Application of Unsupervised Learning Methods in Graph Similarity Search

Cieľom tejto diplomovej práce bolo v spolupráci s firmou Avast navrhnúť systém, ktorý dokáže dolovať znalosti z databázy grafov pomocou metód učenia bez učiteľa. Grafy, určené pre dolovanie, popisujú chovanie počítačových systémov a do databázy prichádzajú anonymne od používateľov softvérových produktov firmy. Grafom v databáze je možné priradiť jednu z dvoch tried: čistý graf alebo malware (škodlivý) graf. Úlohou navrhnutého samoučiacieho systému je nad grafovou databázou nájsť zhluky grafov, v ktorých sa triedy grafov nemiešajú. Zhluky grafov, v ktorých sa nachádza iba jedna trieda grafov, sa dajú interpretovať ako rôzne typy čistých alebo malware grafov a sú užitočným zdrojom ďalších analýz nad grafmi. Pre ohodnotenie kvality zhlukov bola navrhnutá vlastná metrika pomenovaná ako jednofarebnosť. Metrika hodnotí kvalitu zhlukov na základe toho ako veľmi sa v zhlukoch miešajú čisté a malware grafy. Najlepšie výsledky metrika dosiahla, keď boli vektorové reprezentácie grafov vytvorené modelom hlbokého učenia (variačným grafovým autoenkodérom s dvomi relačnými grafovými konvolučnými operátormi)  a pre zhlukovanie nad vektormi bola použitá bezparametrická metóda MeanShift.Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one class of graphs can be interpreted as different types of clean or malware graphs and they are a useful source of further analysis on the graphs. To evaluate the quality of the clusters, a custom metric, named as monochromaticity, was designed. The metric evaluates the quality of the clusters based on how much clean and malware graphs are mixed in the clusters. The best results of the metric were obtained when vector representations of graphs were created by a deep learning model (variational  graph autoencoder with two relation graph convolution operators) and the parameterless method MeanShift was used for clustering over vectors.

Human Factor in Conversation Between Subordinates and Managers

Communication between a pilot and air traffic controller (ATC) is one of the most important factors in the aviation safety. Communication is the exchange of information based on mutual reliance, information precision and accuracy of on both communicating parties

Flight Planning with Respect to Meteorological Conditions

The paper “Flight planning with respect to meteorological conditions” describes how flight planning in commercial air transport depends on meteorological conditions. In first part, the article describes satellite products for meteorological analyses such as IR technologies. In next parts authors are talking about thunderstorm, icing and volcanic ash detection methods in atmosphere. In the last part of the paper are shown some modern diagnostic system for identifying dangerous meteorological phenomena and their potential for flight panning

Construction and Safety Aspects of Glass Used in Aviation Transport

Glass is an essential component of several sectors which significantly form our present-day economy. These include machining, the chemical industry, the electrotechnology industry, construction, the automobile industry, the aviation industry and many others. The aim of this article is to provide some detail about and explain the technology used for manufacturing glass which is subsequently used in the automobile and aviation industries and the specific adaptations which help to improve its characteristics. A general trend is increasing the share of final products having high added value, which is done by finishing glass with special processes of surface modifications, mechanical treatment and thermodynamic processes with targeted special traits, including a wide scale of functional coatings

Onedata4Sci: Life science data management solution based on Onedata

Life-science experimental methods generate vast and ever-increasing volumes of data, which provide highly valuable research resources. However, management of these data is nontrivial and applicable software solutions are currently subject to intensive development. The solutions mainly fall into one of the two groups: general data management systems (e.g. Onedata, iRODS, B2SHARE, CERNBox) or very specialised data management solutions (e.g. solutions for biomolecular simulation data, biological imaging data, genomic data). To bridge this gap between them, we provide Onedata4Sci, a prototype data management solution, which is focused on the management of life science data and covers four key steps of the data life cycle, i.e. data acquisition, user access, computational processing and archiving. Onedata4Sci is based on the Onedata data management system. It is written in Python, fully containerised, with the support for processing the stored data in Kubernetes. The applicability of Onedata4Sci is shown in three distinct use cases -- plant imaging data, cellular imaging data, and cryo-electron microscopy data. Despite the use cases covering very different types of data and user patterns, Onedata4Sci demonstrated an ability to successfully handle all these conditions. Complete source codes of Onedata4Sci are available on GitHub (https://github.com/CERIT-SC/onedata4sci), and its documentation and manual for installation are also provided

Odstranění rozmazání pomocí dvou snímků s různou délkou expozice

In the presented work we study the methods of image deblurring using two images of the same scene with different exposure times, focusing on two main approach categories, so called deconvolution and non-deconvolution methods. We present theoretical backgrounds on both categories and evaluate their limitations and advantages. We dedicate one section to compare both method categories on test data (images) for which we our MATLAB implementation of the methods. We also compare the effectiveness of said methods against the results of a selected single-image de-noising algorithm. We do not focus at computational efficiency of algorithms and work with single-channel images only

Porovnání bílkovinné stability u bílých vín

Thesis deals with comparing the protein stability of white wines. The aim of this thesis is to compare the stability of the proteins for seven particular varieties of the white wines using sodium-calcium bentonite Puranit. Size of the batch 150 g / l of the bentonite was used in the research. Theoretical part are briefly described the nitrogen substances, individual factors effect in the condensation of the proteins, the impact of aging of the wine on the soft feces on its protein stability, opportunities of the using the fining enological agent - bentonite, silicic acid salts, gelatin, tannin, isinglass. The thesis are characterized individual in the research applied the varieties of the white wines. In the practical part is detailed described the procedure of the carried out the experiment, the individual results are reported in the table and graphs. Research showed that after application of the bentonite is significantly changed the protein stability of individualt types of the wine

Odstranění rozmazání pomocí dvou snímků s různou délkou expozice

In the presented work we study methods of image deblurring using two images of the same scene with different exposure times, focusing on two main approach categories, the so called deconvolution and non-deconvolution methods. We present theoretical backgrounds on both categories and evaluate their limitations and advantages. We dedicate one section to a comparison of both method categories on test data (images) for which we use a MATLAB implementation of the methods. We also compare the effectiveness of said methods against the results of a selected single- image de-noising algorithm. We do not focus at computational efficiency of algorithms and work with grayscale images only

Application of Unsupervised Learning Methods in Graph Similarity Search

Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one class of graphs can be interpreted as different types of clean or malware graphs and they are a useful source of further analysis on the graphs. To evaluate the quality of the clusters, a custom metric, named as monochromaticity, was designed. The metric evaluates the quality of the clusters based on how much clean and malware graphs are mixed in the clusters. The best results of the metric were obtained when vector representations of graphs were created by a deep learning model (variational  graph autoencoder with two relation graph convolution operators) and the parameterless method MeanShift was used for clustering over vectors

Analysis of Parameters of Packet Classification Rule Sets

Témou bakalarskej práce je analýza pravidiel používaných pre klasifikáciu paketov v počítačových sieťach. Ako klasifikácia paketov funguje a akú úlohu pri klasifikácií majú klasifikačné pravidlá je popísané v teoretickej časti práce. Formáty klasifikačných pravidiel používaných v reálnych nástrojoch v praxi sú v tejto časti opísané taktiež. Na základe týchto znalostí bol navrhnutý a implementovaný nástroj, ktorý umožňuje analyzovať položky klasifikačných pravidiel štandardnej IP pätice zo sád pravidiel s ľubovoľnými formátmi. Výstupom implementovaného nástroja je parametrový súbor, ktorého rôzne štatistiky a rozdelenia pravdepodobností skúmaných položiek pravidiel popisujú kompozíciu analyzovanej sady pravidiel. Tento parametrový súbor je možné použiť pre generovanie umelých sád pravidiel v nástrojoch ClassBench a ClassBench-ng. V záverečnej časti práce sú skúmané parametrové súbory vzniknuté implementovaným nástrojom z dostupných reálnych sád pravidiel.A theme of bachelor's thesis is an analysis of rules used for packet classification in computer networks. A theoretical part of the thesis introduces packet classification and describes the role of classification rules. This part also presents the format of classification rules utilized in real tools. Based on these information, a tool able to analyze IP 5-tuple classification rules in any format was designed and implemented. Output of the implemented tool is a parameter file containing different statistics and probability distributions of examined rule sets. This parameter file can be used for generating synthetic rule sets using ClassBench and ClassBench-ng tools. The final part of the thesis examines parameter files created by the implemented tool from available real rule sets.