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Diagnosing anxiety and depression from brain electroencephalogram (EEG) signals
Osvald, Martin ; Jaroš, Marta (referee) ; Malik, Aamir Saeed (advisor)
Duševné poruchy predstavujú širokú škálu emócií v našej spoločnosti. Tieto psychické stavy významne ovplyvňujú kognitívne, emocionálne a behaviorálne fungovanie jednotlivcov. Bežné duševné poruchy sa vzťahujú na dve hlavné diagnostické kategórie: depresívne poruchy a úzkostné poruchy. Cielom tejto práce je nájsť novú metódu na detekciu či daný pacient trpí úzkosťou alebo depresiou pomocou klasifikácie EEG. V tejto práci používame kombináciu genetických algoritmov a modelov z hlbokého učení.
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Analysis of Topics and Spreading of Disinformation from Propagandist Web Pages
Kyjovský, Dalibor ; Fajčík, Martin (referee) ; Ondřej, Karel (advisor)
This thesis deals with the issue of disinformation on propaganda websites in the Czech Republic and the design of a system that should be used to analyze and recognize the disinformation character of the content of these websites. The proposed and implemented system is able to download and archive data from propaganda websites and then analyze their content. The system mainly uses knowledge-based and network analyses. The aim of the thesis is to use modern technologies for detecting disinformation in the media. The results may help in further research and in preventing the spread of disinformation.
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Application for Detection of Fake News
Zádrapa, Jan ; Holop, Patrik (referee) ; Malinka, Kamil (advisor)
Problém Fake News je aktuálně jeden z největších problémů moderní společnosti. Miliony lidí denně konzumují zavádějící informace a ani o tom nemusí vědět. Tento problém způsobuje riziko po celém světě, protože přispívá k polarizaci společnosti a ovlivňuje volby pomocí propagandy. Bohužel, zatím není vytvořen dostatek spolehlivých automatizovaných nástrojů pro český jazyk, které by dokázaly tento problém řešit. Tato práce má za cíl takovýto nástroj vytvořit a tím pomoci lidem, kteří denně propadají Fake News.
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Segmentation of logical units in text
Kostelník, Martin ; Kišš, Martin (referee) ; Beneš, Karel (advisor)
Cílem projektu bylo vytvořit systém pro automatickou segmentaci textu do logických celků. Práce staví na systému PERO-OCR a cílí na zlepšení zpracovávání českých historických dokumentů a jejich vyhledávačů používaných knihovníky a vědci. Práce zahrnovala vytvoření a anotace vlastní datové sady složené celkem z 4044 stránek z knih, slovníků a novin. K problému segmentaci textu je přistoupeno inovativních přístupem, kdy je brán jako shlukovací problém jednotlivých řádků textu. Metoda je dvoufázová: nejprve probíhá detekce regionů textu pomocí modelu YOLOv8 a následuje jejich spojení grafovou neuronovou sítí. Vyhodnocení je provedeno pomocí shlukovací metriky V-measure a na testovacím datasetu dosahuje hodnot 77.93 % pro knihy, 95.79 % pro slovníky a 90.23 % pro noviny.
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Measurement design in modern packet, transport, and access technologies
Galovič, Dominik ; Michálek, Jakub (referee) ; Škorpil, Vladislav (advisor)
The diploma thesis focuses on the capabilities of the VeEx VePAL Tx300s analyzer and network analysis. In the theoretical part, the thesis focuses on the most common tests used for analyzing network parameters of networks and its devices. Their principles, advantages, and disadvantages are described. The choice of the tests is deliberately selected to match the capabilities of the analyzer itself. Its basic functions are described in this diploma thesis. After understanding the functioning of individual tests and the analyzer itself, two laboratory tasks were created to familiarize potential students, for whom these tasks were designed, with the basic operation of the analyzer and to acquaint them with basic as well as advanced testing of network parameters, whether of active network elements or the laboratory network itself. An addition is the use of the VeEx VePAL tx300 analyzer, which was purchased previously, and the change of an already made laboratory task.
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Automatic Humor Evaluation
Katrňák, Josef ; Ondřej, Karel (referee) ; Dočekal, Martin (advisor)
The aim of this thesis is to create a system for automatic humor evaluation. The system allow to predict humor and category for english input. The main essence is to create a classifier and train the model with the created datasets to get the best possible results. The classifier architecture is based on neural networks. The system also includes a web user interface for communication with the user. The result is a web application linked to a classifier that allows user input to be evaluated and user feedback to be provided.
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Email spam filtering using artificial intelligence
Safonov, Yehor ; Uher, Václav (referee) ; Kolařík, Martin (advisor)
In the modern world, email communication defines itself as the most used technology for exchanging messages between users. It is based on three pillars which contribute to the popularity and stimulate its rapid growth. These pillars are represented by free availability, efficiency and intuitiveness during exchange of information. All of them constitute a significant advantage in the provision of communication services. On the other hand, the growing popularity of email technologies poses considerable security risks and transforms them into an universal tool for spreading unsolicited content. Potential attacks may be aimed at either a specific endpoints or whole computer infrastructures. Despite achieving high accuracy during spam filtering, traditional techniques do not often catch up to rapid growth and evolution of spam techniques. These approaches are affected by overfitting issues, converging into a poor local minimum, inefficiency in highdimensional data processing and have long-term maintainability issues. One of the main goals of this master's thesis is to develop and train deep neural networks using the latest machine learning techniques for successfully solving text-based spam classification problem belonging to the Natural Language Processing (NLP) domain. From a theoretical point of view, the master's thesis is focused on the e-mail communication area with an emphasis on spam filtering. Next parts of the thesis bring attention to the domain of machine learning and artificial neural networks, discuss principles of their operations and basic properties. The theoretical part also covers possible ways of applying described techniques to the area of text analysis and solving NLP. One of the key aspects of the study lies in a detailed comparison of current machine learning methods, their specifics and accuracy when applied to spam filtering. At the beginning of the practical part, focus will be placed on the e-mail dataset processing. This phase was divided into five stages with the motivation of maintaining key features of the raw data and increasing the final quality of the dataset. The created dataset was used for training, testing and validation of types of the chosen deep neural networks. Selected models ULMFiT, BERT and XLNet have been successfully implemented. The master's thesis includes a description of the final data adaptation, neural networks learning process, their testing and validation. In the end of the work, the implemented models are compared using a confusion matrix and possible improvements and concise conclusion are also outlined.
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Integration of advanced artificial intelligence methods with log management security systems
Sedláček, Jiří ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
Cyber security is a very important aspect of everyone’s daily life. With the ever-expanding cyberspace and its growing influence on the real world, the issue of cyber security is all the more important. The theoretical part of the thesis describes the basic aspects of security monitoring. Also, the process of collecting event logs and their management is briefly described. An important means of security monitoring is the management of security information and events. Its advantages, disadvantages and possible improvements with artificial intelligence are discussed. Security orchestration, automation and response functions are also mentioned in the theoretical part. Machine learning techniques such as neural networks and deep learning are also mentioned. This section also focuses on cyber operations centres in terms of improving the efficiency of human ”manual” labour. A survey of possible machine learning techniques for this use case has been conducted, as the lack of human resources is a critical issue within security operations centres. The practical part of the thesis involves setting out a goal (text sequence classification) that could make the work considerably easier in terms of manually categorizing event logs according to their source. For this set task, security monitoring related data was collected from different log sources. In the practical part, the methods for processing this data are also described in detail. Subsequently, a suitable neural network model was selected and its technical description was performed. Finally, the final data processing and the process of training, validating and testing the model are described. Three scenarios were developed for this process, which are then described in detail in the measurement results.
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Visual Question Answering
Kocurek, Pavel ; Ondřej, Karel (referee) ; Fajčík, Martin (advisor)
Visual Question Answering (VQA) je systém, kde je vstupem obrázek s otázkou a výstupem je odpověď. Navzdory mnoha pokrokům ve výzkumu se VQA, na rozdíl od počítačově generovaných popisů obrázků, v praxi používá jen zřídka. Cílem této práce je zúžit mezeru mezi výzkumem a praxí. Z tohoto důvodu byla kontaktována komunita zrakově postižených a byla jim nabídnuta demonstrativní aplikace VQA a následně byla vytvořena mobilní aplikace. Byla provedena studie s 20 účastníky z komunity. Nejprve účastníci zkoušeli demonstrativní aplikaci po dobu dvou týdnů a následně byli požádáni o vyplnění dotazníku. 80 % respondentů hodnotilo přesnost aplikace VQA jako dostatečnou nebo lepší a většina z nich by ocenila, kdyby jejich aplikace pro generování popisů podporovala také VQA. Po tomto zjištění práce porovná získané znalosti z VQA se znalostmi z popisů v různých scénářích. Byla vytvořena datová sada 111 obrázků různorodých scén s ručně anotovanými popisky. Experiment porovnávající získané znalosti ukázal úspěšnost 69,9 % pro VQA a 46,2 % pro popisy obrázků. V dalším experimentu v 70,9 % případů účastníci vybrali správný popis za pomocí VQA. Výsledky naznačují, že pomocí VQA je možné zjistit více znalostí o detailech obrázků než je to v případě generovaných popisů.
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Classification of Relations between Named Entities in Text
Ondřej, Karel ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.
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