In this paper, we propose an integrated action classification and regression learning framework for the fine-grained human action quality assessment of RGB videos. On the basis of 2D skeleton data obtained per frame of RGB video sequences, we present an effective representation of joint trajectories to train action classifiers and a class-specific regression model for a fine-grained assessment of the quality of human actions. To manage the challenge of view changes due to camera motion, we develop a self-similarity feature descriptor extracted from joint trajectories and a joint displacement sequence to represent dynamic patterns of the movement and posture of the human body. To weigh the impact of joints for different action categories, a class-specific regression model is developed to obtain effective fine-grained assessment functions. In the testing stage, with the supervision of the action classifier’s output, the regression model of a specific action category is selected to assess the quality of skeleton motion extracted from the action video. We take advantage of the discrimination of the action classifier and the viewpoint invariance of the self-similarity feature to boost the performance of the learning-based quality assessment method in a realistic scene. We evaluate our proposed method using diving and figure skating videos of the publicly available MIT Olympic Scoring dataset, and gymnastic vaulting videos of the recent benchmark University of Nevada Las Vegas (UNLV) Olympic Scoring dataset. The experimental results show that the proposed method achieved an improved performance, which is measured by the mean rank correlation coefficient between the predicted regression scores and the ground truths.

Promoting learning passion for students in higher education is a vital and challenging issue. Due to the swift changes in society and technology development, many convenient and interesting technologies increasingly interfere with students’ learning performances and reduce students’ motivation for attending classes. For example, mobile games and social networking have led students to lose their passion for learning. MAPS (Mind Mapping, Asking Questions, Presentation, Scaffolding Instruction) is a flipped teaching model which has been proved as an effective approach to increasing students’ reflective learning, allowing students to devote to self-learning and recovering their passion for learning. Thus, this study employed a MAPS teaching strategy that adopts a point-earning approach to encourage students to learn from peer feedback while the teacher can understand students’ learning from the process. When the class is over, students can exchange the points for other rewards through a bidding mechanism which encourages students to regain passion for learning. The concept of Scaffolding Instruction points out that allowing students to sense positive peer pressure, which is caused by their peers’ improvements, and the atmosphere where their peers are studying hard can enhance their learning motivation and reflection. The empirical results of this study found that peer assessment and feedback can improve the learning effectiveness of students with poor performance.

Liben Building, constructed in 1822 AD, is located in Xinnan Village, Hukeng Town, Yongding District, Longyan City, Fujian Province, China. This square earthen building covers an area of about 2100 square metres. In 1931 AD, the building was sacked and burned down by bandits during a war, leaving behind only the remnants of the walls of the main building. In the meta‐universe environment, firstly, this study adopted blockchain DAO technology to conduct a study on the digital collection, storage, processing, display and dissemination of Liben Building, revealing the problems with digital regeneration of cultural heritage. Then, the questionnaire with 20 questions was designed, and 158 valid completed copies of the questionnaire were collected. Combining influence relationship with sample clustering analysis methods, this paper explored the findings of a study of the historical sites under blockchain digital heritage preservation and protection in the metaverse.

The integration of public art education and new media in colleges and universities remains a trend which cannot be stopped. As new media has had a beneficial impact on teaching content, forms of teaching, and teaching effects, it has also created various challenges for public art education in colleges and universities. While investigating the current state of integrating new media with public art education in colleges and universities, it was found that the impact of new media benefits more than it harms, whether it be online, in-person, theoretical, or practical teaching. Based on the current state, future policy focus, the direction of new media’s changes, and the depth of cultural and art resources, new media and public art education in colleges and universities will continue to deepen integration and show further improvement. Using a survey of more than a dozen universities in the Jiangsu Province and the results of a questionnaire survey of 116 college students, this paper probes into the changes in public art education in colleges and universities in the new media era and explores the future developmental pattern of public art education in colleges and universities both internally and externally.

At present, the Sudoku matrix, turtle shell matrix, and octagonal matrix have been put forward according to the magic matrix-based data hiding methods. Moreover, the magic matrices to be designed depend on the size of the embedding capacity. In addition, by determining the classification of points of pixel pairs after applying a magic matrix and by determining the traversal area, the average peak signal-to-noise ratio (PSNR) can be improved. Therefore, this topic intends to propose a data hiding method based on a 16 × 16 Sudoku matrix by using the 16 × 16 Sudoku matrix and extending it to a double-layer magic matrix. Low-cost data embedding methods are also studied, in order to improve the PSNR and maintain good image quality with the same embedding capacity.