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1. Design of a Human Centered Computing (HCC) based Virtual Reality Simulator to train First Responders Involved in the COVID-19 Pandemic

   Cecil, Joe ; Kauffman, Sam ; Gupta, Avinash ; Mckinney, Vern ; Pirela-Cruz, Miguel ( April 2021 , 2021 IEEE International Systems Conference (SysCon),)

   null (Ed.)

   The COVID-19 pandemic has placed an overwhelming strain on our Nation's ability to treat patients; the number of patients who need to be tested continues to rise. With nurses also becoming infected, the number of trained professionals who can perform tasks such as testing of patients along with providing care involving hooking up patients to ventilators continues to decrease as well. There is a need to explore the adoption of virtual computer based training mediums which will enable new nurses and others to be trained in safe and efficient procedures involving patients during this pandemic period. In this paper, the design of a VR based simulator based on Human Centered Computing (HCC) principles is discussed. The role of HCC factors such as affordance and cognitive load on the comprehension and scene understanding of nurses during training and the acquisition of knowledge of safety procedures and detailed steps (pertaining to nasal sample collection and use of ventilators on patients) has been studied with the involvement of nurse and nurse trainee participants. Adoption of a participatory design approach involving experts (nurses, doctors involved in covid-19 testing and treatment) has provided a foundational basis for design of the training environments and assessment activities. Formal information centric process models of the nasal swabbing procedures and ventilator hookup tasks were created using the engineering Enterprise Modeling Language (eEML). The preliminary results from the assessment activities indicate the positive impact of such HCC based 3Dsimulators in such training of first responders. 

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2. Using Virtual Reality Cleanroom Simulation in a Mixed Nanoelectronics Classroom

   Letavish, Sean ; Meliksetyan, Ani ; Ravel, Victoria ; Ok, Hurriyet. A. ; Milman, Natalie B. ; Adam, Gina C. ( June 2023 , 2023 American Society for Engineering Education (ASEE) Annual Conference & Exposition)

   Given the strategic importance of the semiconductor manufacturing sector and the CHIPS Act impact on microelectronics, it is more imperative than ever to train the next generation of scientists and engineers in the field. However, this is a challenging feat since nanofabrication education uses hands-on cleanroom facilities. Since cleanrooms are expensive, have access constraints due to safety concerns, and offer limited instructional space, class sizes and outreach events are limited. To complement instruction in nanotechnology education, there is some open- or educational-access software, which is computer-based and focuses only on training for individual equipment, not on the typical workflow for device fabrication. The objective of this work was to develop an accessible virtual reality ecosystem that provides an immersive education and outreach on device nanofabrication that is user-friendly for a broad range of audiences. At the George Washington University (GWU), a virtual reality cleanroom prototype has been developed. It consists of a 45-minute gameplay module that covers the process flow for the fabrication of micro-scale resistors, from sample preparation to electrical characterization. We also performed a mixed methods study to investigate how 5 students in a nanoelectronics course utilized this virtual reality cleanroom prototype and what changes they recommend to improve its user interface and learner experience. The study population for this work-in-progress consisted of students enrolled in a nanoelectronics course at GWU during the 2022-2023 school year. Students taking this course can be undergraduate (junior or senior) or graduate (masters or PhD). The research questions for this study were 1) what is the user experience with the virtual reality cleanroom prototype, 2) what challenges, if any, did students experience, and 3) what changes did students recommend to improve the virtual reality cleanroom prototype learner experience? Preliminary results indicate that the students found the virtual reality cleanroom simulator helpful in repeatedly exploring the cleanroom space and the nanofabrication process flow in a safe way, thus developing more confidence in utilizing the actual cleanroom facility. The results of this study will provide insight on the design of future modules with more complicated levels and device process flows. Moreover, the study could inform the development of other virtual reality simulators for other lab activities. The improved usability of the proposed software could provide students in large classes or attending online programs in electrical and computer engineering, as well as K-12 students participating in nanotechnology-related outreach events, the opportunity to conduct realistic process workflows, learn first-hand about nanofabrication, and practice using a nanofabrication lab via trial and error in a safe virtual environment. 

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3. Telehealth Frontiers: Social Telerobots in Developmental and Behavioral Pediatrics

   https://doi.org/10.2196/41060  

   Ahumada-Newhart, Veronica ; Marcin, James ( August 2022 , Journal of Medical Internet Research: Iproceedings)

   COVID-19 has severely impacted health in vulnerable demographics. As communities transition back to in-person work, learning, and social activities, pediatric patients who are restricted to their homes due to medical conditions face unprecedented isolation. Prior to the pandemic, it was estimated that each year, over 2.5 million US children remained at home due to medical conditions. Confronting gaps in health and technical resources is central to addressing the challenges faced by children who remain at home. Having children use mobile telemedicine units (telerobots) to interact with their outside environment (eg, school and play, etc) is increasingly recognized for its potential to support children’s development. Additionally, social telerobots are emerging as a novel form of telehealth. A social telerobot is a tele-operated unit with a mobile base, 2-way audio/video capabilities, and some semiautonomous features.

   In this paper, we aimed to provide a critical review of studies focused on the use of social telerobots for pediatric populations.

   To examine the evidence on telerobots as a telehealth intervention, we conducted electronic and full-text searches of private and public databases in June 2010. We included studies with the pediatric personal use of interactive telehealth technologies and telerobot studies that explored effects on child development. We excluded telehealth and telerobot studies with adult (aged >18 years) participants.

   In addition to telehealth and telerobot advantages, evidence from the literature suggests 3 promising robot-mediated supports that contribute to optimal child development—belonging, competence, and autonomy. These robot-mediated supports may be leveraged for improved pediatric patient socioemotional development, well-being, and quality-of-life activities that transfer traditional developmental and behavioral experiences from organic local environments to the remote child.

   This review contributes to the creation of the first pediatric telehealth taxonomy of care that includes the personal use of telehealth technologies as a compelling form of telehealth care.

    

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4. Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

   https://doi.org/10.1109/FIE56618.2022.9962643  

   Wei-Kocsis, Jin ; Sabounchi, Moein ; Yang, Baijian ; Zhang, Tonglin ( October 2022 , 2022 IEEE Frontiers in Education Conference (FIE))

   This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks. 

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5. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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