Data Science Commons^™

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 …

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 …

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 …

Web-Grading-A Tool To Test Personal Grading Of Renal And Prostate Cancer, Glen Kristiansen, Matthias Schmid, Lars Egevad, Hemamali Samaratunga, Murali Varma, Kaan Inam, Hans-Jürgen Thiesen, Brett Delahunt, Yulin Dai

Faculty, Staff and Student Publications

Only a few pathologists have the opportunity to verify their personal grading through objective assessment. This study introduces a web-based grading platform to facilitate and validate the grading of renal cell carcinoma and prostate cancer. Two representative images of two clinically annotated cohorts of 100 cases each of prostate and renal cell carcinoma were used. Each participant was asked to grade a tumor series utilizing a three tiered grading system. Finally, a Kaplan-Meier curve was drawn, and the log-rank test was used for statistical testing of the p-value. The grading of 22 participants (68%) achieved prognostic significance. Further analysis highlighted …

Discoverpath: A Knowledge Refinement And Retrieval System For Interdisciplinarity On Biomedical Research, Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, 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 …

Evolving Availability And Standardization Of Patient Attributes For Matching, Yu Deng, Lacey P Gleason, Adam Culbertson, Xiaotian Chen, 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 …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

Research Data Management In The Canadian Context: A Guide For Practitioners And Learners, Kristi Thompson, Elizabeth T. Hill, Emily Carlisle-Johnston, Danielle Dennie, Émilie Fortin

Western Libraries Publications

Research Data Management is a term for all the things that researchers do to structure, organize and maintain data before, during and after doing research. RDM is also an emerging discipline that is concerned with researching and developing ways to manage research data more effectively. But what is research data? Where is the push towards formal Research Data Management coming from? What are the requirements of good data management? Research Data Management in the Canadian Context: A Guide for Practitioners and Learners looks at these questions and more, all with a focus on Canadian guidelines, regulations and infrastructure.

Precise Method To Identify Kinase Drug Targets In Complex Diseases: The First Step Towards Sustainable And Effective Treatment, Hasbanny Irisson, Marzieh Ayati

Research Symposium

Background: Kinases are enzymes that have proven to be important drug targets due to their role in critical biological mechanisms such as phosphorylation. Phosphorylation happens when a kinase catalyzes the transfer of a phosphate group to a protein in a phosphorylated site, which then becomes known as the substrate of the kinase. Any dysregulation of protein phosphorylation causes a wide range of complex diseases including cancer. Thus, discovering the links between kinases and their substrates (i.e. predicting kinase-substrate associations (KSAs)) is crucial in developing effective and sustainable treatments. Presently, less than 5% of phosphorylated sites have an associated kinase, and …

A Case Report: A Patient Rescued By Va-Ecmo After Cardiac Arrest Triggered By Trigeminocardiac Reflex After Nasal Surgery, Xu Zhang, Bin Sun, Chen Pac-Soo, Daqing Ma, Liwei Wang

Faculty, Staff and Student Publications

Rationale:

Cardiac arrest (CA) caused by trigeminocardiac reflex (TCR) after endoscopic nasal surgery is rare. Hence, when a patient suffers from TCR induced CA in the recovery room, most doctors may not be able to find the cause in a short time, and standard cardiopulmonary resuscitation and resuscitation measures may not be effective. Providing circulatory assistance through venous-arterial extracorporeal membrane oxygenation (VA-ECMO) can help healthcare providers gain time to identify the etiology and initiate symptom-specific treatment.

Patient concerns:

We report a rare case of CA after endoscopic nasal surgery treated with VA-ECMO.

Diagnoses:

We excluded myocardial infarction, pulmonary embolism, allergies, …

Privacy-Preserving Federated Learning, Dumindu Samaraweera

Math Department Colloquium Series

AI's applicability across diverse fields is hindered by data sensitivity, privacy concerns, and limited training data availability. Federated Learning (FL) addresses this challenge by enabling collaborative machine learning while preserving data privacy. FL allows clients to engage in model training with their local data, avoiding centralized storage. However, even with FL, security threats persist, jeopardizing model integrity and client data privacy. In this presentation, we will explore our latest findings in this area of research, safeguarding sensitive data from attacks through techniques like secure multiparty computation, homomorphic encryption, and differential privacy within the FL framework, enhancing data protection, and expanding …

Using Artificial Intelligence To Learn Optimal Regimen Plan For Alzheimer’S Disease, Kritib Bhattarai, Sivaraman Rajaganapathy, Trisha Das, Yejin Kim, Yongbin Chen, Alzheimer’S Disease Neuroimaging Initiative, Australian Imaging Biomarkers And Lifestyle Flagship Study Of Ageing, Qiying Dai, Xiaoyang Li, Xiaoqian Jiang, Nansu Zong

Faculty, Staff and Student Publications

BACKGROUND: Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

OBJECTIVE: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

METHODS: In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). …

Mapping The Delineation Of Practice To The Amia Foundational Domains For Applied Health Informatics, Todd R Johnson, Eta S Berner, Sue S Feldman, Josette Jones, Annette L Valenta, Damian Borbolla, Gloria Deckard, Laverne Manos

Faculty, Staff and Student Publications

OBJECTIVE: This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice.

MATERIALS AND METHODS: Using an iterative …

Organizing Pmode Dopplergrams Of Jupiter With Matlab, Brady T. Smith, Deborah Gulledge, Cody Shaw, Gerard Williger

The Cardinal Edge

The interiors of the giant planets are poorly known. At the time of writing, such investigations have been limited to measuring gravitational effects from a handful of orbital probes. The most recent attempt to map the interior is via PMODE (the Planetary Multilevel Oscillations and Dynamics Experiment), designed to explore Jupiter’s core by collecting Dopplergrams. Small radial velocity shifts in Jupiter’s upper cloud decks enable us to map its atmospheric dynamics and consequently its interior via Dioseismology (techniques similar to Helioseismology, applied to Jupiter). This campaign produced a vast dataset with more than 50,000 exposures, every 30 seconds, over 24 …

Effects Of Weight Initialization Methods On Ffn's, Ida K. Karem

The Cardinal Edge

Weight initialization is the method of determining starting values of weights in a neural network. The way this method is done can have massive effects on the network[2, 3, 6, 9] and can halt training if not handled properly. On the other hand, if initialization is chosen tactfully it can improve training and accuracy greatly. The initialization method usually called Normalized Xavier will be referred to as Nox in this paper to avoid confusion with the Xavier initialization method. This study analyzes five methods of weight initialization(Nox, He, Xavier, Plutonian, and Self-Root), two of them …

Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas

The Cardinal Edge

As Elon Musk’s influence in technology and business continues to expand, it becomes crucial to comprehend public sentiment surrounding him in order to gauge the impact of his actions and statements. In this study, we conducted a comprehensive analysis of comments from various subreddits discussing Elon Musk over a 14-year period, from 2008 to 2022. Utilizing advanced sentiment analysis models and natural language processing techniques, we examined patterns and shifts in public sentiment towards Musk, identifying correlations with key events in his life and career. Our findings reveal that public sentiment is shaped by a multitude of factors, including his …