Data Science Commons^™

The Legacy Of Colonization And Civil Societies In South Africa, Erika Frydenlund, Melissa Miller-Felton, Bolu Ayankojo

Modeling, Simulation and Visualization Student Capstone Conference

This research analyzes the unique ways that civil societies operate in Sub-Saharan Africa in the context of post-apartheid Cape Town, South Africa. Decades after the demise of apartheid, remnants of inequality remain without the promise of actionable change. We used a computational modeling approach to understand the dynamics of migrants in the receiving community as derived from qualitative interviews conducted with 24 stakeholders in Cape Town, South Africa between 2020 and 2021. Our findings show that the presence of NGOs can promote access to resources and reduce xenophobia if they can have the right influence on government policies.

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

Assessing Frustration Towards Venezuelan Migrants In Columbia: Path Analysis On Newspaper Coded Data, Brian Llinás, Guljannat Huseynli, Erika Frydenlund, Katherine Palacia, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This study analyzes the impact of Venezuelan migrants on local frustration levels in Colombia. The study found a relationship between the influx of Venezuelan migrants and the level of frustration among locals towards migrants, infrastructure, government, and geopolitics. Additionally, we identified that frustration types have an impact on other frustrations. The study used articles from a national newspaper in Colombia from 2015 to 2020. News articles were coded during a previous study qualitatively and categorized into frustration types. The code frequencies were then used as variables in this study. We used path modeling to statistically study the relationship between dependent …

Bridging The Chasm Between Fundamental, Momentum, And Quantitative Investing, Allen Hoskins, Jeff Reed, Robert Slater

SMU Data Science Review

A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.

Comparison Of Sampling Methods For Predicting Wine Quality Based On Physicochemical Properties, Robert Burigo, Scott Frazier, Eli Kravez, Nibhrat Lohia

SMU Data Science Review

Using the physicochemical properties of wine to predict quality has been done in numerous studies. Given the nature of these properties, the data is inherently skewed. Previous works have focused on handful of sampling techniques to balance the data. This research compares multiple sampling techniques in predicting the target with limited data. For this purpose, an ensemble model is used to evaluate the different techniques. There was no evidence found in this research to conclude that there are specific oversampling methods that improve random forest classifier for a multi-class problem.

Content-Based Unsupervised Fake News Detection On Ukraine-Russia War, Yucheol Shin, Yvan Sojdehei, Limin Zheng, Brad Blanchard

SMU Data Science Review

The Ukrainian-Russian war has garnered significant attention worldwide, with fake news obstructing the formation of public opinion and disseminating false information. This scholarly paper explores the use of unsupervised learning methods and the Bidirectional Encoder Representations from Transformers (BERT) to detect fake news in news articles from various sources. BERT topic modeling is applied to cluster news articles by their respective topics, followed by summarization to measure the similarity scores. The hypothesis posits that topics with larger variances are more likely to contain fake news. The proposed method was evaluated using a dataset of approximately 1000 labeled news articles related …

Rethinking" Risk" In Algorithmic Systems Through A Computational Narrative Analysis Of Casenotes In Child-Welfare, Devansh Saxena, Erina Seh-Young Moon, Aryan Chaurasia, Yixin Guan, Shion Guha

Health Services and Informatics Research

No abstract provided.

Sports Data Science Job Requirements, Cam E. Morse

Student Publications

Data science is an extremely fast growing field in which job opportunities are opening in every industry related to data science. Within the data science field is the sports data science industry which has it's own requirements and specificities that may not be present in other industries. In this paper, research is done using multiple job posting websites such as LinkedIn, Indeed, Sportstek jobs, and TeamworkOnline to explore the job descriptions of many different sports data science jobs. These job descriptions are then examined using Python coding to find the frequencies of specific data science skills in the various job …

A Diffusion Network Event History Estimator, Jeffrey J. Harden, Bruce A. Desmarais, Mark Brockway, Frederick J. Boehmke, Scott J. Lacombe, Fridolin Linder, Hanna Wallach

Government: Faculty Publications

Research on the diffusion of political decisions across jurisdictions typically accounts for units’ influence over each other with (1) observable measures or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce network event history analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA’s unique methodological and substantive benefits in applications to policy adoption in …

Moral Injury To Inform Analysis Of Post-Traumatic Stress Disorder, Amanda Julia Manea

Senior Theses

Post-traumatic stress disorder (PTSD) is a mental health condition that almost one out of ten veterans struggle with. Although the National Center for PTSD has made extensive progress in characterizing and developing new treatments for PTSD, most veterans still experience symptoms of PTSD following treatment. Novel avenues of investigation, such as developing algorithms to review electronic health record (EHR) data and better understanding moral injury, are being pursued to address the gap that still exists when it comes to treating veterans. Moral injury is the individual evaluation of exposure to a potentially morally injurious event (PMIE) and can lead to …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Institutional Design And Policy Responsiveness In Us States, Scott J. Lacombe

Government: Faculty Publications

There is significant disagreement on the moderating role of institutions on policy responsive- ness, yet overwhelmingly research in state politics has focused on single institutions. This project leverages a new aggregate scale of state institutions to evaluate if the collective insti- tutional context moderates the influence of public opinion on policy. I use a recently released latent scale of institutional context and find that high levels of accountability pressure strongly strengthen public opinion’s influence on policy for both economic and social policy, while the strength of a state’s checks and balance system is largely unrelated to policy responsiveness. These results …

Biological Correlates Of The Effects Of Auricular Point Acupressure On Pain, Chao Hsing Yeh, Nada Lukkahatai, Xinran Huang, Hulin Wu, Hongyu Wang, Jingyu Zhang, Xinyi Sun, Thomas J Smith

Faculty, Staff and Student Publications

BACKGROUND: To identify candidate inflammatory biomarkers for the underlying mechanism of auricular point acupressure (APA) on pain relief and examine the correlations among pain intensity, interference, and inflammatory biomarkers.

DESIGN: This is a secondary data analysis.

METHODS: Data on inflammatory biomarkers collected via blood samples and patient self-reported pain intensity and interference from three pilot studies (chronic low back pain, n = 61; arthralgia related to aromatase inhibitors, n = 20; and chemotherapy-induced neuropathy, n = 15) were integrated and analyzed. This paper reports the results based on within-subject treatment effects (change in scores from pre- to post-APA intervention) for …

Social Impacts Of Robotics On The Labor And Employment Market, Kelvin Espinal

Dissertations, Theses, and Capstone Projects

Robotics have been introduced into the workplace to perform tasks that human beings have traditionally fulfilled. Complementing or substituting human labor with robotics eliminates human involvement in functions attributable to hazardous environments, heavy lifting, toxic substances, and repetitive low-level tasks. On the other hand, they are meant to be more efficient and cost-effective, saving money, time, and labor. However, since the introduction of robotics in the workforce, societal opposition has been towards this branch of technology in fear of losing employment, wages, and purpose.

Previous studies have reported an overarching societal fear that adopting robotics in the workplace and industry …

Assessing Spurious Correlations In Big Search Data, Jesse T. Richman, Ryan J. Roberts

Political Science & Geography Faculty Publications

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random …

The Daily Patterns Of Emergency Medical Events, Mary Elizabeth Helander, Margaret K. Formica, Dessa K. Bergen-Cico

Social Science - All Scholarship

This study examines population level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the U.S. National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ($R^2$) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours--specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when re-organized to consider monthly, seasonal, daylight-savings vs civil time, and pre-/post- COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.

The Basil Technique: Bias Adaptive Statistical Inference Learning Agents For Learning From Human Feedback, Jonathan Indigo Watson

Theses and Dissertations--Computer Science

We introduce a novel approach for learning behaviors using human-provided feedback that is subject to systematic bias. Our method, known as BASIL, models the feedback signal as a combination of a heuristic evaluation of an action's utility and a probabilistically-drawn bias value, characterized by unknown parameters. We present both the general framework for our technique and specific algorithms for biases drawn from a normal distribution. We evaluate our approach across various environments and tasks, comparing it to interactive and non-interactive machine learning methods, including deep learning techniques, using human trainers and a synthetic oracle with feedback distorted to varying degrees. …

The Shortfalls Of Vulnerability Indexes For Public Health Decision-Making In The Face Of Emergent Crises: The Case Of Covid-19 Vaccine Uptake In Virginia, Lydia Cleveland Sa, Erika Frydenlund

VMASC Publications

Equitable and effective vaccine uptake is a key issue in addressing COVID-19. To achieve this, we must comprehensively characterize the context-specific socio-behavioral and structural determinants of vaccine uptake. However, to quickly focus public health interventions, state agencies and planners often rely on already existing indexes of "vulnerability." Many such "vulnerability indexes" exist and become benchmarks for targeting interventions in wide ranging scenarios, but they vary considerably in the factors and themes that they cover. Some are even uncritical of the use of the word "vulnerable," which should take on different meanings in different contexts. The objective of this study is …

Lessons Learned From Interdisciplinary Efforts To Combat Covid-19 Misinformation: Development Of Agile Integrative Methods From Behavioral Science, Data Science, And Implementation Science, Sahiti Myneni, Paula Cuccaro, Sarah Montgomery, Vivek Pakanati, Jinni Tang, Tavleen Singh, Olivia Dominguez, Trevor Cohen, Belinda Reininger, Lara S Savas, Maria E Fernandez

Faculty, Staff and Student Publications

BACKGROUND: Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals' preventive behaviors, including masking, testing, and vaccine uptake.

OBJECTIVE: In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation.

METHODS: We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a …

Warehouses In The Inland Empire: Displacing Land And Life, Katherine Gelsey

Pomona Senior Theses

The Inland Empire in Southern California embodies unique spatial and social configurations as a consequence of how settler colonialism has manifested locally in the region since the Spanish Mission Period. This work uses GIS software to estimate patterns of land conversion for residential, agricultural, and warehouse land from 2012 to 2022. Preliminary analysis suggests that thousands of people have been displaced by warehouse expansion over the ten-year period. In the twenty-first century, the Southern California logistics industry continues processes of land dispossession and racialized labor exploitation through displacing agricultural and residential land, exposing disproportionately low-income Black and Latine communities living …

Biodiversity Of Philippine Marine Fishes: A Dna Barcode Reference Library Based On Voucher Specimens, Katherine E. Bemis, Matthew G. Girard, Mudjekeewis D. Santos, Kent E. Carpenter, Jonathan R. Deeds, Diane E. Pitassy, Nicko Amor L. Flores, Elizabeth S. Hunter, Amy C. Driskell, Kenneth S. Macdonald Iii, Lee A. Weigt, Jeffrey T. Williams

Biological Sciences Faculty Publications

Accurate identification of fishes is essential for understanding their biology and to ensure food safety for consumers. DNA barcoding is an important tool because it can verify identifications of both whole and processed fishes that have had key morphological characters removed (e.g., filets, fish meal); however, DNA reference libraries are incomplete, and public repositories for sequence data contain incorrectly identified sequences. During a nine-year sampling program in the Philippines, a global biodiversity hotspot for marine fishes, we developed a verified reference library of cytochrome c oxidase subunit I (COI) sequences for 2,525 specimens representing 984 species. Specimens were primarily purchased …

Quantifying The Carbon Stored And Sequestered By The Trees On Pomona College’S Campus, Paola A. Giron-Carson

Scripps Senior Theses

We are experiencing a climate crisis that must be confronted with strategic mitigation. Pomona College contributes to the climate crisis through its emissions for which there is a baseline record. However there is no baseline record of the climate mitigation currently performed by the trees on Pomona’s campus through carbon storage. This study seeks to determine a current baseline quantity of carbon stored and sequestrated by Pomona’s trees as well as possible courses of climate mitigation for Pomona College to take. Initial information gathering was conducted through interviews with several stakeholders. This study was conducted using data collected prior to …

Natural Language Processing In Legal Tech, Jens Frankenreiter, Julian Nyarko

Scholarship@WashULaw

Natural language processing techniques promise to automate an activity that lies at the core of many tasks performed by lawyers, namely the extraction and processing of information from unstructured text. The relevant methods are thought to be a key ingredient for both current and future legal tech applications. This chapter provides a non-technical overview of the current state of NLP techniques, focusing on their promise and potential pitfalls in the context of legal tech applications. It argues that, while NLP-powered legal tech can be expected to outperform humans in specific categories of tasks that play to the strengths of current …

Exploring Information Leakage In Historical Stock Market Data, Edison Hua

Dissertations and Theses

Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the trader’s activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information …

Application Of Sentiment Analysis And Machine Learning Techniques To Predict Daily Cryptocurrency Price Returns, Edward Wu

CMC Senior Theses

This paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price …

Feature Extraction Of Footwear Impression Images For Quality Assessment, Alexandra Hill

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forensic footwear impression analysis is a valuable tool in criminal investigations. Extracting useful features from images of footwear impressions is a critical step in this process. However, the quality of these images can vary widely, making feature extraction challenging. In order to give a quality assessment rating to a footwear impression image, the image should first be analyzed to extract features from the impression. In this paper, we present a method to extract features from a 2D grayscale footwear impression image. A Hierarchical Grid Model implementation has been adapted from use on a 3D dataset to assist in finding features, …

Longitudinal Sport Science Implementation In American Collegiate Men’S Basketball, Jason Stone

Graduate Theses, Dissertations, and Problem Reports (ETD)

The expanding opportunities to implement sport science frameworks in elite-level basketball environments coincide with the sport’s increasing global prominence. Concomitant to these opportunities is the continual growth of the sport technology market (e.g., wearables, force plates) and computational power (e.g., data management tools, coding capabilities), which yields solutions and challenges for both athletes and practitioners. Due to the rapid influx of new sport technologies in high performance environments, particularly American Collegiate Men’s Basketball, more formal and ecologically valid research on how to effectively utilize data derived from them, particularly over long periods of time (i.e., multiple seasons) is needed. To …

Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)

Electrical & Computer Engineering Faculty Publications

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

Deeppatent2: A Large-Scale Benchmarking Corpus For Technical Drawing Understanding, Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M. Jones, Michal Kucer, Diane Oyen

Computer Science Faculty Publications

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million …

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …