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Design of computer exercises for security of information systems
Nakládalová, Kateřina ; Tomašov, Adrián (referee) ; Myška, Vojtěch (advisor)
Nowadays, many services are provided in an online environment through web applications. A large amount of data and sensitive information is thus transmitted through cyberspace, which can be stolen by attackers if the systems that handle it are not sufficiently secure. Education in Information Security is therefore crucial. To this end, a web application in the Java programming language was therefore created to demonstrate attacks aimed at it. The application is constructed using Spring framework and connected to PostgreSQL database. The most common attacks on web applications are demonstrated and described in this thesis which are Brute force attack, SQL injection attack, Cross-site scripting attack and Cross-site request forgery, access control vulnerabilities, sensitive data exposure, data inconsistency in web application and interference with web application parameter manipulation. Specific ways of implementing protection against these attacks are given to ensure their safe use. This work can be used as a useful source of information for web application developers and users who want to gain knowledge about the issues of cyber attacks and thereby reduce the risk of their occurrence.
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Accelerated sensor data analysis using an embedded system with a graphics processing unit
Maczkó, Adam ; Myška, Vojtěch (referee) ; Tomašov, Adrián (advisor)
The thesis deals with two main goals, namely the acceleration of data analysis and the subsequent visualization of this data. The purpose is to speed up the supplied application so that it is suitable for protecting optical infrastructures through real-time analysis of polarization state changes and visualization of its outputs. The thesis describes topics that are important in terms of accelerating computations on the graphics processor, particularly principles of parallelism, parallel programming, processes, threads, and parallel architectures. In addition, the thesis describes the capabilities of the Jetson Nano platform. The output of the thesis is an application that is capable of performing computations on the graphics processor and has a web interface for visualizing the analyzed data. The PyTorch library was used for acceleration on the graphics processor. Visualisation was achieved through the React framework in conjunction with the react-spectrogram and ApexCharts libraries.
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Mobile application for an intelligent beekeeping system
Pecár, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The aim of this thesis is to design and create an application which will allow beekeepers to manage their hives with a mobile phone.The reason for this is centralisation and clarification of all colected data from visits to the hive, where this data could be later used to create statistics.Furthermore, this app contains ways to notify the beekeeper that there is a need of intervention with the hive using their own alerts and statistics of selected properties of a hive. The result of this work is the previously described application.
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Universal data storage for IoT applications
Kadlíček, Matej ; Myška, Vojtěch (referee) ; Šteffan, Pavel (advisor)
The master’s thesis is written about the topic of data warehouse and the technology associated with it. The aim of the theses was to select programming tools for creating a website. Next aim was to design the functionality, appearence and datastorage. Subsequent implementation of design was done after. Theses was divided into 5 chapters. The first one includes selections of programming tools for a creating a backend. Second chapter includes design of database storage. Third chapter includes design of functionality and user interface. Fourth chapter includes implementation of the application and the last one includes testing.
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Smartphone application for optical fiber monitoring system
Vaverka, Jan ; Myška, Vojtěch (referee) ; Dejdar, Petr (advisor)
The main goal of this master thesis is to develop a multi-platform mobile application in the Flutter framework for iOS and Adnroid platforms. The application will remotely communicate with a fiber optic monitoring system through a server. In the introduction of the thesis, the issues of fiber optics and fiber optic security options are described. The next section describes the communication options between the application and the server, also the Flutter framework is described. In the practical part, the communication between the application and the server is proposed. A large part of the practical section is focused on the design and programming of the multiplatform application. The last part is focused on testing the communication between the application and the server.
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Detection Of Road Surface Defects From Data Acquired By A Laser Scanner
Myska, Vojtech
Research in the field of automatic detection of road surface defects has been relativelywidespread in recent years. Most of the existing works solve this issue by processing the imageacquired by camera technology. The contribution of this study is the proposal of the LRS-CNN algorithmfor the detection of defects on road surfaces based on their laser scans. The advantage ofLRS-CNN is the ability to detect so-called microcracks, which can not be recognized from camerarecordings. We have also found that transfer learning methods are not suitable for the use of road defectdetection from their laser scans. Our LRS-CNN algorithm has been trained on unique nonpublicdata and is able to achieve up to 99.33% of success depending on the type of task.
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Graph Convolutional Neural Networks For Sentiment Analysis
Myska, Vojtech
Commonly used approaches based on deep learning for sentiment analysis task operating over data in Euclidean space. In contrast with them, this paper presents, a novel approach for sentiment analysis task based on a graph convolutional neural networks (GCNs) operating with data in Non-Euclidean space. Text data processed by the approach have to be converted to a graph structure. Our GCNs models have been trained on 25 000 data samples and evaluated 5 000 samples. The Yelp data set has been used. The experiment is focused on polarity sentiment analysis task. Nevertheless, a relatively small training data set has been used, our best model achieved 86.12% accuracy.
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Interactive web presentation of audiovisual works
Paulech, Matúš ; Myška, Vojtěch (referee) ; Sikora, Pavel (advisor)
The bachelor thesis contains a presentation of audiovisual works processed by a calibrator based on neural networks (deep learning and artificial intelligence). The work deals with uploading video files to a web application and then playing them on a website. Data, that have been processed by neural networks, are added to the videos. This data is divided according to the classification into given tags, in which it is written what is currently in the image of video. These tags can be used to classify videos, and these classifications are also displayed in real time during video playback.
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Image data segmentation using deep neural networks
Hrdý, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The main aim of this master’s thesis is to get acquainted with the theory of the current segmentation methods, that use deep learning. Segmentation neural network that will be capable of segmenting individual instances of the objects will be proposed and created based on theoretical knowledge. The main focus of the segmentation neural network will be segmentation of electronic components from printed circuit boards.
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Segmentation of multiple sclerosis lesions using deep neural networks
Sasko, Dominik ; Myška, Vojtěch (referee) ; Kolařík, Martin (advisor)
Hlavným zámerom tejto diplomovej práce bola automatická segmentácia lézií sklerózy multiplex na snímkoch MRI. V rámci práce boli otestované najnovšie metódy segmentácie s využitím hlbokých neurónových sietí a porovnané prístupy inicializácie váh sietí pomocou preneseného učenia (transfer learning) a samoriadeného učenia (self-supervised learning). Samotný problém automatickej segmentácie lézií sklerózy multiplex je veľmi náročný, a to primárne kvôli vysokej nevyváženosti datasetu (skeny mozgov zvyčajne obsahujú len malé množstvo poškodeného tkaniva). Ďalšou výzvou je manuálna anotácia týchto lézií, nakoľko dvaja rozdielni doktori môžu označiť iné časti mozgu ako poškodené a hodnota Dice Coefficient týchto anotácií je približne 0,86. Možnosť zjednodušenia procesu anotovania lézií automatizáciou by mohlo zlepšiť výpočet množstva lézií, čo by mohlo viesť k zlepšeniu diagnostiky individuálnych pacientov. Našim cieľom bolo navrhnutie dvoch techník využívajúcich transfer learning na predtrénovanie váh, ktoré by neskôr mohli zlepšiť výsledky terajších segmentačných modelov. Teoretická časť opisuje rozdelenie umelej inteligencie, strojového učenia a hlbokých neurónových sietí a ich využitie pri segmentácii obrazu. Následne je popísaná skleróza multiplex, jej typy, symptómy, diagnostika a liečba. Praktická časť začína predspracovaním dát. Najprv boli skeny mozgu upravené na rovnaké rozlíšenie s rovnakou veľkosťou voxelu. Dôvodom tejto úpravy bolo využitie troch odlišných datasetov, v ktorých boli skeny vytvárané rozličnými prístrojmi od rôznych výrobcov. Jeden dataset taktiež obsahoval lebku, a tak bolo nutné jej odstránenie pomocou nástroju FSL pre ponechanie samotného mozgu pacienta. Využívali sme 3D skeny (FLAIR, T1 a T2 modality), ktoré boli postupne rozdelené na individuálne 2D rezy a použité na vstup neurónovej siete s enkodér-dekodér architektúrou. Dataset na trénovanie obsahoval 6720 rezov s rozlíšením 192 x 192 pixelov (po odstránení rezov, ktorých maska neobsahovala žiadnu hodnotu). Využitá loss funkcia bola Combo loss (kombinácia Dice Loss s upravenou Cross-Entropy). Prvá metóda sa zameriavala na využitie predtrénovaných váh z ImageNet datasetu na enkodér U-Net architektúry so zamknutými váhami enkodéra, resp. bez zamknutia a následného porovnania s náhodnou inicializáciou váh. V tomto prípade sme použili len FLAIR modalitu. Transfer learning dokázalo zvýšiť sledovanú metriku z hodnoty približne 0,4 na 0,6. Rozdiel medzi zamknutými a nezamknutými váhami enkodéru sa pohyboval okolo 0,02. Druhá navrhnutá technika používala self-supervised kontext enkodér s Generative Adversarial Networks (GAN) na predtrénovanie váh. Táto sieť využívala všetky tri spomenuté modality aj s prázdnymi rezmi masiek (spolu 23040 obrázkov). Úlohou GAN siete bolo dotvoriť sken mozgu, ktorý bol prekrytý čiernou maskou v tvare šachovnice. Takto naučené váhy boli následne načítané do enkodéru na aplikáciu na náš segmentačný problém. Tento experiment nevykazoval lepšie výsledky, s hodnotou DSC 0,29 a 0,09 (nezamknuté a zamknuté váhy enkodéru). Prudké zníženie metriky mohlo byť spôsobené použitím predtrénovaných váh na vzdialených problémoch (segmentácia a self-supervised kontext enkodér), ako aj zložitosť úlohy kvôli nevyváženému datasetu.
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