Three years after the COVID-19 pandemic emerged, we have adapted to the new normal, especially in the education field. Learning with video conferences has become our daily activity, and learning tools have gotten more prominent attention to gain student engagement, especially in emergency remote teaching (ERT). Since the trends of metaverse campaigns by meta, augmented reality (AR) has increased recognition in education contexts. However, very little research about the acceptance of augmented reality in video conferences, especially among university students. This paper aims to measure acceptance of AR in video conferences to motivate and inspire students to gain benefits and get impactful technology in the learning process. The research gathered data from a survey of 170 university students (from 5 majors in the study program and 17 different demographic areas) using unified theory of acceptance of technology 2 (UTAUT2). The result reveals that variables significantly impact acceptance: performance expectancy, hedonic motivation, and habit. The least significant but still positive effects are effort expectancy, social influence, and facilitating conditions. The study will provide helpful information on AR technology in video conferences and help top-level management in the university that provides online/distance learning in the early diffusion stage for metaverse in education.

Controlling the time evolution of the population of two states in cavity quantum electrodynamics is necessary by tuning the modified Rabi frequency in which the extra classical effect of electromagnetic field is taken into account. The theoretical explanation underlying the perturbation of potential on spatial regime of bloch sphere is by the use of Bagrov–Baldiotti–Gitman–Shamshutdinova–Darboux transformations [Bagrov et al., “Darboux transformation for two-level system,” Ann. Phys. 14, 390 (2005)] on the electromagnetic field potential in one-dimensional stationary Dirac model in which the Pauli matrices are the central parameters for controlling the collapse and revival of the Rabi oscillations. It is shown that by choosing σ1 in the transformation generates the parabolic potential causing the total collapse of oscillations, while {σ2,σ3} yield the harmonic oscillator potentials ensuring the coherence of qubits.

Due to hypercompetitive internet service provider market, customers can easily move from one companies or operator to another if they did not obtain a good service. This customers' movement is a major issue for companies as the reason most often found why customer churn. Churn management is an important program for companies to maintain valuable customers thus predicting customer churn is crucial. In literature, churn analysis has been widely studied with a various churn analysis techniques. These techniques mostly utilize customer complaint data, customer tenure, customer usage, customer payment behaviour, etc. This paper employs customer opinions from Twitter with some specific keyword. We investigate whether measurements of collective sentiment (mood) states about a product, extracted from Twitter feeds, are correlated to the value of the churn rate of the observed product. This study examines the text content of daily Twitter feeds by Convolution Neural Network that measures positive, negative or neutral sentiment. Cross-validate the resulting mood time series with Granger causality analysis was conducted. A Recurrent Neural Network was then used to test the hypothesis that mood states from Twitter are predictive of changes in churn rate values. The results indicate that the accuracy of churn rate predictions can be improved by the inclusion of specific mood dimensions, that is negative sentiment, but not others. The Mean Average Percentage Error (MAPE) is about 1.47%.

Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.