The Effect of Digital Economy and Artificial Intelligence on The Participants of The School Educational Process

The article examines the nature of the effect of the digital economy and artificial intelligence on the participants of the school educational process. The study deploys a Likert scale questionnaire, focused interview, and IBM SPSS Statistics 22 as the main instruments. The authors determine the status of a teacher in the AI-teacher system and identify the nature of AI’s influence on the students and communication within the teacher-student-teacher system. Support is provided for the hypothesis suggesting that the effect of the digital economy and AI on the participants of the school educational process (teachers and students) is complex and controversial and causes the differences in the teachers’ positions depending on their age, working experience, and the subject of specialization. The primary advantage of the article is the identification of stable relationships in the teacher-student-teacher system developing with the help of AI. The study indicates the teacher’s advantage over AI in teaching technique and social influence, as well as the negative effect of AI on students’ health. For the first time, the authors examine the nature of role changes in the AI-teacher system in the context of a global pandemic. The danger of AI replacing teachers with the help of teachers themselves is identified. The acquired results may be used by theorists and practitioners for further development of the foundations of the system of education and human interaction with AI in the digital economy

Examining Trust and Willingness to Accept AI Recommendation Systems

This paper proposes and tests a conceptual model that identifies the antecedents of trust in AI, which could in turn lead to users’ willingness to accept AI recommendation systems. An online survey was conducted in the context of stock market investment. Responses came from 313 participants with prior investment experiences. Data were analyzed using partial least squares structural equation modeling. Results indicate that attitude towards AI and perceived AI accuracy were positively related to users’ trust in AI. Users’ AI anxiety was negatively related to trust in AI. Furthermore, users’ trust in AI was positively related to their willingness to accept AI recommendation systems. The paper extends previous works by explicating the role of users’ trust in AI and suggests that the uptake of AI systems can be promoted by fostering favorable attitudes, greater perceived AI accuracy, and lowering AI anxiety

Determining the Factors Influencing Cloud Computing Implementation in Library Management System (LMS): A High Order PLS-ANN Approach

The principal component of this paper is to ascertain the prominent variables of technological, organizational, environmental, and financial constructs that influence library cloud computing (LCC) among the library users and professionals in the selected universities of India. This paper discusses the advantages, opportunities, challenges, and Models of Smart Library in the ICT age library management system. The study also commissioned tools viz. EFA, CFA, and structural equation assess the degree to which selected factors were associated with LCC adoption. Empirical research proposed four hypotheses by selecting the technological, organizational, environmental, and financial constructs and 16 manifests in the specified model. The model was then tested on a sample of 510 respondents of 26 major states, central and private universities of India using SEM-ANN. First, SEM was employed to find out which variables had a meaningful influence on LCC. Secondly, the output of ANN outlined the rank of influencing predictors obtained from SEM. It is evident that technological factors, greater scalability (TF_1), tech-readiness (TF_2), and easier back-up (TF_3), are the most robust antecedents of LCC. Whereas in organizational factors- recognized usefulness (OF_1), are the robust manifest, but in environmental factors-geographical reach (EF_1), administrative support (EF_2), conducive application interface (EF_4), are the significant predictors. Eventually, financial factors- cost-saving (FF_1) and better return on investment (FF_2) are the considerable predictors obtained from ANN. The findings further indicate that behavioural intention to adopt the library cloud yielded novel insights that significantly benefit users and stakeholders

Assessment of the status and factors influencing the adoption of cloud computing in knowledge-based companies Case Study: Kerman Science and Technology Park

Cloud computing is one of the most important topics in knowledge-based companies. Small and medium-sized enterprises with a low budget and few human resources are one of the major groups tending to use cloud computing to benefit from this technology. Several components affect the adoption of cloud in these companies, which should be evaluated before making the decision. This study aimed to identify these components and determine how much each component impacts the adoption of cloud in small and medium-sized companies. Accordingly, based on the diffusion of innovation theory and technology-organization-environment (TOE) framework as well as the previous studies, a conceptual model with twelve components was presented. Data were collected via a questionnaire using the descriptive survey method from 59 knowledge-based companies of Kerman Science and Technology Park. In this study, the “need” factor was selected as the desired state and “use” as the current state; then, the mean of the other components was compared with the mean of these two factors. The results of this study showed that based on the gap between the desired state and the current state, the employees’ knowledge of cloud computing, compatibility, complexity, and security and privacy require more attention. Innovation factors, decision makers’ knowledge of cloud computing, benefits, and costs have a better position than other components. Finally, factors effective in the compliance of knowledge-based companies of Kerman Science and Technology Park with cloud computing were ranked using the Vikor method. The need factor (information need), decision makers’ innovation, and benefits were ranked first to third, respectively, and the complexity factor was ranked last among the indicators. Therefore, identifying the current state (not using cloud computing based on the needs or not matching with cloud) and the desired state (using cloud computing based on the needs or matching with the cloud) in knowledge-based companies, based on the criteria or factors whose usefulness was investigated in this study, can be an important step in joining these companies into the cloud, and thus bringing the benefits of this new technology to knowledge-based companies

Machine Learning Centered Energy Optimization In Cloud Computing: A Review

The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing

Multi-dependency and time based resource scheduling algorithm for scientific applications in cloud computing

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.peer-reviewe

Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds

Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms, i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization