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Machine Comprehension Using Commonsense Knowledge
Daniš, Tomáš ; Landini, Federico Nicolás (referee) ; Fajčík, Martin (advisor)
V tejto práci je skumaná schopnosť používať zdravý rozum v moderných systémoch založených na neurónových sieťach. Zdravým rozumom je myslená schopnosť extrahovať z textu fakty, ktoré nie sú priamo spomenuté, ale implikuje ich situácia v texte. Cieľom práce je poskytnúť náhľad na súčasný stav výskumu v tejto oblasti a nájsť sľubné výskumné smery do budúcnosti. V práci je implementovaný jeden z najmodernejších modelov na odpovedanie na otázky a je ďalej použitý na experimenty v rôznych situáciách. Narozdiel od starších prístupov, tento model dosahuje porovnateľné výsledky s najlepšími známymi modelmi aj keď jeho architektúra neobsahuje žiadne prvky zamerané konkrétne na zlepšenie schopnosti zdravo uvažovať. Taktiež boli nájdené štatistické artefakty v populárnej sade dát s otázkami vyžadujúcimi zdravé uvažovanie. Tieto artefakty môžu byť použité štatistickými modelmi na nájdenie správnej odpovede aj v prípadoch, kedy by to nemalo byť možné. Na základe týchto zistení sú v práci poskytnuté odporúčania a návrhy pre výskum do budúcnosti.
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Analysis of GPON frames using machine learning
Tomašov, Adrián ; Horváth, Tomáš (referee) ; Holík, Martin (advisor)
Táto práca sa zameriava na analýzu vybraných častí GPON rámca pomocou algoritmov strojového učenia implementovaných pomocou knižnice TensorFlow. Vzhľadom na to, že GPON protokol je definovaný ako sada odporúčaní, implementácia naprieč spoločnosťami sa môže líšiť od navrhnutého protokolu. Preto analýza pomocou zásobníkového automatu nie je dostatočná. Hlavnou myšlienkou je vytvoriť systém modelov za použitia knižnice TensorFlow v Python3, ktoré sú schopné detekovať abnormality v komunikácií. Tieto modely používajú viaceré architektúry neuronových sietí (napr. LSTM, autoencoder) a zameriavajú sa na rôzne typy analýzy. Tento systém sa naučí na vzorovej vzorke dát a upozorní na nájdené odlišnosti v novozachytenej komunikácií. Výstupom systému odhad podobnosti aktuálnej komunikácie v porovnaní so vzorovou komunikáciou.
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Image classification using artificial intelligence
Labuda, Adam ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This bachelor's thesis address the issue of classification and feature extraction of imagesfrom image. In JAVA platform will create an example that loads a set of images, extracted from symptoms with the help of artificial intelligence provided by the thesis supervisor. Artificial intellihence assumed kind of image. Finally the results are compared. }
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Using machine learning for quality control in industrial applications
Gaško, Viktor ; Dobrovský, Ladislav (referee) ; Parák, Roman (advisor)
Goal of this bachelor´s thesis is to get acquainted with issue of quality control in industrial applications with focus on deep learning. For this and similar issues was created several libraries which have a purpose of simplifying these issues. Main task is to create program for quality control with help of programming language Python and framework Tensorflow. This program will be comprised of three neural network, from which one will identify the approximate position of the part, second its color, and third will check the correctness of its production.
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Artificial Poet
Bančák, Michal ; Szőke, Igor (referee) ; Beneš, Karel (advisor)
The paper presents a work on automatic poetry generation using the Long Short-TermMemory recurrent neural network. The aim of this work is to create an application thatimitates the writing of poems. This is a character-level language modeling in the Slovaklanguage. The neural network model used in the work consists of three layers of LSTM,with 400 hidden units. A collection of poems in the Slovak language with a size of 900k characters was also created for this work. . The final model is generating text that has poemelements. Achieved accuracy of generation is 41.85%.
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Estimation of Respiration Activity from ECG Using Mechine Learning
Ondrejková, Eliška ; Vítek, Martin (referee) ; Plešinger, Filip (advisor)
This Bachelor thesis deals with methods to estimate respiration activity from ECG. For a better understanding of a subject, the anatomy and physiology of the respiratory and cardiovascular systems are described. Furthermore, several estimation methods are explained as well. A public dataset of ECG signals read from polysomnography was used in the practical part. An algorithm for estimation was implemented in the programming language Python using the PyTorch library. Finally, results are discussed and compared to other methods.
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GPGPU parallel computing
Pacura, Dávid ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
The aim of this trim’s thesis is to reveal possibilities and demonstrate parallelization of computation on graphics processors. The paper presents descriptions of available development tools, and then one of them is selected to implement MD5 encryption algorithm and neural network for optical character recognition. Its performance is then compared to its parallel equivalent for conventional processors. In conclusion, problems encountered during development are described, and ways of avoiding them are discussed.
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Analysis of Classification Methods
Juríček, Jakub ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.
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Recognizing People and Their Activities in Video from Security Cameras
Saloň, Juraj Samuel ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to design and develop a system capable of recognizing the activities of people from surveillance cameras. Special attention is paid to the concept of complex situations or events that are defined by relations between identified objects. The first part surveys state-of-the-art techniques for object recognition, object tracking, and recognition of activities relevant to the realized solution. The second part describes the design and implementation of the devised system. It takes advantage of specific relations among two or more objects that are identified in video recordings, such as "person getting out of the car" or "one or more people met with a person of interest and they left together". Results are evaluated on video data extracted from available datasets and manually annotated. The mean average precision metric (MAP) on the data is reported.
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Chatbot Based on Artificial Neural Networks
Richtarik, Lukáš ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
This work deals with the issue of chatbots, which are based on artificial neural networks and generative models. It also describes options and process of designing the chatbot as well as an implementation and testing using BLEU metrics. The work contains multiple experiments with different models of chatbots, their performance evaluation and comparison, user experience and several suggestions for future enhancements.
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