Data Science Commons^™

Helping Frontline Workers In Texas-A Framework For Resource Development, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

First responders disproportionately experience occupational stress when compared to the general population, and COVID-19 has exacerbated this stress. The nature of their duties as law enforcement officers, firefighters, and medics exposes them to repeated trauma, increasing their risk of developing a broad array of mental health issues, including post-traumatic stress disorder (PTSD), substance use disorder (SUD), and compassion fatigue. This paper describes the need for resources for frontline workers and provides a framework for creating and implementing resources. A team of interdisciplinary subject matter experts developed two major resources. The first resource was a 24/7 helpline to support first responders …

Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based …

Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang

I-GUIDE Forum

Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …

Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

I-GUIDE Forum

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …

Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que

I-GUIDE Forum

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer …

Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony

I-GUIDE Forum

To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should …

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

Privacy-Preserving Federated Genome-Wide Association Studies Via Dynamic Sampling, Xiaoqian Jiang, Jaideep Vaidya

Faculty, Staff and Student Publications

MOTIVATION: Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS.

RESULTS: This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We …

The Uses And Limitations Of Citizen Science For Monitoring The Australian Grey Nurse Shark (Carcharias Taurus) Population, Leah Corckran

Independent Study Project (ISP) Collection

iNaturalist is a citizen science photographic database, which is an underutilized resource in photographic identification research studies. Grey nurse sharks are critically endangered and there is a lack of knowledge regarding the estimated population size, longevity, and interactions with fisheries of this species off the coasts of Australia. To determine how photos submitted to iNaturalist can be used in Carcharias taurus conservation, the photographs were evaluated on a number of criteria including: location, date, visibility of spot patterns, visible sex characteristics, and visible injuries. In total, 814 photographs of grey nurse sharks were obtained from the iNaturalist database. Only 23.2% …

Dei: Exploring Academic Reflections Using Natural Language Processing To Create A Roadmap Of Student Success And Foster Inclusive Engineering Education, Rajvir H. Vyas, Nidhi Raviprasad

College of Engineering Summer Undergraduate Research Program

Every year, the College of Engineering (CENG) students and faculty reach out to admitted students through “Text-a-Thon” programs to answer their questions about being a student at Cal Poly. In order to improve CENG outreach efforts, we analyzed these text conversations to predict the likelihood of an admitted student accepting an offer of admission from Cal Poly. Through our research, we discovered key factors that play a role in a student committing to Cal Poly through data-based insights. Additionally, we successfully used a human-on-the-loop system to help create Machine Learning (ML) models that predict satisfaction of response by way of …

A Congressional Twitter Network Dataset Quantifying Pairwise Probability Of Influence, Christian Fink, Nathan Omodt, Sydney Zinnecker, Gina Sprint

Physics Faculty Scholarship

We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. …

Digital Scholarship And Data Science Intersect In Libraries: A Needs Assessment Report, Halie Kerns

Library Created Resources

The following report summarized the results of a needs assessment completed in the fall of 2023 at Binghamton University by the Libraries’ Digital Scholarship team. The aim was to understand how data science-focused programming, as part of the digital scholarship’s offerings, would be utilized on campus. The report evaluates existing literature, summarizes findings from twenty-eight interviews done across campus, and lays out an action plan for the Digital Scholarship team’s future planning.

Improving Semantic Document Classification Accuracy By Integrating Human-Crafted Knowledge, Zachary Weinfeld, Lubomir Stanchev

College of Engineering Summer Undergraduate Research Program

Document classification is a pivotal task in various domains, warranting the development of robust algorithms. Among these, the Bidirectional Encoder Representations from Transformers (BERT) algorithm, introduced by Google, has proven to perform well when fine-tuned for the task at hand. Leveraging transformer architecture, BERT demonstrates stellar language understanding capabilities. However, the integration of BERT with a range of techniques has shown potential for further enhancing classification accuracy. This work investigates several techniques that leverage semantic understanding to improve the performance of document classification models trained with BERT. Specifically, we explore three methods. First, we will balance corpuses afflicted by imbalanced …

Machine Learning Prediction Of Hea Properties, Nicholas J. Beaver, Nathaniel Melisso, Travis Murphy

College of Engineering Summer Undergraduate Research Program

High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our …

Ethics And Social Justice For Ai In Data Science, Arya Ramchander, Kylene Nicole Landenberger

College of Engineering Summer Undergraduate Research Program

The advances of AI raise several critical questions about human values and ethics, highlighting the need for researchers and developers to consider the ethical implications and the risks of neglecting them. In the past few years, student researchers have developed an AI model that allows users to test their surveys for possible breaches of subject confidentiality. This allows the users to gauge the ethicality of their proposal. This summer, we have expanded on this research and launched an interactive model for students and researches to assess their current work for ethical and social justice implications. Using Langchain and Figma, we …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Environmental Loss, Displacement, And Anxiety In Portugal: Analyzing News Articles To Differentiate Manifestations Of Environmental Distress, Robin Greene

Independent Study Project (ISP) Collection

Expressions of environmental distress can take different forms, with different symptoms, causes, and treatments. Existing literature generally identifies three primary categories of environmental distress responses: environmental grief, solastalgia, and eco-anxiety. This paper explores these distinctions and identifies words typically associated with each category in Portuguese news articles using Latent Dirichlet Allocation (LDA). LDA is a Natural Language Processing (NLP) technique that groups words into topics and identifies connections between those words based on how often those words appear together in sequences. This paper uses a modified LDA algorithm called GuidedLDA to identify additional keywords within topics defined by a list …

Surveillance Systems In Western Kenya: Methods, Perceptions, And Effectiveness, Marissa Duffy

Independent Study Project (ISP) Collection

Surveillance is an important tool in monitoring and evaluating infectious disease patterns and trends. Surveillance is vital because it aids public health officials and medical professionals in creating better prevention methods and efficiently managing outbreaks. Kenya is home to many noncommunicable diseases making it an important location to conduct disease surveillance. Within Kenya, each county has its own surveillance unit which tracks and controls outbreaks. In addition, government run surveillance systems were established to determine disease burden, incidence, and patterns in specific at-risk communities around Kenya. One of these major surveillance systems is Population-Based Infectious Disease Surveillance (PBIDS) which has …

Evolving Availability And Standardization Of Patient Attributes For Matching, Elmer V Bernstam, Theresa Cullen, Ramkiran Gouripeddi, Christopher Harle, David F Hesse, Jacob Kean, John Lee, Tanja Magoc, Daniella Meeker, Toan Ong, Jyotishman Pathak, Marc Rosenman, Laura K Rusie, Akash J Shah, Lizheng Shi, Aaron Thomas, William E Trick, Shaun Grannis, Abel Kho

Faculty, Staff and Student Publications

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes …

Discoverpath: A Knowledge Refinement And Retrieval System For Interdisciplinarity On Biomedical Research, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath …