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Column-oriented and Image Data Format Benchmarks
Tarageľ, Marián ; Bartl, Vojtěch (referee) ; Špaňhel, Jakub (advisor)
Cieľom tejto bakalárskej práce je ohodnotiť rôzne dátové formáty pre ukladanie tabulárnych a obrazových dát. K zvládnutiu tejto úlohy táto práca navrhuje nový benchmark dátových formátov. Benchmark je rozdelený do troch benchmarkových skupín. Tie zahŕňajú benchmark nekomprimovaných tabulárnych formátov, komprimovaných tabulárnych formátov a benchmark obrazových úložísk. Celkové výsledky tabulárnych benchmarkov naznačujú, že najlepší tabulárny formát pre rýchle ukladanie a čítanie je Feather a najviac pamäťovo efektívny je Parquet. Výsledky benchmarkov ukladania obrázkov ukazujú, že najrýchlejšie úložisko obrázkov je v SQLite a najmenej miesta vyžaduje formát PNG. Výsledky tejto práce môžu prispieť k lepšiemu pochopeniu správania sa rôznych dátových formátov a pomôcť pri výbere správneho formátu pre tabulárne a obrazové dáta.
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Detekce karet při turnajích v pokru
Kovalets, Vladyslav ; Šilling, Petr (referee) ; Vaško, Marek (advisor)
This bachelor's thesis focuses on the development of an advanced system for automatic recognition and registration of playing cards from video recordings of poker games. The technology of convolutional neural networks, specifically the YOLO network, was chosen as the basic tool. It enables effective identification of cards on the table and in the hands of players even under challenging conditions. The work involved creating an extensive dataset for training and testing the card detector, which achieved a recognition accuracy of 98.7%. An algorithm was designed to minimize detector errors and improve the overall accuracy of the system. The results of the study suggest that the developed system has potential for use in practice.
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Image Inpainting using Deep Learning
Zobaník, Radek ; Kubík, Tibor (referee) ; Šilling, Petr (advisor)
In this thesis, an application was developed for testing and comparing methods for completing missing parts of an image using deep learning, and two methods were trained, pconv with convolutional architecture, and AOT-GAN with GAN architecture. The thesis describes the design of the finished application, its functionality, and important implementation details. A dataset was selected on which the chosen models were optimally trained. Experiments were made on the AOT-GAN model to investigate the impact of the number of AOT blocks in generator on the resulting completed image. All experiments were qualitatively and quantitatively compared. The results showed respectable outcomes when working with natural scenery.
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Detekce malware domén pomocí metod strojového učení
Ebert, Tomáš ; Poliakov, Daniel (referee) ; Hranický, Radek (advisor)
This bachelor thesis deals with the detection of malware domains using machine learning methods learning based on various information obtained about the domain (DNS records, geolocation data etc.). With the rapid proliferation of threats, not only in the form of malware, the current examples are often approaches are insufficient, either in terms of the speed of detection of malware domains or in terms of overall recognition,whether a domain is dangerous. The output of this work is a trained XGBoost classifier model, which has the advantage of fast and efficient real-time detection over blacklist detection, which often acquires domain data with a week delay. For this model, 131,000 malware domains were obtained, using which obtain a high-value model. Using experiments, a score of F1 of 96.8786 % for the XGBoost classifier with a false positive detection rate of 0.004887.
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Semantic segmentation of aerial images
Pazdera, Jiří ; Králík, Jan (referee) ; Adámek, Roman (advisor)
This work deals with semantic segmentation of aerial images and their subsequent use for route planning. The first part represents an introduction to this issue and a theoretical description of the current state of knowledge. The second part describes testing of available segmentation methods, the development of custom dataset, and the training of an existing neural network model. Finally, the possibility of route planning using an appropriate algorithm is demonstrated.
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Automatická kontrola dopravního značení
Čechmánek, Roman ; Klíma, Ondřej (referee) ; Musil, Petr (advisor)
The aim of this work is to create a cost-effective tool capable that would be able to automate the process of traffic sign control. This includes working with records of drives on land communications, created using inexpensive recording devices such as GoPro action cameras or certain dashcams. The control is based on the system localized traffic signs and historical traffic sign mapping data. The result of the work is a system whose input consists of drive records and historical data, and whose output is two files containing information about the inspection results. The first of these is a GEOJSON file, suitable for further processing of the collected data, and an HTML file that provides a simple user interface visualizing the inspection results on an interactive web map.
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Detekce nežádoucích požadavků na webu
Slovák, Michal ; Setinský, Jiří (referee) ; Hranický, Radek (advisor)
This thesis deals with the development of a classifier for detecting unwanted requests to a web server using machine learning methods. This approach requires the creation of an annotated dataset and the analysis of common features and characteristics of illegitimate requests that can be used to categorize them. Furthermore, the paper deals with the selection of an appropriate classification algorithm. The resulting model achieves a weighted F1 score of 99.95 %, is reliable and fast, making it suitable for practical deployment.
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Tool for Processing Municipal Council Voting Data
Janošík, Adam ; Hynek, Jiří (referee) ; Zaklová, Kristýna (advisor)
The aim of this work was to design a generic tool for data transformation of input data into data model of reference. Tool was created to be applicable for any possible dataset. Developed solution was implemented as a Python script, which, according to specified meta file, performs data transformation over input data. The correctness of data transformation into reference model was verified by importing the data in an visualizing app that allowed to check the correctness of the transformed data. Developed solution makes data transformation of different data easier. Tool can be used as a part of the project of data visualization.
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Phishing Webpage Detection using Machine Learning Methods
Polóni, Peter ; Poliakov, Daniel (referee) ; Hranický, Radek (advisor)
Phishingové stránky sú veľmi nebezpečnou hrozbou, čo znamená, že úspešná a spoľahlivá detekcia týchto stránok je veľmi doležitá. Tieto hrozby detekujem s využitím prístupu strojového učenia. Tento prístup je efektívny a dokáže odhaliť aj hrozby, s ktorými sa nikdy predtým nestretol. Ako dôveryhodné zdroje dát URL som využil OpenPhish a PhishTank. Z dôveryhodných URL som nazbieral HTML a JavaScript kód webových stránok. Zber dát som vykonal pomocou programu, ktorý som pre tento účel vytvoril. S využitím vektoru príznakov, ktorý sa skladá z 82 numerických príznakov, som vytvoril štyri klasifikátory. Následne som ich vyladil a experimentálne overil presnosť ich predikcií. Najpresnejší model je XGBoost klasifikátor, ktorý dosiahol vyváženú presnosť až 97.03% a FPR 2.22%, počas predikovania dát, ktoré nikdy predtým nevidel. Výsledky ukazujú, že tento prístup detekcie je schopný identifikovať phishingovú stránku aj v praxi. Toto som overil aj implementovaním webového rozšírenia pre prehliadač Chrome, ktoré detekuje phishigové stránky. Toto rozšírenie je vytvorené nad rámec zadania.
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Analýza malware na úrovni síťových toků
Brázda, Šimon ; Setinský, Jiří (referee) ; Poliakov, Daniel (advisor)
This thesis explores freely available datasets and investigates their applicability to training machine learning models. The ipfixprobe tool was used to extract data from the dataset and the Python language was used for further implementation. In the theoretical part, basic application protocols, network monitoring capabilities at the flow level are discussed. Furthermore, different types of malware and types of machine learning models applicable to network flow classification were discussed. Subsequently, these models were used to test the applicability of the selected dataset, which was thus validated.
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