M-Skyline: Taking sunk cost and alternative recommendation in consideration for skyline query on uncertain data

Problems and countermeasures associated with intercultural adaptation in international education according to the communication action theory model

Due to the development of the Chinese economy, the consolidation of national power worldwide, and the increasing frequency of economic and cultural exchanges with foreign countries, the number of people from various countries who travel to China to engage in exchanges has increased significantly. Given the development of economic globalization, the acceleration of the process of educational internationalization represents a general trend in higher education development and a common requirement for universities. In addition to education, international students also experience cross-cultural adaptation, that is, behavioral adaptation or changes that occur in people in response to changes in their home country, whether in the form of external or internal cultural adaptation. For international students, the problem of cross-cultural adaptation not only hinders their learning progress but also affects their psychology and living conditions. This article explains the construction of Habermas’s theory of communication action. According to this theory, the purpose of communication is to coordinate the common actions of actors, and this coordination is achieved through mutual communication, which is mediated by language. The article also discusses the cross-cultural adaptation experienced by international students in Chinese universities and highlights the importance of developing educational services for international students in the context of international education. Studies have shown that female students exhibit slightly worse psychological adaptation than male students and female students have a slightly higher rate of depression rate than male students. An interpersonal study of 120 international students traveling to China found that most of these students (60%) were able to adapt in terms of their interpersonal relationships. To solve the problems associated with cross-cultural adaptation of international students in China, some countermeasures have been proposed, mainly including active participation in interpersonal communication, an enhanced understanding of the new culture, and the amplification of cultural identity.

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper.

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

The basic idea of the mass of medical growth is to adhere to local market price thinking with a Chinese touch and follow the development policy of “quality first, efficiency first.” It insists on properly handling a series of important relationships betwixt socialism and market economy, the first to drive the rich later, the government and the market, equality and efficiency, short-term growth and long-term development, China and the international economy, ecology and growth of the region. Under the guidance of the qualitative thinking theory, it combines the strategic goals of China's economic qualitative development and actively draws on the research results of other countries. It uses big data algorithms to focus on the impact of qualitative development on tax and income reduction in the real economy. It conducts research experiments on the impact of tax reduction and fee reduction based on big data algorithms on the top-notch growth of the real economy. Its experimental data show that: in 2018, the share of primary, tertiary, and primary sector in China's dimensional economy top-notch growth coordination index was 7.2%, 40.7%, and 52.2%, respectively. Its contribution rate to economic growth was 4.2%, 36.1%, and 59.7%, respectively. From these data, it can be concluded that the top-notch growth of the real economy is getting better and better under the influence of tax reduction and fee reduction by big data algorithms.

Intelligent terminal security technology of power grid sensing layer based upon information entropy data mining

The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure.

Deep Learning and Improved HMM Training Algorithm and Its Analysis in Facial Expression Recognition of Sports Athletes

Facial expressions are an auxiliary embodiment of information conveyed in the communication between people. Facial expressions can not only convey the semantic information that people want to express but also convey the emotional state of the speaker at the same time. But for sports athletes in training and competitions, it is usually not convenient to communicate directly. This paper is based on deep learning and an improved HMM training algorithm to study the facial expression recognition of sports athletes. It proposes the construction of deep learning of multilayer neural network, and the rank algorithm is introduced to carry out face recognition experiments with traditional HMM and class-specific HMM methods. The experimental results show that, with the increase of rank value, the class-specific recognition rate is up to 90%, the detection rate is 98% and the time-consuming is 2.5 min, which is better than HMM overall.

Dynamic Analysis of College Physical Education Teaching Quality Evaluation Based on Network under the Big Data

Improving the quality of college physical education is of great significance to facilitating the integrated development of students' psyches and physical. Establishing a more systematic, effective, and social training needs of education quality evaluation hierarchy is also the centerpiece of the college physical culture education administration. Massive information technology provides new conception and methods to this, and supply advantage sustains for furtherance the education ecology development. Based on the network education system, this paper uses big data to quantify the evaluation indexes of physical education teaching, so as to actualize the timely dynamic evaluation of the process that is physical teaching and learning. This essay constructs the evaluation index system of college physical education teaching quality by combining mensurable and qualitative methods. On the basis of previous studies, an evaluation model of college physical education teaching quality based on artificial intelligence mass data calculation is designed. The experiment authenticates that the model evaluation risk coefficient is 1.93 lower than the optimized model. The experiment also proves that the model is conducive to elevating the education quality.

A Universal Routing Algorithm Based on Intuitionistic Fuzzy Multi-Attribute Decision-Making in Opportunistic Social Networks

With the vigorous development of big data and the 5G era, in the process of communication, the number of information that needs to be forwarded is increasing. The traditional end-to-end communication mode has long been unable to meet the communication needs of modern people. Therefore, it is particularly important to improve the success rate of information forwarding under limited network resources. One method to improve the success rate of information forwarding in opportunistic social networks is to select appropriate relay nodes so as to reduce the number of hops and save network resources. However, the existing routing algorithms only consider how to select a more suitable relay node, but do not exclude untrusted nodes before choosing a suitable relay node. To select a more suitable relay node under the premise of saving network resources, a routing algorithm based on intuitionistic fuzzy decision-making model is proposed. By analyzing the real social scene, the algorithm innovatively proposes two universal measurement indexes of node attributes and quantifies the support degree and opposition degree of node social attributes to help node forward by constructing intuitionistic fuzzy decision-making matrix. The relay nodes are determined more accurately by using the multi-attribute decision-making method. Simulation results show that, in the best case, the forwarding success rate of IFMD algorithm is 0.93, and the average end-to-end delay, network load, and energy consumption are the lowest compared with Epidemic algorithm, Spray and Wait algorithm, NSFRE algorithm, and FCNS algorithm.

An Adaptive Routing-Forwarding Control Scheme Based on an Intelligent Fuzzy Decision-Making System for Opportunistic Social Networks

Routing selection in opportunistic social networks is a complex and challenging issue due to intermittent communication connections among mobile devices and dynamic network topologies. The structural characteristics of opportunistic social networks indicate that the social attributes of mobile nodes play a significant role on data dissemination. To this end, in this paper, we propose an adaptive routing-forwarding control scheme (FPRDM) based on an intelligent fuzzy decision-making system. On the foundation of the conception of fuzzy inference logic, two techniques are used in the proposed routing algorithm. Information fusion of social characteristics of message users and node identification are implemented based on the fuzzy recognition strategy, and the fuzzy decision-making mechanism is applied to control message replication and optimize data transmission. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.8, reduces the average end-to-end delay by nearly 45% as compared with the Epidemic routing protocol, and lowers the network overhead by about 75% as compared to the Spray and Wait routing algorithm.