Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.
Data management plan for the grant, "CICI: UCSS: Secure Containers in High-Performance Computing Infrastructure." Ensuring the security and privacy of high-performance computing (HPC) infrastructures is of utmost importance due to their handling of sensitive data and critical scientific computations. HPC infrastructures commonly employ containers, which provide lightweight and isolated environments for running applications. Nevertheless, containers in HPC infrastructures encounter security challenges, including insecure container images and vulnerabilities related to isolation. Existing container image scanners face a major challenge of low coverage, while current container runtimes struggle to ensure both security and performance for HPC workloads simultaneously. This project addresses these challenges by developing secure containers specifically tailored for HPC infrastructures. The project introduces innovative solutions, including the development of an efficient image vulnerability scanner and a secure container runtime.
Article discusses how visual object tracking aims to continuously localize the target object of interest in a video sequence. To provide the community an overview, in this commentary, the authors discuss visual tracking from different aspects.
Article asserts that the foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. The authors present an instep girth measurement algorithm, and they used a pixel per metric algorithm for measurement; these algorithms were accordingly integrated with the application.
December 6, 2022
Rafiq, Riyad Bin; Hoque, Kazi Miftahul; Kabir, Muhammad Ashad; Ahmed, Sayed & Laird, Craig
Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.
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 the large quantities of farm and meat products that rot and are wasted if correct actions are not taken leading to serious health concerns if consumed. Because there is no proper system for tracking and communicating the status of goods to consumers, a right which according to the authors should be a given, they propose a method of increased communication using Corda private blockchain.
October 27, 2022
Vangipuram, Sukrutha L. T.; Mohanty, Saraju P.; Kougianos, Elias & Ray, Chittaranjan
Authors of the article created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study.
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.
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.
September 5, 2022
Aljedaani, Wajdi; Saad, Eysha; Rustam, Furqan; de la Torre Díez, Isabel & Ashraf, Imran
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.
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.