Entertainment Computing最新文献

Parental involvement is an essential factor that influences a child’s learning, behavior, and every other aspect of life, including online interactions. While holding parents responsible for the consequences of children’s online actions and overall well-being, we often overlook the importance of and need for means that can effectively support parents in interacting and engaging in activities with their children. In the present study, we describe and evaluate a collaborative family game called “CyberFamily” to facilitate parent–child collaboration and leverage family communication, intending to increase cybersecurity awareness among children aged 9–12. We also present the results of two user studies: one conducted with four parent–child dyads to test the feasibility of the game and a second user study conducted with 11 parent–child dyads focusing on evaluating the usability of CyberFamily. Our findings yielded positive feedback and showed that a collaborative family game like CyberFamily can help parents engage with their children’s online activities, leading to discussions and the potential for collaborative learning for both groups. We suggest that future researchers and designers consider and provide an active, engaging role for parents when developing solutions to raise cybersecurity awareness among children, rather than just having parents monitor and control children’s online access and activities.

Dynamic Difficulty Adjustment (DDA) has emerged as a notable solution to address the demand for adaptive gameplay in digital games. However, the DDA domain presents various research challenges that require careful consideration. In response, this study introduces an approach that merges insights from self-adaptive systems with the specific requirements of adaptive gameplay. Our contribution involves extending the DDA-MAPEKit framework, which was previously presented. Constructed on the modular MAPE-K loop foundation, this solution facilitates the assimilation of multiple DDA strategies. The primary objective is to provide tailored treatment for distinct game mechanics (or groups of mechanics) by constructing individual MAPE-K loops for each of them. Notable enhancements include the development of a descriptive player model involving continuous player profile assessment, performance calculation, and modifications to the rules’ system to better align with the player’s conditions. A proof of concept is executed by applying DDA-MAPEKit in a Space Shooter game to assess the viability of the proposed model. Simulations are conducted both with and without the DDA mechanisms. Encouraging results are obtained through a comparative analysis of the gathered data. Evidence points out that incorporating the DDA mechanism, implemented with DDA-MAPEKit, may effectively lead to adapting variables depicting the complexity of game mechanics following the player’s performance. This fact underscores the potential effectiveness of our approach in addressing the challenges within the realm of DDA.

In the era of digital entertainment media, the rapid dissemination of information and the widespread application of social media have a huge impact on user behavior and psychology. For students, the experience of digital entertainment content may have potential adverse effects on their mental health. The aim of this study is to propose a prediction model of university students’ psychological intervention based on optical network transmission based on parallel computing algorithm, in order to improve the computational efficiency and accuracy of the model. Then, a large number of psychological data of college students are collected, and feature extraction and model training are carried out by using machine learning algorithms. Finally, the predictive model of psychological intervention is simulated by parallel computing algorithm. The experimental results show that the optical network transmission based on parallel computing algorithm has high computational efficiency and accuracy in the prediction model of psychological intervention of college students. The model can reliably predict and evaluate the effect of psychological intervention of college students, provide an effective simulation tool for psychological intervention of college students, and help realize personalized and efficient psychological intervention measures.

With the rapid development of digital entertainment technology, people’s demand for green landscape reconstruction and virtual reality (VR) interactive art design is increasing. This study aims to explore the use of digital entertainment technology and entertainment gesture robots, combined with green landscape 3D reconstruction and VR interactive art design, to provide a new art experience. This paper uses digital entertainment technology to reconstruct the green landscape in 3D, and combines the interactive function of entertainment gesture robot to enable users to interact with the virtual environment. Then VR technology is used to present the reconstructed landscape to the user, and through immersive experience, users can feel the immersive art design. Through experimental evaluation and user survey, 3D reconstruction of green landscape and VR interactive art design using digital entertainment technology and entertainment gesture robots can provide a new entertainment experience. Users are more satisfied with the immersive art design expression, and experience the interaction and connection with the natural environment, which provides users with a unique and immersive art experience, further enriching the form and content of digital entertainment.

With the continuous progress of AI intelligent technology, the internet industry has undergone tremendous changes. The application of virtual reality and other digital entertainment technology has changed the classroom mode, and at the same time, current political classrooms place more emphasis on interaction with society. Therefore, this article constructs a political classroom AI intelligent system based on CNN network. This system studies the political classroom from the perspective of AI intelligent construction, using a combination of theory and practice. This article selects some research materials from both domestic and international sources. Firstly, it summarizes and analyzes relevant concepts such as convolutional neural networks, summarizes the importance of political classroom education, and analyzes the problems faced by political classrooms in the context of AI intelligence. Then, experimental analysis was conducted on the AI intelligent system in the political classroom. The experiment proved that the AI intelligent system in the political classroom has good working performance in the actual operation process, and the overall performance is also better than other systems. Implementing a network-based AI intelligent system for the political classroom and applying AI intelligent technology in practical applications can improve the personalization and accuracy of the political classroom.

E-learning is a social network learning method that greatly promotes the dissemination of knowledge. Students can use mobile network terminals for video learning and also share their learning materials, making the learning process interactive and entertaining. This article is based on virtual reality technology and studies the effectiveness of the interactive E-learning mode for political courses in the digital entertainment environment. This online teaching method benefits students and teachers a lot with its unique advantages, and is unanimously favored by experts and scholars. These courses not only provide students with a comprehensive understanding of Marxism, but also help them develop a correct world outlook, values, and ethics. Meanwhile, in the current digital technology environment, interactive online learning has the characteristics of virtualization and entertainment. Students can comment on social hot topics and share their favorite content through various media platforms. The results indicate that the application of artificial intelligence and virtual reality technology in the interactive e-learning mode of political networks has important advantages.

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.