Video recording of Dr. Mark V. Albert's presentation, "Artificial Intelligence and Applications in Genomics Research: Assessment of Mobility in Diverse Patient Populations" and Dr. Heather Wheeler's presentation, "Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries." They were presented at the Transdisciplinary Ancestral Genomic Research Investigations (TAGRI) II Conference held online November 5-6, 2021.

This article uses a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses provide insights into the effects of global mass gatherings on the progression of the COVID-19 pandemic locally and globally.

This article presents a novel approach of introducing a weighted temporal feature to bias a topic clustering toward articles in a similar time frame, performed over a set of finance journal abstracts from 1974 to 2020 to demonstrate how time can be emphasized in trend detection. The authors detect trending finance topics that are not identifiable when we use a standard clustering approach with no temporal bias.

Data management plan for the grant "Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)." Research relating to creating a community infrastructure for researchers using multilayer networks (MLN). This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching.

Data management for the grant, "Collaborative Research: Framework Implementations: CSSI: CANDY: Cyberinfrastructure for Accelerating Innovation in Network Dynamics." Research addressing the lack of a comprehensive cyberinfrastructure that supports innovative research challenges in large-scale, complex, dynamic networks by developing a novel platform, called CANDY (Cyberinfrastructure for Accelerating Innovation in Network Dynamics), based on efficient, scalable parallel algorithm design for dynamic networks and high-performance software development with performance optimization.

This article proposes a software framework for identifying the percentage and sources of communication non-determinism.

This article proposes a novel preconditioned and accelerated Gauss–Siedel algorithm referred to as Symmetric Successive Overrelaxation Preconditioned Gauss-Seidel (SSORGS) to address the signal detection challenges associated with massive MIMO technology.

Data management plan for the grant "2021 NCAE-C-002 University of North Texas."

Article studying the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. The authors propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings.

Article proposing the use of anonymization techniques coupled with graph algorithms over homomorphically encrypted (HE) graphs as a basis of analysis for this accumulated data. This approach ensures individuals’ privacy and anonymity while preserving the usefulness of the plaintext data. This article was originally presented at the 18th International Conference on Security and Cryptography - SECRYPT.

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This article explores a proposed deep-learning-based algorithm called DeepFilter for improving peptide identifications from a collection of tandem mass spectra. The authors find that DeepFilter is believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.

Data management plan for the University of North Texas GenCyber Academy grant. The GenCyber Cybersecurity Program at the University of North Texas is part of the GenCyber program. The program is hosted by the Department of Computer Science and Engineering. The mission as part of the GenCyber program is to engage students at an early age in cybersecurity field and inspire them to become skilled cybersecurity professionals. This is provided by free summer cybersecurity camps for North Texas middle and high school students (7th-11th grade). The goals of the summer camps are to help students at an early age to understand correct and safe on-line behavior, increase students' interest in cybersecurity careers and improve diversity in the cybersecurity workforce of the nation.

Data management plan for the grant "IUCRC Planning Grant University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)." Research concentrating on interdisciplinary research, aiming to initiate and accelerate the transformation of mobility methods from conventional vehicles to electric, connected and autonomous vehicles by creating innovative electric, connected and autonomous technologies. The grant will create the Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT). A partnership between Wayne State University (WSU), University of North Texas (UNT), and Clarkson University (Clarkson), the center not only serves as an apparatus of academic researchers collaborating with industry on important problems, but also provides industry partners opportunities to access advanced synergic research produced from a diverse group of researchers.

This article presents the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

Article evaluating whether advanced prostheses can provide better safety and performance capabilities to maintain and improve quality of life in individuals who are predominantly designated MFCL level K2. This study used a 13 month longitudinal clinical trial to determine the benefits of using a C-Leg and 1M10 foot in individuals at K2 level with transfemoral amputation due to vascular disease.

This article develops a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.

This article surveys previous work on negation with an emphasis on computational approaches.

This article examines the relationships between spatial patterns of long-term ozone exposure and respiratory illness within Dallas-Fort Worth to better understand impacts on health outcomes.

This article reviews and summarizes the state-of-the-art methods and publicly available data sets from proximate sensing to support land-use analysis. Discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing imagery in urban land-use studies.

This article studies the problem of comparing the similarity and dissimilarity between two distinct vehicular trajectories by proposing an adjacency-based metric. This approach has a broad application in building truthfulness by comparing the similarity between two vehicles and evaluating the dissimilarity between two distinct paths in hazardous materials transportation.

Data management plan for the grant, "Collaborative Research: SaTC: CORE: Small: Privacy protection of Vehicles location in Spatial Crowdsourcing under realistic adversarial models." Research to develop new location privacy protection techniques by considering vehicles’ mobility features in the road network, and consequently lead to a more secure and trustworthy computing environment in location-based services (LBSs). As privacy concerns are still among the main obstacles for mobile users to participate in many advanced LBSs, this project is poised to contribute to the wider adoption of LBSs for many applications (e.g. navigation systems and location-based recommendation systems). The project will also provide a set of diverse and interesting topics for undergraduate and graduate students and outreach activities for the community.

Article proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes in order to prevent mosquito-borne infection.

This article provides a big picture of PIoT architecture, vision, and future research scope. The exploratory study of PIoT is in its infancy, which will explore the expansion of new use cases, service requirements, and the proliferation of PIoT devices. This is a preprint version of this article.