Abstract

Abstract
The occurrence of the first illnesses of the inhabitants of Poland and Portugal caused decision-makers to introduce many changes in the functioning of economic units in various areas. This document aims to answer the questions of whether the changes related to the occurrence of COVID-19 had a significant impact on the housing situation of students by answering two questions: (1) How has the pandemic affected the change in the form of residence? (2) What changes in the provisions of the contract do students expect after returning to the full-time form? The empirical study was conducted based on data obtained from a survey. The research was conducted in May and June 2021 on a sample of 599 students at the University of Warmia and Mazury in Olsztyn and the School of Technology and Management of Porto Polytechnic in Portugal. The analysis related to the determination of statistically significant interdependencies of socio-demographic characteristics of respondents with their attitudes, and a multidimensional method of comparative analysis was used, known as correspondence analysis. As a method of recording data in the analysis of correspondence, the Burt matrix was used. The result of the statistical analysis was the identification of structural relationships between variables and objects (respondents). The results showed different behaviours related to housing conditions in Poland and Portugal. Polish students, due to the epidemiological situation, were mostly forced to change their place of residence, which was usually associated with returning to their family home. This trend was not observed for students in Portugal (median response: Housing had not been affected in any way by the pandemic).

Abstract

Abstract
The rapid development of Internet technology and information technology is rapidly changing the way people think, recognize, live, work and learn. In the context of Internet + education, the emerging learning form of a cloud classroom has emerged. Cloud classroom refers to the process in which learners use the network as a way to obtain learning objectives and learning resources, communicate with teachers and other learners through the network, and build their own knowledge structure. Because it breaks the boundaries of time and space, it has the characteristics of freedom, high efficiency and extensiveness, and is quickly accepted by learners of different ages and occupations. The traditional cloud classroom teaching mode has no personalized recommendation module and cannot solve an information overload problem. Therefore, this paper proposes a cloud classroom online teaching system under the personalized recommendation system. The system adopts a collaborative filtering recommendation algorithm, which helps to mine the potential preferences of users and thus complete more accurate recommendations. It not only highlights the core position of personalized curriculum recommendation in the field of online education, but also makes the cloud classroom online teaching mode more intelligent and meets the needs of intelligent teaching.

Abstract
Machine Learning problems are significantly growing in complexity, either due to an increase in the volume of data, to new forms of data, or due to the change of data over time. This poses new challenges that are both technical and scientific. In this paper we propose a Distributed Learning System that runs on top of a Hadoop cluster, leveraging its native functionalities. It is guided by the principle of data locality. Data are distributed across the cluster, so models are also distributed and trained in parallel. Models are thus seen as Ensembles of base models, and predictions are made by combining the predictions of the base models. Moreover, models are replicated and distributed across the cluster, so that multiple nodes can answer requests. This results in a system that is both resilient and with high availability.

Abstract
In the last years, organizations and companies in general have found the true potential value of collecting and using data for supporting decision-making. As a consequence, data are being collected at an unprecedented rate. This poses several challenges, including, for example, regarding the storage and processing of these data. Machine Learning (ML) is also not an exception, in the sense that algorithms must now deal with novel challenges, such as learn from streaming data or deal with concept drift. ML engineers also have a harder task when it comes to selecting the most appropriate model, given the wealth of algorithms and possible configurations that exist nowadays. At the same time, training time is a stronger restriction as the computational complexity of the training model increases. In this paper we propose a framework for dealing with these challenges, based on meta-learning. Specifically, we tackle two well-defined problems: automatic algorithm selection and continuous algorithm updates that do not require the retraining of the whole algorithm to adapt to new data. Results show that the proposed framework can contribute to ameliorate the identified issues.