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This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models. [ABSTRACT FROM AUTHOR]

With the continuous development of Internet of Things technology, this technology is not limited to the laboratory but is more and more applied in the process of commercialization. Large-scale machine communication is one of the three most important scenarios for 5G applications. Its features include larger uplink access, smaller data packets, higher device energy consumption requirements, lower device transmission speed, higher latency, and bursty communication. With the development of the Internet of Things in the 5G era, interpersonal interaction experience has been further optimized and welcomed by the market and adopt Qt/embedded multilayer menu design mode to improve our computer system interaction to facilitate the operation and Maintenance system. The software function of the human-computer interaction system has reached the expected result. By constructing the application of a human-computer interaction system in the analysis of regional tourism competitiveness, we analyze the temporal and spatial changes of tourism competitiveness in four major economic regions in China and combine multiple linear regression and geographic detector models to analyze China's tourism competitiveness. On this basis, we propose to further enhance the tourism competitiveness of areas with weak tourism development infrastructure and encourage potential tourism development areas to complement each other with high-quality resources between regions. [ABSTRACT FROM AUTHOR]

With the continuous development of the computer field, the Internet has become the main way of education in today's era. The development of computer technology and big data has brought innovation and opportunities to the teaching mode of ideological and political courses in colleges and universities. Due to the dynamic change of big data, there are many difficulties in the establishment of ideological and political course models in colleges and universities. This paper studies the innovation of ideological and political course modes in colleges and universities on the network platform under the background of big data. This paper mainly compares the traditional teaching mode and explores the formal modeling method of network platform education. The discrete dynamic modeling technology of the complex system is used to model the formal process of online education. Then, it carries out nonlinear prediction modeling for the operation efficiency and traffic change of the network platform under the big data environment and uses dynamic modeling to predict the learning effect of college students' ideological and political course mode. The results show that the formal method of education under dynamic modeling can improve the defects of traditional teaching and give full play to the advantages of network platform teaching. Improving the teaching mode of network platform can improve students' learning efficiency. Finally, in the dynamic model prediction, the problem of big data affecting the model results is improved, and the accuracy of the prediction model is improved. [ABSTRACT FROM AUTHOR]

Machining activities in recent times have shifted their focus towards tool life and tool wear. Cutting tools have been utilized on a daily basis and play a vital role in manufacturing industries. Prolonged and incessant operation of the cutting tool can lead to wear and tear of the component, thereby compromising the dimensional accuracy. The condition of a tool is estimated based upon the surface quality of the machined component, condition of the machine, and the rate of production. Maintaining the tool health plays a vital role in enhancing the productivity of manufacturing industries. Numerous efforts were experimented by the researchers to maintain the tool health condition. The drawbacks of conventional diagnostic techniques include requirement of high level of human intelligence and professional expertise on the field, which led the researchers to develop intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the condition of single point cutting tool. This article proposes the use of transfer learning technology to detect the condition of single point cutting tool. First, the vibration signals were collected from the cutting tool and plots were made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the single point cutting tool. In this work, the pretrained networks such as VGG-16, AlexNet, ResNet-50, and GoogLeNet were employed to identify the state of the cutting tool. In the pretrained networks, the effect of hyperparameters such as batch size, solver, learning rate, and train-test split ratio was studied, and the best performing network was suggested for tool condition monitoring. [ABSTRACT FROM AUTHOR]

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection. [ABSTRACT FROM AUTHOR]

At the same time that my country has shifted from high-speed development to high-quality development, my country has also put forward new requirements for education development. Due to the limited study time during college, each student's study habits and learning process are also different, and the degree of connection between tennis lessons is high, so there will be polarization when learning tennis. With the development of science and technology, more and more technological innovations are integrated into the classroom, and traditional teaching methods can no longer keep up with the pace of the times. Tennis teaching is a subject of equal proportion between theory and practice. The traditional teaching method simplifies the theory, which makes students to have some bad phenomena when they practice. Aiming at this series of problems, this paper uses algorithms such as softmax function and threshold function to construct an application model of virtual image technology based on the artificial neural network in tennis teaching. The research results of the article show that: (1) the average accuracy rate of the method in this paper is 97.22%, and the highest accuracy rate is 99.17%. The average accuracy rate also tends to increase with the increase of sample size; the recall rate is the highest, and the highest recall rate is 99.36%. The average recall rate is 96.77%; the highest correct rate is close to 100% and is significantly higher than the other three methods; the average correct rate reaches 98.8%; the response time is the shortest; the average response time is 33 ms; and the response time increases with the increase of the sample size. (2) After using this model, tennis skills have been improved, with an average of 12 in situ flips, an average of 7 in situ rackets, an average of 5 in situ forehand draws, and an average of 3 in situ backhand draws. (3) The average forehand and backhand scores of the class after the experiment were 90 and 86; the average forehand and backhand stability were 8 and 7; and the average forehand and backhand accuracy were 31 and 29, respectively. The average depth of forehand and backhand is 36 and 32. (4) Most of the students are satisfied with this model, and they all choose to strongly agree and relatively agree, and the percentage of very agree that helps stimulate learning has reached 60.52%, and no students choose to disagree very much. [ABSTRACT FROM AUTHOR]

Individualized instruction is a type of educational principle. On the one hand, it necessitates the creation of individualized teaching resources, courses, and methods. Students, on the other hand, require a high level of autonomy and the ability to make personalized plans based on their own cognitive characteristics and needs. The big data (BD) era opens up new possibilities for IE (ideological education) work in universities, but it also poses some challenges. IE development will be greatly aided by recognizing opportunities to meet challenges and optimizing and integrating PL (political lesson) resources. The LNN (Lagrange neural network) model has been established. The simulation results show that the LNN network can converge to the optimal solution quickly and effectively and then reconstruct sparse signals. Individualized college PL instruction using LNN and BD technology helps students communicate more effectively and improves the pertinence, immediacy, and positivity of IE. [ABSTRACT FROM AUTHOR]

In recent years, the number and scale of small, medium-sized, and micro enterprises have continued to increase, which is the main force to promote the development of China's national economy, the guarantee of China's rapid and stable economic development, and has a direct impact on social stability and people's livelihood. In China's economic growth, small, medium-sized, and micro businesses play a critical role. However, several limitations limit their current growth stage, preventing them from expanding their size, such as resource and environmental limits, an inappropriate industrial structure, and institutional issues, such as severe market rivalry. To address this issue, this paper develops a micro innovation service platform for small and medium-sized businesses, offers a variety of services to small and medium-sized businesses, fully realizes data sharing and transmission in all aspects using social perception and neural network algorithms, and effectively utilizes social attributes on mobile nodes to assist each node. Also, it investigates the impact of the innovation service platform for small and medium-sized micro firms in practice. After the construction of the innovation service platform for small and medium-sized micro enterprises in 2018, it analyzes the number of transaction projects of platform technology transfer, technology development, technical consultation, and technical services from 2018 to 2020 and analyzes the transaction volume of projects in four aspects. The results show that they have significantly improved, which plays an important role in realizing the transformation of achievements and improving the innovation ability of small and medium-sized micro enterprises in China. At the same time, according to the investigation and analysis of 8 innovative service platforms in the western region, 25% of them have developed well. [ABSTRACT FROM AUTHOR]

As a revolutionary education model, the flipped classroom teaching model has unique advantages over the traditional education model. How to change the teaching method of flipped classroom into a teaching method suitable for college English courses is a problem. The goal of this research is to investigate how to use data mining and few-shot learning technology to investigate the impact of MOOC and flipped classroom task-based college English teaching modes. This work provides a data mining-based decision tree algorithm and examines the enhanced decision tree method. The experimental results of this study demonstrate that two students in each of the two groups believe that the English teaching mode, which is mostly taught by traditional teachers, is very favorable to a thorough understanding of basic knowledge, accounting for 4% of the total. There are 17 students that believe the new teaching methodology is really beneficial, accounting for 34% of the total. This mode was determined to be effective for in-depth knowledge of the basics by a total of 25 students. It can be seen that the flipped classroom model is more popular with students. [ABSTRACT FROM AUTHOR]

Automatic classification and retrieval of fine art collections have received much attention in recent years. In this article, we explore the applicability of convolutional neural networks (CNNs) for art-related image classification tasks. To examine how hyperparameters affect model performance, we use different hyperparameters in our experiments and find that a higher resolution and appropriate training steps with mix-up can improve model performance. To determine how transfer learning affects the final results, we systematically compare the efforts of five weight initializations of the models for different tasks. We show that fine-tuning networks pretrained on a larger dataset have better generalizability. This phenomenon shows the a priori knowledge that models learn in the real world also applies to the art world, and we call this method as big transfer learning (BiT). Through extensive experiments on fine art classification, we demonstrate that the proposed transfer learning approach outperforms the previous work by a large margin and achieves state-of-the-art performance in the art field. Furthermore, to show how computers capture features in paintings to make classifications, we visualized the results of different classification tasks to help us understand the operation mechanism of the models. Additionally, we use our models to retrieve paintings by analyzing different image similarity aspects. The results show that models can be employed to retrieve paintings even if they are computer-generated. [ABSTRACT FROM AUTHOR]

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches. [ABSTRACT FROM AUTHOR]

With the global industrial integration, trade exchanges between countries are becoming more and more frequent and the competition for related international trade logistics is becoming more and more intense. This paper aims to study the optimization design of international trade logistics based on Internet of Things technology. For this reason, this paper proposes an RFID method, through which it is possible to master a journey in the actual international trade logistics process. And it optimizes and improves its related paths, shortens the time delayed on the road, and achieves the purpose of mentioning the carrying capacity. And for the actual use effect, the relevant experiments and analysis parts are designed to simulate it. The experimental results of this paper show that the improved and optimized international trade logistics shortens the carrying time by 28.6% and increases the carrying capacity by 34.2%, which greatly promotes the development of the relevant international trade logistics industry. [ABSTRACT FROM AUTHOR]

Traditional marine pastures have made great contributions to human beings, but traditional marine pastures have also caused major harm to the marine natural environment. Modern technology represented by artificial intelligence has broad application prospects and significant potential and has the ability to innovate and enhance the conventional marine ranching business that is currently in recession. The particularity of China's marine ranching industry determines the combination of technologies such as underwater robots and the Internet of Things with the development of marine ranching to create a sustainable intelligent marine ranching industry in China. The growth of China's national economy is largely influenced by the development of the country's financial services industry. In order to better support the development of the real economy, it is necessary to establish a financial service system with controllable risks and complementary activities. This paper proposes to use the method of Internet of Things data collection combined with sensors to conduct economic analysis. By taking a number of data in recent years for research and analysis, the results show that financial service industry innovation has become an important part of improving the competitiveness of the industry and an important driving force for economic growth. The Internet of Things (IoT) business in China is a fast-growing industry with huge potential. From the perspective of international comparison, our country is currently in the early stage of the development of the Internet of Things, and a certain technology, industry, and application foundation has been established in the early stage of development. In the financial services business, the visual tracking provided by IoT technology has innovative potential. Introducing IoT technology into financial service business and identifying the driving force and process of financial innovation have practical significance for accelerating financial innovation. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm. [ABSTRACT FROM AUTHOR]