Entertainment Computing最新文献

Traditional design methods usually present design ideas through plans and models, but this method can not truly simulate the design effect. Therefore, this research aims to use computer game simulation and virtual robotics technology to provide a more interactive and realistic design experience. The study creates a virtual street environment with elements such as streets, buildings and vegetation through computer game simulation. Participants can move and interact in this environment through virtual robots, experience the process of street landscape design, adjust the street layout, add landscape elements, and observe the design effect in real time according to their own design intentions. Compared with traditional design methods, the experimental results show that the design experience using computer game simulation and virtual robot technology is more vivid, creative and interactive. Participants can better perceive the design effect and adjust the design scheme in real time. This interactive entertainment design experience method can help designers and participants better understand and evaluate the design scheme, and improve the design effect and participation.

The development of digital technology has given music dissemination a technological advantage of high productivity and efficiency. With the continuous improvement of digital technology and the diversification of communication forms, information visualization design pays more attention to the audience’s experience and feelings. This article analyzes the application of pattern recognition based artificial intelligence in music entertainment environments and automatic recognition of music. Digital media can achieve bidirectional information transmission through user participation and feedback, and can provide customers with more entertainment music experiences through entertainment games and other means. This article focuses on the image enhancement processing, note recognition, and automatic recognition conversion of music scores in complex contexts, and develops an automatic music score recognition system to digitize music score information. Through online dissemination, the public can fully experience the beauty of music in the process of communication and interaction, and at the same time, music can be brought into the entertainment environment.

In order to understand the mathematical analysis of human motion vision capture image processing, a mathematical analysis of human motion vision capture image processing based on artificial intelligence is proposed. This paper firstly introduces the research progress, classification and several commonly used vision-based human motion tracking methods of motion capture technology. Secondly, the process and framework of capturing human motion video by using ordinary cameras and marking nodes are proposed, and the automatic tracking algorithm based on Camshift and Kalman filter is adopted. It verifies the effectiveness of the system, changes the traditional way of motion capture, and makes the process of capture more convenient when the capture effect meets the requirements. Finally, the performance of the human motion capture data retrieval algorithm based on video is evaluated comprehensively. It is compared with the latest literature in this field. In terms of time efficiency, for each online retrieval of data set, the proposed algorithm takes 0.056 s, while the methods of other scholars take an average of 1.5 s. Meanwhile, experiments are also conducted on public databases, proving the universality and scalability of the proposed algorithm. The algorithm proposed in this paper has greater advantages than the most advanced method of the same type, which verifies the effectiveness of the algorithm proposed in this paper.

We studied the use of machine learning models for training dance action models, collected dance action data, annotated and classified it, and established a training set for dance action models. We trained the training set to learn the feature representation and pattern recognition capabilities of dance actions. Through training and tuning the model, a model that can accurately recognize and generate dance movements was obtained. Evaluate the similarity between two dance movements and select the appropriate dance movements to form a smooth dance sequence. A planning algorithm was designed based on the kinematic and dynamic characteristics of robots to generate dance action paths suitable for the robot’s body conditions. Considering factors such as joint limitations, body stability, and smooth movement of the robot, generate a reasonable dance motion path while ensuring safety. Through on-site testing and data analysis of the system, it has been verified that it can effectively generate diverse and expressive dance movements, bringing a unique viewing experience to entertainment venues.

In existing English teaching, teachers often find it difficult to accurately understand students’ emotional changes, thus unable to adjust teaching strategies in a timely manner. Therefore, the introduction of computer vision and facial recognition algorithms will help improve the effectiveness of English teaching. This article is based on computer vision technology and facial recognition algorithms. Based on the sample data provided during the learning process, a model is learned and established to recognize facial expressions under different emotions and identify students’ emotional states. Use computer vision technology to capture and analyze students’ facial expressions in real-time. Then, facial recognition algorithms are used to recognize and classify the captured facial features to determine the current emotional state of the students. Finally, based on the students’ emotional state, the system will provide corresponding feedback. This technology based on computer vision and facial recognition algorithms can help teachers better understand students’ emotional changes, adjust teaching strategies in a timely manner, provide personalized learning feedback, and thereby improve students’ learning effectiveness and the quality of English teaching. This technology can also provide students with a better learning experience, enhancing their learning motivation and interest.

The present study describes the evaluation of Regulatia, an immersive web-based educational game for pre-service teachers to promote self-regulated learning (SRL). Based on Zimmerman’s model of SRL, learners immerse themselves in the underwater kingdom Regulatia and must find a way back home. Regulatia fosters the use of SRL-specific strategies and combines game elements with learning content. In this paper, the goal is to evaluate the first functional prototype of the game, examining its usability as well as users’ game experience to create a basis for an effective game in the future. The findings based on a sample of N = 31 pre-service teachers from a Southwestern German university indicate great usability and a good feedback system, high perceived knowledge improvement, and pleasant visual aesthetics. Potential for optimization was revealed for the scope and the level progression of the game.

Context:

In recent years, there has been a significant surge in global Social Engineering (phishing) attacks. This upsurge has prompted governmental bodies, organizations, and educational institutions to formulate strategies aimed at mitigating this threat.

Objective:

The primary objective of this research is to create a game-based solution that educates participants about URLs and evaluates their understanding through multiple-choice questions.

Methodology:

To attain the aforementioned objectives, a multifaceted approach has been adopted in this study. Firstly, an extensive literature review was conducted to gain insights into the problem and prior research on the subject. This review was instrumental in comprehending the game development framework, developmental tools, and various design models for game design. A digital adaptation of the game has been created utilizing the CONSTRUCT 3 platform. Secondly, an empirical evaluation was executed, involving participants engaging with the game and their learning assessed through survey. A survey method was employed to further gauge participants’ knowledge and to solicit feedback on the game’s design.

Results and Conclusion:

The survey results indicate a lack of significant outcomes or dependencies on the dependent variable. fun to play, ease to play, and game-based learning did not significantly predict avoidance behavior while the intention to play and phishing knowledge were the significant positive predictors of avoidance behavior, with intention to play showing the biggest contribution in the models 2 and 3. Correspondingly, the negligible difference between the R2 value and $△$ R2 in models 2 and 3 also confirmed the small variance of model 2 (explained in the paper). Consequently, the research asserts that the assessment of the gaming method has not yielded success and underscores the necessity for enhancements and further evaluation.

Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

The application of digital media in fitness environments can improve the entertainment of the fitness process, allowing users to watch entertainment videos while engaging in fitness activities. At the same time, we can add relevant game elements to the fitness process, such as playing tennis through gesture movements, thereby increasing user interest in fitness participation. Therefore, this article has developed a personalized fitness training system using optical sensors and intelligent algorithms, and conducted testing experiments on blood oxygen saturation under two sports states: running and cycling. This article analyzes the intelligent optimization algorithm for optical signals. This algorithm has strong adaptability and intelligence level, and can directly learn and adjust the model based on the characteristics of the data. The automation performance of the intelligent algorithm can effectively reduce labor costs and improve efficiency. Finally, this article provides a basic analysis and testing of the personalized fitness training system. Through the obtained data, personalized guidance is provided to users, helping people make fitness more scientific and intelligent, and improving fitness efficiency.