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.

Article describes study which sought to evaluate methods for activity recognition for toddlers.

Article states that vaccines, though reliable preventative measures for diseases, also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines including the COVID-19 vaccines. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis.

Article describes how, due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. In this work, the authors explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS.

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.

Data management plan for the grant, "CAREER: Developing a Flexible Serverless Multimedia Streaming Cloud Platform."

Data management plan for the grant, "Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications."

Data management plan for a NSF grant with a name that has not been determined.

Article discusses how with the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. The authors aim to address the privacy problem by protecting both vehicles' sensor data and the detection results.

Article discusses how globalisation has facilitated different industries to eliminate geographical boundaries and equipped organisations to work collectively to produce goods. The authors of the article propose a novel Distributed Ledger Technology (DLT) based transparent supply chain for PSC and proof-of-concept is implemented to analyse the scalability and efficiency of the proposed architecture.

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.

Article addresses the processing of negation in Spanish by presenting a machine learning system that processes negation in Spanish and providing a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.

This article is a study where the authors integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach.

Data management plan for the grant "Travel: NSF Student Travel Grant for 2023 Great Lakes Bioinformatics Conference (GLBIO)"

Data management plan for the grant, "REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates." TaMaLe (Testing and Machine Learning for Context-Driven Systems), a renewal Research Experience for Undergraduates (REU) Site at University of North Texas, engages 10 undergraduate students for 10 weeks with problems in the context-driven system domain. The students explore research problems to improve the reliability and security of context-driven systems. Context-driven systems, such as mobile apps, face constant streams of input from both users and context changes in their environments. Users interact with apps through touch and speech interfaces. These systems also respond to context events that occur in their environments such as changes to network connection, battery level, screen orientation, and more. The combined explosion of possible user events and context event sequences pose new challenges that require cost effective testing solutions. Students and mentors in this REU program work in small teams to develop and empirically evaluate new software testing techniques for context-driven systems using strategies such as reinforcement learning and combinatorial-based techniques.

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

This article presents a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. The authors discuss the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in massive MIMO systems and discuss state-of-the-art mitigation techniques. Recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems are outlined. Finally, future research for massive MIMO systems for 5G and beyond is discussed.

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.

Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models.

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.

Data management plan for the grant, "2023-GenCyber-University of North Texas."

Data management plan for the grant, "GenCyber Teachers Camp." The goal of the UNT GenCyber Teacher Camp is to increase the cybersecurity expertise of middle and high school Computer Science teachers so that they can introduce cybersecurity curriculum into their classrooms. We will recruit 20 teachers who teach Computer Science and STEM-related middle/high school courses. We will create modules that teachers can use to create positive environments for students and thus to motivate them to gain interest in cybersecurity.