Music plays an important part in the lives of people from an early age. Many parents invest in music education of various types for their children as arts and music are of economic importance. This leads to a new trend that the STEAM education system draws more and more attention from the STEM education system that has been developed over several years. For example, parents let their children listen to music since they were in the womb and invest their money in studying music at an early age, especially for playing and learning musical instruments. As far as education is concerned, assessment for music performances should be standardized, not based on the individual teacher’s standard. Thus, in this study, automatic assessment methods for piano performances were developed. Two types of piano articulation were taken into account, namely “Legato” with vibration notes using sustain pedals and “Staccato” with detached notes without the use of sustain pedals. For each type, piano sounds were analyzed and classified into “Good”, “Normal”, and “Bad” categories. The study investigated four approaches for this task: Support Vector Machine (SVM), Naive Bayes (NB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). The experiments were conducted using 4680 test samples, including isolated scale notes and kids’ songs, produced by 13 performers. The results show that the CNN approach is superior to the other approaches, with a classification accuracy of more than eighty percent.

Keywords Brainwaves Marine transportation Virtual realityThis work aims to investigate the possibility of reducing the occurrence of marine accidents by avoiding the unsuitability of marine transportation personnel.Recognizing that a majority of marine accidents arise from the improper reactions of transportation workers, we propose to evaluate the suitability of transportation personnel with the aid of brainwave analysis and Virtual Reality (VR) technology. By analyzing the brainwaves of a person facing operation situations simulated via VR, it may be possible to predict his/her responses in the situations and, thereby, determine if he/she is suitable to be involved in transportation work. Our preliminary experiments show that the brainwave analyses do have the capability of evaluating the appropriateness of the decisions made by a person when he/she faces operation situations.All rights reserved

Digital twin technologies are still developing and are being increasingly leveraged to facilitate daily life activities. This study presents a novel approach for leveraging the capability of mobile devices for photo collection, cloud processing, and deep learning-based 3D generation, with seamless display in virtual reality (VR) wearables. The purpose of our study is to provide a system that makes use of cloud computing resources to offload the resource-intensive activities of 3D reconstruction and deep-learning-based scene interpretation. We establish an end-to-end pipeline from 2D to 3D reconstruction, which automatically builds accurate 3D models from collected photographs using sophisticated deep-learning techniques. These models are then converted to a VR-compatible format, allowing for immersive and interactive experiences on wearable devices. Our findings attest to the completion of 3D entities regenerated by the CAP–UDF model using ShapeNetCars and Deep Fashion 3D datasets with a discrepancy in L2 Chamfer distance of only 0.089 and 0.129, respectively. Furthermore, the demonstration of the end-to-end process from 2D capture to 3D visualization on VR occurs continuously.

This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of "Yes" in the captured EEG, as opposed to a certain pattern of "No" when the user is relaxed. Accordingly, the task is to determine that the captured EEG is "Yes" or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.

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