Species distribution models (SDM) are widely used tool in biogeography, macroecology and nature conservation. With gradual development, it has become an important means used by, for example, in determining the potentially threatened locations by invasive species, or studying the impact of climate change on biodiversity. With the progressive development it becomes obvious that one of the major factors limiting the species distribution modelling are input data. The presence data are most readily available, but they suffer from an uneven collection, for example, with a predominance of records in easily accessible locations. The aim of this work is to show, that popular climate filtering of presence data input, in order to eliminate uneven sampling, affects the final model in a negative way. For this purpose, there were virtual sorts of different species and different prevalence of recorded occurrences on the territory of the Iberian Peninsula generated. Subsequently, species distribution models with and without climate filters were created by using Maxent. They were evaluated by AUC. The difference between virtual reality, which is presented to the suitability of the virtual species, and the resulting model was tested by paired T test. Comparison of the AUC confirmed that the species distribution models based on climate filtering have better discriminative ability. However, it only points to the skilful work with the selected sample bias that already does not reflect reality. In contrast, comparison of the differences between virtual reality and the models with and without climate filtering using a paired T test shows greater congruence between unfiltered models and virtual reality. Thus it was proved that the climate filtering does not lead to higher validity species distribution models.

This work deals with the issue of poviding recommendations for individual users of e-shop based on the obtained user preferences. The work includes an overview of existing recommender systems, their methods of getting user preferences, the methods of using objects' content and recommender algorithms. An integral part of this work is design and implementated for independent software component for Content-based recommendation. Component is able to receive various user preferences and various forms of object's input data. The component also contains various processing methods for implicit feedback and various methods for making recommendations. Component is written in the Java programming language and uses a PostgreSQL database. The thesis also includes experiments that was carried out with usage of component designed on datasets slantour.cz and antikvariat-ichtys.cz e-shops.

This master's thesis describes creation of recommender system that is used in real server cbdb.cz. A~fully operational recommender system was developed using collaborative and content-based filtering techniques. Thanks to many user feedback, we were able to evaluate their opinion. Many recommended books were tagged as desirable. This thesis is extending current functionality of cbdb.cz with recommender system. This system uses its extensive database of ratings, users and books.

This thesis deals with problems of recommender systems and their usage in web applications. There are three main data mining techniques summarized and individual approaches for recommendation. Main part of this thesis is a suggestion and an implementation of web applications for recommending dishes from restaurants. Algorithm for food recommending is designed and implemented in this paper. The algorithm deals with the problem of frequently changing items. The algorithm utilizes hybrid filtering technique which is based on content and knowledge. This filtering technique uses cosine vector similarity for computation.

The thesis addresses the dynamic state estimation problem for the field of robotics, particularly for unmanned aerial vehicles (UAVs). Based on data collected from an UAV, we design several probabilistic models for estimation of its state (mainly speed and rotation angles), including the configurations where one of the sensors is not available. We use Kalman filter and Particle filter and focus on learning the model parameters using EM algorithm. The EM algorithm is then adjusted with respect to non-Gaussian density of some sensor errors and modified using model complexity penalization terms for better generalization. We implement these methods in MATLAB environment and evaluate on separate datasets. We also analyze data from a ground robot and use our implementation of Particle filter for estimation of its position. Powered by TCPDF (www.tcpdf.org)

Abstract This diploma thesis focuses on processes that structure desmid communities. Phylogenetic structure of communities helps to reveal those processes. Phylogenetic structures links ecology of communities and evolutionary history of species in those communities. Totally I analysed 89 desmid communities. Most of them were phylogenetically structured, which agrees with the hypothesis that communities are structured by classical niche-related processes, such as competition and environmental filtering. Usage of environmental information showed the influence of pH on the community structure. Low pH acts as a strong environmental filter. Under the influence of this filter communities are composed of closely relates species. In the absence of this filter communities are structured by competition. Localities with higher pH host communities of distantly related species - phylogenetically overdispersed. There was found no influence of conductivity and geographical distance on phylogenetic structure of desmids communities. Key words: phylogenetic structure, desmids, Desmidiales, environmental filtering, pH

The aim of this work is to explore recommender systems for prediction user's future film ratings according to their previous ratings. Author will describe available algorithms and compare their results with his own algorithm. The goal is to find algorithm with the highest prediction accuracy and find the most important parameters for a good predictions.

{This Diploma thesis deals with Internet censorship. Various technical solutions for Internet censorship are presented together with censorship analysis options. Several possibilities for blocked content access and censorship circumvention in general are discussed. The topic is analyzed from the global point of view and is related to the People's Republic of China.

This thesis describes the requirements analysis, design and implementation of stateful packet filtering to an existing stateless firewall. They also deals with testing of the implemented system. The first two chapters describe the properties NetCOPE development platform for FPGA. They also describes the principle of operation           firewall, which also serves as a requirements specification for stateful firewall. Then describes the detailed design of individual modules to modify the existing firewall and the proposal for the creation of new modules. It also discusses the implementation of the proposed modules and testing for proper operation. Finally, it discuss the current state of the thesis and describes possible future expansion.

In recent years the market share of major real estate companies, at least the Czech ones, has not changed much. Statistical data don't reflect any significal upward trend in volumes of properties for rent or sale. In case the real estate company would like to access larger market share, they have to secure a competitive advantage over the others. One of the ways how to attract more potential customers might be speeding up the company website's property search process. In many cases the website visitors are facing hundreds or thousands of property offers before finding couple satisfactories. The aim of the thesis is to explore possibilities of applicating customer preferences in property trading. The focus is put on research of recommender system algorithms, their characteristics and limtations. The author is evaluating usage of each algorithm variant and its suitability for a real world deployment in a real estate area. Apart from the theoretical part of the work one can find a part, where real estate information system is extended with a framework for implementing recommendation system algorithms. The author is in possesion of production data of a medium sized real estate company. He uses the recommender system framework to build and evaluate example algorithm. Powered by TCPDF (www.tcpdf.org)