Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to: avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.

Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure;dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.

Relationship between Risk Perception, Emotion, and Coping Behavior during Public Health Emergencies: A Systematic Review and Meta-Analysis

Complex mechanisms exist between public risk perception, emotions, and coping behaviors during health emergencies. To unravel the relationship between these three phenomena, a meta-analytic approach was employed in this study. Using Comprehensive Meta-Analysis 3.0, 81 papers were analyzed after selection. The results of the meta-analysis showed that (1) risk perception (perceived severity, perceived susceptibility) and negative emotions (especially fear) are both correlated with coping behaviors;(2) risk perception is strongly correlated with fear and moderately correlated with anxiety;and (3) anxiety predicts the adoption of coping behaviors. The existing research provided an empirical basis for implementing effective coping behavior interventions and implied that management decisionmakers need to consider reasonable interventions through multiple channels to maintain the public's risk perception and emotions within appropriate levels. Finally, future research directions are suggested.

Advanced Dance Choreography System Using Bidirectional LSTMs

Recently, the craze of K-POP contents is promoting the development of Korea's cultural and artistic industries. In particular, with the development of various K-POP contents, including dance, as well as the popularity of K-POP online due to the non-face-to-face social phenomenon of the Coronavirus Disease 2019 (COVID-19) era, interest in Korean dance and song has increased. Research on dance Artificial Intelligent (AI), such as artificial intelligence in a virtual environment, deepfake AI that transforms dancers into other people, and creative choreography AI that creates new dances by combining dance and music, is being actively conducted. Recently, the dance creative craze that creates new choreography is in the spotlight. Creative choreography AI technology requires the motions of various dancers to prepare a dance cover. This process causes problems, such as expensive input source datasets and the cost of switching to the target source to be used in the model. There is a problem in that different motions between various dance genres must be considered when converting. To solve this problem, it is necessary to promote creative choreography systems in a new direction while saving costs by enabling creative choreography without the use of expensive motion capture devices and minimizing the manpower of dancers according to consideration of various genres. This paper proposes a system in a virtual environment for automatically generating continuous K-POP creative choreography by deriving postures and gestures based on bidirectional long-short term memory (Bi-LSTM). K-POP dance videos and dance videos are collected in advance as input. Considering a dance video for defining a posture, users who want a choreography, a 3D dance character in the source movie, a new choreography is performed with Bi-LSTM and applied. For learning, considering creativity and popularity at the same time, the next motion is evaluated and selected with probability. If the proposed method is used, the effort for dataset collection can be reduced, and it is possible to provide an intensive AI research environment that generates creative choreography from various existing online dance videos.

Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach

Our research contributes a new point of view on China's rare earth dynamic risk spillover measurement;this was performed by combining complex network and multivariate nonlinear Granger causality to construct the time-varying connectedness complex network and analyze the formation mechanism using the impulse response. First, our empirical research found that for the dynamic characteristics of China's rare earth market, due to instability, uncertainty, and geopolitical decisions, disruption can be captured well by the TVP-VAR-SV model. Second, except for praseodymium, oxides are all risk takers and are more affected by the impact of other assets, which means that the composite index and catalysts are main sources of risk spillovers in China's rare earth trading complex network system. Third, from the perspective of macroeconomic variables, there are significant multivariate nonlinear impacts on the total connectedness index of China's rare earth market, and they exhibit asymmetric shock characteristics. These findings indicate that the overall linkage of the risk contagion in China's rare earth trading market is strong. Strengthening the interconnections among the rare earth assets is of important practical significance. Empirical results also provide policy recommendations for establishing trading risk protection measures under macro-prudential supervision. Especially for investors and regulators, rare earth oxides are important assets for risk mitigation. When rare earth systemic trading risk occur, the allocation of oxide rare earth assets can hedge part of the trading risk.

A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid-19 Cases

At present, the whole world is experiencing a huge disturbance in social, economic, and political levels which may mostly attributed to sudden outbreak of Covid-19. The World Health Organization (WHO) declared it as Public Health crisis and global pandemic. Researchers across the globe have already proposed different outbreak models to impose various control measures fight against the novel corona virus. In order to overcome various challenges for the prediction of Covid-19 outbreaks, different mathematical and statistical approaches have been recommended by the researchers. The approaches used machine learning and deep learning based techniques which are capable of prediction of hidden patterns from large and complex datasets. The purpose of the present paper is to study different machine learning and deep learning based techniques used to identify and predict the pattern and performs some comparative analysis on the techniques. This paper contains a detailed summary of 40 paper based on this issue along with the use of method they applied to obtain the purpose. After the review it has been found that no model is fully capable of predicting it with accuracy. So, a hybrid model with better training should be employed for better result. This paper also studies different performance measures that researchers have used to show the efficiency of their proposed model.

A Multivariate Deep Learning Model with Coupled Human Intervention Factors for COVID-19 Forecasting

Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. However, most previous AI-based models have failed to consider human interventions when predicting the trend of infectious diseases. This study selected four human intervention factors that may affect COVID-19 transmission, examined their relationship to epidemic cases, and developed a multivariate long short-term memory network model (M-LSTM) incorporating human intervention factors. Firstly, we analyzed the correlations and lagged effects between four human factors and epidemic cases in three representative countries, and found that these four factors typically delayed the epidemic case data by approximately 15 days. On this basis, a multivariate epidemic prediction model (M-LSTM) was developed. The model prediction results show that coupling human intervention factors generally improves model performance, but adding certain intervention factors also results in lower performance. Overall, a multivariate deep learning model with coupled variable correlation and lag outperformed other comparative models, and thus validated its effectiveness in predicting infectious diseases.

Multi Criteria Decision Making based Approach to Assist Marketers for Targeting BoPs Regarding Packaging Influenced Purchase during Covid-19

This study has a novel approach to capture the attitude of Bottom of the Pyramid (BoP) consumers towards Packaging Influenced Purchase (PIP) during the Covid-19 crisis. Over the years, BoPs consumers have established themselves as an emerging market with ample growth and opportunities. The authors suggested a Multiple-Criteria Decision-Making (MCDM) based framework to assist marketers in targeting both urban and rural BoP consumers regarding PIP. Packaging elements and influence of family, extended family, peers have been included in the framework for gaining in-depth understanding. With a sample size of 100 from West Bengal, this focus group-based study can fulfil the BoP literature's existing prominent research gap. Results indicate the difference in attitude for urban and rural BoPs towards PIP during this crisis. The fusion of MCDM based approach and relevant machine learning-based technique aims to assist marketers in identifying, formulating, and redefining an action plan.

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers' perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature's existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness;however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

Femvertising and COVID-19—What Did Brand Owners Broadcast during the Lockdown?

The aim of this study was to analyze, from a gender perspective, advertising broadcasts during a time of crisis. A holistic perspective of the stereotypes, roles, professions, and gender relations represented is offered by utilizing a content analysis of all the advertisements and their corresponding images during broadcast. Methods: a content analysis of 20 variables was conducted;of these, 7 variables were obtained from under the gender perspective of 1.350 images, corresponding to 71 audiovisual spots on YouTube that were broadcasted during the lockdown. Results: this analysis showed the special sensitivity of advertisers when balancing male and female presences, and in projecting an equitable and co-responsible vision between both genders, with special emphasis on gender professions, teleworking, and childcare. Corporate advertising predominates over commercial advertising, which may explain why the discourse and images blur inequalities and imbalances with respect to official statistics. Conclusions: advertisers seem to have noticed the strategic role of introducing gender perspectives into advertising, thus assuming a more social function that better connects them with today's society while also supporting the advances and challenges of equal opportunities.

Customer Attitude toward Digital Wallet Services

The goal of this study is to examine and identify the factors influencing customer attitude toward and intention to use digital wallets (electronic wallets, e-wallets) during and after the COVID-19 pandemic. A total of 257 correctly fulfilled questionnaires from an online survey were summarized. The main features of e-wallet payment systems were classified with a focus on consumer satisfaction via the integration of classic and modern data analysis methods. Structural Equation Modeling (SEM) was preferred to reveal the dependencies between the variables from e-wallets users' perspective. The designed model can discover and explain the underlying relationships that determine the e-wallets' adoption mechanism. The obtained results lead to specific recommendations to stakeholders in the value chain of payment processing. Financial regulatory authorities could employ the presented results in planning the development of payment systems. E-commerce marketers could utilize the proposed methodology to assess, compare and select an alternative way for order payment. E-wallet service providers could establish a reliable multi-criteria system for the evaluation of digital wallet adoption. Being aware of the most important components of e-wallets value, managers can more effectively run and control payment platforms, enhance customer experience, and thus improve the company's competitiveness. As the perceived value of customer satisfaction is subjective and dynamic, measurements and data analysis should be conducted periodically.

A dimension reduction in neural network using copula matrix

In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.

Transmission of COVID-19 through asymptomatic individuals

The rapid emergence of the coronavirus disease 2019 (COVID-19) has already taken on pandemic proportions, resulting thousands of deaths around the world. In the present manuscript, a mathematical model is proposed to investigate the current outbreak of the COVID-19. The model includes multiple transmission pathways and emphasises the role of asymptomatic and symptomatic infected population in the spread of this disease. To predict upcoming situation and a detail analysis of the spread of COVID-19 outbreak, basic reproduction number is calculated using publicly reported data from three different countries, where the outbreak is at its peak (USA), initial level (India) and controlled up to certain level (Japan). Analytical and numerical results of the model indicate that current on-going outbreak of COVID-19 would remain endemic if we do not proceed with extreme vigilance due to the serious risk it poses around the globe.

Improvisational and Dynamic Capabilities as Drivers of Business Model Innovation: An Enterprise Architecture Perspective

Background: This study examines how organizations can achieve business model innovation under the pressing COVID-19 conditions by leveraging two complementary capabilities, i.e., improvisational and dynamic capabilities driven by enterprise architecture (EA). We argue that EA-driven improvisational and dynamic capabilities ensure the adaptiveness of the organization and enable the organization to cope with emerging business model problems and opportunities through an integrated and orchestrated perspective. Method: We used a cross-sectional research approach and collected data from 414 decision-makers and senior practitioners to test our research model's hypotheses. Results: We found that EA-driven improvisational and dynamic capabilities both positively impact business model innovation in tumultuous times. In turn, business model innovation positively impacts organizational performance under COVID-19. Also, we found a positive moderating effect of EA-driven dynamic capabilities on the relationship between improvisational capabilities and business model innovation. Conclusion: The outcomes of this study offer a nuanced understanding of the role of EA-driven capabilities in organizations. We also offer various managerial implications to achieve business model innovation under turbulent conditions.

Biochip Systems for Intelligence and Integration

Disease is one of the major threats to human life and health, and historically there have been many cases which threatened human life due to infectious diseases. In almost all cases, specific triggers for the emergence of disease can be identified, so there is an urgent need for effective detection and identification of most diseases, including infectious diseases. Therefore, this article proposes biochip systems as a tool for disease detection and risk assessment, and explains why they are effective in detecting disease, in terms of their working mechanisms, advantages and disadvantages, specific application scenarios and future trends.

Go Digital! Determinants of Continuance Usage of Mobile Payment Apps: Focusing on the Mediating Role of Gamification

Background: COVID-19 spread over the last two years has been instrumental in shifting physical banking transactions to mobile-based banking transactions. Recently, M-payments have dominated online and point-of-sale (POS) transactions in the Asia-pacific region. Therefore, there was a need to study the factors influencing M-payments. This research has been conducted to determine the significant factors influencing the usage and continuance usage of M-payment apps in an emerging country and particularly how gamified features enhance the usage of M-payments apps.is study is based on the perspectives of the Unified theory of acceptance and use of technology (UTAUT2) and information system success (ISS) theory, and it adds three new determinants—trust, gamified features, and continued use of mobile payments to better explain and forecast users' behavioral intentions and continued use of mobile payment applications (M-payments apps). Method: The research has employed two studies on sample data from young users of M-payment apps (n=898), the dataset was analyzed through structural equation modelling for mediation and moderation analysis in study one. The second study was grounded through Vignette experiments to analyze the effects of the degree of gamified features on the continued usage of M-payments. Results: The results reported that behavioral intention to adopt, and usage of mobile payments are significantly mediated by gamified features and gamified features are partially mediating continuance usage of M-payments. Trust is the key to enabling continuance usage amongst the users of M-payments. These findings extend the understanding of users' continuance intention in the context of payments apps. Conclusion: This study would be helpful in presenting insights for the M-payments service providers and the associated banks to develop strategy for the continuance usage of mobile payment apps.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods.

How Innovation Ecosystem Synergy Degree Influences Technology Innovation Performance—Evidence from China’s High-Tech Industry

The technology innovation of high-tech industries has become an important support for the innovation-driven strategy. This study introduces innovation ecosystem synergy as a moderating variable from a systemic and holistic perspective based on the traditional perspective of innovation factor input-output, and helps construct a technology innovation performance driving model based on the Cobb–Douglas knowledge production function, which enriches the discussion perspective and theoretical model research on technology innovation performance. With a sample of 28 provinces in mainland China, this study empirically analyzed the moderating mechanism of innovation performance by innovation synergy in high-tech industries during the two stages of technology development and technology transformation. The findings of the study are as follows: (1) Independent research and development has a positive and significant impact on technology development performance;product innovation has a positive and significant impact on technology transformation performance;(2) Technology introduction can weaken technology development performance due to technology dependence and the inhibitory effect on independent innovation, and inefficient technology renovation can negatively and significantly affect technology transformation performance.;(3) The degree of synergy has a positive and significant impact on the performance of technology development innovation and technology transformation innovation. The degree of synergy has a positive moderating effect on the innovation performance of independent R&D and technology development, as well as product innovation and technology renovation, and a negative moderating effect on the innovation performance of technology introduction and technology development, but no significant moderating effect on technology renovation and technology transformation performance. The research results can provide a reference for the improvement of the technology innovation performance of regional high-tech industries.

Assessing Risks in Dairy Supply Chain Systems: A System Dynamics Approach

Due to the dynamic nature of the food supply chain system, food supply management could suffer because of, and be interrupted by, unforeseen events. Considering the perishable nature of fresh food products and their short life cycle, fresh food companies feel immense pressure to adopt an efficient and proactive risk management system. The risk management aspects within the food supply chains have been addressed in several studies. However, only a few studies focus on the complex interactions between the various types of risks impacting food supply chain functionality and dynamic feedback effects, which can generate a reliable risk management system. This paper strives to contribute to this evident research gap by adopting a system dynamics modelling approach to generate a systemic risk management model. The system dynamics model serves as the basis for the simulation of risk index values and can be explored in future work to further analyse the dynamic risk’s effect on the food supply chain system’s behaviour. According to a literature review of published research from 2017 to 2021, nine different risks across the food supply chain were identified as a subsection of the major risk categories: macro-level and operational risks. Following this stage, two of the risk groups identified first were integrated with a developed system dynamics model to conduct this research and to evaluate the interaction between the risks and the functionality of the three main dairy supply chain processes: production, logistics, and retailing. The key findings drawn from this paper can be beneficial for enhancing managerial discernment regarding the critical role of system dynamics models for analysing various types of risks across the food supply chain process and improving its efficiency.