Learning from Cedefop’s European skills and jobs survey

The exchange rate is one of the most monitored economic variables reflecting the state of the economy in the long run, while affecting it significantly in the short run. However, prediction of the exchange rate is very complicated. In this contribution, for the purposes of predicting the exchange rate, artificial neural networks are used, which have brought quality and valuable results in a number of research programs. This contribution aims to propose a methodology for considering seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of Euro and Chinese Yuan. For the analysis, data on the exchange rate of these currencies per period longer than 9 years are used (3303 input data in total). Regression by means of neural networks is carried out. There are two network sets generated, of which the second one focuses on the seasonal fluctuations. Before the experiment, it had seemed that there was no reason to include categorical variables in the calculation. The result, however, indicated that additional variables in the form of year, month, day in the month, and day in the week, in which the value was measured, have brought higher accuracy and order in equalizing of the time series.

https://www.mdpi.com/1911-8074/12/2/76/pdf

Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate

The enterprises operating in the e-business area represent an example of a sustainable entrepreneurship. These enterprises belong to those for which the people are the most valuable assets. Human capital management (HCM) represents a modern concept of people management that is based on the business strategy. Via measurement and assessment of the effectiveness of the utilization of human capital and effectiveness of investment in its development, this concept leads to the increase of performance and competitiveness of the enterprise within the context of sustainability. As part of the questionnaire survey, we found that enterprises in Slovakia have little use of the HCM concept. Despite the fact that they consider investing in education or training as the main form of investment in human capital, they do not evaluate the effectiveness of this investment in practice. By analyzing foreign studies, we have found that e-business enterprises consider IT education to be very important for their performance, competitiveness, and sustainability. The aim of the article is to explain the nature of HCM and to propose an approach to assess the effectiveness of investment in education as a part of the HCM process. Implementation of the approach was implemented in a fast-growing medium-sized IT and e-business enterprise where education is mainly implemented in the IT field. We also used an internal enterprise data analysis. The proposed example of HCM implementation in the part of evaluating the effectiveness of investment in education and the article can serve as a theoretical and practical aid to human resources managers in implementing the HCM concept.

Evaluating the Effectiveness of Investment in Human Capital in E-Business Enterprise in the Context of Sustainability

The ability to do business successfully and to stay on the market is a unique feature of each company ensured by highly engaged and high-quality employees. Therefore, innovative leaders able to manage, motivate, and encourage other employees can be a great competitive advantage of an enterprise. Knowledge of important personality factors regarding leadership, incentives and stimulus, systematic assessment, and subsequent motivation factors are parts of human capital and essential conditions for effective development of its potential. Familiarity with various ways to motivate leaders and their implementation in practice are important for improving the work performance and reaching business goals. Pearson’s chi-square test is used to test correlation between the motivation factors relating to career aspiration and education. Following the research results, the fact that there is dependence between the motivation factors relating to career aspiration and completed education can be stated. The motivation factors relating to career aspiration are important, even very important for highly educated employees and employees with upper secondary education. Following the research results, the fact that the requirements and expectations relating to career aspiration are more demanding when the education completed by employees is higher is confirmed.

https://www.mdpi.com/2071-1050/11/18/4985/pdf

Knowledge and Human Capital as Sustainable Competitive Advantage in Human Resource Management

This paper aims to explore the cyber-deception-based approach and to design a novel conceptual model of hybrid threats that includes deception methods. Security programs primarily focus on prevention-based strategies aimed at stopping attackers from getting into the network. These programs attempt to use hardened perimeters and endpoint defenses by recognizing and blocking malicious activities to detect and stop attackers before they can get in. Most organizations implement such a strategy by fortifying their networks with defense-in-depth through layered prevention controls. Detection controls are usually placed to augment prevention at the perimeter, and not as consistently deployed for in-network threat detection. This architecture leaves detection gaps that are difficult to fill with existing security controls not specifically designed for that role. Rather than using prevention alone, a strategy that attackers have consistently succeeded against, defenders are adopting a more balanced strategy that includes detection and response. Most organizations deploy an intrusion detection system (IDS) or next-generation firewall that picks up known attacks or attempts to pattern match for identification. Other detection tools use monitoring, traffic, or behavioral analysis. These reactive defenses are designed to detect once they are attacked yet often fail. They also have some limitations because they are not designed to catch credential harvesting or attacks based on what appears as authorized access. They are also often seen as complex and prone to false positives, adding to analyst alert fatigue. The security industry has focused recent innovation on finding more accurate ways to recognize malicious activity with technologies such as user and entity behavioral analytics (UEBA), big data, artificial intelligence (AI), and deception.

/pdf/threat-defense-cyber-deception-approach-and-education-for-3qejfodbzq.pdf

Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model

The aim of this article is to identify substantial factors affecting the motivation of universities’ students to be actively engaged in the education process and define recommendations for the increase of this motivation. As a result, the sustainability of education at universities will be supported, contributing to the increase of the value of human capital of students and, subsequently, to the generation of value for the stakeholder groups in those enterprises where the graduates will be employed. The research hypothesis is focused on the presence of differences in students’ motivation in relation to their gender, study program, and the year of study. To effectively achieve this aim, the analysis, comparison, and the synthesis of the theoretical background was performed, using available sources of secondary data found in the pieces of domestic and foreign professional literature. The pieces of knowledge obtained were supplemented and combined with pieces of information acquired from the questionnaire survey conducted, focusing on the motivation of students of informatics and management at a university in the Slovak Republic. As tools of statistical analysis, tests of independence suitable for nominal categorical data were applied. It was revealed that young people are motivated to study at a university, specifically at the Faculty of Management Science and Informatics, mainly by the prospect of better chances in the labor market, the possibility of getting a higher salary, and higher qualification. The motivation to study at a university in order to improve the opportunity of getting employed in the labor market was more frequently perceived by women. Despite the fact that the level of teaching is considered to be high by almost 50% of the students regardless of their gender, study program, or the year of study, their motivation also stems from their expectations related to their future jobs. The students of informatics expect to have a team of friendly colleagues, delightful and stimulating working conditions, and the opportunity to do meaningful work. Among the students of management, meaningful work was replaced by the opportunity for self-fulfillment. When focusing on other factors, the differences based on the gender, study program, or the year of study were not statistically significant. Based on these findings, specific measures for the faculty’s management were proposed.

https://www.mdpi.com/2071-1050/11/20/5577/pdf

The Motivation of Students at Universities as a Prerequisite of the Education’s Sustainability within the Business Value Generation Context

In this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical as well as the neural network approach which is an alternative way for time series modelling and forecasting in economics. The goal of this paper is to provide an alternative and reasonable way in modelling dynamic economic time series. We suggest an alternative approach for forecasting time series with non-constant volatility – we suggest and implement several neural network prediction models; we also use a large number of statistical models as well as different optimization techniques for artificial neural network. After discussing the basics of statistical volatility modelling and the basis of artificial neural networks we perform the experiment on real financial data. We quantify several ARCH and GARCH models; we also implement various RBF neural network prediction models. The comparative analysis of out-of-sample forecasts evaluated using MSE evaluation measures is performed. Finally, we state that suggested neural network models performed almost as good as the standard statistical models and are therefore reasonable and acceptable in economic modelling.

Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility

Summary. Enterprises are currently trying to bring new technologies into production and use new procedures and recommendations in various management...

Models of application economic value added in automotive company

Everyday usage of new information and communication technologies and recent changes in the economy increased the importance of educated IT employees with the right combination of technical and business skills. The purpose of this paper is to contribute to the management literature and practice by answering the question of what abilities today’s IT students should have to fulfil the requirements of their future workplace. Our surveys via the questionnaires and semi–structured interviews among IT employers and IT students identify the importance of hybrid skills and the increasing managers’ awareness of this topic. The findings indicated that not only the employers but also the students call for the increase of education efforts to improve these skills. The skills and knowledge recognized in our surveys should be mastered by the university educators to prepare graduates by providing innovative learning and teaching methods. For IT students, it is essential to acquire and develop technical skills as the core ones, but they also need the business skills to become desired professionals who can play a significant role in the future workplaces and the economy as a whole.

/pdf/importance-of-technical-and-business-skills-for-future-it-oyjvwigspi.pdf

Importance of Technical and Business Skills for Future IT Professionals

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

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Maria Durisova

Papers

The Motivation of Students at Universities as a Prerequisite of the Education’s Sustainability within the Business Value Generation Context

Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility

Models of application economic value added in automotive company

Importance of Technical and Business Skills for Future IT Professionals

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.