ABSTRACT
This book chapter provides an overview of Temi, an autonomous, video-oriented personal assistant robot which was deployed within the Centre for Independent Language Learning (CILL) at The Hong Kong Polytechnic University. The artificial intelligence robot was chosen principally because of its role as a Robot as a Service (RaaS). Such a service can deliver greater self-improvement and better learning strategies (e.g. Cohen, A. D. (2014). Strategies in learning and using a second language (2nd ed.). Routledge., Dörnyei et. al., 2015, Wenden, Learner strategies for learner autonomy, Prentice Hall, 1991, Yang, Frontiers in Psychology 12:600, 218–600, 218, 2021) as well as foster beneficial attitudes and skills towards the users' long-term language learning success. Through its cloud-based system, Temi offers users access to dynamic interactions and enhanced CILL services, during the COVID-19 pandemic. As a whole, it appears that the introduction of Temi has proven to be an effective strategy to augment learners' autonomy. It further allows administrators to rethink how CILL services are conducted during human resource shortages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Engaging students' personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student's progress, moreover, it could predict the student's level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however, the importance of the topic has increased exponentially ever since. The research supports the decision-makers in educational institutions as considering personalized data for the student's educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students' personal data in predicting their learning skills as well as their educational level. The research included engaging five well-known clustering algorithms, one of the most successful classification algorithms, and a set of 10 features selection techniques. The research applied two main experiment phases, the first phase focused on predicting the students' learning skills, and the second focused on predicting the students' level. Two datasets are involved in the experiments and their sources are mentioned. The research revealed the success of the clustering and prediction tasks by applying the selected techniques to the datasets. The research concluded that the highest clustering algorithm accuracy is enhanced k-means (EKM) and the highest contributing features selection method is the evolutionary computation method. © 2023 World Scientific Publishing Company.
ABSTRACT
With India's rapid educational growth towards virtual education, the education system has intensified. India has gone through critical condition like COVID-19. There is huge increment in the implementation of online learning management system. This helped the educational institutions to manage their education system without being physically present in the institution. This paper is presenting the challenges required by institutions to transform their manual education system into virtual/online education system. The online learning management system has enhanced the way of teaching and learning process of students. This study will tell us about how online LMS has speeding up the quality of education for the On-Campus enrolled students as well as distance learning students also. In general, LMS are built to provide the course material in a static way only. The modern LMS has the ability for the group discussion which is helping the student to achieve the qualities of team-working. © 2022 IEEE.
ABSTRACT
Due to the COVID-19 pandemic, many robot competitions have been canceled in the past years. To address this problem, we proposed a cloud-based VR platform for the crowdsourcing of embodied human-robot interactions. However, this system only suggested the feasibility of the competition application, and actual competitions had not yet been held and implemented. Therefore, through demonstration experiments in the RoboCup Asia Pacific (RCAP) conducted in a hybrid format with on-site and remote participation, we evaluated the usefulness of using cloud computing on AWS from whether the latency time causes problems in human-robot interaction in a virtual reality environment. [ FROM AUTHOR] Copyright of Advanced Robotics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Professional services firms face evolving client needs and can better meet these needs through digital transformation. We offer the case of Clio, a leading provider of cloud-based legal technology for law firms to better serve their clients. With the onset of COVID-19, the company recognized that remote client access and services, previously embraced by early adopters, would now become essential for all law firms' survival. The company's response resulted in dramatic growth and the transition from a customer base of early adopters to customers spanning most of the innovation adoption curve. Clio's success throughout this period is attributable to three core elements of the company's strategy: (1) Deep, culturally-rooted commitment to customer success, (2) Research-based understanding of the needs of both law firms and their clients, and (3) Industry thought leadership and assistance. These elements generalize beyond Clio and the pandemic and will help guide any organization seeking to become not just a vendor but an essential partner to its customers. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
Purpose: Generally, the use of cloud-based services has proliferated all around the world. This tendency increased dramatically during the coronavirus disease 2019 (COVID-19) pandemic when many people were obliged to conduct business remotely. This research study aims to investigate the intentions to adopt (IA) cloud-based enterprise resource planning (ERP) systems in Jordan, as this sort of research is scarce in developing countries. Specifically, this research study combined the model of Diffusion of Innovation (DOI) and the Technology-Organization-Environment (TOE) framework, in addition to looking at COVID-19 as a moderating variable. Design/methodology/approach: Data were collected from 60 participants working in Jordanian companies that currently use, or have the intention to use, cloud-based ERP systems. An online questionnaire was developed and used to collect responses. The study hypotheses were examined statistically using simple regression analysis and hierarchical multiple regression. Findings: The study results clearly indicate that complexity, observability, employee information technology (IT) skills, organizational culture (OC) and top management support significantly affect the intention to adopt cloud-based ERP systems. In contrast, relative advantage (RA), compatibility, trialability, regulatory environment and competitive pressure had no significant effect on adoption intention. Interestingly, the advent of COVID-19 increased the intention to adopt cloud-based ERP systems. Research limitations/implications: The study's findings will provide theoretical and practical suggestions for the effective adoption of cloud ERP in Jordan, as well as aiding other developing nations in comparable situations in implementing cloud-based ERP systems. Originality/value: The study enriches the knowledge and further examines the underlying reasons for adopting cloud-based ERP systems in a developing country. © 2023, Emerald Publishing Limited.
ABSTRACT
Small Medium and Micro Enterprises (SMMEs) are significant contributors to economic growth and employment and therefore playing a vital role in wealth creation and human welfare. Yet despite their critical importance, SMMEs are yet to realize their full potential due to numerous challenges presented by the nature of operations in a global market, including the emergent hyper digital contexts as presented by COVID-19. Mobile Cloud Based Enterprise Resource Planning (MCB-ERP) systems present opportunities such as on-demand use, low upfront capital expenditure, security and stability, easy upgrade, and low operational and maintenance costs for SMMEs operating in these global markets. This paper reports on a systematic review of 35 peer reviewed journals and conference papers to identify MCB-ERP research that has been undertaken and to identify opportunities, gaps, and challenges in relation to SMMEs. The key findings suggest that researchers have mainly focused on Cloud-Based ERP adoption and implementation factors;the use, benefits, and impact for SMMEs received limited attention;and that SMME readiness for adoption is impacted by a lack of MCB-ERP vendor presence, awareness, and experience. Moreover, most of the research is not contextualized and treats SMMEs as homogenous entities operating in sectors with similar environments and preconditions. Future context specific research investigating MCB-ERPs in specific contexts, and non-ICT based sectors such as agriculture for SMMEs is recommended. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Covid-19 Epidemic has significantly changed how hypospadias patients are delivered to healthcare services, particularly after hypospadias repairs (postoperative care). Some studies reported that using telemedicine schemes by sending digital documentation such as images and videos through cell phones can facilitate an assessment of postoperative monitoring of hypospadias patients. However, this approach raises various concerns, such as managing digital documentation of hypospadias patients, analyzing the data, and the security of individuals' health information. This study proposes a design of cloud-based architecture for early detection and postoperative monitoring of hypospadias patients to address the concerns above. The user acceptance test shows that most users agree that this application may be used for early detection, monitoring hypospadias patients, and helping capture videos and provide labeling to patients' data. © 2022 IEEE.
ABSTRACT
Modern manufacturing enterprises must be agile to cope with sudden demand changes arising from increased global competition, geopolitical factors, and unforeseen circumstances such as the Covid-19 pandemic. Small- and Medium-Sized Enterprises (SMEs) in the manufacturing sector lack agility due to lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of a formal innovation ecosystem for new products or solutions. In recent years, Cloud-based Design and Manufacturing (CBDM) has emerged as an enabler for product realization by integrating various service-based models. However, the existing framework does not thoroughly support the innovation ecosystem from concept to product realization by formally addressing economic challenges and human skillset requirements. The present work considers the augmentation of the Design-as-a-Service (DaaS) model into the existing CBDM framework for enabling systematic product innovations. The DaaS model proposes to connect skilled human resources with enterprises interested in transforming an idea into a product or solution through the CBDM framework. The model presents an approach for integrating human resources with various CBDM elements and end-users through a service-based model. The challenges associated with successfully implementing the proposed model are also discussed. It is established that the DaaS has the potential for rapid and economical product discovery and can be readily accessible to SMEs or independent individuals. [ FROM AUTHOR] Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.
ABSTRACT
Since December 2019, the world still fighting to beat coronavirus (COVID-19). However, coronavirus is continuing its spread in many countries and claimed the lives people. It is not easy to differentiate between COVID-19 symptoms and simple flu symptoms, especially at the first stage of the infection. This is the main challenge where we have to run many tests as possible and isolate any suspicious people 14 days at least to make sure that they are not carrying the virus. This will increase the cost and people may lose their jobs. Therefore, the economy has to continue. Companies and organization start running their business using online tools, this will draw different future and employee need to gain special task to continue their work. In order go back to the normal life, we have track the virus and stay away from infected area or people. In this paper, we propose a secure cloud-based health framework to record patients' readings, give initial diagnose to identify infected areas and control the spread of the virus. The proposed framework will be running in a secure environment to protect patient's records. © 2022 IEEE.
ABSTRACT
The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This study aims to build clusters of similar research papers. Text clustering for research articles is challenging because re-clustering is necessary to handle newly added papers. An incremental clustering algorithm is presented to find similar research papers for COVID-19 related literature. The proposed approach uses an incremental word embedding generation technique to extract feature vectors of the papers. The initial clustering is done by using the K-means algorithm by two NLP feature extraction models;TF-IDF and Word2vec. The clustering results show that the Word2vec outperforms the TF-IDF model. With increasing COVID-19 literature continuously, the ultimate focus is to add the newly published papers to the existing clusters without re-clustering. Title, , and full body of papers are considered for testing the proposed incremental algorithm. Clustering quality is evaluated by the Microsoft language similarity package, which shows clustering of the full-text body outperforms the and title of papers. © 2022 Society for Modeling & Simulation International (SCS)
ABSTRACT
The COVID-19 crisis represents significant challenges for the manufacturing industry, especially for small and medium sized enterprises (SMEs), and further emphasizes the need for supporting the fast and efficient reconfiguration and repurposing of manufacturing lines and supply networks. The European CO-VERSATILE project addresses these challenges by offering the Digital Technopole, a central hub for decentralized services, including manufacturing and supply network simulation, among other vital services in aid of the reconfiguration process. Such simulation services are provided through an external cloud-based platform, the Digital Agora (emGORA). This paper highlights how such novel cloud-based simulation services can be efficiently implemented and offered using the Digital Agora and the Digital Technopole. The presented case study aims to optimize the supply network related to the manufacturing process of silicon facemasks, involving three European SMEs. The implemented solution is offered as cloud-based service to provide scalability, convenient access and reduction of upfront costs. © 2022 Society for Modeling & Simulation International (SCS)
ABSTRACT
The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Purpose: This study aims to assesses the factors that affected the development of cloud-based accounting education (DCBAE) and students' academic performance at Oman Universities. Design/methodology/approach: In this paper a questionnaire was used to collect data for a sample of students. Partial least squares (PLS) were used to assess the hypotheses and model. Findings: Results indicate that human factors and cultural and social factors have a direct positive effect on the DCBAE and students' academic performance. Practical implications: This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Thus, it can help universities (accounting education in particular) in the Sultanate of Oman know the factors that affect the development of accounting education and adopt policies and strategies that depend on cloud computing in education operations, even after the COVID-19 pandemic. Originality/value: The main contribution of this study is to evaluate the factors that affected the DCBAE and the academic performance of students in the Sultanate of Oman from March 15, 2020 to June 2022, a period that witnessed the application of cloud-based education, either in whole or in part using a questionnaire about the opinions of students. © 2022, Omar Ikbal Tawfik and Hamada Elsaid Elmaasrawy.
ABSTRACT
Due to the COVID-19 pandemic, many robot competitions have been canceled in the past years. To address this problem, we proposed a cloud-based VR platform for the crowdsourcing of embodied human-robot interactions. However, this system only suggested the feasibility of the competition application, and actual competitions had not yet been held and implemented. Therefore, through demonstration experiments in the RoboCup Asia Pacific (RCAP) conducted in a hybrid format with on-site and remote participation, we evaluated the usefulness of using cloud computing on AWS from whether the latency time causes problems in human-robot interaction in a virtual reality environment. [ FROM AUTHOR]
ABSTRACT
This study aims to build clusters of similar research papers. Text clustering for research articles is challenging because re-clustering is necessary to handle newly added papers. An incremental clustering algorithm is presented to find similar research papers for COVID-19 related literature. The proposed approach uses an incremental word embedding generation technique to extract feature vectors of the papers. The initial clustering is done by using the K-means algorithm by two NLP feature extraction models;TF-IDF and Word2vec. The clustering results show that the Word2vec outperforms the TF-IDF model. With increasing COVID-19 literature continuously, the ultimate focus is to add the newly published papers to the existing clusters without re-clustering. Title, , and full body of papers are considered for testing the proposed incremental algorithm. Clustering quality is evaluated by the Microsoft language similarity package, which shows clustering of the full-text body outperforms the and title of papers. © 2022 SCS.
ABSTRACT
The COVID-19 crisis represents significant challenges for the manufacturing industry, especially for small and medium sized enterprises (SMEs), and further emphasizes the need for supporting the fast and efficient reconfiguration and repurposing of manufacturing lines and supply networks. The European COVERSATILE project addresses these challenges by offering the Digital Technopole, a central hub for decentralized services, including manufacturing and supply network simulation, among other vital services in aid of the reconfiguration process. Such simulation services are provided through an external cloud-based platform, the Digital Agora (emGORA). This paper highlights how such novel cloud-based simulation services can be efficiently implemented and offered using the Digital Agora and the Digital Technopole. The presented case study aims to optimize the supply network related to the manufacturing process of silicon facemasks, involving three European SMEs. The implemented solution is offered as cloud-based service to provide scalability, convenient access and reduction of upfront costs. © 2022 SCS.
ABSTRACT
The Covid situation forced organisations to rethink their strategies and to use more cloud and networking strategies than ever before. Also the technologies allow better connectivity and this leads to the reformulation of traditional strategies to new digital visions and strategies. New processes are being set up which connect experts, companies and events and new marketing and management strategies arise. In this paper we describe the results of the research performed in a master thesis in cooperation between EuroSPI and the University of Applied Sciences Joanneum, and the paper outlines based on the example of EuroSPI how such digital transformation strategies have been used to restructure EuroSPI and the vision of the EuroSPI conference series to become an integrated digital enabled service network. This way situations like a Covid 19 can lead to improvement and higher business potentials than before. © 2022, Springer Nature Switzerland AG.