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Folk paper-cut art is one of the bright pearls, with a wide range of shapes and a long history, that serves as a model for modern visual communication design in China and encourages designers to think creatively. Integrating emotional elements into a design not only can elicit an emotional response from the viewer but also can make works created using traditional rational thinking more vivid, energetic, and touching. The main focus of this research is on the path and method of incorporating folk paper-cut emotion into visual communication design. The research status of various feature extraction methods and image recognition methods at home and abroad is examined in depth in this paper. For the recognition of distorted images that are not transformed by mathematics, some effective methods are proposed. They are used to recognize artistic images of paper-cut patterns because they can extract effective features that are not affected by translation, rotation, scale change, or small deformation. Experiments have shown that they have a positive impact. The use of folk paper-cut art in visual communication design not only improves the quality of design work but also generates new ideas for the transmission and development of other traditional Chinese cultures. [ABSTRACT FROM AUTHOR]

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users' query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions. [ABSTRACT FROM AUTHOR]

With the rapid development and application of internet technology, people are gradually entering a state of digital life. However, it is difficult for digital assets to be circulated with a unified standard protocol. Blockchain has the characteristics of decentralization, trustworthiness, and immutability, which can be used to solve the above problems. Although virtual marketing technology shows great potential, as a form of organization that is difficult to manage, there is still a lack of experience in the form factors and operation mechanism of a virtual business model. Therefore, combined with the realistic background, this paper built a digital twin innovation and entrepreneurship virtual marketing interactive platform based on blockchain. The article analyzed the data structure of the blockchain and completed the construction of the platform operation mode. It studied the encryption and decryption process of cryptography and expanded the ciphertext space, then the blockchain is encrypted and decrypted again, and finally, a chi-square analysis is performed. After building the platform, it conducted market research and analyzed the main operating indicators of the A platform on the market. Then, it compared the uncertain string preprocessing enumeration length change and the index occupied space between the platform constructed in this paper and the platform in the literature. The comparison results have shown that the index proposed in this paper is 320 MB in 8 iterative calculations, while the bibliographic index starts at 10000 MB and keeps increasing during the iterative calculation process. The two are not in the same order of magnitude, illustrating the superiority of the platform in this paper. [ABSTRACT FROM AUTHOR]

Within a few decades of development, Internet is been refreshing the world's awareness of its development and potentials at an unexpected speed. In recent years, virtual reality technology has begun to be applied to more and more fields. Especially in instant transmission of information, the applications of VR are incomparable. Akin to the wide-ranged advantages of the technology in education and health, the applications of VR in the art of artistic image designing need to be addressed. With this research work, a comprehensive study is presented about the applications of virtual reality in the art of image visualization. The research intends to analyze the possibility of intermingling modern image visualization with the emerging VR technology. Following a systematic approach, the history and development of display design are reviewed besides studying the artistry techniques used in the technology of art image visualization design. The interdisciplinary association of the two fields is explored with the help of relevant algorithms. The model proposed in the paper for the design of art image is based on the visual interactive process, rigorous assembly sequence, and appropriate equipment mode. As a result, the whole design process is more controllable and feasible. The suggested design concept is verified dynamically. Through the collection and comparison of feedback information, the hypothesis algorithm of the model is updated to accommodate the dynamic information so that to reflect the complete image design process. To assess performance of the model, evaluation was conducted from various perspectives. The three basic image operations such as illumination change, occlusion change, and color change were performed by using the two technologies such as the traditional image design and VR-based image design. From the comparison of experimental data, it was revealed that the effects of traditional art image visualization products produced by the art image visualization and that of VR technology are quite different. For instance, the visualization score achieved for the lighting change operation using the VR technology was 95.48%, whereas 85.57% was obtained for the same operation using the traditional image design technology. Similarly, while adding visual effects using the VR technology, an average of 9% higher score was obtained for the said image operations. The promising results attained for the VR-based design testify that the technology is feasible and suitable to be intermingled with the visual design of art image. As a whole, the findings justify that the proposed approach is quite applicable in visual design of art image systems. In view of the significance of the research, the study can be extended to related domains of computer-based designing and visualization as well. [ABSTRACT FROM AUTHOR]

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals. [ABSTRACT FROM AUTHOR]

Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications. [ABSTRACT FROM AUTHOR]

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport. [ABSTRACT FROM AUTHOR]

Modelica is a simulation language that is able to model systems from many different fields of scientific and engineering contexts, which can coexist in individual models. Modelica Standard Library is an open‐source library that contains many ready‐to‐use open‐source models built in Modelica language but does not have any code to determine transmission line parameters from its geometry, thus limiting its usability for power system studies. This paper first describes important issues arising when modelling power transmission lines, then presents how they can be solved using Modelica code. Readers of this paper will, therefore, be able to model power lines from the geometry of conductors using any of the existing (some freely available) Modelica‐capable simulation tools. [ABSTRACT FROM AUTHOR]

Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

Enterprise financial risk refers to the uncertainty of financial status caused by various internal and external factors during investment, financing, operations, and other activities, which may result in losses for the enterprise. Agricultural group enterprises engage in diversified businesses and have a large number of transactions. Characteristics such as natural disasters, financing difficulties, and strong seasonality of income and expenditure make agricultural enterprises face greater financial risks. Based on the perspective of evidence fusion, this paper takes the JS Agricultural Reclamation Group as the research object, puts forward a financial risk early warning model based on evidence theory, and innovatively puts forward the basic probability assignment of evidence obtained by whitening weight function, and uses evidence theory to fuse uncertain risk information. This paper makes an empirical study of 420 subsidiaries of the group and selects 10 subsidiaries as case study objects. The results show that the model proposed in this study can improve the prediction accuracy of the group's intelligent financial decision system and the existing literature and reduce the subjective factors and information loss in the risk assessment process. In addition, this study provides the probability interval of early warning results, which can expose small probability risks and provide a more reliable basis for regulators, enterprise managers, and investors to make efficient and correct decisions. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors' time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, English learning plays an increasingly important role in daily communication. However, the update speed of English textbooks is far lower than the development speed of English. How to reconstruct the text of English textbooks in colleges and universities to improve the learning effect of college English has become an urgent problem to be solved. In order to solve this problem more effectively, this paper proposes a text reconstruction method for college English textbooks from the perspective of language images. First, this paper proposes a text reconstruction network model for college English textbooks, which includes two self-network models, namely, a text feature extraction network and an image feature extraction network. Second, text designs an English text feature reconstruction network to fuse image features and text features to guide the generation of new English texts according to the generated emotions. Finally, through a large number of experiments, it is proved that the text reconstruction method of college English textbooks from the perspective of language images can effectively generate new texts of college English textbooks, enhance the emotional color of college textbooks, and improve the effect of English learning. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect. [ABSTRACT FROM AUTHOR]

Networked education represents a development direction of educational reform, has become a feature of modern education, and has formed a new impetus to the development of education. The change from paper-and-pencil examinations to computer network-based machine examinations is not only a reform of the content and form of the University English Level 4 and 6 examinations, but also a reflection of the reform of the comprehensive assessment system and teaching philosophy of University English teaching. Promoting the development and construction of online college English examination systems in universities, applying machine examinations to various types of tests and formal examinations for college English teaching, and updating the college English question bank through continuous accumulation of professional teachers will have very high research value and practical value in improving the quality of examinations and promoting the standardization of college English examinations. In order to show the individualized chemistry report simultaneously, it is necessary to further ask relevant subject experts, educational measurement experts, and front-line teachers to expand the question attributes; that is, the question attributes extend to module specific knowledge points, cognitive levels, and information literacy ability levels, while most of the pages are combined with Ajax technology to reduce hardware resource dependency and optimize software performance. The paper analyzes the requirements analysis and overall design of the online college English examination system, including the design objectives, design principles, functional design, core algorithm design, architecture design, database design, and security design. The main functions of the online college English examination system, such as personal information management, question bank information management, test paper grouping management, online examination management, paper marking management, and system setting management, are discussed in detail. The main functions of the system, such as personal information management, question bank information management, test paper grouping management, online test management, mark and approve management, and system setting management, are discussed in detail, and the performance test and some function test of the system are also briefly discussed. The whole research process involves the identification of test questions and their attributes in the construction of the question bank, the construction of the adaptive test system, the comprehensive analysis of the mock test results, and the thinking about the application of the high school adaptive academic level test. [ABSTRACT FROM AUTHOR]

With people's higher and higher spiritual requirements, fiber art is more common in people's lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images of art based on deep learning technology to achieve the purpose of convenient display and also analyzes the feature extraction in fiber art. In order to comprehensively compare the performance of the algorithms, this paper compares the performance of four categories: reconstruction speed, number of reconstructed point clouds, diversity of reconstructable categories, and reconstruction stability. The experimental results show that the number of point clouds obtained by the algorithm proposed in this paper far exceeds the other two reconstruction processes, and the average time is 34.4 min. This also shows that such algorithmic processes are more robust to clothing diversity. [ABSTRACT FROM AUTHOR]

With the increasing development of information science and technology and the vigorous use and promotion of new technologies, profound changes have taken place in all aspects of our daily life. With this huge change, the IoT industry was born. And it analyzes and processes the large amount of data generated between them, and finally, it helps the development of the economy. The RFID system studied in this paper is the radio frequency identification system, which is an automatic signal identification system. The relationship between the RFID system and the Internet of Things is that the former will obtain a large amount of Internet of Things information data by identifying the Internet of Things. However, it is difficult to guarantee the analysis and processing of data and the security of data in the RFID system. This paper aims to study the effective processing and security guarantee of a large amount of data obtained by the RFID system after processing the identification of the Internet of Things. It is expected to overcome the problems of conventional related art. This paper proposes the encryption technology for the Internet of Things RFID system, as well as the corresponding algorithm, and establishes a processing system for information data. The experimental results of this paper show that the cryptographic mechanism run by the algorithm PECC has better security performance compared with other cryptographic mechanisms, and its computational complexity can be reduced by 28.35%. [ABSTRACT FROM AUTHOR]

An increasing number of research literature studies about green technology have been accepted by journals of different disciplines due to the rapid technical progress and innovation in all types of industry. This study uses bibliometric tools of CiteSpace and VOSviewer to analyse the key authors' co-citation network, institution cooperation, the keyword clusters of green technology, and the evolution trend of green technology. We find that since 1960s, the number of research papers with green technology theme has been growing. These papers are mainly involved in fields of economics and business, engineering, and chemistry, in which there exist 13 highest cited papers from 2009 to 2020 based on the Web of Science database. In this study, we find the top 20 journals of green technology with the parameter of literature count and centrality. We find that the cooperation of authors is quite weak with the co-authorship analysis, and we obtain the top 12 institutions in 15 countries dominantly in green technology research through country and institution analysis. We conduct a cluster analysis of keywords related to green technology, and we obtain 10 clusters, with three economical clusters, five engineering clusters, and two chemical clusters. Finally, we summarise green technology with the aid of the timeline view function of CiteSpace system. [ABSTRACT FROM AUTHOR]

As China becomes more and more international, the number of people traveling abroad is also increasing. The demand for English recognition is becoming more and more vigorous, and traditional translation software is time-consuming, laborious, and less accurate. This article optimizes the target detection model YOLOV3. Firstly, the image is divided into multiple model structures, and the K-means++ clustering algorithm is used to determine the target detection prior frame value and the high frame of the corresponding frame according to the characteristics of the English image. Then, by using K-means++ clustering algorithm to optimize the anchor parameters, the model structure is better adapted to the English identification dataset scene; finally, the feature information extracted by the DarkNet-53 model is spliced to improve the structure of the YOLOV3 convolutional layer, using 3090 graphics card GPU to perform multiscale training and testing. Experimental results show that the improved YOLOV3 algorithm in this paper has a mAP of 0.95 on the English identification dataset and a detection speed of 50fps, which is 0.11 higher than the mAP before optimization. Therefore, optimizing the YOLOV3 algorithm in this article has a good effect. In the future, English translation will become a necessary software program for Chinese people to go abroad. [ABSTRACT FROM AUTHOR]

With the development of science and technology, more and more fields have begun to use AI to provide convenient services for humans. Artificial intelligence (AI) refers to a new technology that uses human thinking to respond accordingly through computers and robots to assist human beings. Action recognition is an important research project that needs to be broken through in many industries, such as security system, martial arts instruction, and dance training. This paper aims to study a method for action recognition using AI technology and to build a sports training management information system. In this paper, a recognition model and related algorithms using a convolutional neural network (CNN) are proposed, and an intelligent sports training management information system is constructed. The system and the model are tested, the action recognition effect of 60 athletes in a university is tested, and the comparison with the traditional recognition algorithm is carried out. The results show that the CNN recognition accuracy test results used in this paper are generally more than 90%, while the traditional recognition accuracy rate is only about 75%, and the highest is not more than 86%; the training management information system of this paper takes about 15.7 s, and the maximum time is not more than 10 s, while the traditional recognition system takes about 15.7 s, which is about twice the time of the system in this paper. Therefore, it shows that the CNN recognition method in this paper has a significantly better effect on the recognition of athletes' movements, and the sports training management information system constructed in this paper is less time-consuming and faster and has certain feasibility. [ABSTRACT FROM AUTHOR]

In the bidding process of government investment engineering projects, collusion between the government and bidders occurs repeatedly, which seriously affects the quality of engineering projects and the effectiveness of the government investment. Therefore, it is necessary to analyze and discuss the collusion between the government and bidders in government investment engineering projects, so as to provide a healthy and sustainable environment for the government investment engineering bidding market. There are two main types of collusion in engineering bidding: horizontal collusion and vertical collusion, and this paper focuses on the vertical collusion process in the engineering bidding process. A conceptual framework of the grey interest chain based on three stages of benefit creation, benefit distribution, and benefit realization was established, 15 major nodes in the grey interest chain were identified, and a grey interest chain control model was constructed, which further identified and classified the nodes into four levels: key nodes, important nodes, general nodes, and unimportant nodes. Finally, through the application of the model in the case, measures such as establishing a cracking mechanism for grey resource integration, increasing the supervision of grey interest chain, and strengthening post-bid audit are proposed. Measures such as including the preparation of bidding documents into the work assessment system and entrusting consulting units or third parties to prepare bidding documents are proposed to establish a crack mechanism for grey resource integration. In the benefit distribution stage, the penalties for the government and the bidders can be appropriately increased, the responsibilities of enterprises and project leaders can be implemented in the system on a reciprocal basis, and a perfect reputation mechanism information can be established. At the stage of benefit realization, the bidding system should be improved and post-bid audit should be strengthened to increase the difficulty of grey benefit realization. This paper will provide a reference for the prevention and governance of vertical collusion in bidding and tendering. [ABSTRACT FROM AUTHOR]

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect. [ABSTRACT FROM AUTHOR]

At present, with the popularity of multimedia, people's appreciation and interest in ink painting art have been deepened. In the research of art works, this paper proposes a rendering method of three-dimensional ink painting effect based on deep learning model, which can simulate the painting skills of ink painting in terms of intensity and distance. Specifically, first construct a two-dimensional ink texture, then map the horizontal component of the texture using the contour information of the model, and finally adjust the ink tone by controlling the coordinates of the vertical component of the texture through the attribute function. Linear attribute function, quadratic attribute function, and attribute function with light source factor are used for simulation. The results show that this method can be used for ink and wash simulation of three-dimensional scene, making the image more layered and more in line with the artist's actual technology. [ABSTRACT FROM AUTHOR]

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people's daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis. [ABSTRACT FROM AUTHOR]

In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method. This paper takes a single text as the research object, establishes the connection relationship between article sentences, and proposes a method of automatic extraction of text summary based on graph model and topic model. The method combines the text graph model, complex network theory, and LDA topic model to construct a sentence synthesis scoring function to calculate the text single-sentence weights and output the sentences within the text threshold in descending order as text summaries. The algorithm improves the readability of the text summary while providing enough information for the text summary. In this paper, we propose a BERT-based topic-aware text summarization model based on a neural topic model. The approach uses the potential topic embedding representation encoded by the neural topic model to match with the embedding representation of BERT to guide topic generation to meet the requirements of semantic representation of text and explores topic inference and summary generation jointly in an end-to-end manner through the transformer architecture to capture semantic features while modelling long-range dependencies by a self-attentive mechanism. In this paper, we propose improvements based on pretrained models on both extractive and generative algorithms, making them enhanced for global information memory. Combining the advantages of both algorithms, a new joint model is proposed, which makes it possible to generate summaries that are more consistent with the original topic and have a reduced repetition rate for evenly distributed article information. Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed. In several comparative experiments, the evaluation metrics were improved by up to 2.5 percentage points, proving the effectiveness of the method, and a prototype system for an automatic abstract generation was built to demonstrate the results. [ABSTRACT FROM AUTHOR]

The main difficulty of music emotion recognition is the lack of sufficient labeled data. Only the labeled data with unbalanced categories are used to train the emotion recognition model. Not only is accurate labeling of emotion categories costly and time-consuming, but it also requires extensive musical background for labelers At the same time, the emotion of music is often affected by many factors. Singing methods, music styles, arrangement methods, lyrics, and other factors will affect the expression of music emotions. This paper proposes a multimodal method based on the combination of knowledge distillation and music style transfer learning and verifies the effectiveness of the method on 20,000 songs. Experiments show that compared with traditional methods, such as single audio, single lyric, and single audio with multimodal lyric methods, the method proposed in this paper has significantly improved the accuracy of emotion recognition, and the generalization ability has been significantly improved. [ABSTRACT FROM AUTHOR]

Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data. [ABSTRACT FROM AUTHOR]

Benefiting from the convenience of virtualization, virtual machine migration is generally utilized to fulfil optimization objectives in cloud/edge computing. However, live migration has certain risks and unapt decision may lead to side effects and performance degradation. Leveraging modified deep Q network, this paper provided an advanced risk evaluation system. Thorough formulation was given in this paper and a specific integration method was innovated based on uncertain theory. Series experiments were carried on computing cluster with OpenStack. The experimental results showed deep Q network for risk system was reliable while the uncertain approach was a proper way to deal with the risk integration. [ABSTRACT FROM AUTHOR]

The key to solving the problem of redundant financial indicators in addressing financial warning issues is to reduce the dimensionality of the original financial indicators. This paper proposes a model based on the whale optimization algorithm with mixed strategy (IWOA) combined with support vector machine (SVM), namely, the IWOA-SVM early warning model, which simultaneously performs index optimization and dimensionality reduction, and financial risk early warning identification. This paper takes a total of 302 enterprises specially treated in Shanghai and Shenzhen stock exchanges and normal enterprises of the same specification as the research samples to design the model. The results show that the improved whale optimization algorithm has better optimization speed and accuracy and improves the search ability of the original algorithm for the optimal solution. Compared with other dimensionality reduction methods, the IWOA-SVM model has the lowest index dimension after dimensionality reduction and has more excellent recognition effect. The dimensionality reduction results have certain universality for different classifiers, which provides a new idea for the selection of indicators for financial early warning. [ABSTRACT FROM AUTHOR]

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users' preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting. [ABSTRACT FROM AUTHOR]

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy. [ABSTRACT FROM AUTHOR]

While facing complex surroundings, robots need to identify the same intention which is expressed in different ways. In order to solve the problem of assisting robots to get a better intention understanding, a self-tuning multimodal fusion algorithm is put forward in this paper, which is not restricted by the expressions of interacting participants and environment. The multimodal fusion algorithm can be transferred to different application platforms. Robots can own the understanding competence and adapt new tasks by changing the content of the robot knowledge base. Compared with other multimodal fusion algorithms, this paper attempts to transfer the basic structure of feed-forward neural networks on discrete sets, which has strengthened the consistency and perfect the complementary relations between multiple mode, and has realized the simultaneous operation of fusion operator's self-tuning and intention search. There are three kinds of modes selected in the paper: speech, gesture, and scene objects, where the single modal classifiers are trained separately. This method conducted a human-computer interaction experiment on the bionic robot Pepper platform, which proved that the method can effectively improve the accuracy and robustness of robots in aspects of understanding human intentions, and reduce the uncertainty about intention judgment in a single modal interaction. [ABSTRACT FROM AUTHOR]

A natural game interaction method can bring a good game experience to users, but pen-type interaction, which is one of the natural human-computer interaction methods, rarely appears in games, especially three-dimensional games. The main reason is that most pen-type interactive devices are developed for two-dimensional applications such as writing and painting. Whether the pen-based interaction can be extended from two-dimensional to three-dimensional is the key to whether the pen-based interaction can become one of the leading interactive methods of the game. The pen tablet is one of the most widely used pen-type interactive devices, and a virtual tour is an essential part of the game. Therefore, this paper takes this as a breakthrough and proposes a virtual roaming control method based on the pen tablet. First, we analyze the performance characteristics of the pen tablet and get the data information available for the pen tablet. Then, we design various virtual roaming actions such as walking, running, jumping, viewing angle rotation, view zooming, space-free displacement, acceleration, and deceleration and establish an association with the operation of the pen tablet. At the same time, the maximum likelihood function constraint was added to improve the error tolerance rate of viewing angle rotation. Finally, two games were developed to verify their effectiveness. The experimental results show that the pen tablet performs well in completing virtual roaming tasks, especially when users need to achieve similar writing and roaming tasks without switching devices back and forth. This maintains the user's habit of writing with pen and paper and improves the interactive experience. The method is a new exploration of the application of pen-based interaction in games, which lays the foundation for future game development based on pen-based interaction. [ABSTRACT FROM AUTHOR]

With the fast advancement of science and technology, numerous music systems have emerged. However, there are few folk music in the music industry, for example, pop music, which leads to the inability of folk music lovers to find their favorite music. For this problem, this paper develops a national system of smart concert hall based on a neural network and wireless Internet of things (IoT). It establishes the architecture model of the wireless IoT system of the concert hall by describing the neural network, neuron model, and BP neural network model in detail. Besides, this paper develops the smart music national system based on this model, completes the functional module design of the national music system, and uses the music attribute recommendation model. Finally, it analyzes the scoring performance of the national music system. The results show that when the number of recommended folk music songs is less than 20, the lowest MAE is 0.73. [ABSTRACT FROM AUTHOR]

With the continuous development of communication technology, computer technology, and network technology, a large amount of information such as images, videos, and audios has grown exponentially, and people have started to be exposed to massive multimedia contents, which can easily and quickly access the increasingly rich music resources, so new technologies are urgently needed for their effective management, and automatic classification of audio signals has become the focus of engineering and academic attention. Currently, music retrieval can be achieved by selecting song titles and singer names, but as people's living standards continue to improve, the spiritual realm is also enriched. People want to be able to select music with different types of emotional expressions with their emotions. It mainly includes the basic principles of audio classification, the analysis and extraction of music emotion features, and the selection of the best classifier. Two classification algorithms, hybrid Gaussian model and AdaBoost, are used to classify music emotions, and the two classifiers are combined. In this paper, we propose the Discrete Harmonic Transform (DHT), a sparse transform based on harmonic frequencies. This paper derives and proves the formula of Discrete Harmonic Transform and further analyzes the harmonic structure of musical tone signal and the accuracy of harmonic structure. Since the timbre of musical instruments depends on the harmonic structure, and similar instruments have similar harmonic structures, the discrete harmonic transform coefficients can be defined as objective indicators corresponding to the timbre of musical instruments, and thus the concept of timbre expression spectrum is proposed, and a specific construction algorithm is given in this paper. In the application of musical instrument recognition, the 53-dimensional combined features of LPCC, MFCC, and timbre expression spectrum are selected, and a nonlinear support vector machine is used as the classifier. The classification recognition rate is improved by reducing the number of feature dimensions. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, mankind has entered the era of big data. The Internet records all kinds of information, and the amount of information generated in the future is in a state of explosive growth. With the development of enterprises, the data of accounting information have grown, and its security has also been threatened. Once the accounting information is leaked, it will have a great impact on the company. Therefore, it is very necessary to protect accounting information data. This paper aims to study how to protect accounting information data based on big data and cloud computing. Based on this, this paper proposes an attribute-based encryption method based on big data and cloud computing and analyzes the ABE scheme of a single authority and multiple authorities. The multiauthority ABE scheme can effectively solve the problems of low computational efficiency, poor security performance, and high time overhead in the single-authority ABE scheme. The experimental results of this paper show that, under the single-authority ABE scheme in the information data protection of enterprise A, the confidentiality of data is 54% at the highest and 46% at the lowest, which is generally not very high. In the information data protection of the multiauthority ABE scheme in enterprise A, the highest data confidentiality is 86% and the lowest is 79%. The highest is 32% more than the single-authority ABE scheme. The lowest is also 33% more than that. Generally speaking, the confidentiality of its data is very high. In terms of reliability, integrity, and effectiveness, the single-authority ABE scheme is not as good as the multiauthority ABE scheme. It shows that the multiauthority ABE scheme proposed in this paper has a powerful information data security protection function and can be effectively applied to accounting information data protection. [ABSTRACT FROM AUTHOR]

With the current exchange and communication between different countries becoming more and more frequent, the language conversion of different countries has become a difficult problem. The analysis of a series of problems in cross-language discourse conversion, the study of the discourse conversion path, and innovation motivation based on the deep learning theory of cross-language transfer, it has theoretical and practical significance. This paper aims at the technical difficulties in speech conversion methods to effectively utilize the local mode information of signal time spectrum and the long-term correlation of speech signal. A discourse conversion method based on convolutional recurrent neural network model is proposed. In the model, the extended convolutional neural network is used to model the long-term correlation of speech signals. In the part of speech fundamental frequency estimation, the prosodic information generated by the decomposition of the fundamental frequency by continuous wavelet transform is used as the training target of the fundamental frequency estimation model. The experimental results show that the speech transformation method based on the convolutional cyclic network model proposed in this paper has better quality and intelligibility than the speech transformed by the contrast method. [ABSTRACT FROM AUTHOR]

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students' class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students' learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students' inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application. [ABSTRACT FROM AUTHOR]

Depression is one of the most common psychological problems faced by human society. Because of less social experience, low psychological endurance, and the multiple responsibilities of future families and society, college students have become one of the most vulnerable groups to suffer from depression. This paper explores an automatic identification method to identify patients with early depression tendency through deep mining of online information of campus social platform users. First, we comprehensively analyze the common characteristics of emotion and behavior on the campus social platform for depression. Secondly, the experimental corpus is formed by preprocessing operations such as deprivation, word segmentation, and denoising of the original data. Finally, the depression recognition is transformed into a text classification problem, and a shallow support vector machine and a deep convolutional neural network model are, respectively, constructed based on the experimental corpus. Combined with the features of depression blog, the algorithm was further improved, and a dual-input convolutional neural network algorithm compatible with multiple features was proposed. The experiments showed that the recognition rate was effectively improved. [ABSTRACT FROM AUTHOR]

In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data. [ABSTRACT FROM AUTHOR]

Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance. [ABSTRACT FROM AUTHOR]

Venture capital firms are always faced with insufficient information and insufficient time when evaluating whether startups are worth investing in. This paper focuses on how to combine the public information of startups with the decision tree algorithm to assist investors in project screening. By extracting the public information of 1104 AI and big data companies from January 2016 to June 2017 and the financing progress in the following 18 months, this paper finds that: the six indicators of having a working background in well-known companies, being reported by well-known media, having patents and being invested by excellent institutions, working experience is highly related to this venture, and having well-known financing consultants can effectively help investors screen out projects that can obtain sustainable financing, that is, high potential projects. Considering the problem of data availability in the real world, combined with industry experience, this paper makes a more detailed variable mining for semistructured information, such as the team resume of start-ups, and selects a decision tree algorithm that is insensitive to missing values and has strong interpretability to amplify the value of fragmented information. Finally, by the improvement of the algorithm, a project screening model that can meet the needs of investment practice is designed, and the fusion mode of public information and private information is discussed, which has a more complete guiding significance for optimizing investment work. [ABSTRACT FROM AUTHOR]

In recent years, great changes have taken place in the economic environment, policies, competitive environment, and market of colleges and universities. Furthermore, factors including the reduction of number of students every year has led to the continuous decline in the number of students enrolled in colleges and universities. Facing this problem, it is essential that colleges and universities should build their own brand value and attract more high school graduates with the brand value of colleges and universities. Nowadays, higher education has been popularized and internationalized, and the competition among colleges and universities has gradually increased. In fact, colleges and universities can obtain high-quality school running resources by relying on their own loyalty, reputation and social status, and can also achieve greater development space in the field of education. Therefore, university managers began to pay attention to their own brand value. The brand value of colleges and universities cannot be directly reflected, but needs to be reflected from different aspects. For this problem, this paper uses analytic hierarchy process (AHP) to study the brand value of colleges and universities. By selecting many aspects as the indicators of the dynamic evaluation system of brand value of colleges and universities, this paper constructs a dynamic evaluation model of brand value of colleges and universities based on analytic hierarchy process. After analysis, the total score of the brand value of a university is 84.12 and the grade is "B." Based on the analysis of the results, it can be concluded that the overall score of the university is high and has a certain brand value. However, it is weak in talent training and academic reputation. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the prediction method of future development measures and indicators of fault block reservoir in the current oilfield practical application, a prediction method of fault block reservoir measures and indicators based on the random forest method and LightGBM is proposed, which can help the oilfield make more effective decisions in the middle and later development. Firstly, using the advantages of random forest (RF) in dealing with high-dimensional data sets, the main controlling factors are selected by feature analysis. Then, the measure prediction model is established by using the 1DCNN-LightGBM algorithm. Firstly, 1DCNN processes the reservoir dynamic data and then trains the LightGBM model with the extracted time series characteristics and static data characteristics as input to predict the measure indexes of fault block reservoir. The evaluation results show that the prediction models proposed in this paper have good performance and can obtain more accurate prediction results and more stable prediction performance. It provides a basis for the future planning and optimization of the oilfield. [ABSTRACT FROM AUTHOR]

With the popularization of higher education, China's higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students. [ABSTRACT FROM AUTHOR]

Improved SVM algorithm improves the efficiency of College English teaching effect evaluation and meets the requirements of College English teaching evaluation. Based on the relevant theories, this paper constructs the evaluation index system with teachers and students as the main body and takes the questionnaire survey results as the input samples of the LSSVM algorithm. Compared with the evaluation accuracy of an optimized BP neural network and the category weighted gray target decision-making method, the results show that the evaluation accuracy of optimized LSSVM algorithm is 96.26%. Taking SIT as an example, this paper uses the optimized LSSVM algorithm to evaluate its teaching effect and obtains that teachers' literature and teaching contents are important factors to improve the effect of English teaching. Therefore, this paper introduces the intelligent voice system to optimize the English teaching design of SIT. The teaching design is optimized from the dimensions of teaching objectives, learning situation, teaching content, teaching media and curriculum materials, and teaching procedures. [ABSTRACT FROM AUTHOR]

Under the vision of achieving carbon neutrality by 2060, it is urgent to introduce appropriate carbon reduction policy for city road traffic. This paper establishes a three-layer neural network model to predict the carbon emission from private cars based on urban private car trajectory data, simulates and analyzes the carbon emission from private cars, travel cost, personal income, and government revenue under the four policy perspectives, and evaluates and compares the emission reduction effects under four policy perspectives. Next, this paper evaluates the government revenue from the perspective of carbon tax and policy mix and compares the individual consumer utility of two-commodity and three-commodity mix, as well as the total social benefits under the four policy perspectives. The results show that the policy mix has better implementation effect on carbon emission reduction, personal income, and travel cost. The implementation effect of the single carbon tax policy is better in terms of government revenue. The implementation effect of the single carbon trading policy is better in terms of social benefit. In addition, as the carbon tax rate increases, the consumer utility tends to decline. Finally, this paper puts forward specific policy implementation proposals based on the above simulation analysis. [ABSTRACT FROM AUTHOR]

With the frequent occurrence of international trade communication, in order to improve the quality of communication, this paper proposes a study on the translation of international trade English phrases and grammar. First, with the help of a rectangular window function, the composition principle of international trade English phrases is determined. Then, the horizontal feature aggregation point method is introduced to build a mathematical model of the characteristic identification of the English phrases. Finally, the sparse matrix representation of the source phrase is constructed to complete the extraction and preprocessing of the English phrase features. The input English sentence is converted into the output sentence, the form and POS factors of the English semantic translation are extracted, and a lemma is introduced to obtain the surface form of international trade English language factors. According to the international trade grammar analysis method, this paper decomposes the translation model, decomposes English sentences into small phrases for translation, and completes the research on international trade English phrase and grammar translation. The experimental results show that this method has high accuracy in the feature extraction of international trade English phrases, and the error rate is low, which is feasible. [ABSTRACT FROM AUTHOR]