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System for Recognizing Disinformation in Web Environment
Večerka, Lukáš ; Žádník, Martin (referee) ; Strnadel, Josef (advisor)
This work deals with the design, implementation, and verification of a system for automatic recognition of disinformation on the web. It addresses the issue of disinformation spread in the online environment and its impact on society. It focuses on training several Czech transformer language models for disinformation recognition and further automatic extraction of content from Czech online newspapers and their analysis using text classification and natural language processing through deep learning methods. The results of these analyses are then presented in a web user interface with the aim of providing a platform for verifying articles, authors, and sources. The interface could be used for data annotation by experts for continuous improvement of language models.
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Generating Code from Textual Description of Functionality
Zobal, Ondřej ; Nosko, Svetozár (referee) ; Smrž, Pavel (advisor)
Tato práce se zabývá vývojem rozšíření do editoru Visual Studio Code, které pomůže vývojářům udržet kvalitu kódu jazyka Python 3. Rozšíření poskytuje možnost generování komentářů a docstringů, návrhu nových jmen proměnných. Rozšíření využívá velké jazykové modely Transformer s řídkou pozorností pro zpracování výsledků. Výsledky bohužel nekonkurují současné konkurenci, jakou je například GPT-3.5-turbo.
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Programming a robotic arm with ChatGPT
Kuba, Josef ; Bambušek, Daniel (referee) ; Materna, Zdeněk (advisor)
This bachelor thesis aims to develop a virtual assistant that allows users without advanced technical knowledge to effectively control a robotic arm. The thesis uses ChatGPT technology along with \uv{function calling} to generate commands for the robotic arm API based on user input. The focus is on developing and testing appropriate inputs for ChatGPT (prompt engineering), to create an intuitive and user-friendly interface. Testing with users revealed opportunities for improvement and provided valuable feedback for further development. Users were able to create simple object manipulation programs without much difficulty. The results show that the creation of such an assistant is possible and that the main challenge is to specify the correctly designed system input for proper code generation. The paper also compares the performance and efficiency of ChatGPT versions 3.5 Turbo and 4, emphasizing the importance of choosing the appropriate version for a particular application.
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Query Answering over Wikipedia for Mobile Devices on the Android Platform
Kováč, Andrej ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
p { margin-bottom: 0.1in; direction: ltr; line-height: 120%; text-align: left; widows: 2; orphans: 2; }p.western { font-family: "Times New Roman",serif; }p.cjk { font-family: "Times New Roman"; }p.ctl { font-family: "Times New Roman"; font-size: 12pt; }a:link { color: rgb(0, 0, 255); } This bachelor thesis deals with the development of a system for query answering over Wikipedia for mobile devices running Android operating system. In this technical report theoretical knowledge related to this topic is described as well as the implementation process of a server system and client side application. Part of this thesis is dedicated to testing of the system and in the final part the potential for future development is drafted.
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Generating Code from Textual Description of Functionality
Kačur, Ján ; Ondřej, Karel (referee) ; Smrž, Pavel (advisor)
The aim of this thesis was to design and implement system for code generation from textual description of functionality. In total, 2 systems were implemented. One of them served its purpose as a control prototype, the second one was the main product of this thesis. I focused on using smaller non-pre-trained models. Both systems used Transformer type model as their cores. The second system, unlike the first, used syntactic decomposition of both code and textual descriptions. Data used in both systems originated from project CodeSearchNet. Targer programming language to generate was Python. The second system achieved better quantitative results than the first one, with accuracy of 85% versus 60%. The system managed to auto-complete correct code to finish the function definition, with bigger time delay. This thesis is almost exclusively dedicated to the second system.
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Word2vec Models with Added Context Information
Šůstek, Martin ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
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Syntactic Analyzer for Czech Language
Beneš, Vojtěch ; Otrusina, Lubomír (referee) ; Kouřil, Jan (advisor)
Master’s thesis describes theoretical basics, solution design, and implementation of constituency (phrasal) parser for Czech language, which is based on a part of speech association into phrases. Created program works with manually built and annotated Czech sample corpus to generate probabilistic context free grammar within runtime machine learning. Parser implementation, based on extended CKY algorithm, then for the input Czech sentence decides if the sentence can be generated by the created grammar and for the positive cases constructs the most probable derivation tree. This result is then compared with the expected parse to evaluate constituency parser success rate.
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Methods of Document Summarization on the Web
Belica, Michal ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic analysis. Result of the work is a design and implementation of summarization module for Python language. Final part of the work contains evaluation of summaries generated by implemented summarization methods and their subjective comparison of the author.
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A Classification of a Syndicated Content
Matušov, Izidor ; Očenášek, Pavel (referee) ; Smrčka, Aleš (advisor)
This work deals with a classification of a syndicated content as the possible way of organizing the content. The classification uses algorithms for natural language processing. The main contribution is applying word sense disambiguation algorithm for enhancing the classification, eliminating the learning stage, and using a readability test for improving user experience. The application is implemented as an extensible server-client model. The future work is discussed in the end.
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