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Semantic Similarity Methods in Folksonomies
Kadlec, Jan ; Otrusina, Lubomír (referee) ; Schmidt, Marek (advisor)
Bakalářská práce byla vypracována na studijním pobytu na "Aalborg University" v Dánsku, a byla zpracována v angličtině. Folksonomie jsou nový, uživateli řízený přístup ke klasifikaci a důležitá čast Web 2.0. Jsou také jediným přístupem, který je schopen udržet krok s dnešní rychlostí expanze webu, tím že předá uživatelům odpovědnost za klasifikaci. Pokud folksonomie obsahují dostatečné množství dat, dají se k mnohému využít. Tato práce se zaměřuje na metody sémantické podobnosti ve folksonomiích. Cílem této práce bylo odzkoušet mnohé metody na vzorku tří datových sad - delicious.com, Last.fm a medworm.com. Toto bylo vykonáno za pomocí kotvících dat z WordNetu, Open Directory Project a zdravotně orientované ontologie. Výsledky přinesené touto prací indikují, že metody sémantické podobnosti mohou být použity k úspěšnému měření podobností v mnohých doménách.
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Automatic Photography Categorization
Veľas, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.
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Automatic Image Labelling
Lukáč, Michal ; Řezníček, Ivo (referee) ; Hradiš, Michal (advisor)
This thesis focuses on automatic image labelling to semantic categories. It describes the theory of classif cation and local features detection. It explains fundamental machine learning models used for image tagging, and how such models can be learned with Gradient descent. It propose solution with hierarchy for ImageNet and tagging images with attributes. MapReduce computing model is considered for learning on big data sets. In the last part it is described implementation, experimental and test results.
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Annotation Editor for Semantic Analysis of Text
Šťastná, Barbora ; Otrusina, Lubomír (referee) ; Dytrych, Jaroslav (advisor)
The thesis introduces the most crucial terms related with text annotation, and shows some electronic tools for annotation of electronic documents. It describes an annotation editor which provides the user with a graphic interface and allows for annotation of web documents. It also proposes modifications to the editor which would make it more intuitive, efficient, and user-friendly. The thesis follows with the description of implementation of the said modifications, and their testing.
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Application of virtual local area networks for effective and flexible data network design
Brázda, Libor ; Burda, Karel (referee) ; Novotný, Vít (advisor)
This thesis is aimed to the problems of virtual local networks. In the first chapters are explained both basic and advanced methods of designing and management of virtual LAN networks. The following chapters are dedicated to the methods of effective management VLAN networks and to the protocol Spanning Tree, preventing formation of loops in redundant networks, and their exploitation in VLANs. The practical part describes the application of the implementation of the technology of virtual local networks in experimental network, containing various types of devices. Based on this theory, a laboratory assignment has been designed for the training of the students in solving such problems. The scope of the laboratory assignment is divided into simpler partial tasks, which even a less advanced student should be able to configure and test.
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Keyword Extraction from Scientific Articles
Kyjovský, Marek ; Schmidt, Marek (referee) ; Smrž, Pavel (advisor)
The main goal of this thesis is to explore basic methods which is using for extraction of important words from articles. After that try to understand character of using keywords from the available set of testing English articles. Based on these findings, try to design and to implement a system which is using this methods. Then created system testing on the real English articles and after that try to analyse results.
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High-Performance Platform for Malware Research
Plaskoň, Pavol ; Bartík, Vladimír (referee) ; Kolář, Dušan (advisor)
Anti-malware companies analyze large number of files every day. In order to speed up their analysis, many automatized tools were implemented. Detection definitions that detect malicious software are often generated automatically. Information about currently spreading malware is scattered across several tools and they are sometimes too generic. This work proposes a new tool that will aggregate, prioritize, and evaluate all the available information. Due to large amount of incoming data, high performance and scalability of the system is necessary. Files, detection definitions, and other objects will be tagged using the given information directly or inferred. Collected information will be accessible via interface for further analysis and statistics. Everything was implemented, tested and put into production.
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Automatic Keyword Detection
Mašláňová, Marcela ; Karafiát, Martin (referee) ; Smrž, Pavel (advisor)
The main goal of this work is to survey the field of the automatic keywords tagging in a text and apply this background for automatically generating back-of-the-book indexes. Human made indexes are expensive and that's why we are looking for (semi)-automatic methods indexes. The theoretical part of this thesis deals with collocations, which are an important part of generated indexes. The practical part of the work applies selected methods to testing data and summarize results of experiments.
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Automatic Photography Categorization
Veľas, Martin ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.
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