Data Science Commons^™

Covid-19 In Casinos: Analysis Of Covid-19 Contamination And Spread With Economic Impact Assessment, Anastasia (Stasi) D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Abstract:

The COVID-19 pandemic caused tremendous disruption for casinos, with the virus causing various lengths of shutdowns, capacity restrictions, and social distancing strategies such as machine removals or section closures. Although most of the world has now eased off these measures, it is important to review lessons learned to understand, and better prepare for similar circumstances in the future. We present Monte Carlo slot floor simulation software customized to simulate players spreading COVID-19 on the slot floor. We simulate the amount of touch surface contamination; the number of potential surface contact exposure events per day, and a proximity exposures statistic …

Statistical Methods To Generate Artificial Slot Floor Data For The Advancement Of Casino Related Research, Courtney Bonner, Anastasia (Stasi) D. Baran, Jason D. Fiege, Saman Muthukumarana

International Conference on Gambling & Risk Taking

Abstract:

A common difficulty when researching gambling topics is the availability of high-quality data sets for development and testing. Due to the high level of secrecy within the gambling industry, if data is obtained for research purposes it is often prohibitively obfuscated, incomplete, or aggregated. Although these data have allowed for advancement in academic work, it leaves both the researchers and readers left wondering about what would be possible if more detailed data sets were available. To mitigate the paucity of data available to researchers, we present a Markov chain-based statistical process for producing artificial event data for a simulated …

Payments In Gambling, Kasra Ghaharian

International Conference on Gambling & Risk Taking

A considerable body of gambling-related research has addressed the task of segmenting a sample population of gamblers into homogenous sub-groups. Typically, “static” features are used as model inputs for cluster analysis, where variables are aggregated for each individual over a specified period of time; for example, the total amount wagered per gambler over the course of a study period. Engineering features in this way fails to capture the intricacies of a gambler’s behavior over time. Recent works have begun to address this limitation by using time-series data as model inputs and by employing trajectory analysis. While these methods incorporate the …

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …

The Rocket: Analyzing Rtp (Return To Player), Payoff Distribution And Player Behavior In Crash Games, Mikhail M. Sher, Robert Haywood Scott Iii, Jonathan A. Daigle

International Conference on Gambling & Risk Taking

Abstract

Rocket is a crash game developed by DraftKings, an American publicly traded online casino, sports betting and fantasy sports company. DraftKings Rocket is a game played with a rising rocket. Players must exit the rocket at any point before the rocket crashes. In that case they receive the payoff in accordance to the multiplier of their exit point. If the rocket crashes before the player bails, player’s payoff is 0 (and they lose their bet).

The game boasts an unprecedented 97% RTP (Return to Player). For comparison, Atlantic City casino slots typically have a 91-92% RTP, while Vegas casino …

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid

The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access)

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …

Deephtlv: A Deep Learning Framework For Detecting Human T-Lymphotrophic Virus 1 Integration Sites, Johnathan Jia, Johnathan Jia

The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access)

In the 1980s, researchers found the first human oncogenic retrovirus called human T-lymphotrophic virus type 1 (HTLV-1). Since then, HTLV-1 has been identified as the causative agent behind several diseases such as adult T-cell leukemia/lymphoma (ATL) and a HTLV-1 associated myelopathy or tropical spastic paraparesis (HAM/TSP). As part of its normal replication cycle, the genome is converted into DNA and integrated into the genome. With several hundreds to thousands of unique viral integration sites (VISs) distributed with indeterminate preference throughout the genome, detection of HTLV-1 VISs is a challenging task. Experimental studies typically use molecular biology …

Consumers' Perceptions Of Digital Privacy In The United States And Japan, Destiny Randle

Whittier Scholars Program

The purpose of my study is to explore the contours of contemporary consumer privacy protections derived from legislation, regulations and publicly available company policies as a way to get a better understanding of how consumer data is protected. A few examples ranging from company-based consumer protection in the United States to data breaches in Japan will be explored and examined. Finally, this paper includes a comparative survey of consumer perceptions and concerns related to personal data privacy in the U.S. and Japan. As a way to assess the degree to which digital privacy and personal data breaches have adversely influenced …

Interactive Dashboard Of Diabetes In The Us, Marc Butler

Campus Research Day

The contribution of this research project is the construction and interactive dashboard in order to facilitate the visualization of diabetes-related data to the public

Open Data Indicates That Collegedale Could Be A Bluezone, Tristan Deschamps, Alva Johnson

Campus Research Day

A blue zone is an indicator of exceptional health in a community. Adventists have a blue zone community in Loma Linda, but there has been little research into other Adventist populated areas that could be blue zones. Therefore, our goal is to show that open data suggests that a blue zone may exist near Southern Adventist University, specifically in Collegedale. This data has been gathered from different federal sources, including, the CDC, the US Census Bureau, the Tennessee Department of Health, official state records, and federal documents that are available to the public.

Visualizing The Spread Of Western Music Throughout The World Using Big Data, Dakota C. Cookenmaster

Campus Research Day

Music, perhaps the most prevailing form of art throughout the ages, has impacted the world in countless ways. Due to the vast magnitude of published musical compositions, it is difficult to comprehend the full extent of how Western music has spread from Europe to the rest of the world. Our contribution is a presentation of the history of music throughout the ages, highlighting the countries of publication by year since the 15th century. Our visualization also exhibits the top 10 most prevalent composers within the British Library, with additional information such as the composers’ number of works and lifespan.

Using Azure Automl To Analyze The Effect Of Attendance And Seat Choice On University Student Grades, Ac Hýbl

Campus Research Day

Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University's attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models.

Visualizing Literary Narratives With A Graph-Centered Approach., Meg Ermer

Campus Research Day

The art of storytelling is multifaceted and nonlinear, involving multiple characters, themes, and symbols while often jumping between the present and past. While media forms such as novels can encapsulate these complexities, it is often difficult to visualize a narrative in an easy-to-understand format. Our contribution is a graph-based system to let users organize and visualize those narratives. Events and characters are represented as nodes and their relationships are represented as edges. Neo4J is used as a database management system to store the graph and to run queries on it, and Streamlit and Pyvis are used to represent the database …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

Using California Harmful Algae Risk Mapping To Predict Sea Lion Strandings, Florybeth La Valle, Sydney Socquet

Seaver College Research And Scholarly Achievement Symposium

Domoic acid (DA) is a toxin produced by marine diatoms of the genus Pseudo-nitzschia (Pn) and bioaccumulates in California sea lions (Zalophus californianus). DA toxicosis can cause neurological issues and death, and the rate at which Z. californianus become stranded due to this condition has been increasing since it was first diagnosed in a marine mammal in 1998. We compared geotemporal data of sea lion strandings with data from the California Harmful Algae Risk Mapping (C-HARM) Model to analyze patterns that may indicate when and where a sea lion stranding due to DA toxicosis will occur. C-HARM geographically visualizes the …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn

SMU Data Science Review

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …

Deep Learning For Online Fashion: A Novel Solution For The Retail E-Commerce Industry, Zachary O. Harris, Gowtham G. Katta, Robert Slater, Joseph L. Woodall Iv

SMU Data Science Review

The online shopping experience for clothing can be further enhanced by implementing Deep Learning techniques, such as Computer Vision and personalized recommendation systems. Automation, as a principle, can be applied to solving problems surrounding efficacy, efficiency, and security. It also provides a layer of abstraction for the user during the online shopping experience. This research aims to apply Deep Learning methods and principles of automation to augment the e-commerce fashion market in a novel way. After using these methods, it was found that Convolutional Autoencoders and Item-to-Item Based Recommenders may be used to accurately and precisely recommend articles of clothing …

A Chairpersons Guide To Managing Time And Stress, Christian K. Hansen

Academic Chairpersons Conference Proceedings

In this interactive workshop we discuss time and stress management specifically from the perspective of a department chairperson responsible for leading an academic department through numerous internal and external challenges. The focus will be on practical strategies for effective use of time, not only at a personal level, but also at a department wide level.

Text And Data Mining Applications For Teaching Music Bibliography, Taylor Greene, Laurie Sampsel

Library Presentations, Posters, and Videos

Text and data mining (TDM) is a process of increasing interdisciplinary potential and one with many practical applications for music graduate students. TDM, however, remains a topic rarely introduced in the music bibliography course. Understandably, talk of artificial intelligence, algorithms, and programming languages are intimidating to music students, but thanks to software applications, knowledge about these computer science topics are not required to participate in research using TDM. This presentation explores ways to introduce digital humanities to music students through TDM.

In our presentation, we will discuss two approaches to incorporating TDM into the music bibliography course, focusing on two …