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Object tracking using computer vision
Klapal, Matěj ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This diploma thesis deals with posibilities of tracking object movement using computer vision algorithms. First chapters contain review of methods used for background subtraction, there are also listed basic detection approaches and thesis also mentions algorithms which allows tracking and movement prediction. Next part of this work informs about algoritms implemented in resulting software and its graphical user interface. Evaluation and comparison of original and modified algorithms is stationed at the end of this text.
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Text Mining Based on Artificial Intelligence Methods
Povoda, Lukáš ; Tučková,, Jana (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
This work deals with the problem of text mining which is becoming more popular due to exponential growth of the data in electronic form. The work explores contemporary methods and their improvement using optimization methods, as well as the problem of text data understanding in general. The work addresses the problem in three ways: using traditional methods and their optimizations, using Big Data in train phase and abstraction through the minimization of language-dependent parts, and introduction of the new method based on the deep learning which is closer to how human reads and understands text data. The main aim of the dissertation was to propose a method for machine understanding of unstructured text data. The method was experimentally verified by classification of text data on 5 different languages – Czech, English, German, Spanish and Chinese. This demonstrates possible application to different languages families. Validation on the Yelp evaluation database achieve accuracy higher by 0.5% than current methods.
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Identification and characterization of malicious behavior in behavioral graphs
Varga, Adam ; Burget, Radim (referee) ; Hajný, Jan (advisor)
Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.
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New methods for emotion recognition from text
Onderka, Jakub ; Burget, Radim (referee) ; Mašek, Jan (advisor)
This master’s thesis is about a method for sentimental analysis, especially machine learning methods without teacher. In detail are described method for semantic modeling LSA, pLSA a LDA. It was created a LDA implementation in Java language, which was used to emotional classification of 860 Czech documents to six different emotional categories. Maximal accuracy was 24 % if optimized parameters was used.
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Image search using similarity measures
Harvánek, Martin ; Mašek, Jan (referee) ; Burget, Radim (advisor)
There are these methods implemented: circular sectors, color moments, color coherence vector and Gabor filters, they are based on low-level image features. These methods were evaluated after their optimal parameters were found. The finding of optimal parameters of methods is done by measuring of classification accuracy of learning operators and usage of operator cross validation on images in program RapidMiner. Implemented methods are evaluated on these image categories - ancient, beach, bus, dinousaur, elephant, flower, food, horse, mountain and natives, based on total average precision. The classification accuracy result is increased by 8 % by implemented modification (HSB color space + statistical function median) of original method circular sectors. The combination of methods color moments, circular sectors and Gabor filters with weighted ratio gives the best total average precision at 70,48 % and is the best method among all implemented methods.
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Monitoring of Internet servers
Pospíchal, Ondřej ; Burget, Radim (referee) ; Komosný, Dan (advisor)
This thesis deals with the availability of devices and the availability of remote connection to devices via SSH in the network PlanetLab worldwide. The theoretical part consists of the description of the network PlanetLab, services ping and SSH, program environment, in which was the practical part made, and parallelization of processes, which was used during optimizing the program. The practical part describes the creation, functions and outputs of the application, which continuously tests servers in the network PlanetLab. Analyzing the results of the program it was found that the availability of PlanetLab devices is relatively stable, but unsatisfactory. More than half of the devices of this network do not respond to neither one of mentioned services.
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Nonlinear analysis and prediction of network traffic
Člupek, Vlastimil ; Burget, Radim (referee) ; Vychodil, Petr (advisor)
This thesis deal with an analysis of network traffic and its properties. In this thesis are discussed possibilities of prediction network traffic by FARIMA model, theory of chaos with Lyapunov exponent and by neural networks. The biggest attention was dedicated to prediction network traffic by neural networks. In Matlab with using Neural Network Toolbox were created, trained and tested recurrent networks for prediction specific types of network traffics, which was captured on local network. There were choosed Elman network, LRN and NARX network to test the prediction of network traffic, results were discussed. Thesis also introduce area of application ability prediction of network traffic, there is introduce design of system for dynamic allocation bandwidth with particular description of its prediction part. Thesis also states possible use designed system for dynamic allocation of bandwidth.
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Echo state neural network for stock market prediction
Pospíchal, Ondřej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.
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Internet coordinating systems
Krajčír, Martin ; Komosný, Dan (referee) ; Burget, Radim (advisor)
Network coordinates (NC) system is an efficient mechanism for prediction of Internet distance with limited number of measurement. This work focus on distributed coordinates system which is evaluated by relative error. According to experimental results from simulated application, was created own algorithm to compute network coordinates. Algorithm was tested by using simulated network as well as RTT values from network PlanetLab. Experiments show that clustered nodes achieve positive results of synthetic coordinates with limited connection between nodes. This work propose implementation of own NC system in network with hierarchical aggregation. Created application was placed on research projects web page of the Department of Telecommunications.
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Increasing quality of facial images using sequence of images
Svorad, Adam ; Mezina, Anzhelika (referee) ; Burget, Radim (advisor)
Diplomova praca sa zameriava na oblast zaostrovania obrazkov tvari. V teoretickej casti prace budu prezentovane moderne metody zaostrovania obrazkov pomocou jedineho obrazku a metody editacie obrazkov. Prakticka cast sa zameria na pristupy rekonstrukcie obrazkov zo sekvencie poskodenych obrazkov. Viacere modely neuronovych sieti so vstupom pre viacero obrazkov budu zhotovene a vyhodnotene. Alternativny pristup v podobe balika nastrojov na editaciu obrazkov bude taktiez predstaveny. Tieto nastroje budu vyuzivat najmodernejsie pristupy k editacii obrazkov s cielom spojit vizualne prvky tvari zo vstupnej sekvencie obrazkov do jedneho finalneho vystupu. V zavere prace budu vsetky metody navzajom porovnane.
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