This diploma thesis is focused on issues connected to possibilities of developement of mathematically talented pupils from the first grade. In the theoretical part there basic terms related to the education of talented pupils in th Czech Republic are summarised and explained. In next part motivation, processes of motivatiom and the most frequent usage of Math competitions as a part of the education in the Czech Republic are presented. The base of the practical part of this diploma thesis is a set of non standardized tasks and problems, whose target is to activate the cognitive developement of talented pupils. Part of the practical part of this thesis is a protocol about an experiment and analysis of the particular tasks and activities for talented pupils. The main aim of this thesis is to emphasise the importance of the developement of talented pupils and compile/form enriching tasks for talented pupils during Maths lessons. KEYWORDS Talent, talent models, motivation, cognitive process, non standardized tasks, aktivity.

The diploma thesis deals with the sources of trust in artificial intelligence, and how these sources are conditioned by social representations. It examines what risks the user is aware of when using artificial intelligence and how various factors affect the user's thinking. Artificial intelligence is represented in my work by intelligent virtual assistants (IVA). Based on semi-structured research interviews with their users, I have identified as the main sources of trust: neutrality, belief in the future, fulfillment of expectations and closeness. The first two sources are constant and based on social structure, while the last two sources of trust are based directly on user experience and are variable over time. It was also found that social representations have a significant impact on sources of trust. I divided all social representations into three categories - mechanicality, personification, intangibility, according to which the user's perception of artificial intelligence could be assessed and, based on that, his thinking about it. Because of this, I have proved that the trust of users is conditioned by social representations. An important part of trust is risk awareness. In my work, I have identified six main risks that users are aware of: unexpected software error, mechanical error, misuse of...

This master thesis explores the media representation of deepfakes. The first part summarizes previous research followed by a comprehensive review of deepfakes, including the technology allowing for their emergence, current uses and methods of regulation and detection. The second part connects the phenomenon with important theoretical concepts such as social construction of reality and the crucial role of media in this process. The empirical part consists of research combining two methods - quantitative content analysis and qualitative critical discourse analysis. The research analysis is focused on media articles dealing with deepfakes in order to find out how the media represent this phenomenon. The results show that current media discourse of deepfakes is strongly negative as the media frame them as a security threat. This negative representation is highly speculative since journalists often invent their own stories of future disastrous consequences of the technology for national security due to lack of current examples. The findings show an apparent hierarchy of the harms posed by deepfakes which is present in media coverage, and reflects gender sereotypes and inequality in the current society. Harm in the form of non-consensual fake pornography targeting women is neglected in the media...

[ES] Esta investigación contribuye al avance de la modelación sustitutiva como una técnica poderosa en el campo de la simulación computacional que ofrece numerosas ventajas para resolver eficientemente problemas complejos. En particular, este estudio destaca el papel crucial de la modelación sustitutiva en la gestión de aguas subterráneas. El impacto del cambio climático es un enfoque central, y el primer estudio tiene como objetivo construir modelos de datos sustitutivos para evaluar los efectos del cambio climático en los recursos de aguas subterráneas, también en el futuro. El estudio implica la comparación entre métodos estadísticos y diferentes tipos de Redes Neuronales Artificiales (ANN). La eficacia de los modelos sustitutivos se demostró en el norte de la Toscana (Italia), pero puede extenderse fácilmente a cualquier área de interés. El método estadístico adoptado implica analizar datos históricos de precipitación y temperatura junto con niveles de agua registrados en pozos de monitoreo. Inicialmente, el estudio explora posibles correlaciones entre índices meteorológicos e índices de agua subterránea; si se identifica una correlación, se emplea un análisis de regresión lineal. Estas relaciones establecidas se utilizan luego para estimar los futuros niveles de agua subterránea en función de las proyecciones de precipitación y temperatura obtenidas de un conjunto de Modelos Climáticos Regionales, bajo dos Trayectorias de Concentración Representativa: RCP4.5 y RCP8.5. Posteriormente, se implementaron tres modelos distintos de Inteligencia Artificial (AI), AutoRegressive No Lineal con Entradas Exógenas (NARX), Memoria a Largo y Corto Plazo (LSTM) y Red Neuronal Convolucional (CNN) para evaluar el impacto del cambio climático en los recursos de aguas subterráneas para el mismo caso de estudio. Específicamente, estos modelos fueron entrenados utilizando directamente datos históricos de precipitación y temperatura como entrada para proporcionar niveles de agua subterránea como salida. Después de la fase de entrenamiento, los modelos de IA desarrollados se utilizaron para prever los futuros niveles de agua subterránea utilizando las mismas proyecciones de precipitación y temperatura y escenarios climáticos descritos anteriormente. Los resultados resal-taron diferentes salidas entre los modelos utilizados en este trabajo. Sin embargo, la mayoría de ellos predice una disminución en los niveles de agua subterránea como resultado de futuras variaciones en la precipitación y temperatura. Notablemente, el modelo LSTM emerge como el enfoque más prometedor para predecir futuros niveles de agua subterránea. Dentro del mismo campo, se desarrolló una ANN con la capacidad de simular las condiciones de agua subterránea en la cuenca cerrada de Konya, Turquía, uno de los sitios piloto investigados como parte del proyecto InTheMED. Este modelo sirve como herramienta para examinar los impactos potenciales del cambio climático y las políticas agrícolas en los recursos de agua subterránea dentro de la región. El objetivo final de esta aplicación es proporcionar una herramienta fácil de usar, basada en la red neuronal entrenada. La simplicidad inherente del modelo sustitutivo, con una interfaz directa y resultados fáciles de entender, juega un papel crucial en los procesos de toma de decisiones. En cuanto al transporte de contaminantes, se implementó una ANN para resolver diferentes problemas directos e inversos. El problema directo trata sobre la evaluación de concentraciones en pozos de monitoreo, mientras que el probl-ma inverso implica la identificación de fuentes de contaminantes y su historial de liberación. Demostró eficiencia al abordar problemas de transporte tanto directos como inversos, ofreciendo resultados confiables con una carga computacional reducida. El estudio también aborda el desafío de la interpretabilidad de las ANNs y el llamado "problema de generalización" a través de las Redes Neuronales Informadas por la Física (PINNs) / [CA] Aquesta investigació contribueix a l'avanç de la modelació substitutiva com una tècnica potent en el camp de la simulació computacional que ofereix nombroses avantatges per a resoldre eficientment problemes complexos. En particular, aquest estudi destaca el paper crucial de la modelació substitutiva en la gestió d'aigües subterrànies. L'impacte del canvi climàtic és un enfocament central, i el primer estudi té com a objectiu construir models de dades substitutius per avaluar els efectes del canvi climàtic en els recursos d'aigües subterrànies, també en el futur. L'estudi implica la comparació entre mètodes estadístics i diferents tipus de Xarxes Neuronals Artificials (ANN). L'eficàcia dels models substitutius es va demostrar al nord de la Toscana (Itàlia), però pot estendre's fàcilment a qualsevol àrea d'interès. El mètode estadístic adoptat implica analitzar dades històriques de precipitació i temperatura juntament amb nivells d'aigua registrats en pous de monitorització. Inicialment, l'estudi explora possibles correlacions entre índexs meteorològics i índexs d'aigua subterrània; si s'identifica una correlació, s'emplea una anàlisi de regressió lineal. Aquestes relacions establertes s'utilitzen després per estimar els futurs nivells d'aigua subterrània en funció de les projeccions de precipitació i temperatura obtingudes d'un conjunt de Models Climàtics Regionals, sota dues Trajectòries de Concentració Representativa: RCP4.5 i RCP8.5. Posteriorment, es van implementar tres models diferents d'Intel·ligència Artificial (IA), AutoRegressive No Lineal amb Entrades Exògenes (NARX), Memòria a Llarg i Curt Terme (LSTM) i Xarxa Neuronal Convolucional (CNN) per avaluar l'impacte del canvi climàtic en els recursos d'aigües subterrànies per al mateix cas d'estudi. Específicament, aquests models van ser entrenats utilitzant directament dades històriques de precipitació i temperatura com a entrada per proporcionar nivells d'aigua subterrània com a sortida. Després de la fase d'entrenament, els models d'IA desenvolupats es van utilitzar per predir els futurs nivells d'aigua subterrània utilitzant les mateixes projeccions de precipitació i temperatura i escenaris climàtics descrits anteriorment. Els resultats van destacar diferents sortides entre els models utilitzats en aquest treball. No obstant això, la majoria d'ells preveu una disminució en els nivells d'aigua subterrània com a resultat de futures variacions en la precipitació i temperatura. Notablement, el model LSTM emergeix com l'enfocament més prometedor per predir futurs nivells d'aigua subterrània. Dins del mateix camp, es va desenvolupar una ANN amb la capacitat de simular les condicions d'aigua subterrània a la conca tancada de Konya, Turquia, un dels llocs pilot investigats com a part del projecte InTheMED. Aquest model serveix com a eina per examinar els impactes potencials del canvi climàtic i les polítiques agrícoles en els recursos d'aigua subterrània dins de la regió. L'objectiu final d'aquesta aplicació és proporcionar una eina fàcil d'usar, basada en la xarxa neuronal entrenada. La simplicitat inherent del model substitutiu, amb una interfície directa i resultats fàcils d'entendre, juga un paper crucial en els processos de presa de decisions. Pel que fa al transport de contaminants, es va implementar una ANN per resoldre diferents problemes directes i inversos. El problema directe tracta sobre l'avaluació de concentracions en pous de monitorització, mentre que el problema invers implica la identificació de fonts de contaminants i el seu historial de lliberació. Va demostrar eficiència en abordar problemes de transport tant directes com inversos, oferint resultats fiables amb una càrrega computacional reduïda. L'estudi també aborda el repte de la interpretabilitat de les ANNs i el denominat "problema de generalització" a través de les Xarxes Neuronals Informades per la Física (PINNs). / [EN] This research contributes to the advancement of surrogate modelling as a powerful technique in the field of computational simulation that offers numerous advantages for solving complex problems efficiently. In particular, this study emphasizes the pivotal role of surrogate modeling in groundwater management. By integrating key factors like climate change and leveraging machine learning, particularly neural networks, the research facilitates more informed decision-making, significantly reducing the computational cost of complex numerical models. The impact of climate change is a central focus and the first study aims to construct surrogate data-driven models for evaluating climate change effects on groundwater resources, also in the future. The study involves a comparison between statistical methods and different types of artificial neural networks (ANNs). The effectiveness of surrogate models was demonstrated in Northern Tuscany (Italy) but can easily extend to any area of interest. The adopted statistical method involves analyzing historical precipitation and temperature data along with groundwater levels recorded in monitoring wells. Initially, the study explores potential correlations between meteorological and groundwater indices; if a correlation is identified, a linear regression analysis is employed to establish relationships between them. These established relationships are then used to estimate future groundwater levels based on projected precipitation and temperature obtained from an ensemble of Regional Climate Models, under two Representative Concentration Pathways, namely RCP4.5 and RCP8.5. Then, three distinct Artificial Intelligence (AI) models, Nonlinear AutoRegressive with eXogenous inputs (NARX), Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) were implemented to evaluate the impact of cli-mate change on groundwater resources for the same case study. Specifically, these models were trained using directly historical precipitation and temperature data as input to provide groundwater levels as output. Following the training phase, the developed AI models were utilized to forecast future groundwater levels using the same precipitation and temperature projections and climate scenarios described above. The results highlighted different outputs among the models used in this work. However, most of them predict a decrease in groundwater levels as a result of future variations in precipitation and temperature. The study also presents the strengths and weaknesses of each model. Notably, the LSTM model emerges as the most promising approach to predict future groundwater levels. Within the same field, an ANN was developed with the capability to simulate groundwater conditions in the Konya closed basin, Turkey, one of the pilot sites investigated as part of the InTheMED project. This model serves as a tool for examining the potential impacts of climate change and agricultural policies on groundwater resources within the region. The final goal of this application, is to provide a user-friendly tool, based on the trained neural network. The inherent simplicity of the surrogate model, with a straightforward interface and results that are simple to understand, plays a crucial role in decision-making processes. Shifting to pollutant transport, an ANN was implemented to solve different direct and inverse problems. The direct problem deals with the evaluation of concentrations in monitoring wells, while the inverse problem involves the identification of contaminant sources and their release history. It demonstrated efficiency in addressing both direct and inverse transport problems, offering reliable results with reduced computational burden. The study also addresses the interpretability challenge of ANNs and the so called "generalization problem" through Physics-Informed Neural Networks (PINNs reducing the gap between data-driven modeling and physics-based interpretations. / Secci, D. (2024). Surrogate models, physics-informed neural networks and climate change [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/205793

The main focus of thesis are two information systems in the company and their overall analysis. These key systems are Orsoft information system and Oracle system. An important objective is the economic information systems to assess, describe and compare their use in accounting, the advantages and disadvantages, to propose any improvement of the economic information systems. The prime objective is an assessment of these two information systems and their proposals for possible improvements. The company Fezko Thierry first used 13 years of information system Orsoft. Then it was decided to implement Oracle system. One of these information systems will be described more in depth, the different accounting modules, the use of, any improvement of this system and the work of this information system. The operational objective of work is the development of economic systems, accounting modules and a description of the implementation of the Oracle accounting system to the company. After experience with both systems, it was found that the system is reliable Orsoft. Workers are also of the opinion that it is better to work with reliable data in the system Orsoft than working with many and many dates in Oracle.

Resumé v anglickém jazyce Summary This thesis is an attempt to show two authors and mainly two of their major works created in two different cultures, concluded by comparison of these two key works. Vladimír Neff, through his five-part cycle of novels Sňatky z rozumu (and following) dealing to the broad extent with the lives of members of Born and Nedobyl families, has proved his outstanding narrative skills which, together with a fair dose of irony and perfect knowledge of historical facts, make this pentalogy Neff's life work that in a remarkable manner made its way into the history of Czech literature. The author draws very accurate picture of Prague changing over a hundred of years and portrays the glamour of the age that is to be noted for the pursuit of economic and technological progress. Thanks to psychologically very thoroughly and accurately depicted key figures of the founders of abovementioned families, some of which were given the typically bourgeois features by Neff, the readers become close witnesses of their rise and fall. Jaroslaw Iwaszkiewicz in his relatively extensive work Čest a sláva presents the life of two generations on the historical and social background depicted in great detail. It captures the hasty and sometimes breakneck changes in the modern history and the fate of the Polish...

This theses focuses on intellectually talented pupils and from the narrower point of view on pupils of the Czech language at the second level of an elementary school. In the theoretical part, the author deals with a talent for the Czech language, definitions and concepts of a given issue, existing models, characteristics of gifted pupils and approaches of Czech schools towards the education of these pupils. This theses also focuses on linguistic intelligence and personality of teachers of gifted pupils. The aim is to reveal what are the experiences of the teachers of the Czech language with the gifted students and what differences are there between gifted individuals and ordinary students. For this solution is used a structured interview method. The next aim is to find out the attitudes of gifted pupils towards a taught subject. A semi-structured interview method is used to meet this aim. The final aim is to find differences between results of talented pupils and ordinary pupils in the subject of the Czech language. As an appropriate method was chosen a didactic test.

The diploma thesis presents a differentiation and description of various forms of interpersonal interaction with emphasis on cooperation and cooperative behaviour from the perspective of sociology, social psychology and psychology of personality. As the research is focused on preschool children, the characteristic traits of this age are specified, specific methods and forms suitable for the development of cooperative behaviour are described as well as the role of the teacher in this process. Moreover, the aim of fostering cooperative behaviour is linked to the educa?tional purpose outlined in FEP PE and Learning: The Treasure Within (four pillars of education). Furthermore, variables related to individual dispositions in respect to cooperative behaviour are briefly analysed. In the research section of the thesis, activities aiming at fostering cooperative behavior in preschool children are presented and evaluated based on their testing in a pre-school: whether/how they really worked in respect to eliciting cooperative behaviour in children.

Control theory of complex systems by utilization of artificial intelligent algorithms is relatively new science field and it can be used in many areas of technical practise. Best known algorithms to solved similar tasks are genetic algorithm, differential evolution, HC12 Nelder-Mead method, fuzzy logic and grammatical evolution. Complex solution is presented at selected examples from mathematical nonlinear systems to examples of anthems design and stabilization of deterministic chaos. The goal of this thesis is present examples of implementation and utilization of artificial algorithms by multi-objective optimization. To achieve optimal results is used designed software solution by multi-platform application, which used Matlab and Java interfaces. The software solution integrate every algorithms of this thesis to complex solution and it extends possible application of those approaches to real systems and practical world.

This master's thesis offers a brief introduction to evolutionary computation. It describes and compares the genetic programming and grammar based genetic programming and their potential use in automatic software repair. It studies possible applications of grammar based genetic programming on automatic software repair. Grammar based genetic programming is then used in design and implementation of a new method for automatic software repair. Experimental evaluation of the implemented automatic repair was performed on set of test programs.