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In this paper, we consider the course of the coronavirus disease (COVID-19) in human patients. We investigate anamnesis, examination, and clinical analysis data, as well as other features that can affect the severity and mortality of COVID-19. Based on these features, we develop a set of machine learning and statistical models that can predict the severity of the coronavirus disease and its outcome for inpatients and outpatients. The main contribution of this work is the development of the CT Calculator service, which is integrated in the Moscow city medical information system. This service allows one to assesses the degree of changes in the lung tissue of COVID-19 patients in an express mode without computed tomography (CT) scan, as well as predict the degree of lung damage. The developed machine learning models make it possible to determine the degree of risk for mild and severe forms of the coronavirus disease depending on various factors. [ABSTRACT FROM AUTHOR]

The problem of tracking people in a video stream with the aim of counting them is studied. Modern video surveillance systems, such as the Moscow video surveillance system, use hundreds of thousands of cameras. The use of modern methods developed for working on a single computer with an expensive graphical processor is economically inefficient for such large-scale systems. In this paper, a distributed tracking algorithm is proposed. It makes it possible to reduce the amount of computational resources due to detecting people in a sparse set of frames. The detection is performed on servers installed in a data center, while the video stream is processed by local camera computation nodes. The experimental evaluation showed that the proposed algorithm provides acceptable quality at the detection rate of 4/3 Hz. [ABSTRACT FROM AUTHOR]

The paper describes an approach to the implementation of 'Programming technologies' curriculum in senior school (grades 9-11), which combines development of skills for algorithm design with learning specific languages and technologies. [ABSTRACT FROM AUTHOR]