In the digital age, chatbots are widely applied in education. chatbots have become prevalent in education, proving to enhance learning outcomes. However, Students often face challenges in time and task management, which simultaneously test their ability to continue learning and cope with setbacks. To address this, we developed a "Learning Butler" chatbot system. incorporating the GPT language model and prompt engineering to support students' task and schedule management. This system fosters grit and a growth mindset through interactive dialogues, boosting their perseverance and adaptability in the face of challenges. thereby enhancing learning outcomes. Experiment results show significant improvements in students' grit, growth mindset and overall learning outcomes. Furthermore, surveys and web usage records also indicate the chatbot's beneficial impact on education.

The virtual reality (VR) market is expected to reach 202.7 billion dollars by 2028, at a compound annual growth rate of 24.74% over the forecast period 2023–2028. It motivates innovative VR services in touring, E-commerce, and social activities, and effective VR video streaming becomes essential. However, VR services are envisaged to consume a large amount of bandwidth, but current research primarily focuses on multimedia streaming for each individual user without considering the opportunity of view synthesis for multicast to reduce wireless resource consumption further. In this article, we formulate a new optimization problem VR Content Sharing and Multicasting (VCSM) and prove the NP-hardness. Then, we propose an approximation algorithm, named Efficient View Synthesis and Multicasting (EVSM), to select multicast views and their Modulation and Coding Schemes (MCS) for wireless VR services. Afterward, we extend EVSM to support dynamic user behaviors and increase scalability with distributed mobile edge computing. We also explore the intrinsic properties of view selections to find the optimal solution for regular user deployment. Experiment results show that EVSM can effectively reduce bandwidth consumption for VR services by more than 50$\%$.

This study investigates the effectiveness of a simultaneous multi-situational learning system with embodied recognition to enhance learners' outcomes. Traditional situational learning systems provide a single situation for knowledge explanation or application, which limits learners' ability to fully immerse themselves in the learning environment and receive immediate feedback. To address this, a simultaneous multi-situational learning system was proposed to create a digital space with multiple situations, allowing learners to immerse themselves in different scenarios simultaneously and facilitating reflective learning. An experimental study with 60 undergraduate students in a Hospitality Japanese course compared a control group using the traditional situational learning system with an experimental group using the proposed simultaneous multi-situational learning system. Results showed the proposed system significantly improved learning outcomes, understanding, and interest in learning. It enables the prompt creation of situational learning environments, contextualizes knowledge, and fosters reflective learning with real-time feedback. Overall, this study contributes to the field of education by providing a more effective approach to situational learning that can be easily implemented in classroom settings.

With the emergence of innovative applications for Virtual Reality (VR) in touring, E-commerce, and social activities, high-quality VR video streaming becomes essential. To support numerous wireless VR users, this paper aims to leverage video synthesis techniques to effectively reduce the multicast bandwidth consumption. It synthesizes the view in the video for a user from the one of a nearby user with similar Field of View (FoV), under the virtual distance and view angle constraints. We first formulate a new optimization problem, named VR Content Sharing and Multicasting (VCSM), and prove the NP-hardness. Then, we propose View Sharing Relation Graph (VSRG) to model the synthesis relation between each pair of views. We then design a new algorithm, named Bandwidth-Efficient Multicast with Synthesis (BEMS) to select multicast views and the corresponding MCS in wireless networks. Simulation results show that BEMS can effectively reduce bandwidth consumption by more than 50% compared with state-of-the-art wireless transmission schemes.

Inspired by the self-exploring learning approach, this paper proposes an object-learning system in which a robot interacts with objects to obtain their features and construct object concepts. The system consists of three kinds of features: interaction features, visual features, and intrinsic features. When the robot interacts with an object, it observes the changes in the object to obtain its interaction features. At the same time, the robot learns the visual features of the object. The intrinsic features are the properties of the object. Models of the relationships among the three kinds of features are constructed through an Artificial Bee Colony based Random Forest algorithm and a Convolutional Neural Network. The established models help the robot to predict the properties of new objects and to make decisions. Two experiments are constructed in this paper: the service-providing task and the stacking task. In the former, the robot decides on an appropriate object, using the object concept models, to accomplish an appointed task. In the second experiment, the robot uses a Gated Recurrent Neural Network to learn the stacking sequence of various objects. All the experimental results demonstrate that the robot can build object concept models by interacting with objects, and can utilize these models to accomplish various tasks.

Rainfall measurement is subjected to various uncertainties due to the complexity of measurement techniques and atmosphere characteristics associated with weather type. Thus, this article presents a video-based disdrometer to analyze raindrop images by introducing artificial intelligence technology for the rainfall rate. First, a high-speed CMOS camera is integrated into a planar LED as a backlight source for appropriately acquiring falling raindrops in different positions. The falling raindrops can be illuminated and used for further image analysis. Algorithms developed for raindrop detection and trajectory identification are employed. In a field test, a rainfall event of 42 continuous hours has been measured by the proposed disdrometer that is validated against a commercial PARSIVEL2disdrometer and a tipping bucket rain gauge at the same area. In the evaluation for 5-min rainfall images, the results of the trajectory identification are within the precision of 87.8%, recall of 98.4%, and F1 score of 92.8%, respectively. Furthermore, the performance exhibits that the rainfall rate and raindrop size distribution (RSD) obtained by the proposed disdrometer are remarkably consistent with those of PARSIVEL2disdrometer. The results suggest that the proposed disdrometer based on the continuous movements of the falling raindrops can achieve accurate measurements and eliminate the potential errors effectively in the real-time monitoring of rainfall.

Human vision is highly sensitive to perceive the contrast for interpreting images, so that the neurons of the brain will be excited to make recognition of the surroundings. In this study, the test patterns were inspired to reach the neurons excitation via the proposed pixel resonance concept, and then the quantum concept of physics was employed with random walk behaviour to analyse the test patterns for multiple interpretations. In simulation, similar alphabetic letters (C, O) and Chinese characters (玉, 王) are selected. Subsequently, various extents of noise are added including the different mean values and related standard deviations for the two test patterns. The results showed that the corresponding resonance maps with various morphologies can generate multiple resonance outputs. Furthermore, the simulation results indicated that the peak signal‐to‐noise ratio curves match those of the resonance maps perceived by using human vision. Based on this approach, it is concluded that the proposed pixel resonance algorithm can effectively simulate the multiple interpretations of the human vision system. Further, a novel physical model was presented to generate multi‐output patterns for a single image.