Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning

The PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model. Since the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data. To adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed. I have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods. By critically evaluating this architecture, I proved its validity and suggested directions for future research

E-Learning

E-learning enables students to pace their studies according to their needs, making learning accessible to (1) people who do not have enough free time for studying - they can program their lessons according to their available schedule; (2) those far from a school (geographical issues), or the ones unable to attend classes due to some physical or medical restriction. Therefore, cultural, geographical and physical obstructions can be removed, making it possible for students to select their path and time for the learning course. Students are then allowed to choose the main objectives they are suitable to fulfill. This book regards E-learning challenges, opening a way to understand and discuss questions related to long-distance and lifelong learning, E-learning for people with special needs and, lastly, presenting case study about the relationship between the quality of interaction and the quality of learning achieved in experiences of E-learning formation

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 22nd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 29 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with foundational research with a clear significance for software science

The frequency of falls in children judo training

Purpose: Falling techniques are inseparable part of youth judo training. Falling techniques are related to avoiding injuries exercises (Nauta et al., 2013). There is not good evidence about the ratio of falling during the training in children. Methods: 26 children (age 8.88±1.88) were video recorded on ten training sessions for further indirect observation and performance analysis. Results: Research protocol consisted from recording falls and falling techniques (Reguli et al., 2015) in warming up, combat games, falling techniques, throwing techniques and free fighting (randori) part of the training session. While children were taught almost exclusively forward slapping roll, backward slapping roll and sideward direct slapping fall, in other parts of training also other types of falling, as forward fall on knees, naturally occurred. Conclusions: Judo coaches should stress also on teaching unorthodox falls adding to standard judo curriculum (Koshida et al., 2014). Various falling games to teach children safe falling in different conditions should be incorporated into judo training. Further research to gain more data from groups of different age in various combat and non-combat sports is needed

Fear of crime and victimization among the elderly participating in the self-defence course

Purpose. Self-defence training could enhance seniors´ defensive skills and fitness. There is lack of evidence about fear and concerns of seniors participating in the self-defence course. Methods. 18 elderly persons (16 female, 1 male; age 66.2, SD=5.86) participated in the self-defence course lasting 8 training units (each unit 60 minutes). Standardized tool for fear of crime and victimization analysis previously used in Euro-Justis project in the Czech Republic (2011) was used in pretest and posttest. Results. We explored the highest fear of crime by participants in their residence area after dark (mean=2,77; median=3; SD=0,80), lower fear at the night in their homes (mean=2,29; median=2; SD=0,75) and in their residence area at the daytime (mean=2,00; median=2; SD=0,77) at the beginning of the course. We noticed certain decrease of fear of crime after the intervention. Participant were less afraid of crime in their residence area after dark (mean=2,38; median=2; SD=0,77), they felt lower fear of crime at the night in their homes (mean=2,00; median=2; SD=0,48) and in their residence area at the daytime (mean=1,82; median=2; SD=0,63). Conclusions. The approach to self-defence teaching for elderly should be focused not just on the motor development, but also on their emotional state, fear of crime, perception of dangerousness of diverse situations and total wellbeing. Fear of crime analysis can contribute to create tailor made structure of the self-defence course for specific groups of citizens