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Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR]

Ink element is one of the unique cultural symbols in China, which has profound cultural heritage and spiritual connotation. Animation is a form of art design, and it is a good way to convey and express information in an intuitive artistic image. The use of ink and wash elements for animation design has important value and significance in the start of Chinese culture. It can develop animation design, innovate animation creation forms, and can highlight Chinese style. In this article, we have studied the creation process, creation format, and creation method of using angular digital technology to make ink VR design. It combines art and technology and combines practical experience with wireless sensors. Based on HTML5 interactive technology, we have studied its advantages more deeply, better introduced the theme of ink element animation, solved the problem of realizing ink effect in 3D virtual environment, and better introduced ink animation in virtual reality environment. During the production process, students will learn how to simulate digital objects, render ink and wash effects, build a virtual environment in the later stage, and explore the knowledge, operation, and meaning of ink and wash elements and the animation design process. This paper studies the wireless sensor and HTML5 interactive technology and applies it to the design of ink and wash animation VR, aiming to promote its vigorous development and application. [ABSTRACT FROM AUTHOR]

At present, virtual-reality fusion smart experiments mostly employ visual perception devices to collect user behavior data, but this method faces the obstacles of distance, angle, occlusion, light, and a variety of other factors of indoor interactive input devices. Moreover, the essence of the traditional multimodal fusion algorithm (TMFA) is to analyze the user's experimental intent serially using single-mode information, which cannot fully utilize the intent information of each mode. Therefore, this paper designs a multimodal fusion algorithm (hereinafter referred to as MFA, Algorithm 4) which focuses on the parallel fusion of user's experimental intent. The essence of the MFA is the fusion of multimodal intent probability. At the same time, this paper designs a smart glove based on the virtual-reality fusion experiments, which can integrate multichannel data such as voice, visual, and sensor. This smart glove can not only capture user's experimental intent but also navigate, guide, or warn user's operation behaviors, and it has stronger perception capabilities compared to any other data glove or smart experimental device. The experimental results demonstrate that the smart glove presented in this paper can be widely employed in the chemical experiment teaching based on virtual-reality fusion. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the activity, resistance, clonality of MIC distribution, and the correlation between virulence and resistance genes and biofilm formation of omadacycline (OMC) in clinics for Streptococcus agalactiae isolates from China. 162 isolates were collected retrospectively in China. The S. agalactiae were collected from the body's cervical secretions, wound secretions, ear swabs, secretions, semen, venous blood, cerebrospinal fluid, pee, etc. The MIC of OMC against S. agalactiae was determined by broth microdilution. The inhibition zone diameters of OMC and other common antibiotics were measured using filter paper. D-test was performed to determine the phenotype of cross resistance between erythromycin and clindamycin. In Multilocus sequence typing (MLST), some commonly-detected resistance genes and virulence gene of these S. agalactiae isolates were investigated using polymerase chain reaction (PCR). Biofilms were detected by crystal violet staining. Our data demonstrated the correalation of the biofilm formation and OMA antimicrobial susceptibility of S.agalactiae clinical isolates with the carrier of virulence gene scpB. Conclusively, OMC exhibits the robust antimcirobial activity against clinical S. agalactiae isolates from China compared with DOX or MIN, and the carrier of the virulence gene scpB might correlate with the biofilm formation in OMC-resistant S. agalactiae. [ABSTRACT FROM AUTHOR]

Digitalization brings challenges and new opportunities to the development of landscape gardening, "smart gardening," which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts "digital landscape design logic" to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools. [ABSTRACT FROM AUTHOR]

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]

In order to improve the effect of ballet training, this paper combines digital technology to improve the motion recognition process, analyzes the imaging process, analyzes the system process combined with the human node model, and combines digital feature recognition technology to construct a ballet training motion correction system. Moreover, this paper inputs the ballet training action recognition results into the system and compares the standard actions to judge the rationality of the actions and builds a ballet training action correction system based on digital feature recognition. In addition, this paper designs experiments to conduct ballet training movements in the system effect evaluation. The experimental research verifies that the digital feature recognition technology proposed in this paper can play an important role in ballet movement recognition and has a good action correction effect. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, social sport is more and more valued and favored by the people as a universal and nationwide sport, but it should be noted that social sport involves a wide range of aspects, but due to its particularity, it is also constrained by economic development. In view of these needs and limitations, three technical methods of entropy method, RSR (rank-sum ratio), and TOPSIS are introduced, to sort out the development of social sports, national physical development, social sports guidance, and the number of people to be measured in various places based on the relevant technologies of smart cities in this paper, realize the application analysis of social sports in the public service level. This paper aims to provide a scientific evaluation and evaluation method for social sports information cloud services. The results of simulation experiments show that the evaluation of social sports information cloud service quality based on smart cities is effective, and the comprehensive application of methods can be implemented perfectly, and the further promotion and popularization of social sports services can be realized. [ABSTRACT FROM AUTHOR]

With a tremendous growth in the number of scientific papers, researchers have to spend too much time and struggle to find the appropriate papers they are looking for. Local citation recommendation that provides a list of references based on a text segment could alleviate the problem. Most existing local citation recommendation approaches concentrate on how to narrow the semantic difference between the scientific papers' and citation context's text content, completely neglecting other information. Inspired by the successful use of the encoder-decoder framework in machine translation, we develop an attention-based encoder-decoder (AED) model for local citation recommendation. The proposed AED model integrates venue information and author information in attention mechanism and learns relations between variable-length texts of the two text objects, i.e., citation contexts and scientific papers. Specifically, we first construct an encoder to represent a citation context as a vector in a low-dimensional space; after that, we construct an attention mechanism integrating venue information and author information and use RNN to construct a decoder, then we map the decoder's output into a softmax layer, and score the scientific papers. Finally, we select papers which have high scores and generate a recommended reference paper list. We conduct experiments on the DBLP and ACL Anthology Network (AAN) datasets, and the results illustrate that the performance of the proposed approach is better than the other three state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

With the continuous development of network communication technology, the domestic network has covered the homes of ordinary people, but the indoor coverage system of the network cable network needs to be further improved. The optical fiber distribution system is a signal coverage system integrating GSM, TD-SCDMA, and other standards. It is mainly composed of system access unit, system expansion unit, and system remote unit. Wireless network is an important communication technology, which is easy to use and can be connected wirelessly over long distances. The purpose of this paper is to study an optical fiber distribution system for indoor coverage design of wireless networks, so as to achieve faster and more efficient coverage. In this paper, a wireless network signal processing algorithm is proposed, and a systematic method for designing the wireless network indoor coverage of the optical fiber distribution system is proposed, and the algorithms are compared and tested. The test results in this paper show that the signal strength obtained by the algorithm in this paper is −54.83 dB, while the traditional artificial design algorithm is only −45.97 dB. In addition, the wireless network base point of the algorithm in this paper is also 3 lower than the traditional one. Moreover, the coverage rate achieved by the algorithm in this paper is 3.17% higher than that of the network cable network base point layout given by the genetic algorithm, and the average received signal strength is 3.34 dB higher. In addition, the algorithm in this paper takes 9.1 s, 11.2 s, and 5.3 s for the three scenarios of underground parking lot, large shopping mall, and residential residential area, while the traditional manual design algorithm takes 87.9 s, 108.8 s, and 51.7 s, respectively. The algorithm in this paper is only about 1/10 of the traditional artificial design algorithm. Therefore, it can be concluded that the coverage effect of the wireless network indoor coverage system of the optical fiber distribution system designed in this paper is significantly improved. [ABSTRACT FROM AUTHOR]

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper. [ABSTRACT FROM AUTHOR]

The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

Due to the poor effect of the traditional vocational education platform, it is difficult to meet the requirements of the current Internet education platform. To solve this problem, this paper designs a vocational education platform system based on blockchain and Internet of Things (IoT) technology. First, the system access control strategy based on blockchain technology is introduced into the teaching system so as to improve the security of the teaching system. Then, in order to solve the single point of failure and efficient transmission problems of access control in the IoT environment, this paper proposes an access control strategy based on main side chain cooperation. This strategy introduces a side chain to expand the blockchain and improve the data carrying capacity of the blockchain. To meet the requirements of high-speed data transmission, this paper designs an access control model suitable for the main side chain blockchain structure. Experimental results show that compared with other algorithms, the data request speed of the system in this paper is faster, and the storage space is smaller. Therefore, the proposed technology based on blockchain and IoT can meet the use requirements of vocational education platforms. [ABSTRACT FROM AUTHOR]

Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent. [ABSTRACT FROM AUTHOR]

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm composition network from the perspective of machine learning, based on an in-depth study of related literature. At the same time, this paper examines the characteristics of music and develops a model for recognising musical emotions. Using the model's information entropy of pitch and intensity to extract the main melody track, note features are extracted from bar features. Finally, the cosine of the vector included angle is used to judge the similarity between feature vectors of several adjacent sections, allowing the music to be divided into several independent segments. The emotional model of music is used to analyze each segment's emotion. By quantifying music features, this paper classifies and quantifies music emotion based on the mapping relationship between music features and emotion. Music emotion can be accurately identified by the model. The model's emotion recognition accuracy is up to 93.78 percent, and the algorithm's recall rate is up to 96.3 percent, according to simulation results. The recognition method used in this paper has a higher recognition ability than other methods, and the emotion recognition result is more reliable. This paper can not only meet the composer's auxiliary creative needs, but it can also help intelligent music services. [ABSTRACT FROM AUTHOR]

As an important part of the education system, college physical education directly affects the comprehensive development of college students' physical and mental quality. It is necessary to build a scientific and efficient evaluation index of college physical education teaching quality. Physical fitness monitoring is an important indicator for the quality evaluation of physical education. However, how to achieve lightweight, portable, and high-accuracy quantitative physical fitness monitoring is currently a major challenge. In order to solve the above challenges, this paper proposes a method of constructing a physical education quality evaluation index based on wearable devices. The wearable device collects human ECG signals, calculates the exercise intensity of participating students, and realizes quantitative evaluation of the quality of physical education teaching. Aiming at the problems of complex equipment and low accuracy of the existing exercise intensity detection methods, this paper proposes an ECG signal wave group detection algorithm based on a one-dimensional convolutional neural network (1D-CNN) to obtain the heart rate variability signal more accurately. After obtaining the ECG feature vector, the SVM classifier is used to predict the exercise intensity. In order to verify the effectiveness of the method in this paper, the real data collected from students of one university and a public available dataset are selected for experiments. The experimental results show that the method proposed in this paper achieves a good performance. [ABSTRACT FROM AUTHOR]

From the emergence of the concept of the Internet of Things to today, China has always been very concerned about the development of the Internet of Things technology. The research and application of Internet of Things technology has become an important measure for the country to promote supply-side reform. The Internet of Things technology is the core innovation content of the country and an important measure to realize the smart city. This paper analyzes the cutting-edge research results in the field of Internet of Things abroad. This paper focuses on the application of IoT technology in English terminology research. This paper specifically analyzes the feature extraction methods of Internet of Things terms and studies the application content of Internet of Things technology in English-Chinese translation. We specifically conduct translation research on the content in "The Internet of Things: Enabling Technologies, Platforms, and Use Cases," focusing on the translation of scientific and technological terms. As a kind of technical English terminology, IoT English terminology has the characteristics of strong professionalism and rigorous semantics. This article specifically translates the original part of the practice report and classifies the terms that appear in it, including IoT English terms for which canonical translations have been made and IoT English terms that do not have standardized translations. The research will further carry out the investigation and analysis work. The research is to identify the terms that have standardized translation, especially the detailed research of the terms of the industry field. This paper selects standard translations and summarizes the translation methods of terms from three aspects: abbreviations, compound words, and semi-technical words. The research results provide guidance for terminology translation. This article translates terms that do not yet have a canonical translation. Based on terminology characteristics and existing terminology translation methods, this paper proposes a variety of translation methods such as literal translation, decomposing and combining, and non-translation. The study described in detail with examples. The research results of this paper are expected to provide some reference for translators engaged in the translation of scientific and technological documents. [ABSTRACT FROM AUTHOR]

Under the modern environment, the reconstruction of enterprise's core competitiveness depends not only on capital and technical strength, but also on the overall strength of its human resources. At the same time, effective allocation and rational use of talents are needed to create good performance for enterprises. Enterprise human resource management is the key part of the whole enterprise management. At the same time, it is also a necessary preparation for the continuous development and innovation of enterprises. In the whole process of human resource management, the core work is person-post matching. Only by promoting the reasonable implementation of person-post matching can other management work be carried out smoothly. This paper expounds two major elements in human resource management, namely, the concept and measurement of person-post matching and the principle of person-post matching. And the factors in the matching of people and posts are analyzed. This paper probes into the implementation of person-post matching in enterprise human resource management. Based on this, this paper puts forward a depth model of accurate matching between people and posts based on ability perception. On the basis of studying the optimization of human resource scheduling, this paper takes into account three factors: resource constraints, heterogeneity of employee efficiency and time sequence relationship, and uses integer linear programming theory to model the system with the shortest construction period as the goal. The research shows that the accuracy of this algorithm can reach about 94%, which is about 8% higher than the traditional algorithm. It has certain superior performance. This will provide some reference for related researchers. [ABSTRACT FROM AUTHOR]

With the rise of cultural and creative industries, cultural creativity has gradually become an important factor to promote the value of design in the future, and it is also a trend to integrate "cultural elements" into the design of products. At present, the cultural and creative industries in Western countries and Taiwan are the mainstay of their economic development. We should actively absorb their successful experiences and, with the support of national policies, carry out effective and lasting development of them, as well as continuously improve the quality of cultural and creative products. In today's steady economic development, the emotionalization of cultural and creative consumption has gradually formed a new consumption trend. When cultural and creative consumers buy stationery, they will inevitably have three situations: purchasing instinct, purchasing behavior, and reflection. Therefore, this paper adopts the method based on machine learning to conduct in-depth research on the users of cultural and creative products of "Forbidden Day and Night Set Gift Box." Through the research on the cultural and creative consumption intention of "Forbidden Day and Night Set Gift Box," it can effectively promote the development of domestic cultural and creative enterprises, and then promote the customer satisfaction of cultural and creative enterprises. This paper makes a detailed analysis of it from the perspectives of data processing, feature engineering, classification prediction models, and future development directions. By studying the cultural and creative behavior of users, a deep learning model based on neural network is established. The feature extraction, feature preprocessing, feature selection, and asymmetric data collection in the process of data processing are discussed in depth. In order to further improve the prediction accuracy and conduct more in-depth research, this paper establishes a deep learning prediction model based on depth. This model is experimentally validated and it can be observed that the model is 10% more efficient than the traditional model, so the model can learn data better from the user's behavior in several aspects, and the proposed and practice of this model has good practical significance. [ABSTRACT FROM AUTHOR]

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy. [ABSTRACT FROM AUTHOR]

Transformers are an essential part of power production. Insulating paper began to be widely used in transformers in the 1990s. The superior aramid nanofiber as the matrix gives the aramid nano-insulating paper excellent mechanical properties, insulation performance, temperature resistance, and flexibility. At first, the heat resistance and service life of insulating paper should be satisfied for use in electrical equipment. With the continuous development of power equipment, people have put forward higher requirements on the properties of insulating paper, especially heat resistance and electrical properties. Insulation paper made of aramid fibers have better thermal stability and more advantages in electrical and mechanical properties, which can significantly improve the service life and safety of electrical appliances. The purpose of this article is to study the use of aramid nanopaper-based insulating materials in transformers to explore the effect of transformer discharge mechanism on aramid nanopaper-based insulating materials. This paper proposes to design multiple deep learning models to identify the discharge mode of the voltage transformer, find the characteristic signal, and carry out related tests on the discharge signal of different modes, and find the maximum temperature value of the aramid nanopaper-based insulating material for industrial use. The experimental results in this paper show that the aramid nanopaper-based insulating material can be used in transformers discharge detection well, and the safety rate is increased by 20%. [ABSTRACT FROM AUTHOR]

Under the present background, optimizing the existing urban rail transit network is the focus of urban rail transit construction at present. Based on DL, this paper constructs the optimization algorithm of urban rail transit network route planning. According to the current urban layout and urban planning, build a suitable rail transit network line form; according to the function, the types of urban rail transit stations are divided, and the optimization of urban rail transit network lines is realized. In addition, according to the K short path algorithm, this paper calculates the effective path between any stations of rail transit and, according to the model, allocates the passenger flow to each path. Experimental results show that the accuracy of real-time traffic flow prediction by this algorithm can reach 94.98%, which is about 9% higher than other methods. This algorithm can effectively optimize the route planning of urban rail transit network. This verifies the effectiveness of the route planning optimization algorithm proposed in this paper. Using the algorithm in this paper for line planning can get good real time, rationality, and optimality. [ABSTRACT FROM AUTHOR]

Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users' satisfaction. [ABSTRACT FROM AUTHOR]

The modernization of education necessitates the enhancement of instructional quality. This paper begins with a basic overview of the current state of film and television art education and then goes on to examine the current state of film and television art education and develop a virtual simulation-based teaching mode for film and television art. This paper develops an instructional quality evaluation model to assess the feasibility of this teaching mode. In this model, GA is used to optimize BPNN's initial weights and thresholds, reducing the time it takes BPNN to find the weights and thresholds that satisfy the training termination conditions. Improve BPNN's prediction accuracy and convergence speed in relation to instructional quality evaluation results. The evaluation accuracy of this algorithm can reach 94.31 percent, which is 6.35 percent higher than other methods. Furthermore, the use of virtual simulation technology in the teaching of film and television art in higher education institutions can have a significant impact and have far-reaching research implications. The findings of this study can be used to improve the teaching of film and television art in institutions of higher learning. [ABSTRACT FROM AUTHOR]

This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality. [ABSTRACT FROM AUTHOR]

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor's level in the identification of various lung diseases. [ABSTRACT FROM AUTHOR]

In recent years, China's foreign trade has continued to develop and achieved remarkable results, and the economic development trend has been steady and positive overall. However, although there are new opportunities for the development of China's foreign trade, it also faces new challenges and dilemmas. Therefore, in the current international trade situation, it is of great significance to study the impact of China's foreign trade on industrial structure and find the key points for the development of endogenous economic dynamics on this basis. China's industrial structure has undergone a clear process of upgrading and is a major trading country for industrially manufactured goods. The results of the analysis of the subsectors show that the trend of structural upgrading within the industry is equally significant. At the same time, there is often a significant trend of synergistic changes in the percentage of processing trade and the percentage change of trade in high-tech products. Based on this, this paper analyzes the correlation between China's foreign trade structure and industrial structure based on the correlation of mutual influence. The evolutionary algorithm has the advantages of a strong ability to search for global optimal solutions and good robustness, which is applied as the core algorithm of this paper. The study in this paper examines the interaction between industrial structure and trade structure from the perspective of matching the association between industrial structure and trade structure on the basis of measuring the industrial structure and trade structure at the regional level. The experimental results demonstrate the validity of the model in this paper. [ABSTRACT FROM AUTHOR]

By combining virtual reality, the digital twin system has been deeply applied in many places. Especially with the upsurge of national sports and more attention to sports, people have also begun a more in-depth study on the strategy of sports training. This paper aims to explore the application of the Brazilian jujitsu training strategy and interactive control method in the new era. Based on the sports skills of Brazilian jujitsu, combined with digital twin technology and wireless sensor network technology, this paper develops a sports training system based on Brazilian jujitsu. It can collect the action information of the trainer and twin it into the simulation system so as to carry out better and more effective confrontation training. The experimental results show that the average delay time is 5.2 ms and the maximum delay time is 6.5 ms, which is less than the limit delay time of 20 ms. Compared with the control group, it achieved quite good results. It shows the effectiveness of the system designed in this paper for trick jujitsu training. [ABSTRACT FROM AUTHOR]

The central issue in finance is how to select a portfolio in the financial market. The traditional artificial fish swarm algorithm (AFSA) is optimized in this paper, and the improved AFSA is used to solve the portfolio model. This model generates a uniform distribution operator using uniform distribution and combines it with the basic fish swarm algorithm. Uniform variation occurs when the variance of the optimal value of continuous convergence is within the allowable error. In this manner, the fish can escape the trap of the local extremum, obtaining the global optimal state. To validate the feasibility of improving AFSA, this paper conducts simulation experiments on portfolio problems using MATLAB tools. Experiments show that this model has an accuracy of 93.56 percent, which is 8.43 percent higher than that of the NSGA-II model and 3.76 percent higher than that of the multiobjective optimization model. The experiment shows that the algorithm in this paper can solve these types of problems well and that, using this model, the optimal portfolio investment decision scheme satisfying investors can be obtained. The optimized AFSA presented in this paper can serve as an important reference for investment portfolios and has a wide range of application possibilities in the investment market. [ABSTRACT FROM AUTHOR]

Based on the concept of "smart tourism," this paper designs and implements a tourism management information system based on PSO-optimized NN. The foreground tourism web page of the system adopts DIV + CSS mode for page planning and layout, PHP as the client script language, and SQL server as the database to store and analyze user information. At the same time, the system adds personalized components to the user's search ranking results, so that the routes and scenic spots presented in front of users in the result interface are more in line with users' consumption habits. In order to verify the performance of the model and algorithm constructed in this paper, several experiments were carried out in this paper. Experimental results show that the prediction accuracy of this algorithm is 94.67% and the recall rate is 96.11%. This algorithm can overcome the disadvantages of traditional algorithms and provide some effective suggestions for tourism management. At the same time, this paper applies the concept of "smart tourism" to specific tourism informatization, which can promote the transformation and upgrading of tourism industry structure and further enhance the overall development level of tourism industry. [ABSTRACT FROM AUTHOR]

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques. [ABSTRACT FROM AUTHOR]

Due to the small size and weak characteristics of small objects, the performance of existing object detection algorithms for small objects is not ideal. In this paper, we propose a small object detection network based on feature information enhancement to improve the detection effect of small objects. In our method, two key modules, information enhancement module and dense atrous convolution module, are proposed to enhance the expression and discrimination ability of feature information. The detection accuracy of this method on PASCAL VOC, MS COCO, and UCAS-AOD data sets is 81.3%, 34.8%, and 87.0%, respectively. In addition, the detection results of this paper in detecting small objects are slightly (0.2% and 0.1%) higher than the current advanced algorithms (YOLOv4 and DETR, respectively). Moreover, when these two modules are integrated into other algorithms, such as RFBNet, it can also bring considerable improvement. [ABSTRACT FROM AUTHOR]

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

This paper is an in-depth study of international trade quantification models based on deep neural networks. Based on an in-depth analysis of global trade characteristics, a summary of existing problems, and a comparative analysis of various prediction methods, this paper constructs the ARIMA model, BP neural network (BPNN) model, and deep neural network (DNN) model to make a comprehensive comparison of international trade quantification. The results show that the nonlinear model has a global trade quantification has some advantages over linear models, and the deep model shows better prediction performance than the shallow model. In addition, preprocessing of the time series is considered to improve the prediction accuracy or shorten the model training time. The empirical modal analysis method (EMD) is introduced to decompose the time series into eigenmodal functions (IMFs) of different scales. Then the decomposed IMF series are arranged into a matrix using principal component analysis (PCA) to reduce the dimensionality and extract the data containing the most stock index information features; these features are then input into BPNN and DNN for combined prediction, thus constructing the combined models EMD-PCA-BPNN and EMD-PCA-DNN. Based on Melitz's heterogeneous firm trade theory and its development by Chaney, a quantitative trade model incorporating production heterogeneity is constructed through a multisector extension. This paper adopts a general equilibrium analysis, which makes the modeling process pulse clear. The completed model has high flexibility and scalability, which can be applied to quantitative analysis of various problems. [ABSTRACT FROM AUTHOR]

Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of machine learning in different areas, many researchers focus on the security of Internet access through machine learning methods. The most common form of machine learning is supervised learning. Supervised learning requires a large number of labeled samples, but it is difficult to obtain labeled samples in practical applications. This paper introduced an unsupervised deep learning algorithm based on seq2seq, which combined with the recurrent neural network and the autoencoder structure to realize an intelligent boundary security control mechanism. The main methods proposed in this paper are divided into two parts: data processing and modeling. In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum. In the modeling phase, the network was optimized according to the principle of minimizing the reconstruction error. From the comparative experiments, the proposed method's AUC on the public data set reached 0.99, and its performance is better than several classical supervised learning algorithms, proving that the proposed method has an efficient defense against abnormal network access. [ABSTRACT FROM AUTHOR]

Scholars have been suffering from scams involving counterfeit academic journals for a long time, and there has been a relatively clear analysis of the causes of counterfeiting and the governance path for it in existing studies. However, the authors reexamined the counterfeiting and found the problem still prevails. Through the analysis of the multiple players involved in counterfeiting, the author points out that the current personnel evaluation mechanism and journal evaluation system are the root causes of counterfeiting and thus offer corresponding suggestions for further improvement. [ABSTRACT FROM AUTHOR]

Scientific and accurate prediction of high-tech industries is of great practical significance for government departments to grasp the future economic operation and formulate development strategies. In this paper, aiming at some shortcomings of neural network (NN) applied in economic forecasting, GANN was introduced to construct the economic forecasting model of high-tech industry. Genetic algorithm (GA) has simple calculation and strong robustness and can generally ensure convergence to the global optimum, which effectively overcomes the shortcomings of NN using gradient descent method. In order to verify the feasibility of the economic forecasting model in this paper, the comparative experiments of different models are carried out in this paper. Experimental results show that the proposed algorithm has faster convergence speed and greater generalization ability, and the average error rate is reduced to about 1%. The prediction accuracy of this model reached 95.14%, which was about 11.93% higher than the previous model. Applying the economic forecasting model in this paper to the economic forecasting of high-tech industries can provide the means and reference value for the government to formulate regional future economic development plans, forecast, and control the economic growth and development direction. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [ABSTRACT FROM AUTHOR]

In recent years, with the development of deep neural network becoming more and more mature, especially after the proposal of generative confrontation mechanism, academia has made many achievements in the research of image, video and text generation. Therefore, scholars began to use similar attempts in the research of music generation. Therefore, based on the existing theoretical technology and research work, this paper studies music production, and then proposes an intelligent music production technology based on generation confrontation mechanism to enrich the research in the field of computer music generation. This paper takes the music generation method based on generation countermeasure mechanism as the research topic, and mainly studies the following: after studying the existing music generation model based on generation countermeasure network, a time structure model for maintaining music coherence is proposed. In music generation, avoid manual input and ensure the interdependence between tracks. At the same time, this paper studies and implements the generation method of discrete music events based on multi track, including multi track correlation model and discrete processing. The lakh MIDI data set is studied. On this basis, the lakh MIDI is pre-processed to obtain the LMD piano roll data set, which is used in the music generation experiment of MCT-GAN. When studying the multi track music generation based on generation countermeasure network, this paper studies and analyzes three models, and puts forward the multi track music generation method based on CT-GAN, which mainly improves the existing music generation model based on GAN. Finally, the generation results of MCT-GAN are compared with those of Muse-GAN, so as to reflect the improvement effect of MCT-GAN. Select 20 auditees to listen to the generated music and real music and distinguish them. Finally, analyze them according to the evaluation results. After evaluation, it is concluded that the research effect of multi track music generation based on CT-GAN is improved. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

The cold chain logistics route of fresh product export is characterized by large quantity and complexity, which is prone to cause transportation risks of different degrees in the process of fresh product export transportation and affects the decision-making effect of the cold chain logistics route. Therefore, in order to improve the ability of cold chain logistics route planning and shorten the transportation time, an optimization method of fresh product export cold chain logistics route decision considering transportation risk was proposed. This paper analyzes the basic characteristics and classification of cold chain logistics by means of risk quantification and uses the K-nearest neighbor algorithm to predict the risk of traffic congestion, so as to shorten the transportation time. Ahp process is used to construct a risk factor judgment matrix and determine the index weight of risk factors, so as to reduce the error of path planning. A genetic algorithm is introduced to construct the optimal decision function of the cold chain logistics route of new product export and realize the optimization of cold chain logistics route decision of fresh product export. Experimental results show that the method presented in this paper can effectively improve the decision-making effect of cold chain logistics route and select the shortest and most smooth transportation path to complete logistics distribution. The decision-making accuracy of the route decision effect is 90%, and the transportation time is 31.45 min, which has certain feasibility and applicability. [ABSTRACT FROM AUTHOR]

The implementation of the strategy of rural revitalization is a major ministerial work made by the Nineteenth National Congress of the Communist Party of China and is the general grasp of contemporary agriculture, peasants, and rural work. In recent years, with the rapid development of remote sensing technology and deep learning technology, the demand for the technology for the classification of crops on satellite remote sensing images based on deep learning technology has increased in agricultural insurance and land survey. Therefore, this paper trains one, which is 85.9%–92.8%, the accuracy of corn classification is 77%–93%, and the accuracy of forest classification is 77%–87.6%. Subsequently, the overall accuracy of classifying all directories through the multi-temporal validation data set between May 2017 and October 2017 reached 92.6%. Such a multi-time combination method can be used for monthly, timely, and efficient iteration of agricultural insurance and crop yield estimation, which will be more accurate each time. These methods can also be further applied to the growth and change monitoring of large agricultural planting areas, adding bricks and tiles to China's agricultural remote sensing. If the countryside is to be revitalized, agriculture must develop rapidly at the same time, industries must flourish, ecology must be livable, rural customs must be civilized, and life must be prosperous. Modern agriculture is a comprehensive circulation system with high yield, high quality, low consumption, ecology, environmental protection, and high efficiency. The development of modern agriculture is inseparable from the industrialization of agriculture, the globalization of agriculture, the digitization of agriculture, the integration of agriculture, the adjustment of agricultural structure, and agricultural innovation. Only the continuous development of modern agriculture can make rural revitalization enter a new journey. [ABSTRACT FROM AUTHOR]

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model. [ABSTRACT FROM AUTHOR]

Mining and utilizing cultural heritage resources and creating and developing creative and cultural industries have become the priority direction of economic development, setting off a wave of cultural heritage resource development, and industrial transformation. Which cultural heritage resources can have the high value of industrial transformation has become one of the research topics that have attracted much attention. In view of this problem, research is of great significance to the development of cultural heritage resources and the field of industrial transformation. With the in-depth research on resource development and industrial transformation, the research on artificial neural network (ANN) in cultural industry transformation is gradually carried out, and its performance advantages are of great significance to solve the problem of value evaluation. This paper aims to study the application of the value assessment method based on the BP neural network (BPNN) in the development of cultural heritage resources and industrial transformation. Through the analysis and research of BPNN and cultural heritage resource development and industrial transformation, it can be applied to the construction of resource value assessment methods to solve the problem of improving the value level of cultural heritage resource development and industry. This paper analyzes BPNN, cultural heritage, and value evaluation methods, conducts experimental analysis on the performance of the algorithm, and uses related theoretical formulas to explain. The results show that the evaluation method has passed the random consistency test, and the results are valid. The obtained index popularity weight value is 0.134, the economic benefit weight value is 0.093, and the resource correlation degree weight value is 0.074, which can be used as the key criteria for resource value evaluation. The classification of resources through resource value assessment can provide theoretical support for the development and industrialization of cultural heritage and can meet the needs of improving the value and quality of cultural heritage development and industrial transformation, and the development level and satisfaction have been greatly improved. [ABSTRACT FROM AUTHOR]

With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved. [ABSTRACT FROM AUTHOR]

Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods. [ABSTRACT FROM AUTHOR]

In view of the low level of gymnastics facilities monitoring and intelligent management of gymnastics venues, which cannot effectively manage gymnastics venues in real time, this paper proposes a method of gymnastics facilities monitoring and intelligent optimization distribution of gymnastics venues in the Internet of Things. This paper will build an information monitoring model, introduce a particle swarm optimization algorithm to participate in the location layout, and explore the actual effect of gymnastics and the Internet of Things. The research results show that (1) the system can measure the mechanical error of facilities and ensure that the controllable fault tolerance rate is less than 1%. (2) The quality of system monitoring is evaluated in accuracy, time, delay, and satisfaction, and the results are basically satisfactory, but the accuracy and time still need to be improved. (3) Using the evaluation system of sports facilities to test the temperature, humidity, facility pressure, and energy consumption suitable for gymnastics and to verify the injury tendency of athletes. When the damage tendency is between 70% and 100%, the actual damage rate is 1. (4) The speed of the PSO algorithm is faster than other methods, which is used to optimize the layout of gymnastics venues and has a certain role in promoting the construction of gymnastics venues. The system model designed in this paper performs well in gymnastics and needs to be further improved and optimized. [ABSTRACT FROM AUTHOR]

The online individual behavior analysis is an important means for mining user interests. The user retweeting behavior prediction is typical problem for online individual behavior analysis. In order to make online learning behavior prediction method more suitable for the application of large-scale datasets, the improved condensed K nearest neighbor (ICKNN) method is proposed in this paper. Inspired by the idea of compressing samples in the condensed nearest neighbor (CNN) algorithm, this proposed method has adopted the Hadoop platform to parallelize the traditional CNN algorithm. For the traditional CNN method, as the value of K increases, the compression ratio decreases and so as the efficiency. The proposed ICKNN method can parallelize the traditional CNN method under the Hadoop framework to enhance efficiency. The proposed ICKNN method in this paper is validated by actual Twitter retweeting dataset. It can be seen that the proposed method in this paper has a higher compression rate than the traditional CNN algorithm. In terms of accuracy, the classification accuracy of the proposed ICKNN method has decreased compared with the traditional KNN method. However, the time consumed by the ICKNN method has significantly reduced compared with the traditional KNN method and CNN method, which can greatly improve the efficiency. [ABSTRACT FROM AUTHOR]