult data do not generalize well to child data. Recent deep domain adaptation approaches have improved the generalizability of models trained on a source domain to a target domain with few labeled samples. We propose that incorporating steps of deep transfer learning, e.g. weights initialization from the pre-trained source model and freezing model layers, may …

Using Data Visualization To Analyze Big Data In Social Networks, Tracey J. Hayes

Communication & Leadership Faculty Scholarship

Today social networks allow protests to develop using complex components and strategies; furthermore, new tools for digital analysis allow scholars to study patterns and connections in those social movements analyzing online protests and the complex rhetorical work and connections occurring within an online protest (Hayes, 2016). The tools and programs available to study social media in many ways make the process easier, in regards to the amount and type of data available. Nonetheless, this increase in available data presents challenges as data must be collected, sorted, selected, and analyzed. The options present many difficult choices as much of this is …

The Role Of Privacy Within The Realm Of Healthcare Wearables' Acceptance And Use, Thomas Jernejcic

Masters Theses & Doctoral Dissertations

The flexibility and vitality of the Internet along with technological innovation have fueled an industry focused on the design of portable devices capable of supporting personal activities and wellbeing. These compute devices, known as wearables, are unique from other computers in that they are portable, specific in function, and worn or carried by the user. While there are definite benefits attributable to wearables, there are also notable risks, especially in the realm of security where personal information and/or activities are often accessible to third parties. In addition, protecting one’s private information is regularly an afterthought and thus lacking in maturity. …

Modeling The Stock Market Through Game Theory, Kylie Hannafey

Honors College Theses

Game Theory is used on many occasions to help us understand interactions between decision-makers. The famous Nash equilibrium is a steady state in a model that shows the interaction of different players, in which no player can do better by choosing a different action if the actions of the other players do not change. These two concepts can be applied to numerous situations that vary in types of players, but for our research, we are focusing on businesses in the stock market. The main objective is to use Game Theory to analyze data collected from the stock market, model our …

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 …

Interrupting The Propaganda Supply Chain, Kyle Hamilton, Bojan Bozic, Luc Longo

Conference papers

In this early-stage research, a multidisciplinary approach is presented for the detection of propaganda in the media, and for modeling the spread of propaganda and disinformation using semantic web and graph theory. An ontology will be designed which has the theoretical underpinnings from multiple disciplines including the social sciences and epidemiology. An additional objective of this work is to automate triple extraction from unstructured text which surpasses the state-of-the-art performance.

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.

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data, Yuqi Si, Elmer V Bernstam, Kirk Roberts

Faculty, Staff and Student Publications

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

Sentiment-Oriented Metric Learning For Text-To-Image Retrieval, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

In this era of multimedia Web, text-to-image retrieval is a critical function of search engines and visually-oriented online platforms. Traditionally, the task primarily deals with matching a text query with the most relevant images available in the corpus. To an increasing extent, the Web also features visual expressions of preferences, imbuing images with sentiments that express those preferences. Cases in point include photos in online reviews as well as social media. In this work, we study the effects of sentiment information on text-to-image retrieval. Particularly, we present two approaches for incorporating sentiment orientation into metric learning for cross-modal retrieval. Each …

Network-Based Analysis Of Early Pandemic Mitigation Strategies: Solutions, And Future Directions, Pegah Hozhabrierdi, Raymond Zhu, Maduakolam Onyewu, Sucheta Soundarajan

Northeast Journal of Complex Systems (NEJCS)

Despite the large amount of literature on mitigation strategies for pandemic spread, in practice, we are still limited by naive strategies, such as lockdowns, that are not effective in controlling the spread of the disease in long term. One major reason behind adopting basic strategies in real-world settings is that, in the early stages of a pandemic, we lack knowledge of the behavior of a disease, and so cannot tailor a more sophisticated response. In this study, we design different mitigation strategies for early stages of a pandemic and perform a comprehensive analysis among them. We then propose a novel …

Virtual Network Function Embedding Under Nodal Outage Using Deep Q-Learning, Swarna Bindu Chetty, Hamed Ahmadi, Sachin Sharma, Avishek Nag

Articles

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear …

The Impact Of Twitter On The National Hockey League And Its Players, Benjamin Strauss

Honors Projects in Data Science

This study offers a new perspective on collecting and analyzing Twitter data surrounding the National Hockey League (NHL) to identify any trends or relationships between the data and overall performance during the 2021 abbreviated season. This paper provides and in-depth analysis by studying a sample of sixty of the top NHL players, specifically those who are typically top performers in the league, spanning over all thirty-one teams and all positions, this study was able to identify a deeper and broader perspective of what implications can be drawn from analyzing data from Twitter to both predict and reflect both individual player …

Three-Way Analysis-Based Ph-Uv-Vis Spectroscopy For Quantifying Allura Red In An Energy Drink And Determining Colorant's Pka, Erdal Dinç Prof., Nazangül Ünal, Zehra Ceren Ertekin

Journal of Food and Drug Analysis

Three-way analysis-based pH-UV-Vis spectroscopy was proposed for quantifying allura red in an energy drink product without the need for chromatographic analysis, and determining the colorant’s pKa without using any titration technique. In this study, UV-Vis spectroscopic data matrices were obtained from absorbance measurements at five different pH levels from pH 8 to pH 12 and arranged as a three-way array (wavelength x sample x pH). In the three-way analysis procedure, parallel factor analysis (PARAFAC) was implemented to decompose the three-way array into a set of trilinear components. Each set of three components relates to spectral, pH and relative concentration profiles …

Mass Incarceration In Nebraska: Data And Historical Analysis Of Inmates From 1980-2020, Anna Krause

Honors Program: Senior Projects (Public)

This study examines Nebraska Department of Corrections inmate data from 1980-2020, looking specifically at inmate demographics and offense trends. State-of-the-art data analysis is conducted to collect, modify, and visualize the data sources. Inmates are organized by each decade they were incarcerated within. The current active prison population is also examined in their own research group. The demographic and offense trends are compared with previous local and national research. Historical context is given for evolving trends in offenses. Solutions for Nebraska prison overcrowding are presented from various interest groups. This study aims to enlighten all interested Nebraskans on who inhabits their …

Node-Independent Method For Gastroenterological Signal Processing Based On Cubic Splines, S.A. Bakhromov

Bulletin of TUIT: Management and Communication Technologies

This paper discusses a local cubic spline function built independently of node points using basic functions. the size of the calculations required to find the parameters to be determined during the construction of the spline function does not depend on the number of node points. Local-based splines are used to build such spline functions. Restoration of the gastroenterological signal was performed on the basis of the spline-function model discussed in the article. The result of a cubic spline-function error independent of the node points was compared with the result of the Lagrange classical polynomial error (Table 2).

Developing Institutional Skills For Addressing Big Data: Experiences In Implementation Of Aacsb Standard 5, Sumantra Sarkar, Joy Gray, Scott R. Boss, Emmet Daly

Accountancy Faculty Publications

The explosion of data coupled with firms’ desire to utilize it is driving rapid changes in the desired skillset for accounting and assurance professionals. Educational institutions are considering how to catch up to these requirements, while accreditors are also modifying standards to reflect changes in desired skillsets. We present evidence from two institutions’ efforts to update their courses to address contemporary skill requirements, accompanied by discussion from a Big 4 professional. We find that despite significant differences between the two institutions and their approaches, similar challenges were encountered, and similar feedback was obtained from students. We conclude with a proposal …

A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa

Masters Theses & Doctoral Dissertations

The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data …

Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami

Masters Theses & Doctoral Dissertations

Steganography in computer science refers to the hiding of messages or data within other messages or data; the detection of these hidden messages is called steganalysis. Digital steganography can be used to hide any type of file or data, including text, images, audio, and video inside other text, image, audio, or video data. While steganography can be used to legitimately hide data for non-malicious purposes, it is also frequently used in a malicious manner. This paper proposes JRevealPEG, a software tool written in Python that will aid in the detection of steganography in JPEG images with respect to identifying a …

Bilateral Variational Autoencoder For Collaborative Filtering, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral Variational Autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form …

Contract Information Extraction Using Machine Learning, Zachary E. Butcher

Theses and Dissertations

The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …

Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra

Master's Theses

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …

Predictive Modeling And Estimation Of The Doubling Time Of Confirmed Cases Of Covid-19 In Niger, Ibrahim Sidi Zakari, Hadiza Galadima

Community & Environmental Health Faculty Publications

Modeling is increasingly used to assess scenarios and make projections on the future course of new coronavirus disease. This allows for better planning of care as well as a relaxation or tightening of the restrictive measures decreed by the government and the health authorities. The data analyzed in this study covers the period from March 19 to June 05, 2020 and allowed predictions of new cases of COVID-19 based on a growth model with a growth rate that changes linearly over time. In addition, we calculated and predicted the doubling time of the number of positive cases in each region …

Multi-Objective Database Queries In Combined Knapsack And Set Covering Problem Domains, Sean A. Mochocki, Gary B. Lamont, Robert C. Leishman, Kyle J. Kauffman

Faculty Publications

Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of …

Clustering Web Users By Mouse Movement To Detect Bots And Botnet Attacks, Justin L. Morgan

Master's Theses

The need for website administrators to efficiently and accurately detect the presence of web bots has shown to be a challenging problem. As the sophistication of modern web bots increases, specifically their ability to more closely mimic the behavior of humans, web bot detection schemes are more quickly becoming obsolete by failing to maintain effectiveness. Though machine learning-based detection schemes have been a successful approach to recent implementations, web bots are able to apply similar machine learning tactics to mimic human users, thus bypassing such detection schemes. This work seeks to address the issue of machine learning based bots bypassing …

Explainable Recommendation With Comparative Constraints On Product Aspects, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.

In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative …

Introduction To The Mathematical Analysis Of Data Ams 450, Harrison Dekker

Library Impact Statements

No abstract provided.