Data Science Commons^™

Design Of Secure Communication Schemes To Provide Authentication And Integrity Among The Iot Devices, Vidya Rao Dr.

Technical Collection

The fast growth in Internet-of-Things (IoT) based applications, has increased the number of end-devices communicating over the Internet. The end devices are made with fewer resources and are low battery-powered. These resource-constrained devices are exposed to various security and privacy concerns over publicly available Internet communication. Thus, it becomes essential to provide lightweight security solutions to safeguard data and user privacy. Elliptic Curve Cryptography (ECC) can be used to generate the digital signature and also encrypt the data. The method can be evaluated on a real-time testbed deployed using Raspberry Pi3 devices and every message transmitted is subjected to ECC. …

Getting Started Analyzing Data In Spss, Kristi Thompson

Western Libraries Presentations

SPSS is a popular package for analyzing data. This session will discuss how to get started on a simple quantitative analysis project using SPSS. Topics covered will include getting summary statistics, creating and modifying variables, creating graphs, running simple analyses, and interpreting SPSS output.

Lstm-Sdm: An Integrated Framework Of Lstm Implementation For Sequential Data Modeling[Formula Presented], Hum Nath Bhandari, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal

Arts & Sciences Faculty Publications

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.

Recall Distortion In Neural Network Pruning And The Undecayed Pruning Algorithm, Aidan Good, Jiaqi Lin, Hannah Sieg, Mikey Ferguson, Xin Yu, Shandian Zhe, Jerzy Wieczorek, Thiago Serra

Faculty Conference Papers and Presentations

Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a dataset may be more negatively affected. In this work, we study such relative distortions in recall by hypothesizing an intensification effect that is inherent to the model. Namely, that pruning makes recall relatively worse for a class with recall below accuracy and, conversely, that it makes recall relatively better for a class with recall above accuracy. In addition, we propose a new pruning algorithm aimed …

Marginal Proportional Hazards Models For Clustered Interval-Censored Data With Time-Dependent Covariates, Kaitlyn Cook, Wenbin Lu, Rui Wang

Statistical and Data Sciences: Faculty Publications

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana’s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered intervalcensored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on …

Policy-Based Redactable Signatures, Zachary Kissel

Computer and Data Science Faculty Publications

In this work we make progress towards solving an open problem posed by Bilzhause et. al, to give constructions of redactable signature schemes that allow the signer to limit the possible redactions performed by a third party. A separate, but related notion, called controlled disclosure allows a redactor to limit future redactions. We look at two types of data, sets and linear data (data organized as a sequence). In the case of sets, we limit redactions using a policy modeled by a monotone circuit or any circuit depending on the size of the universe the set is drawn from. In …

The Impact Of Covid-19 On Opioid-Related Overdose Deaths In Texas, Bhanumathi Gopal, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Prior to the COVID-19 pandemic, the United States was facing an epidemic of opioid overdose deaths, clouding accurate inferences about the impact of the pandemic at the population level. We sought to determine the existence of increases in the trends of opioid-related overdose (ORO) deaths in the Greater Houston metropolitan area from January 2015 through December 2021, and to describe the social vulnerability present in the geographic location of these deaths. We merged records from the county medical examiner's office with social vulnerability indexes (SVIs) for the region and present geospatial locations of the aggregated ORO deaths. Time series analyses …

Scgwas: Landscape Of Trait-Cell Type Associations By Integrating Single-Cell Transcriptomics-Wide And Genome-Wide Association Studies, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.

RESULTS: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in …

Federated Learning Algorithms For Generalized Mixed-Effects Model (Glmm) On Horizontally Partitioned Data From Distributed Sources, Yong Chen, Xiaoqian Jiang

Faculty, Staff and Student Publications

OBJECTIVES: This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package ('lme4') as the baseline model.

METHODS: The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve …

An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary

Conference Papers

In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Preparation Of Core-Shell-Structured Rdx@Pvdf Microspheres With Improved Thermal Stability And Decreased Mechanical Sensitivity, Fangchao Zhao, Yanchun Li

Faculty, Staff and Student Publications

Reducing the sensitivity of high-energy simple explosives is the key technology in improving the practical application of high-energy insensitive powder. As the most widely used high-energy explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is limited in application due to its high sensitivity. In this work, polyvinylidene fluoride (PVDF) was used as an energetic binder. Core-shell-structured RDX@PVDF microspheres are produced using electrospray assembly technology and fully characterized by thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive spectroscopy, and mechanical sensitivity. Their thermal stability and mechanical sensitivity are directly related to the weight fraction of the added PVDF. Moreover, core-shell-structured RDX@PVDF microspheres …

Clinical Decision Support Malfunctions Related To Medication Routes: A Case Series, Dean F Sittig

Faculty, Staff and Student Publications

OBJECTIVE: To identify common medication route-related causes of clinical decision support (CDS) malfunctions and best practices for avoiding them.

MATERIALS AND METHODS: Case series of medication route-related CDS malfunctions from diverse healthcare provider organizations.

RESULTS: Nine cases were identified and described, including both false-positive and false-negative alert scenarios. A common cause was the inclusion of nonsystemically available medication routes in value sets (eg, eye drops, ear drops, or topical preparations) when only systemically available routes were appropriate.

DISCUSSION: These value set errors are common, occur across healthcare provider organizations and electronic health record (EHR) systems, affect many different types of …

Complement Component C4 Structural Variation And Quantitative Traits Contribute To Sex-Biased Vulnerability In Systemic Sclerosis, Shervin Assassi, Susanna M Proudman, Mandana Nikpour, Nicolas Hunzelmann, Gianluca Moroncini, Jeska K De Vries-Bouwstra, Gisela Orozco, Anne Barton, Ariane L Herrick, Chikashi Terao, Yannick Allanore, Carmen Fonseca, Marta Eugenia Alarcón-Riquelme, Timothy R D J Radstake, Lorenzo Beretta, Christopher P Denton, Maureen D Mayes, Javier Martin

Faculty, Staff and Student Publications

Copy number (CN) polymorphisms of complement C4 play distinct roles in many conditions, including immune-mediated diseases. We investigated the association of C4 CN with systemic sclerosis (SSc) risk. Imputed total C4, C4A, C4B, and HERV-K CN were analyzed in 26,633 individuals and validated in an independent cohort. Our results showed that higher C4 CN confers protection to SSc, and deviations from CN parity of C4A and C4B augmented risk. The protection contributed per copy of C4A and C4B differed by sex. Stronger protection was afforded by C4A in men and by C4B in women. C4 CN correlated well with its …

Analyzing The Production And Use Of Fossil Fuels: A Case For Data Mining And Gis, Alejandro Conde

Geography and the Environment: Graduate Student Capstones

As technology progresses and data grows both larger and more complex, techniques are being developed to keep up with the exponential growth of information. The term “data mining” is a blanket term used to describe an approach to find anomalies and correlations in a large dataset. This approach involves leveraging data mining software to manipulate and prepare data, apply statistics to quantify trends and characteristics in the data from a high level, and potentially apply advanced techniques like machine learning to identify patterns that wouldn’t be apparent otherwise. In this case study, data mining aided a GIS in displaying substantial …

The Link Between Democratic Institutions And Population Health In The American States, Julianna Pacheco, Scott Lacombe

Government: Faculty Publications

Context: This project investigates the role of state-level institutions in explaining variation in population health in the American states. Although cross-national research has established the positive effects of democracy on population health, little attention has been given to subnational units. The authors leverage a new data set to understand how political accountability and a system of checks and balances are associated with state population health. Methods: The authors estimate error correction models and two-way fixed effects models to estimate how the strength of state-level democratic institutions is associated with infant mortality rates, life expectancy, and midlife mortality. Findings: The authors …

Svat: Secure Outsourcing Of Variant Annotation And Genotype Aggregation

Faculty, Staff and Student Publications

BACKGROUND: Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions.

RESULTS: We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. SVAT uses homomorphic encryption to encrypt the data at …

Development Of The Invasive Candidiasis Discharge [I Can Discharge] Model: A Mixed Methods Analysis, Joshua A Hendrickson, Masaad Saeed Almutairi, Faris S Alnezary, Anne J Gonzales-Luna, Edward J Septimus, Kevin W Garey

Faculty, Staff and Student Publications

Patients with invasive candidiasis (IC) have complex medical and infectious disease problems that often require continued care after discharge. This study aimed to assess echinocandin use at hospital discharge and develop a transition of care (TOC) model to facilitate discharge for patients with IC. This was a mixed method study design that used epidemiologic assessment to better understand echinocandin use at hospital discharge TOC. Using grounded theory methodology focused on patients given echinocandins during their last day of hospitalization, a TOC model for patients with IC, the invasive candidiasis [I Can] discharge model was developed to better understand discharge barriers. …

Diagnostic Value Of Ultrasound In Children With Transverse Testicular Ectopia, Xiaodong Liu, Junhai Jiang, Guanglun Zhou, Jianguo Wen

Faculty, Staff and Student Publications

OBJECTIVE: The study aimed to investigate the diagnostic value of ultrasound in children's transverse testicular ectopia (TTE).

MATERIALS AND METHODS: We retrospectively studies all TTE cases diagnosed in our hospital from January 2017 to December 2021. All cases were evaluated by ultrasound examination and compared to physical examination and diagnostic laparoscopy results.

RESULTS: This study included 14 TTE patients in total, with a median age was 1.08 years. In the 14 TTE, physical examination found 10 TTE cases, of which nine testes were located in the opposite scrotum, one testis was located in the opposite groin, and the other four …

Aging After Stroke: How To Define Post-Stroke Sarcopenia And What Are Its Risk Factors?, Aila Malik, Nuray Yozbatiran, Elaine Magat, Gerard E Francisco, Hulin Wu, Walter R Frontera

Faculty, Staff and Student Publications

BACKGROUND: Sarcopenia, generally described as "aging-related loss of skeletal muscle mass and function", can occur secondary to a systemic disease.

AIM: This project aimed to study the prevalence of sarcopenia in chronic ambulatory stroke survivors and its associated risk factors using the two most recent diagnostic criteria.

DESIGN: A cross-sectional observational study.

SETTING: A scientific laboratory.

POPULATION: Chronic stroke.

METHODS: Twenty-eight ambulatory chronic stroke survivors (12 females; mean age=57.8±11.8 years; time after stroke=76±45 months), hand-grip strength, gait speed, and appendicular skeletal muscle mass (ASM) were measured to define sarcopenia. Risk factors, including motor impairment and spasticity, were identified using regression …