This diploma thesis deals with the semantics of Dutch adjectives, their categorization and internal classification. The first section observes and analyses the criteria for delimiting adjectives as an independent word class and establishing adjectival subcategories, as dictated by the most influential Dutch grammar publications. One key area of focus is the concept of gradability and its role in delimiting and classifying adjectives. These definitions and classifications are confronted using the scalar approach, according to which adjectives represent a scale of properties and may be sorted depending on which type of scale they express. The practical section comprises a corpus study whose aim is to examine the validity of the assumptions formulated in the theoretical introduction regarding the gradability of adjectives. Using a sample of 100 adjectival lemmas with the lowest coefficient value for the likelihood of gradability, acquired from the Dutch language corpora CGN and SoNaR, the study follows a set of morphological, syntactic, semantic and discourse variables which may warrant the use of the comparative or superlative even with adjectives which are generally considered to be ungradable. The results of the analysis suggest that it is more appropriate to regard gradability as a statistical...

In this paper we present C. S. Peirce's take on the difference between science and practice in order to identify the role practice plays in his view of the universe. This take is based on a number of notions about the general nature of signs, inquiries, inferences and arguments, which we discuss. We then survey Peirce's classification of science, show the factors it is based on and examine the mutual relations of the various fields of scientific study. This lets us finally posit practice in the realm of qualities and reactions and show the limits of scientific inquiry into certain matters. We illustrate our findings on a number of examples.

The aim of the thesis is the classification of magnetic resonance images by Deep Learning models. The goal was to predict Alzheimer's disease on the dataset created by Alzheimer's Disease Neuroimaging Initiative (ADNI). To prepare the dataset, we built two processing pipelines, which align, normalise and remove irrelevant features from brain scans. We used the processed scans for a 2D and 3D dataset. We designed a few models based on convolutional and previously proposed architectures. Although, many studies published astonishing results on ADNI classification, the results of our experiments do not support previous research in this area. Contrary to what was previously thought, we found that the accuracy strongly depends on the dataset splitting. If we split the dataset by patients, not by scans, the accuracy drops significantly. We presented an overview of several previously published architectures and our experiments showing results of these architectures on the datasets generated by random splitting or subject-based splitting. We also pointed out how the dataset splitting choice changes the performance of our models. The work is a natural extension of study [Fung et al., 2019]. 1

Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...

Deep learning is usually applied to static datasets. If used for classification based on data streams, it is not easy to take into account a non-stationarity. This thesis presents work in progress on a new method for online deep classifi- cation learning in data streams with slow or moderate drift, highly relevant for the application domain of malware detection. The method uses a combination of multilayer perceptron and variational autoencoder to achieve constant mem- ory consumption by encoding past data to a generative model. This can make online learning of neural networks more accessible for independent adaptive sys- tems with limited memory. First results for real-world malware stream data are presented, and they look promising. 1

Machine learning models are difficult to employ in biology-related research. On the one hand, the availability of features increases as we can obtain gene expressions and other omics information. On the other hand, the number of available observations is still low due to the high costs associated with obtaining the data for a single subject. In this work we, therefore, focus on the set of problems where the number of observations is smaller than the number of features. We analyse different combinations of feature selection and classification models and we study which combinations work the best. To assess these model combinations, we introduce two simulation studies and several real-world datasets. We conclude that most classification models benefit from feature pre-selection using feature selection models. Also, we define model-based thresholds for the number of observations above which we observe increased feature selection stability and quality. Finally, we identify a relation between feature selection False Discovery Rate and stability expressed in terms of the Jaccard index. 1

Soil crediting in the Czech Republic is based on so-called land boned soil-ecological units (BPEJ), this unit serves to evaluate both absolute and relative production capacity of agricultural land. The thesis points out the need to update this system, caused mainly by climate change, which causes the shift of the existing climate regions but also the emergence of new (not yet classified) climate regions. These changes have a direct impact on soil production capacity. In the thesis there is an overview of legal regulations that are influenced by the system of certified soil-ecological units (BPEJ).

Thesis focuses on the comparison of the technical parameters of the 1920s window and refurbished one to the current conditions. The refurbished window was tested according to the relevant tests in the STV testing laboratory in Zlín and based on the results these values were compared. Thesis also includes the economic balance of the problem. Fur-thermore, there is a proposal for improving the insulation properties of double windows described. The diploma thesis is a follow-up to the bachelor's thesis, so the refurbished win-dow was again tested according to the three most important technical requirements. It was a test of air permeability, load resistance to wind pressure and water tightness. The heat transfer coefficient was also calculated.

The thesis deals with data acquisition from crowdfunding and their analysis. The theoretical part is focused on the description of available technologies and algorithms for data analysis. In the practical part the data collection is realized. Data mining and text mining algorithms are applied in this section for data.

VAŠÍČKOVÁ, V. IFRS 9 – impact on non-bank entities. Diploma thesis. Brno: Mendel University, 2019. Diploma thesis is focused on influence of IFRS 9 on non-bank entities. Classification, measurement, impairment is described in the first part of thesis. Then comparation of IAS 39 and IFRS 9 are performed. In second part of thesis there is performed impact of IFRS 9 on selected non-bank entities – financial instruments of selected entities are classified and measured according IFRS 9 and there is calculated expected credit loss. Impact is quantified using percentual changes in assets and liabilities.