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 "2021 NCAE-C-002 University of North Texas."

Article utilizing active learning and support vector machine (SVM) algorithms to classify facial action units (AU) for human facial expression recognition. Experimental results show that the proposed algorithm can effectively suppress correlated noise and achieve higher recognition rates than principal component analysis and a human observer on seven different facial expressions.

Article summarizing the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials, (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities.

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, "CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems." Research seeking to reinvent on-chip networks for GPU-accelerated systems to remove a communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck.

This article aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI), to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs), and to develop a classifier to predict the fat distribution clusters using the BSDs.

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.

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 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.

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 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.

This article is from the 26th ACM Conference on Innovation and Technology in Computer Science Education and discusses the design, effectiveness, and curricular impacts of an introductory data science course focused on practical programming skills and allowing students to concurrently complete the course in Python, R, or MATLAB. Students indicated a preference for the multi-language course design and the course became the recommended first programming course for a newly developed and approved undergraduate data science majors.

This article describes a system for pupil size estimation with a user interface to allow rapid adjustment of parameters and extraction of pupil parameters of interest in order to identify Parkinson's disease (PD) as early as possible.

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.

Article on deep-learning framework for edge intelligence EDL-EI (event-driven deep learning for edge intelligence). To verify the proposed framework, the authors include a case study of air-quality scenarios based for the most polluted cities in South Korea and China.

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

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.

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 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.

Article that introduces Natlog, a lightweight Logic Programming language, sharing Prolog's unification-driven execution model, but with a simplified syntax and semantics. The authors' proof-of-concept Natlog implementation is tightly embedded in the Python-based deep-learning ecosystem with focus on content-driven indexing of ground term datasets. As an overriding of the authors symbolic indexing algorithm, the same function can be delegated to a neural network, serving ground facts to Natlog's resolution engine. The open-source implementation is available as a Python package at t https://pypi.org/project/natlog/.

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.

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.