Data Science Commons^™

Advanced Full-Text Search Based On Synonyms In Postgres, Joey Bodoia

CMC Senior Theses

This paper discusses the advanced full-text search queries based on synonyms that are supported in Chajda, which is a postgres extension and corresponding python library for highly multi-lingual full-text search in postgres. This discussion will include the motivations for using advanced queries based on synonyms, examples of how to use these advanced queries in Chajda, current limitiations of the advanced queries, and performance testing of the advanced queries.

Comparison Of Caregiver- And Child-Reported Quality Of Life In Children With Sleep-Disordered Breathing, Phoebe Kuo Yu, Kaitlyn Cook, Jiayan Liu, Raouf S. Amin, Craig Derkay, Lisa M. Elden, Susan L. Garetz, Alisha S. George, Sally Ibrahim, Stacey L. Ishman, Erin M. Kirkham, S. Kamal Naqvi, Jerilynn Radcliffe, Kristie R. Ross, Gopi B. Shah, Ignacio E. Tapia, H. Gerry Taylor, David A. Zopf, Susan Redline, Cristina M. Baldassari

Statistical and Data Sciences: Faculty Publications

Objective. Caregivers frequently report poor quality of life(QOL) in children with sleep-disordered breathing (SDB).Our objective is to assess the correlation between care-giver- and child-reported QOL in children with mild SDBand identify factors associated with differences between caregiver and child report.

Study Design. Analysis of baseline data from a multi-institutional randomized trialSetting. Pediatric Adenotonsillectomy Trial for Snoring, where children with mild SDB (obstructive apnea-hypopnea index\3) were randomized to observation or adenotonsillectomy.

Methods. The Pediatric Quality of Life Inventory (Peds QL)assessed baseline global QOL in participating children 5 to12 years old and their caregivers. Caregiver and child scores were compared. Multivariable regression …

Robust Testing Of Paired Outcomes Incorporating Covariate Effects In Clustered Data With Informative Cluster Size, Sandipan Dutta

Mathematics & Statistics Faculty Publications

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on …

Towards A Better Understanding Of The Development Of Collaborative Consumption Pricing, Funda Sarican

2022

With the rise of social platforms, consumers started creating value by sharing human and physical resources. As a result, there has been an increase in collaborative consumption. My papers focus on the peer-to-peer travel accommodation service Airbnb, a popular online market for short-term housing rentals.

In paper one, an integrated framework for collaborative consumption pricing is developed by adopting the hedonic demand theory and leveraging the multilevel modeling method that accounts for various factors from nested data. This paper contributes to the research literature by extending the usage of multilevel modeling across cities to consider city effects and broadening the …

The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection, Liu Liu

Theses and Dissertations--Computer Science

Answer set programming (ASP) has long been used for modeling and solving hard search problems. These problems are modeled in ASP as encodings, a collection of rules that declaratively describe the logic of the problem without explicitly listing how to solve it. It is common that the same problem has several different but equivalent encodings in ASP. Experience shows that the performance of these ASP encodings may vary greatly from instance to instance when processed by current state-of-the-art ASP grounder/solver systems. In particular, it is rarely the case that one encoding outperforms all others. Moreover, running an ASP system on …

Batch Normalization Preconditioning For Neural Network Training, Susanna Luisa Gertrude Lange

Theses and Dissertations--Mathematics

Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks. Despite its success, BN is not theoretically well understood. It is not suitable for use with very small mini-batch sizes or online learning. In this work, we propose a new method called Batch Normalization Preconditioning (BNP). Instead of applying normalization explicitly through a batch normalization layer as is done in BN, BNP applies normalization by conditioning the parameter gradients directly during training. This is designed to improve the Hessian matrix of the loss …

Statistical Theory For Specialized Linear Regression Adjustment Methods Compared To Multiple Linear Regression In The Presence And Absence Of Interaction Effects, Leon Su

Theses and Dissertations--Statistics

When building models to investigate outcomes and variables of interest, researchers often want to adjust for other variables. There is a variety of ways that these adjustments are performed. In this work, we will consider four approaches to adjustment utilized by researchers in various fields. We will compare the efficacy of these methods to what we call the ”true model method”, fitting a multiple linear regression model in which adjustment variables are model covariates. Our goal is to show that these adjustment methods have inferior performance to the true model method by comparing model parameter estimates, power, type I error, …

Addressing Ascertainment Bias In The Study Of Cardiovascular Disease Burden In Opioid Use Disorders - Application Of Natural Language Processing Of Electronic Health Records, Jade Huang Singleton

Theses and Dissertations--Epidemiology and Biostatistics

In the United States, the prevalence of long-term exposure to opioid drugs, for both medically and nonmedically indicated purposes, has increased considerably since the mid-1990’s. Concerns have emerged about the potential health effects of opioid use. There is also growing interest in other possible connections with opioid use including cardiovascular disease. Electronic health records (EHR) contain information about patient care in the form of structured codes and unstructured notes. Natural language processing (NLP) provides a tool for processing unstructured textual data in EHR clinical notes and extracts useful information for research with structured formats. The purpose of this dissertation was …

Exploration Of Lignin-Based Superabsorbent Polymers (Hydrogels) For Soil Water Management And As A Carrier For Delivering Rhizobium Spp., Toby Adjuik

Theses and Dissertations--Biosystems and Agricultural Engineering

Superabsorbent polymers (hydrogels) as soil amendments may improve soil hydraulic properties and act as carrier materials beneficial to soil microorganisms. Researchers have mostly explored synthetic hydrogels which may not be environmentally sustainable. This dissertation focused on the development and application of lignin-based hydrogels as sustainable soil amendments. This dissertation also explores the development of pedotransfer transfer functions (PTFs) for predicting saturated hydraulic conductivity using statistical and machine learning methods with a publicly available large data set. A lignin-based hydrogel was synthesized, and its impact on soil water retention was determined in silt loam and loamy fine sand soils. Hydrogel treatment …

Largemouth Bass In The Upper Mississippi River: An Evaluation Of Management Strategies And Understanding Potential Factors Influencing Dynamic Rate Functions, Kylie Beth Sterling

Graduate Theses/Dissertations

The Upper Mississippi River (UMR) supports ecologically and economically important commercial and recreational fisheries. One recreational fishery in the UMR is the Largemouth Bass fishery. Recreational fisheries can be effectively managed using information on population dynamics, though little is known about Largemouth Bass population dynamics in large river ecosystems. Therefore, the objectives of this study were to 1) evaluate recruitment, growth, and mortality of three Largemouth Bass populations in the UMR, specifically within Pools 4, 8, and 13, and 2) to use those estimates of recruitment, growth and mortality to inform exploitation models to evaluate best management practices for each …

Correlation Does Not Imply Correlation: A Thesis On Causal Influence And Simpson’S Paradox, Emily Naitoh

Scripps Senior Theses

In our data-driven world, it has become commonplace to attempt to find
causal relationships. One of the themes of this thesis is to show methods of
determining causation. The second theme follows a saying in mathematics,
"correlation does not imply causation". We will also discuss situations where
correlation does not even imply correlation itself. These cases are described
by Simpson’s paradox in an exploration of different areas of mathematics
and computer coding.

Data, Knowledge Practices, And Naturecultural Worlds: Vehicle Emissions In The Anthropocene, Lindsay Poirier

Statistical and Data Sciences: Faculty Books

This chapter details the various techno-cultural assemblages giving rise to data collected to model and measure anthropogenic worlds, arguing that data-based technologies both represent and co-produce the Anthropocene. It begins with a review of scholarship emerging at the intersection of science and technology studies and information studies that advances understanding of data infrastructure and knowledge practices, and their role within the anthropogenic assemblages that shape history. Drawing on a case study describing how vehicle emissions are measured and regulated in the US, I examine the materialities and mutability of technologies designed to produce data about air quality, along with the …

Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …

Exploring Factors That Predict Marketing Of E-Cigarette Products On Twitter: Infodemiology Approach Using Time Series, Nnamdi C. Ezike, Allison Ames Boykin, Page D. Dobbs, Huy Mai, Brian A. Primack

Health, Human Performance and Recreation Faculty Publications and Presentations

Background:

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective:

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods:

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected …

Interpretable Machine Learning For Self-Service High-Risk Decision Making, Charles Recaido

All Master's Theses

This research contributes to interpretable machine learning via visual knowledge discovery in General Line Coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and GLC are combined to create a visual self-service machine learning model. Two variants of GLC known as Dynamic Scaffold Coordinates (DSC) are proposed. DSC1 and DSC2 can map in a lossless manner multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane using a dynamic scaffolding graph construction algorithm.

Hyperblock analysis is used to determine visually appealing dataset attribute orders and to reduce line occlusion. It is shown that hyperblocks can generalize decision tree …

Wilderness And The Geotag: Exploring The Claim That "Geotagging Ruins Nature" In The Alpine Lakes Wilderness, Wa, Mara Gans

All Master's Theses

This research explores the claim that “geotagging ruins nature” by quantifying and qualifying patterns in geotag use and visitors’ experiences in the Alpine Lakes Wilderness, in Washington, United States. Many have raised concerns that geotags increase recreational visitation to public lands, which subsequently contributes to negative resource impacts. Others, however, claim that geotagging has made the outdoors more accessible to less privileged communities and raise concerns that condemning geotags will perpetuate the exclusion of certain groups from outdoor recreation. This debate is studied within federally designated Wilderness, which is legally defined as “untrammeled by man,” a definition rooted in problematic …

Hydrocarbon Pay Zone Prediction Using Ai Neural Network Modeling., Darren D. Guedon

Graduate Theses, Dissertations, and Problem Reports (ETD)

This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas.

During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs …

Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley

Graduate Theses, Dissertations, and Problem Reports (ETD)

This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a …

A Workflow For Unconventional Reservoirs Optimization Using Supervised Machine Learning In Conjunction With Orthorhombic Elasticity Modeling, Aymen Ab Ali Alhemdi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Due to the anisotropy and heterogeneous nature of unconventional reservoirs like shale, a comprehensive parametric study to optimize hydraulic fracture treatment for such reservoirs is a tough challenge, especially when natural fractures are present. Most of the current frac simulators do not consider the anisotropy of rock elasticity in the shales. Besides, using the fracture simulation linked with reservoir simulation for the parametric study to understand the impact of multiple different design parameters on fracture propagation and production is time expensive and low efficient. The study proposes a workflow including a new orthorhombic (OB) rock algorithm to interpret geomechanical properties …

Applying Machine Learning Algorithms For Face Mask Detections, Mackenzie Frato

Williams Honors College, Honors Research Projects

Goal: Apply multiple machine learning techniques to Face Mask images to detect if a student is wear a Face Mask and/or wearing it incorrectly or not at all. Methodology: Use 2-3 different machine learning techniques to develop this program. Will choose these techniques as I research over the semester. The best technique will be the final one used, but many will be explored. Validation techniques will be used to see which is the best technique. Timeline: Choose Dataset - October 1st, Choose techniques - October 31st, Research techniques/validation - November 31st, Begin writing code - December 13th, Finish code - …