This thesis focuses on the use of artificial intelligence in photojournalism. Its goal is to explore which tasks could be performed by this technology and how it could streamline journalist's work. The research method is grounded theory that was formulated based on findings from research - practices of pressrooms and their attitudes towards technologies. The initial part of the paper therefore examines specific tools and tasks that artificial intelligence could do in practice. This is done by collecting data from representatives of the largest Czech media houses, technology journalists, press agencies and representatives of photo banks. Secondary sources for research were academic work as well as popular-science resources. From these areas, the discussion proceeds to investigation and analysis of findings about the possibilities of working with images, from managing photo archives and video archives and automatic data retrieval, to machine vision or even generating of an original illustration photo based on selected parameters. This provides a foundation for further discussion on a set of upcoming tools that could make journalists' job easier, especially with regard to the ever-increasing demand for speed, accuracy and quality of news reporting. With that, the paper proceeds to analyse management...

Modern Technologies in Medicine and Law Abstract This thesis deals with the application of modern technologies in medicine from the perspective of law. The primary aim of this work is to analyse the given provisions of the Civil Code, Act on Health Services and Act on Medical Devices, and also to determine whether the current legislation represents a suitable legal framework able to respond to the implementation of modern technology in the healthcare sector. In connection with this analysis, author presents possibilities of legislative changes that would respond to these modern technologies. The work inter alia deals with the question of whether the use of some of these technologies within the provision of healthcare services can be considered compliant with the principle of lege artis. In the first chapter, the reader is introduced to the topic of the thesis. This chapter defines the basic terms and presents an overview of the legislation related to the chosen topic. The second chapter represents a main part of the thesis, where author deals with the topic of artificial intelligence. In this chapter, the reader is acquainted with the term of artificial intelligence and the definition of its legal status. Subsequently, author evaluates whether the current legislation constitutes appropriate legal frameworks...

This diploma thesis deals with the use of artificial intelligence tools and automated journalism in the Czech News Agency. Specifically, it deals with the very first use of algorithmically generated news reports, which the agency published during the election in 2018. It compares municipal and Senate elections from this year with the outputs of ČTK news from municipal elections in 2014 and Senate elections in 2016. It uses qualitative and quantitative analysis methods. The diploma thesis also deals with the method how the generation of reports in 2018 worked in ČTK and shows and explains examples of source codes by which the report generator was programmed. It will also offer other ways and areas in which the Czech News Agency uses or is about to use automated journalism and its tools. One part of the thesis is also an interview with the technical director of ČTK Jan Kodera, who explained how automated journalism works in the Czech media environment of the press agency. The author also outlines some scientific disciplines related to artificial intelligence, which are required for the needs of this thesis. At the same time, he will also offer a number of examples of how automation tools are used in other newsrooms around the world.

This thesis deals with phenomena of spatial visualization, application of spatial visualization in instruction and its educational specifics. The present thesis addresses the questions of definition of spatial visualization in instruction, specification of educational specifics and exploration of the relations between spatial visualization and cognitive processes. The experimental part of the thesis is focused on relations between the form of presentation of subject matter (planar visualization and spatial visualization), spatial intelligence, or the ability of mental rotation and the results of learning at students of lower secondary (ISCED 2) school. Elaboration of the theory given field of educational means, the thesis seeks to contribute to the development of theoretical bases of pedagogy with the context of increasing importance of technology for support education.

Identifikační záznam Richterová, Daniela. On line fundraising a mikrofinancování v sociální síti a na Webu 2.0 [Online fundraising and microfinace in the context of social networks and Web 2.0]. Praha, 2011. 102 s. Diplomová práce. Univerzita Karlova v Praze, Filozofická fakulta, Ústav informačních studií a knihovnictví 2011. Vedoucí diplomové práce Mgr. Denisa Kera, Ph.D. Abstract The main focus of this thesis is to characterize online fundraising and microfinance tools in the context of Web 2.0 and social media. The main goal is to analyze and assess the existing philanthropic portals in the Czech Republic and abroad. The thesis includes a theoretical explanation of terminology, principles and technologies of Web 2.0 and social media platforms and their connection to online philanthropy. New phenomena such as crowdfunding, P2P lending and crowdsourcing are clarified. The analytical part focuses on the evaluation of fundraising portals Network for Good and GlobalGiving and microfinace portals Kiva and LoanBack. Czech philanthropic online projects are represented by Šance pro draha, myELEN, Daruj správně and Skutečný dárek. Particular emphasis is given to monitoring and evaluating the extent of Web 2.0 principles and technologies integration. The conclusions contain suggestions for implementing the findings...

The work deals with multidimensional decision-making in the outsourcing of accounting work; it is designed for accounting companies with more than 30 employees. These companies deal with hundreds of accounting tasks of various extent and nature which would be assigned to individual accountants. Theoretically speaking, the problem of assigning tasks to individual accountants can be resolved by well-known methods of operational research; however, certain qualities of accountants are very vague and are hard to quantify. These qualities include creativity, loyalty, credibility towards clients, etc. The qualities make the application of traditional algorithms (e.g. scheduling algorithms) very difficult. Moreover, certain accounting tasks are unique and can be assigned only by experienced managers, not algorithms. This work focuses on a fuzzy description of tasks and accountants and a two-level assignment process. In preliminary sorting, individual tasks to be assigned are classified as routine, which can be assigned through a fuzzy algorithm, or non-routine, which are assigned to individual employees through the manager . fuzzy expert system dialogue.

The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

This dissertation thesis deals with the application of expert systems and soft computing methods in field of power oil transformers. The main work is divided into theoretical and practical part. First, the theoretical part presents the basic elements of the transformer, and approaches to its diagnosis. The work focused mainly on the diagnostics of the insulation system, and diagnostic methods and approaches in this specific area. Next part describes the basics of expert systems and other soft computing methods such as: fuzzy logic, neural networks, genetic algorithms and their combinations and extensions. At the end of the theoretical part, the possibility of optimization approaches by means of artificial intelligence and its application in fuzzy model optimization are described. The practical part begins with description of the used data file that runs through the entire work. The work is then divided into four parts, namely in parts which deal with the expert system for transformer diagnostics, DGA module, prediction module, and optimization using artificial intelligence. The section describing the expert system gives specific information about the particular expert system. The means and techniques used for constructing given system are described, and then the complete system design and description of all subsystems and modules are presented. The next section describes the developed DGA module and all selected approaches to its implementation and expansion. At the end of the chapter, the results of comparison between all implemented methods are evaluated. The third part deals with the prediction module and describes its design and construction, including description of the main parts which are based on the selected predictive approaches. Also, the predictions of selected quantities from the data file are included. There are two predictive approaches being used: the one step prediction, and the multiple step prediction. The comparison of prediction accuracy and computational cost of given methods is presented at the end of this chapter. The last part deals with the possibilities of optimization using artificial intelligence methods, namely differential evolution, PSO, and genetic algorithms. Both the single-objective and the multi-objective optimization are considered. The methods are compared in a series of synthetic tests and then applied to optimize the fuzzy models of DGA tests from an earlier part of this work. The dissertation also includes chapters: "The Aims", "The Contribution of the Work", and a list of publications, products, and projects of the author.

This work concerns the problematic of language modeling in automatic speech recognition. Currently widely used techniques for advanced language modeling based on statistical approach are described in the first part of work - class based language models, factored language models and neural network based language models. In the next section, implementation of neural network based language model is described. Results obtained on "Pražský mluvený korpus" and "Brněnský mluvený korpus" corpora (1 170 000 words) are reported, with perplexity reduction around 20%. Also, results obtained after rescoring N-best lists with spontaneous speech are reported, with absolute improvement in accuracy by more than 1%. In the conclusion, possible uses of the work are mentioned, along with possible extensions in the future. Finally, main weaknesses of current statistical language modeling techniques are described.

Statistické jazykové modely jsou důležitou součástí mnoha úspěšných aplikací, mezi něž patří například automatické rozpoznávání řeči a strojový překlad (příkladem je známá aplikace Google Translate). Tradiční techniky pro odhad těchto modelů jsou založeny na tzv. N-gramech. Navzdory známým nedostatkům těchto technik a obrovskému úsilí výzkumných skupin napříč mnoha oblastmi (rozpoznávání řeči, automatický překlad, neuroscience, umělá inteligence, zpracování přirozeného jazyka, komprese dat, psychologie atd.), N-gramy v podstatě zůstaly nejúspěšnější technikou. Cílem této práce je prezentace několika architektur jazykových modelůzaložených na neuronových sítích. Ačkoliv jsou tyto modely výpočetně náročnější než N-gramové modely, s technikami vyvinutými v této práci je možné jejich efektivní použití v reálných aplikacích. Dosažené snížení počtu chyb při rozpoznávání řeči oproti nejlepším N-gramovým modelům dosahuje 20%. Model založený na rekurentní neurovové síti dosahuje nejlepších publikovaných výsledků na velmi známé datové sadě (Penn Treebank).