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Support tool for initial phase of penetration testing
Žáček, Dominik ; Gerlich, Tomáš (referee) ; Sikora, Pavel (advisor)
This thesis deals with the development of an advanced tool designed to make team penetration testing more efficient. The tool works by automatically assigning tasks to penetration testers based on skills and historical performance. The theoretical part of the thesis analyzes in detail various methods for solving the assignment problem, in particular the Hungarian method and linear programming. The theoretical part continues with the design of a two-step algorithm for task assignment. Then, the principle of the neural networks underlying the second step of the assignment is described in detail. Unique methods for generating two datasets have also been developed as part of the work. An interface for task assignment has been implemented and metrics to determine the quality of the assignment have been proposed. The result is a tool that significantly streamlines the assignment of tasks to penetration testers and increases the overall efficiency of penetration testing teams.
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Visual Anomaly Detection in Industrial Production
Hrabica, Jan ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This thesis deals with the problem of unary classifiers for anomaly detection in industrial production. It starts with a discussion of classification as a general problem, classification methods and some of their evaluations, and then discusses the main categories of architectures used. Practical part describes the process of scene creation for the acquisitions of a datesed. Acquired dataset is then used for teaching a classifier, on which is then performer a number of experiments to determine its performance.
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Generative Neural Network for Creating Synthetic Photorealistic Images
Hora, Adam ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
The main objective of this work is to select and design a neural network model that will be able to generate realistic images thematically fitting the selected dataset. The architecture used for the solution is Deep convolutional generative adversarial network. This network is than implemented in the Python programming language using the Tensorflow application programming interface and its included interface Keras. Finally, the model is trained on the selected dataset and the resulting generated images are presented. The final model and individual images are then evaluated using various quality assessment methods.
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Automated creation of deep neural network models for image classification
DOHNAL, Patrik
The aim of the thesis is to design and implement a system that can automatically create deep neural networks (DNN) models for image classification. Additionally, the aim is to review the current state-of-the-art and to validate the system's functionality on two different datasets. A genetic algorithm is used to find the best approximate DNN model. Additionally, several approaches to encode the genetic information of DNN models are explored. Furthermore, several experiments with the VGG-16 architecture were conducted to find the best possible system base. The thesis also includes a discussion on the practice of model training and how problems that can arise during the automatic training of DNN models are avoided. The implementation is written in Python with Tensorflow library.
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Artificial Intelligence Poetry
Jurečka, Otakar ; Zmrzlá, Petra (referee) ; Kotásek, Miroslav (advisor)
Tato bakalářská práce podává přehled o programech, které jsou určeny k psaní básnických textů. Obsahuje informace o tom, jak se s jednotlivými představenými aplikacemi pracuje, a jejich schopnosti jsou demonstrovány jejich zkušebním použitím. Tyto vygenerované texty jsou analyzovány. Je také vysvětleno fungování těchto počítačových programů. Dále je také zmíněna historie umělé inteligence, její prvotní myšlenka i její novější technologický vývoj.
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Vehicle Make and Model Recognition
Gregor, Adam ; Špaňhel, Jakub (referee) ; Juránek, Roman (advisor)
V praktické části diplomové práce byla realizována úloha ozpoznání výrobce a modelu vozidla (VMMR). V první části byla pro účely strojového učení sestavena datová sada vozidel sestávající se z obrázků z Internetu. Takto bylo získáno přes 6 milionů obrázků aut, autobusů, motorek a dodávek, použitelných pro úlohu VMMR. Dále byla v rámci ex- perimentů na část datové sady použita standardní klasifikace, kdy na enkodér navazuje klasifikační vrstva realizovaná použitím neuronové sítě, a přístup, kdy za pomocí metody supervised contrastive learning byly embeddingy z enkodérů shlukovány za účelem snazší klasifikace. Jelikož první uvedený přístup vracel přesnější výsledky, byl použit v dalších experimentech. V nich se použilo větší množství obrázků z naší datové sady k natrénování klasifikátoru pro VMMR. Další klasifikátory byly natrénovány na datových sadách Stan- ford Cars a Comprehensive cars. Posléze bylo při porovnávání funkčnosti klasifikátorů na různých datových sadách shledáno, že klasifikátor trénovaný na naší datové sadě si vedl nejlépe.
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Identification of specified segments in the audio signal using machine learning
Pařízek, Radim ; Galáž, Zoltán (referee) ; Zvončák, Vojtěch (advisor)
The bachelor thesis deals with the design of a system for the identification of natural environmental sounds in audio recordings. The datasets and models used for this type of tasks are surveyed and their structure is described. A system for the identification of sounds in one layer and in two layers has been proposed for seven selected labels. The classifier used for this system was created by fine-tuning a transformer model from the Hugging Face platform. The results of two training approaches and one identification system were evaluated.
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Approximation of functions determining colony activity using neural networks
Nevláčil, Jakub ; Ligocki, Adam (referee) ; Honec, Peter (advisor)
Bees as a primary pollinator are an indispensable contribution to global agriculture and food production. However, their numbers have been constantly declining in recent times, primarily due to climate change, parasites or the effect of pesticide use. Understanding their behavior and reliably determine their activity and health could significantly prevent or slow down their decline. That is why this work deals with the development of a device for the acquisition of useful data from beehives, which could be used to determine the activity and health of the bees. Furthermore, this work deals with analysis of the accumulated data using machine learning methods with an emphasis on determining the activity and health of the bees.
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Essays on Data-driven, Non-parametric Modelling of Time-series
Hanus, Luboš ; Vácha, Lukáš (advisor) ; Witzany, Jiří (referee) ; Ellington, Michael (referee) ; Trimborn, Simon (referee)
This thesis consists of four contributions to the literature on data-driven and non-parametric modelling of time series. In the first paper, we study the synchronisation of business cycles and propose a multivariate co-movement measure based on time-frequency cohesion. We suggest that economic inte- gration may lead to increased co-movement of business cycles, which may reflect the benefits of convergence and coordination of economic policies. The second paper presents a new methodology for identifying persistence in macroeconomic variables. Using time-varying frequency response func- tions, we identify heterogeneous persistence effects in US macroeconomic variables. The third and fourth papers propose data-driven techniques for probabilistic forecasting of time series using deep learning. We introduce a multi-output neural network that selects the most appropriate distribution for the data. The distributional neural network is valuable for modelling data with non-linear, non-Gaussian and asymmetric structures. The third paper demonstrates the usefulness of the method by estimating information-rich macroeconomic fan charts and distributional forecasts of asset returns. In the last paper, we present the distributional neural network to obtain the proba- bility distribution of electricity price...
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Intracranial hemorrhage localization in axial slices of head CT images
Kopečný, Kryštof ; Chmelík, Jiří (referee) ; Nemček, Jakub (advisor)
This thesis is focused on detection of intracranial hemorrhage in CT images using both one-stage and two-stage object detectors based on convolutional neural networks. The fundamentals of intracranial hemorrhage pathology and CT imaging as well as essential insight into computer vision and object detection are listed in this work. The knowledge of these fields of studies is a starting point for the implemenation of hemorrhage detector. The use of open-source CT image datasets is also discussed. The final part of this thesis is a model evaluation on a test dataset and results examination.
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