Abstract
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.

Abstract

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

Abstract
Microgrids (MGs) are the important entities of distribution systems and more distributed generations (DGs) need to be considered to achieve maximum benefits under the full potential of MGs. By considering the important role of power converters in MGs, problem formulation with model predictive control (MPC) of reconfigurable converter (VSC) is implemented for AC/DC microgrid. The uncertainties due to the loads and sources (RESs) are investigated. Active front end (AFE) rectifier regulates the DC voltage and power is controlled through direct power MPC (DPMPC) during grid connection. Model predictive voltage control (MPVC) regulates AC load voltage in islanded mode. Furthermore, the transition between grid-connected and standalone is thoroughly investigated. MATLAB/Simulink® software authenticates the model performance evaluation of the suggested scheme for different loads. The proposed scheme shows superior attributes with reduced THD.

Abstract
By means of a cross-cultural virtual teams project involving classrooms in Scotland, Germany, and Portugal, students were exposed to the challenges of collaborating internationally with the intention of increasing their intercultural competency. Intercultural sensitivity and intercultural communication competency were measured using responses to surveys before and after the 6-week project. Students reported, among other aspects, a heightened awareness of the difficulties of intercultural communication. Despite a general appreciation of the project and its outcomes, negative results, such as an increased dislike of intercultural interaction, emerged. Contradictory results warrant further investigation with data from future collaborations.

Abstract
Grade prediction has been for a long time a subject that interests both teachers and researchers. Before the digital age this type of predictions was something nearly impossible to achieve. With the increasing integration of Learning Management Systems in education, grade prediction seems to have become a viable option. The general adoption of this type of systems brings to the research area a database known as “registry”, or more simply known as logged data. Using this new source of information several attempts regarding the prediction of student grades have been proposed. The methodology proposed in this study is capable of, analyzing student online behavior, using the information collected by the Moodle system and making a prediction on what the final grade of the student will be, at any point in the semester. Our novel approach uses the gathered information to examine the academic path of the student in order to determine an interaction pattern, then it tries to establish a link with other, present or past, known successful paths. Making this comparison, the model can automatically determine if a student is going to fail or pass the course, which then would leave a space for the teacher or the student to circumvent the situation. Our results show that the system is not only viable, as it is also robust to make prediction at an early stage in the course.