Abstract: The role of social network mass media increased greatly in the recent years. We investigate news publications in Twitter from the point of view of Network Science. We analyzed news data posted by the most popular media sources to reveal the most significant news over some period of time. Significance is a qualitative property that reflects the news impact degree at society and public opinion. We define the threshold of significance and discover a number of news which were significant for society in period from July 2014 up to January 2015.

Abstract: In spite of extensive research of uncertainty issues in different fields ranging from computational biology to decision making in economics, a study of uncertainty for cloud computing systems is limited. Most of works examine uncertainty phenomena in users’ perceptions of the qualities, intentions and actions of cloud providers, privacy, security and availability. But the role of uncertainty in the resource and service provisioning, programming models, etc. have not yet been adequately addressed in the scientific literature. There are numerous types of uncertainties associated with cloud computing, and one should to account for aspects of uncertainty in assessing the efficient service provisioning. In this paper, we tackle the research question: what is the role of uncertainty in cloud computing service and resource provisioning? We review main sources of uncertainty, fundamental approaches for scheduling under uncertainty such as reactive, stochastic, fuzzy, robust, etc. We also discuss potentials of these approaches for scheduling cloud computing activities under uncertainty, and address methods for mitigating job execution time uncertainty in the resource provisioning.

Abstract: In this work we consider the problem of reconstruction of unknown density based on a given sample. We present a method for density reconstruction which includes B-spline approximation, least squares method and Monte Carlo method for computing integrals. The error analysis is provided. The method is compared numerically with other statistical methods for density estimation and shows very promising results.

Abstract: We investigate the total least square problem with Chebyshev norm instead of the traditionally used Frobenius norm. Using Chebyshev norm is motivated by seeking for robust solutions. In order to solve the problem, we make link with interval computation and use many of results developed there. We show that the problem is NP-hard in general, but it becomes polynomial in the case of a fixed number of regressors. This is the most important result for practice since usually we work with regression models with a low number of regression parameters (compared to the number of observations). We present not only an precise algorithm for the problem, but also a computationally cheap heuristic. We illustrate the behavior of our method in a particular probabilistic setup by a simulation study.