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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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Big Data Processing in Industry 4.0
Trubka, Jakub ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis deals with collecting, processing, and storing big data obtained from monitoring industry machines. The designed and implemented system focuses on extensibility and scalability attributes of the realised solution. The survey part of the text briefly describes existing solutions and discusses collecting and processing big industrial data. A special attention is also paid to the big data storage technology. The crucial part of the thesis then refers to the design and realisation of the system and its individual components, as well as its testing and final evaluation.
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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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Machine Learning for Natural Language Question Answering
Sasín, Jonáš ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis deals with natural language question answering using Czech Wikipedia. Question answering systems are experiencing growing popularity, but most of them are developed for English. The main purpose of this work is to explore possibilities and datasets available and create such system for Czech. In the thesis I focused on two approaches. One of them uses English model ALBERT and machine translation of passages. The other one utilizes the multilingual BERT. Several variants of the system are compared in this work. Possibilities of relevant passage retrieval are also discussed. Standard evaluation is provided for every variant of the tested system. The best system version has been evaluated on the SQAD v3.0 dataset, reaching 0.44 EM and 0.55 F1 score, which is an excellent result compared to other existing systems. The main contribution of this work is the analysis of existing possibilities and setting a benchmark for further development of better systems for Czech.
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Multilingual Open-Domain Question Answering
Slávka, Michal ; Dočekal, Martin (referee) ; Fajčík, Martin (advisor)
Táto práca sa zaoberá automatickým viacjazyčným zodpovedaním na otázky v otvorenej doméne. V tejto práci sú navrhnuté prístupy k tejto málo prebádanej doméne. Konkrétne skúma, či: (i) použitie prekladu z angličtiny je dostačujúce, (ii) multilinguálne systémy vedia využiť preklad otázky do iných jazykov (iii) alebo je výhodnejšie nepoužívať žiaden preklad. Porovnávam použitie anglického systému založeného na modeli T5, ktorý využíva strojový preklad s natívne viacjazyčnými systémami založenými na viacjazyčnom modeli MT5. Anglický systém so strojovým prekladom mierne prekonáva svoje jednojazyčné náprotivky vo viacerých úlohách. Napriek tomu, že tento model bol natrénovaný na väčšom množstve dát zlepšenie nie je dostatočne signifikantné. To ukazuje, že použitie natívne viacjazyčných systémov je sľubným prístupom pre budúci výskum. Tiež prezentujem metódu získavania dokumentov v rôznych jazykoch pomocou algoritmu BM25 a porovnávam ju s anglickým retrievalom. Používanie viacjazyčných dôkazov sa javí ako prospešné a zlepšuje výkonnosť systému systémov.
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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
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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Non-Supervised Sentiment Analysis
Karabelly, Jozef ; Landini, Federico Nicolás (referee) ; Fajčík, Martin (advisor)
Cieľom tejto práce je odprezentovať prehľad aktuálneho výskumu v oblasti analýzy sentimentu bez priameho učiteľa a identifikovať potenciálne smery výskumu. Okrem toho práca predstavuje novú účelovú funkciu na predtrénovanie, ktorá nevyžaduje priamy supervíziu. Rozšírenie modelu predstavenou účelovou funkciou, pridanie vrstvy neurónovej siete a následné samotné natrénovanie ukazujú sľubné výsledky. Rozšírený model naznačil schopnosť zakódovať abstraktné reprezentácie celkového sentimentu, emócií a sarkazmu. Pre účely použitia predstavenej účelovej funkcie bol nazbieraný vlastný dataset. Na základe experimentov vykonaných s rozšíreným modelom sú odprezentované možné smery výskumu a budúce vylepšenia.
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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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Brno Communication Agent
Jurkovič, Juraj ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
The goal of this thesis is explore and subsequently apply techniques and technical solutions in development of information agents. Thesis primarily focuses on solving individual sub tasks using state of the art systems, interconnecting these systems, their adoption for specific domain and implementation of individual modules of communication agent system. User interface is based on multi-platform chat application Telegram. Information extraction from user input is executed by Dialogflow. Several external services are used for user request fulfillment. Elasticsearch is used for searching structured data. For answering open domain questions from free text we use R-net implementation. The resulting can have both ,its knowledge base and range of requests it can fulfill, easily extended and can be deployed to chat platform of choice.
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