National Repository of Grey Literature 193 records found  previous11 - 20nextend  jump to record: Search took 0.00 seconds. 
Searching Semantically Annotated Texts
Grešová, Katarína ; Smrž, Pavel (referee) ; Dytrych, Jaroslav (advisor)
This thesis deals with the issue of semantic searching over indexes of big text data. The aim of this thesis is to design and implement a search engine with web user interface enabling dynamical configuration of access to indexes and editing annotations in the text. The thesis analyzes the current search engine solution and its shortcomings, which results in a specification of requirements for a search engine that is suitable for common use and fulfils the potential of all search engine related tools. The thesis also describes the design, implementation and testing of the resulting system, which also includes an extension in a form of global constraints, which increases the accuracy of the requested search result description.
Analysis of Social Media Content Discussing Czech Mobile Operators
Pavlů, Jan ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The main topic of this thesis is sentiment analysis of posts obtained from a social networks. The posts are about czech mobile network operators. The essential part of implemented system is also data visualization. The sentiment analysis is done using machine learning techniques. Downloaded posts are cleaned, lemmatized and transformed to feature vectors. Stochastic Gradient Descent algorithm is used for classification. Analyzed data are visualized in charts and as the list of posts. The system provides tools for text categorization. The accuracy, precision, recall and F1 score of sentiment analysis is about 75%. The accuracy of post categorization is high (about 80%), but precision, recall and F1 score are low (about 30%). This is the reason why post categorization isn't automatically done. The benefit of the system it that it automatically collects data from different sources, analysis them and displays them. It also provides tools for manual change of sentiment/categories which can lead to better system characteristics with some help of users.
Identifying Entity Types Based on Information Extraction from Wikipedia
Rusiňák, Petr ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper presents a system for identifying entity types of articles on Wikipedia (e.g. people or sports events) that can be used for identifaction of any arbitrary entity. The~input files for this system are a list of several pages that belong to this entity and a list of several pages that do not belong to this entity. These lists will be used to generate features that can be used for generation of the list of all pages belonging to this entity. The fatures can be based on both structured information on Wikipedia such as templates and categories and non-structured informations found by the analysis of natural text in the first sentence of the article where a defining noun that represents what the article is about will be found. This system support pages written in Czech and English and can be extended to support other languages.
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.
Extending System for Acquiring, Processing, and Analysing Large Web Text Collections
Matějka, Jiří ; Dytrych, Jaroslav (referee) ; Smrž, Pavel (advisor)
The aim of the thesis is to extend the existing system for collecting, downloading, processing and analyzing web pages. This work deals with the automation of all processes, brings new tools into the existing system and offers new versions of some tools involved in the processing system and also offers new procedures and ideas.
Automatic Assignment of Student Projects in Python
Kyseľ, Juraj ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
This work focuses on automatic evaluation of student codes in Python programming language. Emphasis is placed primarily on feedback, to guide students in the correct Python code writing practices. In the practical part, the information obtained is used to design a system that alerts beginning programmers to errors and tries to teach them the right structures  in the form of python idioms. Further, the thesis describes individual parts of the system, its inputs and outputs. As a case study, the system was put  in place to support the teaching of the subject Scripting Languages  at the FIT VUT in Brno in the academic year 2017/2018. The thesis summarizes the experience of this deployment and evaluates the advantages and problems of the processed solution.
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.
Search for People in Recordings from Security Cameras
Jezerský, Matouš ; Hradiš, Michal (referee) ; Smrž, Pavel (advisor)
This thesis deals with the design and implementation of a system, which allows to search for and recognize people in video recordings. The presented design is based on a preceding research in theory relating to the topics of face and people recognition. Furthermore, the system design is implemented using convolutional neural networks for face recognition, while the implementation primarily utilizes the libraries dlib and OpenFace. The design and implementation use parallelization and distribution of tasks among multiple devices to reduce computation time, while also bearing in mind the practical applications of such system, such as working with limited amounts of available information regarding the person we seek. The precision of people detection and recognition of the implemented system is about 70% to 80%, based on the performed task. Among other uses, the system can be utilized to find a particular person in a video recording, to estimate the number of passes through the monitored space of one person, or the number of passes in total, or to find unknown people in the monitored space.
Machine Learning in Strategic Games
Vlček, Michael ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.
Computer as an Intelligent Partner in the Word-Association Game Codenames
Obrtlík, Petr ; Hradiš, Michal (referee) ; Smrž, Pavel (advisor)
This thesis deals with associations between words. Describes the design and implementation of a system that can represent a human in the word-association game Codenames. The system uses the Gensim and FastText libraries to create semantic models. The relationship between words is taught by the analysis of the text corpus CWC-2011.

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2 Smrž, Petr
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