Data Science Commons^™

Janus: A Python Package For Agent-Based Modeling Of Land Use And Land Cover Change, K. E. Kaiser, A. N. Flores, C. R. Vernon

Geosciences Faculty Publications and Presentations

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, and still provide clear linkages between the agent, their environment, and …

Improving Trust In Research: Supporting Claims With Evidence, Daniel B. Wright

Educational Psychology, Leadership, and Higher Education Faculty Research

Trust in science is important, and Open Education Studies aims to publish trusted research. Two issues are addressed here: access to the data on which the research is based and how these data are analyzed. Some guidelines from other entities are discussed. As a new journal our guidelines should be influenced by the opinions of readers and authors, and as such we welcome discussion of how to ensure trust in the research OES publishes.

Automatic Delamination Segmentation For Bridge Deck Based On Encoder-Decoder Deep Learning Through Uav-Based Thermography, Chongsheng Cheng, Zhexiong Shang, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

Concrete deck delamination often demonstrates strong variations in size, shape, and temperature distribution under the influences of outdoor weather conditions. The strong variations create challenges for pure analytical solutions in infrared image segmentation of delaminated areas. The recently developed supervised deep learning approach demonstrated the potentials in achieving automatic segmentation of RGB images. However, its effectiveness in segmenting thermal images remains under-explored. The main challenge lies in the development of specific models and the generation of a large range of labeled infrared images for training. To address this challenge, a customized deep learning model based on encoder-decoder architecture is proposed …

Pathways To The Native Storyteller: A Method To Enable Computational Story Understanding, Aramide O. Kehinde

College of Computing and Digital Media Dissertations

The primary objective of this thesis is to develop a method that uses machine learning algorithms to enable computational story understanding. This research is conducted with the aim of establishing a system called the Native Storyteller that plans and creates storytelling experiences for human users. The paper first establishes the desired capabilities of the system and then deep dives into how to enable story understanding, which is the core ability the system needs to function. As such, the research places emphasis on natural language processing and its application to solving key problems in this context. Namely, machine representation of story …

Utilizing Neural Networks And Wearables To Quantify Hip Joint Angles And Moments During Walking And Stair Ascent, Megan V. Mccabe

ENGS 88 Honors Thesis (AB Students)

Wearable sensors were leveraged to develop two methods for computing hip joint angles and moments during walking and stair ascent that are more portable than the gold standard. The Insole-Standard (I-S) approach replaced force plates with force-measuring insoles and achieved results that match the curvature of results from similar studies. Peaks in I-S kinetic results are high due to error induced by applying the ground reaction force to the talus. The Wearable-ANN (W-A) approach combines wearables with artificial neural networks to compute the same results. Compared against the I-S, the W-A approach performs well (average rRMSE = 18%, R^{2 …}

Variability In The Analysis Of A Single Neuroimaging Dataset By Many Teams, Rotem Botvinik-Nezer, Tom Schonberg, Russell A. Poldrack, Zachary J. Cole, Matthew R. Johnson, Phui Cheng Lim, Evan N. Linz, Douglas H. Schultz, Joshua E. Zosky, Narps Management Team, Jean M. Vettel, More Than 100 Other Co-Authors

Department of Psychology: Faculty Publications

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, …

Chemostratigraphy Of Carbonate Gravity Flows Of The Wolfcamp Formation In Crockett County, Midland Basin, Texas, Alex Blizzard, Julie Bloxson

Electronic Theses and Dissertations

Sediment gravity flows into deep-water environments are important stratigraphic traps in lithologically diverse reservoirs generating multiple plays for hydrocarbon exploration. These highly heterogeneous deposits can be studied by utilizing chemostratigraphy and higher-order sequence stratigraphy; being an accurate method for reservoir characterization. Studying these gravity flows along a carbonate platform’s slope can further expand an understanding of the stratigraphy that is filling adjacent basins. The application of elemental analyses can support in identifying mineralogy that impact reservoir quality, especially when conventional testing cannot be applied.

This study utilizes five cores containing the Wolfcamp Formation from the southeastern slope of the Central …

Integrating Finance Dictionary In Lexicon-Based Approach With Machine Learning Algorithm To Analyse The Impact Of Opec News Sentiment On Financial Market, Ling Wu

Student Works (2020-2029)

Since last few decades, machine learning algorithm which trains computers to learn from experience, is one of the most rapidly developing techniques which settles in the intersection research field of statistics and computer science. This research aims to build a properly trained machine learning classifier to study the impact of Organization of Petroleum Exporting Countries (OPEC) news sentiment on stock prices of six Malaysian public listed companies (energy sector) in the main board of Bursa Malaysia. The data used in this research are collected during the period 2012-2017. To carry out the research, firstly, lexicon-based approach is used to analyze …

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom

Research & Publications

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

Privacy-Preserving Recommendation System Using Federated Learning, Rahul Basu

Theses

Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that …

A Web-Based, Positive Emotion Skills Intervention For Enhancing Posttreatment Psychological Well-Being In Young Adult Cancer Survivors (Empower): Protocol For A Single-Arm Feasibility Trial, John M. Salsman, Laurie E. Mclouth, Michael Cohn, Janet A. Tooze, Mia Sorkin, Judith T. Moskowitz

Behavioral Science Faculty Publications

BACKGROUND: Adolescent and young adult cancer survivors (AYAs) experience clinically significant distress and have limited access to supportive care services. Interventions to enhance psychological well-being have improved positive affect and reduced depression in clinical and healthy populations but have not been routinely tested in AYAs.

OBJECTIVE: The aim of this protocol is to (1) test the feasibility and acceptability of a Web-based positive emotion skills intervention for posttreatment AYAs called Enhancing Management of Psychological Outcomes With Emotion Regulation (EMPOWER) and (2) examine proof of concept for reducing psychological distress and enhancing psychological well-being.

METHODS: The intervention development and testing are …

Linked Data For The Real World: Leveraging Metadata For Cataloging, Rachel S. Evans, Robin Fay, Linh Uong

Presentations

Will the promise of linked data actually save us time? How will catalogers and machines work together to streamline recording of data and authority maintenance work, allowing catalogers and metadata practitioners to focus more on data stewardship and less on being data scribes? Will Real World Objects (RWOs) and linked data help bridge the gap between traditional cataloging and the larger semantic web communities of practice, ensuring that library metadata supports our users’ search behaviors, those FRBR User Tasks? Or will it just provide more maintenance work down the road? This session will explore the potential of linked data and …

Online Graduate Certificate In Gis, Joanna Burkhardt

Library Impact Statements

No abstract provided.

Using Case-Level Context To Classify Cancer Pathology Reports, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao-Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We …

An Application Of Machine Learning To Explore Relationships Between Factors Of Organisational Silence And Culture, With Specific Focus On Predicting Silence Behaviours, Stephen Barrett Dr

Articles

Research indicates that there are many individual reasons why people do not speak up when confronted with situations that may concern them within their working environment. One of the areas that requires more focused research is the role culture plays in why a person may remain silent when such situations arise. The purpose of this study is to use data science techniques to explore the patterns in a data set that would lead a person to engage in organisational silence. The main research question the thesis asks is: Is Machine Learning a tool that Social Scientists can use with respect …

Sensor Data Analysis In Smart Buildings, Manuel A. Mane Penton

Publications and Research

Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the …

Using Data Mining To Identify The Most Influential Factors In Training Results, Xiaoqing Wu, Daanial Ahmad

Publications and Research

Data Science is used as a tool to find hidden facts in the data. We want to find out what factors such as ‘AGE’, ‘TAX’, ‘PUPIL-TEACHER RATIO’, ‘PER-CAPITA INCOME’ contribute the most to housing prices. To answer this question, we studied the dataset of “Boston Houses Prices”. By applying the Lasso Regression (a Data Mining Technique) on the data set of “Boston Houses Prices” we identified the influential factors in the linear model. As a conclusion we found that there were six inputs which contributed the most to the prices of houses and those inputs are as follow: (i) CRIM-per …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests …

Philosophical Perspectives, Jochen Albrecht

Publications and Research

This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …

Finding Trends In Big City Health Issues With Data Visualization, Shridhar Kulkarni

Dissertations and Theses

In recent years, data visualization has become one of the most effective tools to understand and identify unseen features of the large datasets available. An open source data set available for health issues for big cities across the United States was obtained. There are numerous indicators presented in the dataset including Demographics, Chronic Health Diseases, Social and Economic Factors, Food Safety, Mortality Rates, Cancer and Life Expectancy Rates. The dataset encompassed myriad of demographics as well as specific data for a number of US cities. The data was explored in different methods in Data points in terms of the demographic …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

Feature Extraction And Analysis Of Binaries For Classification, Micah Flack

Annual Research Symposium

The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we …

A Data Scientist Looks At Covid-19 Part I: Local And National Statistics, Anthony Breitzman

College of Science & Mathematics Departmental Research

No abstract provided.

Data Science Meets Compliance, Christian Clarke

Petersheim Academic Exposition

No abstract provided.

Design Principles Influencing Secondary School Counselors' Satisfaction Of A Decision-Support System, Kodey S. Crandall

Masters Theses & Doctoral Dissertations

In the current era of accountability, secondary school counselors are expected to use data to drive program decision-making, identify and implement evidence-based interventions to create systemic change, and utilize emerging technology. Research shows it is difficult for school counselors to meet any of these expectations. A decision support system (DSS) is a technology that takes minimal effort to learn and can assist in decision-making processes. This design science research builds and evaluates an IT artifact, a decision-support system, in an attempt to solve the problems facing school counselors. To develop this system, four design principles (system usefulness, interface quality, information …

Digital Forensic Readiness: An Examination Of Law Enforcement Agencies In The State Of Maryland, James B. Mcnicholas Iii

Masters Theses & Doctoral Dissertations

Digital forensic readiness within the law enforcement community, especially at the local level, has gone mostly unexplored. As a result, a current lack of data exists that examines the digital forensic readiness of individual agencies, the possibility of proximity relationships, and correlations between readiness and backlogs. This quantitative, crosssectional research study sought to explore these issues by focusing on the state of Maryland. The study resulted in the creation of a digital forensic readiness scoring model that was then used to assign digital forensic readiness scores to thirty (30) of the one-hundred-forty-one (141) law enforcement agencies throughout Maryland. It was …

The Effectiveness Of Transfer Learning Systems On Medical Images, James Boit

Masters Theses & Doctoral Dissertations

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned …

The Influence Of Peer And Parental Norms On First-Generation College Students’ Binge Drinking Trajectories, Graham T. Diguiseppi, Jordan P. Davis, Matthew K. Meisel, Melissa A. Clark, Mya L. Roberson, Miles Q. Ott, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Introduction: First-generation college students are those whose parents have not completed a four-year college degree. The current study addressed the lack of research on first-generation college students’ alcohol use by comparing the binge drinking trajectories of first-generation and continuing-generation students over their first three semesters. The dynamic influence of peer and parental social norms on students’ binge drinking frequencies were also examined. Methods: 1342 college students (n = 225 first-generation) at one private University completed online surveys. Group differences were examined at Time 1, and latent growth-curve models tested the association between first-generation status and social norms (peer descriptive, peer …