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Nowadays, robots are widely applied in life as well as industrial production, medicine, rescue, learning, and entertainment. There are many kinds of robots using different modern technologies such as manipulators, movement, and biomimetic robots; each type is applied according to its own role. Robots are also applied in training, especially in STEM training implemented with mobile robots. In this paper, we proposed models to design mobile robot combined with Android OS Tablet with a user interaction screen, voice control model, and AIDL IPC interactive model for remote control. We research the mobile robot using RockChip AI Processor RK3399Pro, which focuses on understanding the structure and operating mechanism of the AI Processor that RockChip robot uses, along with researching Android with IPC AIDL architecture and Automatic Voice Recognition technology to control robot to turn left, turn right, move forward, backward, and stop the movement by voice with three languages: Korean, English, and Vietnamese. We experimented on 1850 recording audio files including 1013 female voices and 837 male voices with 'latest_short,' 'latest_long,' and 'command_and_search' models. The results obtained a fairly high average confidence of 95% for English and Vietnamese languages and 87% for Korean. From results of this study, it will serve as a foundation for the research and development of a system to support students in learning by interacting with mobile robots through voice with multi-language. STEM education in the form of gamification of the curriculum, especially for extensive research on training programs can be applied in mobile robots with more flexibility. [ABSTRACT FROM AUTHOR]

• Progressive weight ShallowNet for analysis of spontaneous facial emotions. • Limit the alternative path for the gradient at the earlier stage and increase gradually. • The network is able to explore more feature space and vulnerable to perturbations. COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content. The proposed approach is based on novel left data mapping (LDM), pixel repetition method (PRM), RC4 encryption, and checksum computation. The input image of size [Formula: see text] is upscaled by using PRM that guarantees reversibility with lesser computational complexity. The binary secret data are encrypted using the RC4 encryption algorithm and then the encrypted data are grouped into 3-bit chunks and converted into decimal equivalents. Before embedding, these decimal digits are encoded by LDM. To embed the shifted data, the cover image is divided into [Formula: see text] blocks and then in each block, two digits are embedded into the counter diagonal pixels. For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels. A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection. The average peak signal to noise ratio (PSNR) of the stego-images obtained is 41.95 dB for a very high embedding capacity of 2.25 bits per pixel. Furthermore, the embedding time is less than 0.2 s. Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper. All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT-based smart health.