Data Science Commons^™

The Integration Of Big Data In Fintech: Review Of Enhancing Financial Services Through Advanced Technologies, Soudeh Pazouki, Mohammad B. Jamshidi, Mirarmia Jalali, Arya Tafreshi

Management Faculty Publications

Big data analytics is revolutionizing the FinTech industry, offering new opportunities for real-time decision-making, personalized financial services, and improved risk management. By leveraging advanced technologies like machine learning and artificial intelligence, financial institutions can efficiently detect fraud, predict market trends, and create innovative solutions tailored to customer needs. Big data also plays a critical role in promoting financial inclusion through alternative credit scoring models, providing access to credit for underserved populations and fostering broader participation in the financial system.

However, the integration of big data into FinTech is not without its challenges. Issues such as data privacy concerns, regulatory complexities, …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Information Technology & Decision Sciences Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

A Two-Phase Learning Approach Integrated With Multi-Source Features For Cloud Service Qos Prediction, Fuzan Chen, Jing Yang, Haiyang Feng, Harris Wu, Minqiang Li

Information Technology & Decision Sciences Faculty Publications

Quality of Service (QoS) is a key factor for users when choosing cloud services. However, QoS values are often unavailable due to insufficient user evaluations or provider data. To address this, we propose a new QoS prediction method, Multi-source Feature Two-phase Learning (MFTL). MFTL incorporates multiple sources of features influencing QoS and uses a two-phase learning framework to make effective use of these features. In the first phase, coarse-grained learning is performed using a neighborhood-integrated matrix factorization model, along with a strategy for selecting high-quality neighbors for target users. In the second phase, reinforcement learning through a deep neural network …

An Overview Of The Special Issue, Rachel Gibson, Trent Buskirk

Data Science Faculty Publications

[Introduction] Across the quantitative social sciences, researchers increasingly face significant challenges and opportunities prompted by the arrival of new sources of very rich, highly granular, and often unstructured digital data. While traditional methods such as surveys and content analysis tools remain indispensable for measuring individual attitudes, behaviors, demographic characteristics, and media messaging online, they often struggle to capture the complex multimodal information streams and metadata generated by social media platforms, mobile devices, sensors, and tracking applications. Collecting and analyzing these diverse new forms of content, dynamic moment-to-moment behaviors, and naturally occurring interactions has become a pressing and exciting research task-one …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Training Set Augmentation And Harmonization Enables Radiomic Models To Detect Early Onset Of Lung Cancer, Claire Huchthausen, Menglin Shi, Gabriel L.A. Sousa, James Larner, Einsley Janowski, Jonathan Colen, Krishni Wijesooriya

Data Science Faculty Publications

Radiomics-based machine learning models have the potential to detect lung cancer at inception from CT scans and transform patient outcomes. Low malignancy rates in early-development pulmonary nodules (PNs) and variable image acquisition hinder development of clinically applicable radiomics-based early detection models. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We first trained machine learning models to predict PN malignancy using radiomic features from scans of early-development benign and malignant PNs (n = 187) harmonized using ComBat. Observing near-chance performance, we augmented training with later-development benign and malignant PNs (n = 225). We evaluated …

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Biotime 2.0: Expanding And Improving A Database Of Biodiversity Time Series, Maria Dornelas, Laura H. Antão, Amanda E. Bates, Viviana Brambilla, Jonathan M. Chase, Cher F. Y. Chow, Ada Fontrodona-Eslava, Anne E. Magurran, Inês S. Martins, Faye Moyes, Alban Sagouis, Samuel Adu-Acheampong, Daniel Acquah-Lamptey, Dušan Adam, Penelope A. Ajani, Aitor Albaina, Pablo Almaraz, Jeongseop An, Roger Sigismund Anderson, Madelaine Jean Robertson Anderson, Alexsander Z. Antunes, Ivan Arismendi, Linda Armbrecht, Pedro Aros-Mardones, Sreejith Kalpuzha Ashtamoorthy, Narayanan Ayyappan, Gal Badihi, Joseph J. Bailey, Andrew H. Baird, Mark Edward Baird, Sreekumar Vadakkethil Balakrishnan, José António L. Barão-Nóbrega, Adi Barash, Miguel Barbosa, Jos Barlow, Claus Bässler, Matthieu Beaumont, Natalie Beenaerts, Tiago Octavio Begot, Wallace Beiroz, Ricardo Beldade, David M. Bell, Alecia Bellgrove, Jonathan Belmaker, Lisandro Benedetti-Cecchi, Cassandra E. Benkwitt, Pamela Medina-Van Berkum, Brandon T. Bestelmeyer, Matthew C. Betts, Maxwell Kelvin Billah, Anne D. Bjorkman, Magdalena Błażewicz, Christopher P. Bloch, Shane A. Blowes, Antonio Bode, Juliano A. Bogoni, Thomas Bolger, Timothy C. Bonebrake, Erik Bonsdorff, Roberta Bottarin, Luke N. Brokensha, Rob W. Brooker, Andrew J. Brooks, Helge Bruelheide, Thiago Almeida Bueno, Claire Laguionie, Mariana Lopes Campagnoli, James Cant, Erica Pellegrini Caramaschi, Alexandre Caron, Tadhg Carroll, Tancredi Caruso, Juan Carvajal-Quintero, Giuseppe Castaldelli, Edward Castañeda-Moya, Pedro V. Castilho, Sonia Zanini Cechin, Shahar Chaikin, Uchangi Manjunatha Chandrashekara, Tory J. Chase, Chaolun Allen Chen, Jorge José Cherem, Sei-Woong Choi, Erica M. Christensen, Alexander V. Christianini, Jackson Wing Four Chu, Peter Coad, Carl Van Colen, Lise Comte, Elisabeth J. Cooper, J. Hans C. Cornelissen, Eddy Cosson, Unai Cotano, Luc Crevecoeur, Shannan Kyle Crow, Graeme S. Cumming, Vanessa S. Daga, Gabriella Damasceno, Gergana N. Daskalova, Claire H. Davies, Robert A. Davis, Frank P. Day, Sussy De-La-Zerda, Amy Elizabeth Deacon, Indradatta De Castro-Arrazola, Steven Degraer, Kharran Deonarinesingh, Juan C. Diaz-Ricaurte, Christopher R. Dickman, Tara Dirilgen, Ciaran John Dolan, J. Emmett Duffy, Timothy E. Dunn, Giselda Durigan, Ciara Dwyer, Steven Earl, Dor Edelist, Graham John Edgar, Sally Edmonson, Ashley K. Elgin, Kari Elsa Ellingsen, Sarah C. Elmendorf, Ruth S. Eriksen, S. K. Morgan Ernest, Ruben Escribano, Paula Cabral Eterovick, Brian S. Evans, Jason D. Everett, Vesela Evtimova, Dan A. Exton, Andrew J. Fairbairn, Filipe Moreli Fantacini, Fabiano Turini Farah, Fábio Zanella Farneda, Mario E. Favila, Philippe Fernandez-Fournier, Braulio Fernández-Zapata, Diogo F. Ferreira, Carola Ferronato, Christopher R. Du Feu, Alessandra Fidelis, David A. Fifield, Vilmar Picinatto Filho, Walter Mesquita Filho, Robert N. L. Fitt, Carlos A. H. Flechtmann, William R. Fraser, Donna L. Fraser, Lídia Freixas, John Fryxell, Garrett J. Fundakowski, Scott Stanley Gabara, Elise Gallois, Mariana Garcia Criado, Emili García-Berthou, Joaquim Garrabou, Andrew R. Gates, Roberto Cazzola Gatti, Anna Gavioli, Tal Gavriel, Benoit Gendreau-Berthiaume, Xingli Giam, Carina Gjerdrum, Michael Glemnitz, Jasmin Annica Godbold, Daniel Gómez-Gras, Rodrigo Barbosa Gonçalves, Andy Goold, Richard R. Gordon, Menachem Goren, Fernando Vilas Boas Goulart, William G. Gould, Meagan M. Graboski, Nicholas A. J. Graham, Maurício Eduardo Graipel, Laura J. Grange, Aaron C. Greenville, Gary D. Grossman, Valeria A. Guinder, Peter Haase, Gary N. Haskins, Kris Havstad, Luise Hermanutz, Michael Julian Hames Hickford, Pamela Hidalgo, Pedro Higuchi, Andrew S. Hoey, Gert Van Hoey, Annika Hofgaard, Kristen T. Holeck, Robert D. Hollister, Richard T. Holmes, Mia Odell Hoogenboom, Joaquín Hortal, Tammy Horton, Chih-Hao Hsieh, Christine L. Huffard, Ida-Maria Huikkonen, Allen H. Hurlbert, Julian Hynes, Pascal Irz, Natalia Macedo Ivanauskas, Akemi Iwayama, Darren K. James, Ute Jandt, Anna M. Jażdżewska, Merlijn Jocque, Sophie T. Johnston, Samuel E. I. Jones, Faith A. M. Jones, Julia A. Jones, Edite Jucevica, Ugis Kagainis, Maiko Kagami, Jungwon Kang, Xuejia Ke, Erin Colleen Keeley, Rebecca Kinnear, Kari Klanderud, Uwe Klinck, Roel Van Klink, Stefan Klotz, Carolien Kockaert, Halvor Knutsen, Matti Koivula, Alessandra Kortz, Peter Kriegel, Chao-Yang Kuo, David J. Kushner, Rosina Kyerematen, Raphaël Lagarde, Lesley T. Lancaster, Ori Frid Landau, Wouter Van Landuyt, Eric R. Larson, Mai Lazarus, Cheol Min Lee, Jonathan S. Lefcheck, Jonas J. Lembrechts, Renato A. Ferreira De Lima, Romullo Guimarães Lima, Nathália G. S. Lima, Cristina Linares, Sandra C. Lindstrom, Francisco Lloret, John David Lloyd, Cleonice Maria Cardoso Lobato, David M. Lodge, Peter Richard Long, Celeste López-Abbate, Adrià López-Baucells, Julio Louzada, Maite Louzao, Antonella Lugliè, Micheli Ribeiro Luiz, S. Ellen Macdonald, Joshua S. Madin, André Lincoln Barroso Magalhães, Rajindra Mahabir, David Maphisa, Thomas Edward Martin, Marcio Martins, Patrick T. Martone, Silvia Matesanz, Shin-Ichiro S. Matsuzaki, Thomas J. Matthews, Iain Mccombe Matthews, Connie J. Maxwell, Kent P. Mcfarland, Brian J. Mcgill, Diane Marie Mcknight, Michael J. Mcwilliam, Jason Meador, Henning Meesenburg, Kristin Meier, Viesturs Melecis, Peter L. Meserve, Christoph F. J. Meyer, Anders Michelsen, Natali Olivia Roman Miiller, Marco Milardi, Nataliya Milchakova, Robert J. Miller, Jonathan Millett, Tom Moens, Luciano F. A. Montag, Jon Moore, Jörg Müller, Akhil Murali, Shauna Ann Murray, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nakamura, Sasi Nayar, Francis Neat, James A. Nelson, Michael Paul Nelson, Boris P. Nikolov, Rym Nouioua, Collins Ayine Nsor, Michael O' Connor, Edward Adzesiwor Obodai, Amy Marie Offland, Romà Ogaya, Hisako Ogura, Thomas A. Okey, Julian D. Olden, Luiz Gustavo Rodrigues Oliveira-Santos, Jeffrey C. Oliver, Esben Moland Olsen, Vladimir G. Onipchenko, Daniel Oro, Dais Ozolins, Krzysztof Pabis, Bachisio Mario Padedda, Facundo X. Palacio, Alain Paquette, Sinta Trilestari Pardede, David M. Patersib, Sarah Pausina, Raphaël Pélissier, Steven C. Pennings, Josep Penuelas, Felipe Walter Pereira, Nivaldo Peroni, Sergio Picó, Francesca Pilotto, Hudson Tercio Pinheiro, Oscar Pizarro, Roberto Pizzolotto, Francesco Pomati, Paulo Santos Pompeu, Dominique Ponton, Eric Post, Nicolas Poulet, Juha Pöyry, Steven J. Presley, Herbert H. T. Prins, Pieter Provoost, Kathleen L. Prudic, Vignesh Punjayil, Petr Pyšek, Pascal Querner, Juan Pablo Quimbayo, Indar W. Ramnarine, Daniel C. Reed, Peter Bernard Reich, Suzanne M. Remillard, Cerren Richards, Anthony James Richardson, Itai Van Rijin, Victor H. Rivera-Monroy, Christian Rixen, Kevin Peter Robinson, Ricardo Rocha, Ricardo R. Rodrigues, Cassy Rodrigues, Bjørn De Roos, Denise De C. De Rossa-Feres, Loreta Rosselli, Peter Charles Rothlisberg, Ana Rubio, Lars G. Rudstam, Catalina S. Ruz, Nancy B. Rybicki, Gunther Van Ryckegem, Andrew L. Rypel, Jon P. Sadler, Victor Satoru Saito, Sofia Sal, Renato Portela Salomão, Nathan J. Sanders, Flavio A. M. Santos, Tiago Gomes Dos Santos, Swapan Kumar Sarker, Sara E. Scanga, Marcus Schaub, Jochen Schmidt, Inger Kappel Schmidt, Robert L. Schooley, Alfred Schultz, Alberto Scotti, Amanda Serpell-Stevens, Filipe C. Serrano, Elizabeth H. Shadwick, Matthew Shaft, Thomas W. Sherry, Erika Mayumi Shimabukuro, Jacek Siciński, Caya Sievers, Fernando Rodrigues Da Silva, Ana Carolina Da Silva, Juliana M. Silveira, Tadeu Siqueira, Arunkumar Kavidapadinjattathil Sivadasan, Prasad Theruvil Parambil Sivan, Agnija Skuja, Amalia L. Slaughter, Jasper A. Slingsby, Joseph R. Smith, Bruno Eleres Soares, Martin Solan, Flaviana Maluf Souza, Gabriel B. G. Souza, Joshua L. Sprague, Ulrich Stachow, J. John Stadt, Christopher D. Stallings, Radoslav Hristov Stanchev, Emily H. Stanley, Brian M. Starzomski, Jose Mauro Sterza, Maarten Stevens, F. Gary Stiles, Stefan Stoll, Rick D. Stuart-Smith, Yzel Rondon Súarez, Laura Super, Sarah R. Supp, Tapio Sutela, Iain M. Suthers, Anna Suuronen, Kerrie M. Swadling, Daniel K. Szydlowski, Hisatomo Taki, Sara Jeanne Snell Taylor, Pablo A. Tedesco, Nils Teichert, Akira Terui, Gary P. Thiede, Anne Thimonier, Oliver Thomas, Peter Allan Thompson, Simon Thorn, Jeremy S. Tiemann, Luís Felipe Toledo, Anne Tolvanen, Maria Teresa Zugliani Toniato, Ignasi Torre, Marcos Adriano Tortato, Kumiko Totsu, Andrew Trant, Robert R. Twilley, Hirokazu Urabe, Pierre Valade, Nelson Valdivia, Martha Isabel Vallejo, Thomas J. Valone, Jan Vanaverbeke, Tiago Silveira Vasconcelos, Teppo Vehanen, Fábio Venturoli, Hans M. Verheye, Hendrik Jannes Wietse Vermeulen, Arne Verstraeten, Marcelo Vianna, Rui Vieira, João Paulo Santos Vieira-Alencar, Marc Vilella, Jean Ricardo Simões Vitule, Lien Van Vu, Robert B. Waide, Paige S. Warren, Joseph Paul Wayman, Sara L. Webb, Benjamin Weigel, Ellen A. R. Welti, Fritha West, Fulgor Westermann, Matthew A. Whalen, Ethan P. White, Claire E. Widdicombe, Richard Williams, Mark Williamson, Michael R. Willig, Sonja Wipf, Eric J. Woehler, Alje Woldering, Kerry D. Woods, Wu-Bing Xu, Ruthy Yahel, Zeren Yang, Kyle J. A. Zawada, Camila Zornosa-Torres, Assaf Zvuloni

Biological Sciences Faculty Publications

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

Modeling Non-Normal Distributions With Mixed Third-Order Polynomials Of Standard Normal And Logistic Variables, Mohan D. Pant, Aditya Chakraborty, Ismail El Moudden

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous data associated with many real-world events often exhibit non-normal characteristics, which contribute to the difficulty of accurately modeling such data with statistical procedures that rely on normality assumptions. Traditional statistical procedures often fail to accurately model non-normal distributions that are often observed in real-world data. This paper introduces a novel modeling approach using mixed third-order polynomials, which significantly enhances accuracy and flexibility in statistical modeling. The main objective of this study is divided into three parts: The first part is to introduce two new non-normal probability distributions by mixing standard normal and logistic variables using a piecewise function of …

Secure Federated Learning Via Neural Cryptography With Homomorphic Operations, Espen Sele, Ferhat Ozgur Catak, Jungwon Seo, Murat Kuzlu

Engineering Technology Faculty Publications

This study examines neural cryptography with homomorphic operations as an alternative secure aggregation method for federated learning (FL). It proposes a novel neural cryptographic system supporting homomorphic addition on fixed-point encrypted data, and consisting of three networks, namely (1) an encryption network (Alice), (2) a homomorphic network (HO), and (3) a decryption network (Bob), along with an adversarial Eve network. Using the MNIST dataset, the proposed Neural Homomorphic Operation System (NHOS) is evaluated against a plaintext baseline and the CKKS scheme, a widely used public-key homomorphic encryption method. The results show that the proposed NHOS approach offers a satisfying performance, …

5g-Enabled Iot Gateway For Educational Purposes, Murat Kuzlu

Engineering Technology Faculty Publications

The project aims to develop and implement a 5G-IoT gateway for efficient management and interconnection of IoT devices through advanced sensing and communication technologies. Sensing technologies encompass modern approaches for detecting and measuring physical properties with high accuracy across manufacturing, smart grids, healthcare, smart cities, and other domains. Communication technologies represent the latest developments in high-speed data transmission, offering enhanced reliability and network capacity. This 5G-IoT gateway functions as an educational platform, providing students and educators with hands-on experience in emerging technologies. The system creates opportunities for practical learning and research in telecommunication technologies by enabling direct engagement with 5G …

T3-Ciders: Train-The-Trainer And Community Building To Increase Cyberinfrastructure Adoption In Cybersecurity Research And Education, Wirawan Purwanto, Mohan Yang, Peng Jiang, Shanan Chappell Moots, Masha Sosonkina, Hongyi Wu

University Administration Publications

T³-CIDERS is a train-the-trainer program to increase the adoption of advanced cyberinfrastructure (CI) and data skills into the fabric of research and education in cybersecurity and cyber-related disciplines. T³-CIDERS trains faculty, researchers, and students as “future trainers” (FTs) with hands-on technical and instructional skills to enable more people to effectively leverage CI in cybersecurity. The program includes a series of technical pre-training modules, a weeklong summer institute, ongoing learning engagements conducted over an academic year; it culminates with the FTs conducting locally tailored CI-infused training events at their respective home institutions. Ultimately, T³-CIDERS aims to build a “CI+cybersecurity” community of …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

A Universal Implementation Of Radiative Effects In Neutrino Event Generators, Júlia Tena-Vidal, Adi Ashkkenazi, Lawrence B. Weinstein, Peter Blunden, Steven Dytman, Noah Steinberg

Physics Faculty Publications

Due to the similarities between electron-nucleus (eA) and neutrino-nucleus scattering (νA), eA data can contribute key information to improve cross-section modeling in eA and hence in νA event generators. However, to compare data and generated events, either the data must be radiatively corrected or radiative effects need to be included in the event generators. We implemented a universal radiative corrections program that can be used with all reaction mechanisms and any eA event generator. Our program includes real photon radiation by the incident and scattered electrons, and virtual photon exchange and photon vacuum polarization diagrams. It …

Check Your Data Before You Wreck Your Model: The Impact Of Careless Responding On Substance Use Data Quality, Abby L. Braitman, Anna M. Petrey, Jennifer L. Shipley, Rachel Ayala Guzman, Emily Renzoni, Alison Looby, Adrian J. Bravo

Psychology Faculty Publications

Background: The accuracy of survey responses is a concern in research data quality, especially in college student samples. However, examination of the impact of removing participants from analyses who respond inaccurately or carelessly is warranted given the potential for loss of information or sample diversity. This study aimed to understand if careless responding varies across a number of demographic indices, substance use behaviors, and the timing of survey completion.

Method: College students (N = 5809; 70.7% female; 75.7% White, non-Hispanic) enrolled in psychology classes from six universities completed an online survey assessing a variety of demographic and substance use-related information, …

Generating Real-World Evidence In Early Alzheimer's Disease: Considerations For Applying The Target Trial Emulation Framework To Study The Safety Of Anti-Amyloid Therapies, Xiaojuan Li, Sonal Singh, Bahareh Rasouli, Jennifer Lyons, Noelle M. Cocoros, Richard Platt, Ivan Abi-Elias, Jerry H. Gurwitz

Department of Medicine Faculty Publications

Anti-amyloid beta monoclonal antibodies (anti-Aβ mAbs) have received approval from the US Food and Drug Administration for the treatment of patients with mild cognitive impairment or mild dementia due to Alzheimer's disease (collectively known as early AD) based on evidence from clinical trials. However, whether findings from these trials are generalizable to the real world is uncertain. We need reliable evidence on the real-world safety of these treatments to inform decision making for clinicians, patients, and caregivers. Using lecanemab as an exemplar, we outline the key considerations in designing and implementing an observational study on safety and utilization outcomes using …

T3-Ciders: Fostering A Community Of Practice In Ci-And Data Enabled Cybersecurity Research Through A Train-The-Trainer Program, Wirawan Purwanto, Mohan Yang, Peng Jiang, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

We present a training program named T³-CIDERS, the Train- The-Trainer approach to fostering cyberinfrastructure (CI)- and Data-Enabled Research in CyberSecurity. T³-CIDERS is a train-the-trainer program for advanced cyberinfrastructure (CI) skills that is designed to be synergistic with research, teaching, and learning activities in cybersecurity and cyber-related disciplines. The participants, termed 'future trainers' (FTs), are trained in effective instructional design and CI hands-on materials from DeapSECURE, developed in a previous CyberTraining program. T³-CIDERS aims to enhance cybersecurity research and education through broader adoption of advanced CI techniques such as artificial intelligence, big data, parallel programming, and platforms like high-performance computing (HPC) …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Out Of Order And Causally Correct: Ready-Event Discovery Through Data-Dependence Analysis, Erik John Jensen, James Leathrum Jr., Christopher Lynch, Katherine Smith, Ross Gore

Electrical & Computer Engineering Faculty Publications

Data-dependence analysis can identify causally-unordered events in a pending event set. The execution of these events is independent from all other scheduled events, making them ready for execution. These events can be executed out of order or in parallel. This approach may find and utilize more parallelism than spatial-decomposition parallelization methods, which are limited by the number of subdomains and by synchronization methods. This work provides formal definitions that use data-dependence analysis to find causally-unordered events and uses these definitions to measure parallelism in several discrete-event simulation models. A variant of the event-graph formalism is proposed, which assists with identifying …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu

Computer Science Faculty Publications

Private inference applies cryptographic techniques like homomorphic encryption, garble circuit and secret sharing to keep both sides privacy in a client-server setting during inference. It is often hindered by the high communication overheads, especially at non-linear activation layers such as ReLU. Hence ReLU pruning has been widely recognized as an efficient way to accelerate private inference. Existing approaches to ReLU pruning typically rely on coarse hypothesis, which assume an inverse correlation between the importance of ReLU and linear layers or shallow activation layers have less importance for universal models, to assign the budgets according to the layer while preserving the …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …