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The Dunhuang murals, notably the paintings on the interior walls of China's Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map's hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

Since December 2019, the outbreak of novel coronavirus pneumonia has brought great challenges to global public health, which is the most serious epidemic over the past hundred years. The urban rail transit is an important part of public transport in large cities with characteristic of intensive passengers and confined space, which is easy to become viral infection intermediary. In order to prevent and control the situation of the epidemic, the police's public security department for urban rail transit and the urban rail transit operation company have established a three-layer filter network, which is composed of safety inspection, patrol and temporary interrogation, and intelligent police service, and this network implements the deep learning technology to identify key persons, prohibited luggage, and the body temperature of passengers. For the problem of uncertainty in total passenger flow and its density, this paper proposes a method for re-establishing the passenger flow model to focus on data monitoring, and resetting the threshold value of alarm to control the passenger density. In view of the difficulty of passenger identification caused by mask during the epidemic, this paper proposes a systematic schema of timely adjusting face recognition algorithm, modifying the alarm threshold, using iris recognition system, carrying out information collision comparison, deep mining and intelligent judging, which discover the high-risk groups of epidemic prevention and control in time. China's police's public security department for urban rail transit aims at prevention of virus input, infection, riot, fake new, scientific prevention and control, and has made precise policy implementation to hold urban rail transit's covid-19 intelligent prevention and control work, finally won the battle and effectively guaranteed the people's life safety and health. [ABSTRACT FROM AUTHOR]

The research results of this paper provide new ideas and perspectives for the construction of trading market of China's carbon emission rights, so as to better alleviate the difficulties in China's energy conservation and emission reduction. Data evaluation and image analysis based on wavelet transform image processing technology and deep learning were used in the study. Moreover, wavelet transform was used to extract the edge, and compared with the results of several other methods for extracting edges, so as to further study and analyze the trend of carbon emissions trading. The fluctuations in the price of carbon emissions trading, the shift of state, and the period of ups and downs are affected by many random factors. Through research algorithms, we can estimate the situation, seize opportunities, and forecast the prospects. In addition, based on the research, this paper obtains the relevant strategies for carbon emissions trading and value assessment in China. [ABSTRACT FROM AUTHOR]

With the development of China's Internet, wireless network technology has been upgraded, and the rapid development of wireless network technology requires more advanced intelligent information processing technology to match. Therefore, based on the development of wireless network, this paper proposes intelligent tourism Management scheme. Firstly, the Wide & Deep Learning exploratory tourism route recommendation model was constructed. Then the Wide & DSSM exploratory recommendation algorithm combining the traditional recommendation algorithm with the depth model was proposed. Finally, the model and algorithm were tested through experiments. In this paper, the algorithm was used to study the semantic space vectors of both the user dimension and the Travel line dimension, and the data of the user dimension was fully utilized, which brought a significant improvement to the performance of the recommended system. [ABSTRACT FROM AUTHOR]

At present, China has great difficulty in obtaining the reliability of teaching data sources. In order to further improve the effectiveness of data mining and reduce the difficulty of data acquisition, this paper studies the design and simulation of integrated education information teaching system based on fuzzy logic. Bayesian algorithm can perform data mining, feature recognition and classification on data in big data, so that it can effectively process massive data sources. By weighting the different network structures, the number of undirected edges in the network is reduced, and then small data sets that can be processed by multiple traditional algorithms are sampled from the big data set, and data is generated by using the Bayesian network toolkit Samiam. The modules respectively generate data sets of different sizes and construct a teaching data source generation model. The experimental results show that RSEM on Child and Alarm data can take less time and achieve an accuracy of 86.17% compared with the whole data set under the same effect. This paper proposes a Bayesian network structure integration model, which can solve the problem of data acquisition difficulties, and is also a further improvement of data mining technology. [ABSTRACT FROM AUTHOR]

This paper attempts to apply recurrent neural networks (RNN) to price forecasts and financial trading. Compared with previous neural networks models, the recurrent neural network can better use the previous information to infer subsequent events, which is more suitable for price time series analysis. Long Short-Term Memory (LSTM) has made structural changes to the RNN to avoid long-term dependency problems. The empirical research uses the 2010–2017 price panel data of four kinds of soybean futures in China's futures market, and confirms the model's improved predictive ability through statistical tests. The empirical analysis of futures trading verifies the practice of these model strategies in terms of risked return. This paper improves and expands the application of recurrent neural networks model, and provides a new idea for applying artificial neural network algorithm to futures trading. [ABSTRACT FROM AUTHOR]