This paper presents an entirely new approach to the use of virtual reality (VR) in the educational process for the needs of Industry 4.0. It is based on the proposed comprehensive methodology, including the design, creation, implementation and evaluation of individual courses implemented in a VR environment. An essential feature of the new methodology is its universality and comprehensiveness. Thanks to that, it can be applied in such areas as higher education, aviation, automotive, shipbuilding, energy and many others. The paper also identifies the significant advantages and disadvantages of VR-based education that may determine its use scope and profile. In addition, on the basis of the proposed methodology, a model of a training station using VR technology has been developed to enable the realization of training classes in the field of firefighting activities that should be undertaken during the hazard arising from the operation of a numerically controlled production machine. Results of the conducted training using this station were also presented. The study showed the potential of training based on a virtual environment to improve participants’ skills and knowledge. The development and implementation of adequate courses in the VR environment can reduce costs and increase the safety and efficiency of employees’ performed activities.
Internet traffic monitoring is a crucial task for the security and reliability of communication networks and Internet of Things (IoT) infrastructure. This description of the traffic statistics is used to detect traffic anomalies. Nowadays, intruders and cybercriminals use different techniques to bypass existing intrusion detection systems based on signature detection and anomalies. In order to more effectively detect new attacks, a model of anomaly detection using the Hurst exponent vector and the multifractal spectrum is proposed. It is shown that a multifractal analysis shows a sensitivity to any deviation of network traffic properties resulting from anomalies. Proposed traffic analysis methods can be ideal for protecting critical data and maintaining the continuity of internet services, including the IoT.
Gamification, in its nature, combines not only games but also the whole psychological environment. Thanks to this, a properly prepared implementation of gameplaying can encourage people to compete with others and achieve the set tasks and goals. A person feels fulfilled that through his actions has performed a mission or reached a new level. It stimulates them to continue their activity and self-improvement to be better and beat their records. Its advantage is also that it does not have to be limited to one technology or method—it can be realized both through a simple scenario and a corkboard with results, it can also be embedded, e.g., in a virtual or augmented reality. This article focuses on the gamification of dyslexia, a common disorder of developmental disorders among pupils. It affects about 10%–15% of school-age children. The research narrowed the field of the study to one of the aspects of developmental dyslexia—dysorthography and making spelling mistakes by people affected by this disorder. This work aims to present an original application which is using gamification as a supportive tool for the learning of school children with diagnosed dyslexia. The conducted study was based on the implementation of original algorithms and scenarios of gamification on mobile devices, especially smartphones. School children are following a gamification approach for a specified period. As a conclusion, it can be stated that the proposed framework and gamification can help in the learning of people with dyslexia.
The optimal computer network performance models require accurate traffic models, which can capture the statistical characteristic of actual traffic. If the traffic models do not represent traffic accurately, one may overestimate or underestimate the network performance. The paper presents confirmation of the self-similar nature of the selected protocols in the computer network communication layer. It shows that the good measure of self-similarity is a Hurst factor.
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