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Big Data Processing in Industry 4.0
Stredánsky, Dávid ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
Main goal of this thesis is to create application for industrial big data processing. Final application uses bearing vibration data. The application's design is inspired by Lambda architecture for big data processing. The application monitors data recieved from sensors in real time and enables periodic batch processing. Known methods from bearing condition monitoring, such as root mean square, deviation or skewness extraction are used in batch processing. Data prediction method Prophet is tested out in this thesis. Final web appli- cation is written in the Python language with the use of Dash framework and results are stored in MySQL database.
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Recommender System for Web Articles
Kočí, Jan ; Kesiraju, Santosh (referee) ; Fajčík, Martin (advisor)
Tématem této bakalářské práce jsou doporučovací systémy pro webové články. Tato práce nejdříve uvádí nejpopulárnější metody z této oblasti a vysvětluje jejich principy, následně navrhuje požití vlastní architektury, založené na neuronových sítích, která aplikuje metodu Skip-gram negative sampling na problematiku doporučování. V další části pak implementuje tuto architekturu společně s několika dalšími modely, požívající algoritmus SVD, collaborative filtering s algoritmem ALS a také metodu Doc2Vec k vytvoření vektorové reprezentace z obsahu získaných článků. Na závěr vytváří tři evaluační metriky, konkrétně metriky RANK, Recall at k a Precision at k, a vyhodnocuje kvalitu implementovaných modelů srovnáním výsledků s nejmodernějšími modely. Kromě toho také diskutuje o roli a smyslu doporučovacích systémů ve společnosti a uvádí motivaci pro jejich používání.
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Support for Codenames Game on Mobile Phone with OS Android
Hurta, Martin ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an support application for word association board game Codenames on mobile phones with operating system Android. The solution consists of detection and recognition of the game board using the OpenCV and Tess-two libraries and Google Firebase ML Kit tools and providing support during the game, including an optional level of assistance and the ability to play on multiple devices with Google Play Games services. These features motivate the user to further use the application and provide data in~the form of generated game records, that are useful for further development and validation of association models or strategies for automatic playing.
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Combat Management in Starcraft II Game by Means of Artificial Intelligence
Krajíček, Karel ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis focuses on the use of Artificial Intelligence and design of working module in Real-Time Strategy (RTS) game, StarCraft II. The proposed solution uses Neural Network and Q-learning for combat management. For implementation, the StarCraft 2 Learning Environment has been used as a means of communication between the designed system and the game. Evaluation of the system is based on its ability to make progress over time.
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Active Learning with Neural Networks
Beneš, Štěpán ; Fajčík, Martin (referee) ; Hradiš, Michal (advisor)
The topic of this thesis is the combination of active learning strategies used in conjunction with deep convolutional networks in image recognition tasks. The goal is to observe the behaviour of selected active learning strategies in a wider array of conditions. The first section of the thesis is dedicated to the theory of active learning, followed by the motivation and challenges of combining them with convolutional neural networks. The goal of this thesis is achieved by a series of experiments, in which the behaviour of active learning strategies is tested for dependencies on the difficulty of the dataset, quality of the learning model, number of training epochs, the size of a batch of samples added in each iteration, the oracle's consistency and the usage of pseudo-labeling technique. The results show the dependency of continuous active learning on the number of training epochs in each iteration and the difficulty of a given dataset. Chosen strategies also seem somewhat resistant to the oracle's faults. The benefits of using pseudo-labeling come hand in hand with the quality of the learning model. Finally, traditional active learning strategies have shown in some cases that they are capable of keeping the pace with modern, tailored strategies.
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Incremental Web Crawling With Bubing System
Ondřej, Karel ; Fajčík, Martin (referee) ; Škoda, Petr (advisor)
This bachelor thesis deals with modification of BUbiNG system for incremental crawling. The paper describes the main problems related to incremental Internet crawling and the use of other open-source systems for incremental crawling. As a result, BUbiNG system supports re-visiting pages using two commonly used strategies. The first strategy always re-visits page after the same interval. The second strategy adjusts the interval between visits according to the frequency of page changes.
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Reinforcement Learning for Starcraft Game Playing
Chábek, Lukáš ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This work focuses on methods of machine learning for playing real-time strategy games. The thesis applies mainly methods of Q-learning based on reinforcement learning. The practical part of this work is implementing an agent for playing Starcraft II. Mine solution is based on 4 simple networks, that are colaborating together. Each of the network also teaches itself how to process all given actions optimally. Analysis of the system is based on experiments and statistics from played games.
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Support for Codenames Game on Mobile Phone with OS Android
Grossmann, Jan ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This bachelor's thesis deals with creation of an application for support for the Codenames game on mobile phone with Android operating system. Application helps user with game strategy and simplify selection of the clue. First I discuss existing solutions and their imperfections. Based on this experience, I analyze designed solution and then, the very implementation with usage of Java programming language, involving storing data with database system or optical recognition. Finally, I undertake user testing, which I also describe in detail.
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Automation of Verification Using Artificial Neural Networks
Fajčík, Martin ; Husár, Adam (referee) ; Zachariášová, Marcela (advisor)
The goal of this thesis is to analyze and to find solutions of optimization problems derived from automation of functional verification of hardware using artificial neural networks. Verification of any integrated circuit (so called Design Under Verification, DUV) using technique called coverage-driven verification and universal verification methodology (UVM) is carried out by sending stimuli inputs into DUV. The verification environment continuously monitors percentual coverage of DUV functionality given by the specification. In current context, coverage stands for measurable property of DUV, like count of verified arithemtic operations or count of executed lines of code. Based on the final coverage, it is possible to determine whether the coverage of DUV is high enough to declare DUV as verified. Otherwise, the input stimuli set needs to change in order to achieve higher coverage. Current trend is to generate this set by technique called constrained-random stimulus generation. We will practice this technique by using pseudorandom program generator (PNG). In this paper, we propose multiple solutions for following two optimization problems. First problem is ongoing modification of PNG constraints in such a way that the DUV can be verified by generated stimuli as quickly as possible. Second one is the problem of seeking the smallest set of stimuli such that this set verifies DUV. The qualities of the proposed solutions are verified on 32-bit application-specific instruction set processors (ASIPs) called Codasip uRISC and Codix Cobalt.
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Static Analysis of CodAL Language Source Code
Fajčík, Martin ; Přikryl, Zdeněk (referee) ; Hynek, Jiří (advisor)
The goal of bachelor's thesis is to design and implement extensions devoted to source code static analysis and automatic corrections used in CodAL language editors. This form of analysis is convenient e.g. for the source code semantic checks. The thesis consists of theoretical and practical part. Role of the theoretical part is to overview with extension development related to Eclipse platform, especially with the CodAL language editor, CodAL language itself and to define problems of this language which are suitable to be solved on the static analysis level. Practical part includes specific implementation details of the particular static analysis elements and automatic corrections. These extended CodAL language editors are available in integrated development environment Codasip Studio based first and foremost on the Eclipse platform and project CDT. Codasip Studio has been developed by company Codasip Ltd. in collaboration with Lissom research team.
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