Studia Informatica

Volume: 1-2(22)
Year: 2018
Publishing: Publishing House of University of Natural Science
Scientific Editor: Wojciech Penczek,
Review Board: PDF File
ISSN: 1731-2264

Barczak A., Barczak M.,

Spreading information in distributed cloud systems using Gossip algorithm

pp. 5-20

Abstract: In a following article problem of a information sharing in distributed systemis described, as wellways of solving that problem with emphasize on Gossip protocol are presented. Furthermore the application has been creating allowing to test the Gossip protocol in a lab environment. Gossip protocol is highly parameterized and can be working in several modes. The main goal of the article is to examine the work of the Gossip algorithm, depending on the chosen mode and values of parameters, and analyses of a results.
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Mikułowski D., Mańkowski J.,

An approach of explaining math function graphs through the sound representation for blind students

pp. 21-30

Abstract: One of the key abilities that should be learned by every student is mastering math skills. It is particularly difficult for blind students who have problems with access to information that is usually presented graphically for common pupils. In mathematics one such information is the presentation of graphs. However there are several drawing techniques for the blind, but they are expensive, and hard for common using. One way to solve this problem may be using a special sound representation of math graphics for the blind students. In this article, an approach allowing to provide making such presentation possible is presented. Briefly it is grounded on the conversion of graphic data of the math drawing to sound wave with proper assets.
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Stano P.M.,

The collapse of sequential Bayesian estimator in two-target tracking problem

pp. 31-46

Abstract: Track coalescence is a phenomenon that occurs in multi-target tracking applications where certain types of manoeuvres performed simultaneously by several targets can utterly confuse algorithms that track their positions. In its simplest form, the phenomenon occurs when two similar objects, initially well separated, get close to each other and follow similar manoeuvres for a period of time sufficient to confound the tracking algorithm so that when the objects finally depart from each other the tracking algorithm is prone to provide erroneous estimates. This two-target track coalescence is discussed in this paper with the focus on the compound coalescence, when two identical tracks follow the midpoint of two well separated targets. First, the problem is illustrated on a classic problem of tracking two targets manoeuvring in a clutter, which is modeled as a nonlinear stochastic system. It is shown how, otherwise accurate and precise, estimates obtained by a standard particle filter eventually collapse leading to the coalescence of tracks. The phenomenon is given theoretical explanation by the analysis of Bayesian update operator acting on L2-space of probability densities that reveals that the coalescence is an unavoidable consequence of the probabilistic mixing between distributions describing positions of two targets. Finally, the practical consequences of these theoretical results are discussed together with potential approaches to deal with track coalescence in real applications.
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Trusz M., Tserakh U.,

GARCH(1,1) models with stable residuals

pp. 47-57

Abstract: The focus of this paper is the use of stable distributions for GARCH models. Such models are applied for the analysis of financial and economic time series, which have several special properties: volatility clustering, heavy tails and asymmetry of residuals distributions. Below we compare the properties of stable and tempered stable distributions and describe methodologies for constructing models and subsequent estimation of parameters using the maximum likelihood method. We also analyze an example of building models on real data in order to illustrate that tempered stable distributions could be used in financial time series models. Moreover, such distributions can show better results in comparison with traditionally used distributions.
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