Smart media and application

In the fourth industrial revolution, various inter-mediation such as theories, techniques, or implementation would be used. It contains computational intelligence, applied soft computing and fuzzy logic and artificial neural networks, intelligent contents security, model driven architecture and meta-modeling, multimedia contents processing and retrieval, vehicular N/W, big data, intelligence information processing, convergence/complex contents, smart learning, intelligent contents design management, methodology and design theory, intelligent media contents convergence/complex media, social media and collective intelligence, and social media big data analytic. To consider these interesting and significant issues which have related to the fast developing area of smart media and application, the editors have edited this special issue Smart Media and Application, and finally, it selected eleven papers. In Motion data acquisition method for motion analysis in golf by Hwang et al,1 it studied the process a system that obtains the same motion as the actual one by acquiring information on the motion of the subject using 15 inertial sensors, and using the information on the actual joint length and initial direction of the subject extracted from a depth camera. As a result, it shows that measurement error of information on the joint length and the foot stance of subjects extracted through the depth camera ranges from a minimum of 4.4% to a maximum of 6.94%. In NSCT domain–based secure multiple watermarking technique through lightweight encryption for medical images, which is written by Thakur et al,2 it defined non-subsampled contourlet transform (NSCT) domain, in which the proposed scheme first partitions the host image into sub-components and then calculates the entropy values. In Environmental monitoring system for intelligent stations by Li et al,3 it proposed the design of the high-speed rail station environmental monitoring system based on the LoRa communication technology, and it aims to enable the real-time monitoring of temperature, humidity, illuminance, and noise decibels in high-speed railway stations. Efficient dummy generation for considering obstacles and protecting user location is by Song et al.4 This paper was to study an efficient dummy creating techniques to improve user privacy protection, and as a result, it showed that it improved on other recent techniques. Key node selection based on a genetic algorithm for fast patching in social networks by Kim et al5 provided considerable amounts of personal information and shares this information with friends without space-time limitations. It tried to improve the patch propagation speed; it was important to select key nodes that are the starting points of the patch process. In this paper, the authors proposed a key node selection scheme based on a genetic algorithm to find the most significant contribution nodes of the patch propagation. Following the simulation result, it showed the proposed scheme propagates patches more rapidly than the existing one. In Effective computer-assisted pronunciation training based on phone-sensitive word recommendation by Jo et al,6 they proposed a computer-assisted pronunciation training system targeting unacceptable pronunciation due to confusion among contextual allophones, a problem that often emerges from phoneme-based feedback provided during pronunciation training. Finally, it showed that the proposed system results in the improvement of pronunciation skills through training sessions that use recommended words containing phoneme pairs that were initially pronounced incorrectly. In Dataset retrieval system based on automation of data preparation with dataset description model by Mun et al,7 it tried to overcome the problem by proposing a dataset description model that can express the requirements for data processing and dataset retrieval system based on automated data preparation and it could possibly provide good quality datasets for statistical learning applications using data preparation methods such as data acquisition, refinement, and organization. In An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia data analysis by Wang et al,8 it used 3DCNN in CNN part and ConvLSTM in RNN part. In A web-based group decision support system for multicriteria problems written by Conceição et al,9 it used a multiagent system to combine and process this information, using virtual agents that represent each decision-marker. The high level of usability that the system provides will contribute to an easier acceptance and adoption of this kind of systems. In Intelligent and semantic threshold schemes for security in cloud computing by Ogiela and Snasel,10 it tried to enhanced with new classes of solutions, including semantic protocols of secret sharing, and it is subjected to the process of splitting and hiding that could take various forms including that of services. In Estimation of a physical activity energy expenditure with a patch-type sensor module using artificial neural network by Kang et al,11 it proposed the most accurate method using a wireless patch-type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, it achieved RMSE of 0.1893, R2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. We would like to thank you our sincere appreciation of the valuable contributions to all authors.