Pagrindinis šio darbo tikslas - išanalizuoti lietuvių kalbos semantinio anotavimo procesą ir sukurti metodologiją, leisiančią įgyvendinti semantinio anotavimo proceso automatizavimą. Kompiuteris, kitaip nei žmogus, nesupranta ar tekstas yra rišlus ir prasmingas, ar neturi jokio rišlumo ir prasmės. Tai ypač atsiliepia verčiant tekstus, lyginant tos pačios kalbos tekstus vieną su kitu ir t.t. Semantinis anotavimo procesas leidžia išspręsti šią problemą sukurdamas metodiką, kuri leidžia aprašyti žodžių sąsajas sakiniuose, tačiau norint tekstą semantiškai suanotuoti rankiniu būdu, reikia turėti specifinių žinių ir tai reikalauja didelių laiko sąnaudų. Norint išvengti šių problemų semantinį procesą būtina automatizuoti. Šiame darbe buvo išanalizuotas lietuvių kalbai tinkantis semantinio anotavimo procesas, sukurta metodologija, leidžianti įgyvendinti semantinio anotavimo proceso automatizavimą. Metodologijos pagrindu JAVA programavimo kalba įgyvendintas automatizuoto semantinio anotavimo proceso realizavimas, atliktas eksperimentas ir pateiktos išvados. / Major aim of this work – analyze Lithuanian language semantic annotation process and develop methodology which let implement automate semantic annotation process. The computer, unlike the person, does not understand did the text is coherent and meaningful, or have no coherence and meaning. This is particularly vulnerable to the translation of the text, compared with the same language texts with one another, etc. Semantic annotation process allows to solve the problem of creating a methodology that enables to describe links between words in sentences, but in order to semantically annotate the text manually, you need to have specific knowledge and it requires time-consuming. To avoid these problems it is necessary to automate the process of semantic. In this work was analyzed the Lithuanian language suitability for semantic annotation process, developed methodology to implement semantic annotation of process automation. Methodology based Java programming language implementation of automated semantic annotation process realization of the experiment was conducted and the following conclusions.

Recognition and comparison of image is one of the main problems and area of the field of computer vision. This thesis adds to these two issues the third, the recognition image semantics, so called annotations or labels. This work uses the knowledge of methods of recognizing the similarity of images to create a tool that is able based on training dataset of images and annotations, create a group most likely annotation for the test set of images. This work presents several types of test datasets suitable for the detection of annotation information for images. Subsequently, best set with the necessary training dataset size and enough information about annotations is selected. Based on this training dataset algorithm is designed for easy loading test set without large demands on computer performance. Evaluation of annotation information is done based on different similarity algorithms. At the beginning of this work was to use a simple, but not very effective method of MSE and comparison of color histograms, but gradually it was necessary to move to using more advanced methods (such as Tamura, Gabor, CEDD nebo různé druhy hostistogramů). The results of this comparison are then taken to evaluate the likelihood of the annotation for the image specified test set. The last part is an evaluation of the accuracy of annotation based on information from the test set.

This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform  input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.

Automatic classification of devices in computer network can be used for detection of anomalies in a network and also it enables application of security policies per device type. The key to creating a device classifier is a quality data set, the public availability of which is low and the creation of a new data set is difficult. The aim of this work is to create a tool, that will enable automated annotation of the data set of network devices and to create a classifier of network devices that uses only basic data from network flows. The result of this work is a modular tool providing automated annotation of network devices using system ADiCT of Cesnet's association, search engines Shodan and Censys, information from PassiveDNS, TOR, WhoIs, geolocation database and information from blacklists. Based on the annotated data set are created several classifiers that classify network devices according to the services they use. The results of the work not only significantly simplify the process of creating new data sets of network devices, but also show a non-invasive approach to the classification of network devices.