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An essential role in improving educational informatization and encouraging new approaches to education is played by educational technology, a specialist field that supports both theorising and doing in this area. When it comes to learning educational technology, students must have excellent professional knowledge and abilities, as well as a thorough understanding of data. Harr-NMF feature extraction for educational technology teaching large data systems is the focus of this project. With the help of big data technology, the construction of a teaching resource sharing system provides useful solutions for the storage and sharing of current and future massive teaching resources, enables fast and accurate analysis and retrieval of massive teaching resources and data, improves the security and scalability of the system while maintaining system stability, and promotes education teaching informatization to a new level. The system can be used to analyze and retrieve data quickly and accurately. By mapping different types of Harr-NMF features into a gradient matrix using grayscale projection, pattern classification is performed through the simplified gradient matrix as features. The algorithm complexity of feature extraction is eventually reduced to a great extent by the fast advantage of feature extraction by this algorithm. The Harr-NMF algorithm and the similarity relation matrix are used to obtain the optimal parameter values and the corresponding affiliation matrix and class centers, thus providing effective initial values for Harr-NMF, which effectively solves the problems of slow convergence and easy to fall into local minima of the Harr-NMF algorithm. The algorithm of this paper is compared with several improved algorithms of Harr-NMF in terms of convergence, recognition rate, and other performance to verify the feasibility and effectiveness of this paper. With a combination of qualitative and quantitative approaches, the data literacy of teaching educational technology is dissected and constructed to enhance its data literacy and improve the quality of education technology discipline personnel training. [ABSTRACT FROM AUTHOR]

An essential role in improving educational informatization and encouraging new approaches to education is played by educational technology, a specialist field that supports both theorising and doing in this area. When it comes to learning educational technology, students must have excellent professional knowledge and abilities, as well as a thorough understanding of data. Harr-NMF feature extraction for educational technology teaching large data systems is the focus of this project. With the help of big data technology, the construction of a teaching resource sharing system provides useful solutions for the storage and sharing of current and future massive teaching resources, enables fast and accurate analysis and retrieval of massive teaching resources and data, improves the security and scalability of the system while maintaining system stability, and promotes education teaching informatization to a new level. The system can be used to analyze and retrieve data quickly and accurately. By mapping different types of Harr-NMF features into a gradient matrix using grayscale projection, pattern classification is performed through the simplified gradient matrix as features. The algorithm complexity of feature extraction is eventually reduced to a great extent by the fast advantage of feature extraction by this algorithm. The Harr-NMF algorithm and the similarity relation matrix are used to obtain the optimal parameter values and the corresponding affiliation matrix and class centers, thus providing effective initial values for Harr-NMF, which effectively solves the problems of slow convergence and easy to fall into local minima of the Harr-NMF algorithm. The algorithm of this paper is compared with several improved algorithms of Harr-NMF in terms of convergence, recognition rate, and other performance to verify the feasibility and effectiveness of this paper. With a combination of qualitative and quantitative approaches, the data literacy of teaching educational technology is dissected and constructed to enhance its data literacy and improve the quality of education technology discipline personnel training. [ABSTRACT FROM AUTHOR]

Although Internet technology brings invaluable benefits to all walks of life, the security of network and information is becoming more and more prominent. The leakage of personnel information caused by Internet security incidents causes irreparable harm and loss to individuals or enterprises. E-commerce is a virtual transaction mode based on Internet technology. Its security requirements for transactions are more stringent than those for traditional transaction modes. Traditional identity authentication technology can no longer meet its security needs. People urgently need a reliable identity authentication system, meaning to ensure the security of e-commerce. According to the actual application scenarios of the algorithm in e-commerce, this paper, based on the research on face recognition technology, focuses on the in-depth research on face detection technology under the background of big data in order to introduce face recognition technology in this paper. This paper proposes a feature-based face matching algorithm. The face image is preprocessed to improve the accuracy of real-time face detection and reduce the false detection rate. Based on the research of traditional facial feature extraction technology, a virtual sample set that can effectively support a traditional facial feature extraction algorithm is constructed to solve the problem of insufficient training samples in practical applications. The experimental results showed that the accuracy of the method in this paper can reach up to 79.5% and that the minimum time consumption is only 0.142 s. Compared with the traditional method, the accuracy rate is higher and the time consumption is shorter. [ABSTRACT FROM AUTHOR]

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance. [ABSTRACT FROM AUTHOR]

The security prediction and control program of sensor network based on big data analysis is studied. First of all, in view of the shortcomings of existing security measures, this paper regards security standards as a challenge. Based on the changes of network security before and after the attack, the concept of "network security line" is proposed to select and simplify the security measures that can meet the needs and affect the real security and monitor the counting process of the network. Then, for counterattack, several models of topology security measurement based on stochastic process are developed to check the topology security. Finally, the experimental design of the risk assessment method is found by creating a risk receiving data model, deleting data features, and recreating distributed data. Experimental results show that the efficiency of this method is above 90% when the estimated distance is 10–40 m. The working power is 196∼461 Hz, lower than the normal standard. The time delay is less than 0.3 s, and the real-time performance is better than ordinary models. The relative error is less than 3%, and the accuracy is higher. [ABSTRACT FROM AUTHOR]

Traditional exercise rehabilitation mode information is closed, it is difficult for users to obtain exercise rehabilitation information, and the exercise rehabilitation data of users are not fully utilized. With the combination of big data technology and the Internet of Things and exercise rehabilitation, we also face the problem of processing massive amounts of data. Whether these data can be used well has become the key to obtaining good business benefits and improving user experience. This paper applies the Internet of Things and big data technology to the physical exercise rehabilitation system to improve sports data processing and combine with the big data processing technology to explore the factors affecting exercise rehabilitation to improve the effectiveness of physical exercise rehabilitation. Moreover, this paper proposes a posture recognition algorithm based on human vision and defines the human posture described by the feature, which improves the classifier's ability to classify different posture data. Finally, this paper carries out experimental analysis to verify the effect of the method proposed. The research results show that the method constructed in this paper has a good physical exercise rehabilitation effect. [ABSTRACT FROM AUTHOR]

The cornerstone of future social education is the development of skills. This work enhances the big data algorithm and presents an effective association algorithm that may boost relevance in order to improve the accuracy of educational talent employment. Furthermore, this paper employs data mining association analysis technology to identify educational talent attributes that are disproportionately important in determining the problem of student employment, as well as to eliminate attributes that are unrelated to the problem, in order to achieve the goal of screening educational talent attributes. In addition, this paper combines an improved algorithm to build an education talent employment recommendation system, builds a system function structure based on the actual situation, and combines experimental research to verify the effect of the model built in this paper. From the experimental research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation. [ABSTRACT FROM AUTHOR]

Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation "α" between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation "α" and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users' access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the logistics industry has generally introduced information technology, and the construction of intelligent warehouse management is the development trend of the logistics industry. In order to improve the operation efficiency of intelligent warehouse and make full use of freight resources, this paper proposes an intelligent logistics inspection system based on big data. The system takes the platform data of unmanned monitoring points with multiple data sources as the research object and also uses a deep learning-based method for parcel detection and tracking in order to overcome the defects of traditional parcel detection and tracking technology. The role of the system is to obtain the parcel flow data of each unmanned monitoring point and then upload these important data to the scheduling system of the warehouse to realize the intelligent management of the warehouse. [ABSTRACT FROM AUTHOR]

With the reshuffle of the global industrial structure, some industrial parks in China are facing problems such as unclear industrial positioning, unclear agglomeration effect, and lack of impetus for independent innovation. It is urgent to seek a way of transformation and upgrading. The arrival of the era of big data also provides new opportunities for the transformation of China's industrial parks. In this context, the model of "character towns" proposed by Zhejiang Province provides a new concept for breaking the bottleneck of industrial park transformation and also provides a platform and space for the application of data technology and digital governance in the era of big data. Based on the theory of creative destruction and self-organization, this paper holds that the essence of industrial park transformation under the concept of character towns is the self-organization process of creative destruction and high-quality reconstruction. After sorting out the development process of parks in China, this paper divides industrial parks into production-oriented parks, consumption-oriented parks, and trade-oriented parks according to the economic activities classified by classical economics. By constructing a six-stage creative destruction model, this paper analyzes the path selection of transformation and upgrading of three types of parks under the concept of character towns and proposes that industrial structure replacement for the production-oriented parks, upgrading the consumption structure for the consumption-oriented parks, and change of trade control points for the trade-oriented parks are the possible transformation paths for the three types of parks. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, people's lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people's footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users' privacy data based on data mining algorithms so as to truly ensure that users' privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user's identity from being identified and the user's private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users' privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks. [ABSTRACT FROM AUTHOR]

Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC systems. [ABSTRACT FROM AUTHOR]

The development of big data and wireless communication technology has expanded the channels for Western culture to enter China. Diversified teaching forms make us dizzy, which has a great impact on traditional English teaching. This paper aims to study the dilemma and countermeasures of English teaching reform based on multiculturalism under the background of big data and wireless communication. It analyzes the connotation of multiculturalism, the characteristics of the era of big data, the problems existing in college English teaching, and the necessity of college English teaching reform. On the basis of feasibility, in order to understand the current situation of English teaching in colleges and universities, this paper takes teachers and students of a college in this province as the research object, conducts a questionnaire survey on them, and finds out how to better use the background of big data and wireless communication through data analysis. Combined with the multicultural trend of the times to carry out English teaching, the study concluded that under the current technological background, our English teaching must adhere to the student-centered approach and take the improvement of teachers' quality as an important means. [ABSTRACT FROM AUTHOR]

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton ∗ km. In the end, the three of them were selected as company Z's transportation suppliers. [ABSTRACT FROM AUTHOR]

China's economy has grown significantly since the turn of the century, and its domestic situation has become more stable. The employment and employment issues that college graduates encounter across the nation, however, demonstrate the opposite tendency. The general public's attention has turned to "difficulty in getting employment," which has emerged as a crucial component of the overall social employment problem. Additionally, it examines the environment and elements that influence college students' employment, such as the social, economic, and policy elements as well as the university, employer, and individual elements. This study examines the development of college students' entrepreneurial service system by using colleges and universities as the research object and integrating theoretical analysis with empirical research. The M Apriori technique is then parallelized using a Spark and Credible neural network-based approach. This solution makes full use of Spark's benefits based on in-memory processing and data item RDD storage by adopting data parallelism and a local rather than global strategy. The M Apriori algorithm is parallelized and ported to the Spark platform for parallelization, enhancing Spark MLlib. The simulation test and analysis are completed in the end. The study's findings demonstrate the algorithm's excellent accuracy, which is 8.65% greater than that of the Apriori method. Finally, the experimental findings demonstrate the effectiveness of the method used in this paper. [ABSTRACT FROM AUTHOR]

This paper aims to build an English translation query and decision support model using big data corpus and applies it to business English translation. Firstly, the existing convolutional network is improved by using depth-separable convolution, and the input statements are mapped to the depth feature space. Secondly, the attentional mechanism is used to enhance the expressive ability of input sentences in deep feature space. Then, considering the sequential relationship, use long short-term memory (LSTM) neural network as a decoder block to generate the corresponding translation of the input sentence. Finally, nonparametric metric learning module is used to improve the model in an end-to-end way. Wide range of experiments on the multiple corpora have shown the proposed model has better real-time performance while maintaining high precision in translation and query, and it has a certain practical application value. [ABSTRACT FROM AUTHOR]

Software engineering is one of the most active fields of entrepreneurship and innovation in the world, and it is also the core field of the information technology industry. Software talents as the foundation and support are an important weight to determine the future direction of my country's software engineering. How to make colleges and universities cultivate compound software talents with innovative ability and engineering ability and how to guide students to closely combine innovative thinking with social practice are a major challenge faced by the current software process education in colleges and universities in my country. At present, the overall quality of software engineers is poor, which cannot meet the needs of enterprises and training objectives. This paper puts forward the application of the OBE (outcome-based education) model in the training of software talents, which can effectively solve the current problems of talent quality and social demand. The analysis shows that there is a high correlation between collaborative education and satisfaction, and the collaborative education model can effectively improve satisfaction. The investment of scientific research funds can effectively improve the overall quality of scientific research team members. The OBE talent training mode can effectively improve the overall test effect, whether in the experimental set or the test set, the test result of the OBE talent training structure is still the highest, the accuracy rate can reach 94.23%, the recall rate can reach 94.51%, and the F1 value can reach 95.13%. It is fully explained that the identification accuracy is the highest when the OBE talent-training structure is adopted. [ABSTRACT FROM AUTHOR]

Cultural inheritance and innovation is an important measure to enhance the vitality of traditional culture and realize the sharing of national culture. Mongolian folk music, as an important part of Chinese cultural resources, plays an irreplaceable role in inheriting Mongolian culture. How to better inherit the music culture of ethnic minorities has been a hot topic in the music education circle in recent years. However, when we focus on the campus inheritance of ethnic minority music, we have to pay attention to the original living space of ethnic minority music and its unique local inheritance method. What is the relationship between them? What are the similarities and differences? The investigation of the relationship between the two is an indispensable content for exploring the inheritance of minority music. This paper aims to study the innovation of the inheritance path of Mongolian folk song music culture, endow Mongolian folk song music with a new era connotation in the new era, and let the folk song music culture be inherited and revitalized in the in-depth contact with the masses. This paper proposes a method to study user needs with the help of dissemination big data, identify malicious traffic with the help of the Internet of Things, find the most touching music and cultural elements of users, innovate and integrate the Mongolian nation, avoid malicious traffic from invading Mongolian folk songs, and enhance the attractiveness of Mongolian folk songs. Experiments have shown that Mongolian folk songs have a strong appeal, and the participation rate of young people in Mongolian folk songs has increased by 20%. [ABSTRACT FROM AUTHOR]

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton ∗ km. In the end, the three of them were selected as company Z's transportation suppliers. [ABSTRACT FROM AUTHOR]

Recently, data integrity for multiagent-based big data environments has been challenging. This paper presents a blockchain-based Merkle DAG structure (M-DAG) for audit data integrity. M-DAG resolves the problem that arises due to the multicopy of a large data volume in a big data environment. It employed Boneh–Lynn–Shacham's (BSL) signature to verify the integrity of identical multicopy on big data environments. The proposed M-DAG audit mechanism uses a consortium chain algorithm for decentralized traceability and audit to archive reliable data. The evaluation has been carried out for the efficiency of the data integrity audit. [ABSTRACT FROM AUTHOR]

The development of big data and wireless communication technology has expanded the channels for Western culture to enter China. Diversified teaching forms make us dizzy, which has a great impact on traditional English teaching. This paper aims to study the dilemma and countermeasures of English teaching reform based on multiculturalism under the background of big data and wireless communication. It analyzes the connotation of multiculturalism, the characteristics of the era of big data, the problems existing in college English teaching, and the necessity of college English teaching reform. On the basis of feasibility, in order to understand the current situation of English teaching in colleges and universities, this paper takes teachers and students of a college in this province as the research object, conducts a questionnaire survey on them, and finds out how to better use the background of big data and wireless communication through data analysis. Combined with the multicultural trend of the times to carry out English teaching, the study concluded that under the current technological background, our English teaching must adhere to the student-centered approach and take the improvement of teachers' quality as an important means. [ABSTRACT FROM AUTHOR]

Cultural inheritance and innovation is an important measure to enhance the vitality of traditional culture and realize the sharing of national culture. Mongolian folk music, as an important part of Chinese cultural resources, plays an irreplaceable role in inheriting Mongolian culture. How to better inherit the music culture of ethnic minorities has been a hot topic in the music education circle in recent years. However, when we focus on the campus inheritance of ethnic minority music, we have to pay attention to the original living space of ethnic minority music and its unique local inheritance method. What is the relationship between them? What are the similarities and differences? The investigation of the relationship between the two is an indispensable content for exploring the inheritance of minority music. This paper aims to study the innovation of the inheritance path of Mongolian folk song music culture, endow Mongolian folk song music with a new era connotation in the new era, and let the folk song music culture be inherited and revitalized in the in-depth contact with the masses. This paper proposes a method to study user needs with the help of dissemination big data, identify malicious traffic with the help of the Internet of Things, find the most touching music and cultural elements of users, innovate and integrate the Mongolian nation, avoid malicious traffic from invading Mongolian folk songs, and enhance the attractiveness of Mongolian folk songs. Experiments have shown that Mongolian folk songs have a strong appeal, and the participation rate of young people in Mongolian folk songs has increased by 20%. [ABSTRACT FROM AUTHOR]

Big data refers to a large amount of diverse digital information that requires new processing modes to have higher decision-making power. Big data is known for its large capacity, complex types, and high application value. At present, big data is also considered as a modern service technology. The Internet of Things technology originated from the third scientific and technological revolution, which refers to the connection of objects and networks through information sensor devices according to agreed protocols. Competitive aerobics is a sport that can perform continuous, complex, and high-intensity complete sets of movements under the accompaniment of music. The complete set of competitive aerobics combines four sets of difficulty to demonstrate the strength, balance, and flexibility of the athletes, and through the continuous combination of movements, it shows the diversity of the seven steps of competitive aerobics and the coherence of the calisthenic movements. The purpose of this paper is to investigate the current situation of competitive aerobics based on the big data of the Internet of Things. It is expected that, through the IoT and big data technology, the current situation of aerobics competition will be investigated and analyzed, the current industry situation will be analyzed, and the scientific and reasonable training methods of competitive aerobics will be explored. This paper uses a questionnaire to investigate the competitive aerobics, analyzes the problems and deficiencies reflected in the questionnaire, and proposes feasible countermeasures. The experimental results of this paper show that there are 20 coaches with undergraduate education, accounting for 49%. Therefore, in the recruitment of coaches, education and experience must be combined; it is normal for athletes to train 2–3 times a day, and the training frequency can be adjusted according to individual circumstances. [ABSTRACT FROM AUTHOR]

In order to improve the effect of English teaching, this paper combines data mining algorithms to construct an intelligent learning system. Moreover, starting from the actual situation, this paper combines the actual needs of English teaching to construct a blended learning system framework. Blended learning provides a new idea for English teaching. As a result, the purpose of this work is to do some empirical research on how to successfully implement blended learning in college English classes. The findings of the study suggest that blended learning may help students improve their English application skills. Furthermore, this article discovered that students' independent learning capacity, learning passion, and systematic comprehension of maritime equipment were enhanced to some amount using data mining and cloud classroom blended learning resource construction and teaching practise. Finally, this work integrated theoretical and experimental research to demonstrate that the system developed in this paper has a specific impact. [ABSTRACT FROM AUTHOR]

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

Nearest neighbor (NN) models play an important role in the intrusion detection system (IDS). However, with the advent of the era of big data, the NN model has the disadvantages of low efficiency, noise sensitivity, and high storage requirement. This paper presents a neighbor prototype selection method based on CCHPSO for intrusion detection. In the model, the prototype selection and feature weight adjustment are performed simultaneously and k-nearest neighbor (KNN) is used as the basic classifier. To deal with large-scale optimization problems, a cooperative coevolving algorithm based on hybrid standard particle swarm and binary particle swarm optimization, which employs the divide-and-conquer strategy, is proposed in this paper. Meanwhile, a fitness function based on the accuracy and data reduction rate is defined in the CCHPSO to obtain a set of appropriate prototypes and feature weights. The KDD99 and NSL datasets are used to assess the effectiveness of the method. The empirical results indicate that the data reduction rate of the proposed method is very high, ranging from 82.32% to 92.01%. Compared with all the data used, the proposed method can not only achieve comparable accuracy performance but also save a lot of storage and computing resources. [ABSTRACT FROM AUTHOR]

The rapid development of information technology and Internet makes the sports information resources retrieval service more convenient and quick; sports policy in recent years lays a foundation for the development of the Internet + sports, the development of sports industry in the process of our country economy level of development status, and the development of sports industry into the era of information and big data. This paper takes OpenStack cloud platform as the research basis (1) to realize the sharing of sports industry information resources in OpenStack cloud technology and (2) to realize big data analysis of sports industry and (3) empirical research on big data of sports industry. The main content is to realize the construction of sports resources informatization based on the OpenStack cloud platform. Through the analysis and empirical study of the big data of the sports industry, the influence of the development of the sports industry in the process of China's economic development is discussed. In this paper, the experimental results show that the sports industry showed a positive impact in the process of economic development, the sports economy for the development of the economy, the contribution rate reached 11.77%, the sports industry for the development of the economy, the pull rate of 1.056%, based on the cloud platform of information resources sharing of data analysis, sports industry for the development of the economy has a positive role in promoting. [ABSTRACT FROM AUTHOR]

In view of the poor effect of badminton players' physical fitness and response training, this paper puts forward the construction method of badminton players' pace training model based on big data, constructs badminton players' pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players' pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete's pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements. [ABSTRACT FROM AUTHOR]

Economic globalization has become an unavoidable result of the rapid development of global economy. However, while economic globalization brings speed development and convenience to businesses, it also brings risks and challenges. With the advancement of network technology, Big Data has become increasingly intertwined with people's lives. Big Data is gradually being applied to the method of people's lives, in business, medical care, science and technology, production, and life. The production of Big Data for enterprises provides a large amount of data and information analysis library and for the management of enterprises provides a lot of convenience. Business management has also changed with the addition of big data. It is the top priority of enterprise development and survival to cope with the current economic and information globalization era which brings hidden dangers and risks. Based on the background of economic globalization and Big Data, this study analyzes the importance of enterprise risk management in the context of Big Data. This study mainly adopts the literature method and survey method to summarize what problems exist in enterprise risk management, highlights the causes of the problems in enterprise risk management, and gives corresponding solutions according to the existing problems. This paper aims at the study of enterprise risk management in the big environment, hoping to have certain suggestions and guidance for the relevant enterprises. [ABSTRACT FROM AUTHOR]

Currently, private data leakage and nonlinear classification are two challenges encountered in big data mining. In particular, few studies focus on these issues in support vector machines (SVMs). In this paper, to effectively solve them, we propose a novel framework based on the concepts of differential privacy (DP) and kernel functions. This framework can allocate privacy budgets and add artificial noise to different SVM locations simultaneously, which makes the perturbation process freer and more delicate. In addition, under this framework, we propose three algorithms, DP SVMs that perturb the training data set, perturb the kernel function, and utilize mixed perturbation (DPSVM-TDP, DPSVM-KFP, and DPSVM-MP, respectively), all of which can realize accurate classification while ensuring that the users' privacy is not violated. Moreover, we conduct privacy analysis on these algorithms and prove that they all satisfy ε , 0 − DP. Finally, we conduct experiments to evaluate the algorithms in terms of different aspects and compare them with the DPSVM with dual-variable perturbation (DVP) algorithm (DPSVM-DVP) to determine the optimal perturbation method. The results show that DPSVM-KFP can achieve the highest data utility and strictest privacy protection with the shortest running time. [ABSTRACT FROM AUTHOR]

With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users' demand and Items' attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes. [ABSTRACT FROM AUTHOR]

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user's historical access information and interaction records, the user's access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall. [ABSTRACT FROM AUTHOR]

Since the 21st century, thanks to the continuous achievements in the computer field, the Internet technology has developed rapidly in just a few years until it has swept the world. In the torrent of the information age, big data technology came into being. The application of big data technology is extremely high, and it is widely used in many industries. Based on the excellent performance of big data technology in the education industry, this paper will discuss the application of big data technology in the evaluation and analysis of college English education informatization. Under the condition that the evaluation standard is relatively abstract, the traditional method of evaluating through the subjective perspective of human beings has a large interference surface, and it is difficult to make the evaluation work substantive, data-based, and fair, which directly leads to evaluation and analysis work being difficult to carry out. Based on the abovementioned situation that the evaluation and analysis are relatively limited, this article will introduce the role of the big data technology algorithm model in the evaluation and analysis work. Through big data technology algorithms, qualitative problems that are difficult to evaluate are converted into quantitative problems that are easier to analyze and compare and then get rid of the limitations faced by traditional evaluation algorithms. The interference caused by human subjective factors to evaluation and analysis should be reasonably avoided as much as possible. This makes the evaluation and analysis results more objective and fair. This article is mainly based on the relevant algorithms under the big data technology to mine, classify, and purify the data with strong correlation with the important relevant indicators that affect the degree of college English informatization and carry out the experimental calculation of the evaluation of the relevant indicators. The results are as follows: we provide a reliable basis for the evaluation of the degree of informatization in college English and then conduct more scientific and rigorous evaluation and analysis. In the future development of big data technology, the database will be continuously supplemented with valuable teaching data, so that the results of big data technical analysis are more and more consistent with the real situation. [ABSTRACT FROM AUTHOR]

In view of the current educational environment, the development of higher education cannot be separated from the support of information technology. The era of big data has had various impacts on higher education and accelerated the development of higher education. Cloud computing technology has good application prospects for the data analysis and mathematical modeling content involved in the reform of higher education. This study analyzes and calculates the concerns of the four main contents involved in the educational reform of a higher education institution as the research object. In order to quantify the application effects of different cloud computing technologies, the paper introduces three typical cloud computing technologies to analyze the research data and establish related mathematical models. The results show that the prediction accuracy based on the immune cloning algorithm is the best. Based on the calculation results, it can be found that the corresponding data of the two main contents of teaching scene setting and teaching quality detection show an obvious polynomial function relationship. [ABSTRACT FROM AUTHOR]

In order to improve the effect of XSS intrusion intelligent detection, this paper proposes an application of big data-oriented XSS intrusion intelligent detection based on class image processing in the construction of university campus network. In this application, image like processing method is used for data acquisition, data cleaning, data sampling, feature extraction, and other data preprocessing; design a word vector quantization algorithm based on neural network to realize word vector quantization and get word vector big data; through theoretical analysis and derivation, a variety of deep neural network intelligent detection algorithms with different depths are realized; through experiments, it is found that the average recognition rate of each deep DNN for the class I big data set is about 99.44%, the variance is about 0.000002, and the standard deviation is about 0.001589; the average recognition rate of class II big data set is about 99.77%, the variance is about 0.000006, and the standard deviation is about 0.002427. The experimental results show that this method has the characteristics of high recognition rate, good stability, and excellent overall performance. [ABSTRACT FROM AUTHOR]

The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network's input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education. [ABSTRACT FROM AUTHOR]

In order to study the storage and management mechanism of raster data and vector data for different purposes in data services, a research method of big data storage and indexing mechanism based on spatiotemporal information cloud platform is proposed. This paper discusses the application of big data storage index in virtualization platform, cloud management software, and storage management, so that Hadoop cluster can use the dynamic expansion ability of cloud platform to obtain better expansion ability. High performance statistical applications for geographical conditions are constructed. A high performance geostatistical analysis system Hadoop-Geostatistics is designed and implemented. A variety of spatial statistics index calculation, flow, and MapReduce algorithm was realized. The experimental results show that in the cluster environment, the time consumption is basically the same as that of the single index calculation, while in the single computer environment, when the comprehensive index is calculated in parallel for 10,000 statistical objects, the system performance drops rapidly and reaches an early inflection point. In the comprehensive statistical concurrent calculation, when the time consumption reaches 5 × 10^7, the amount of calculation data is as high as 7000, which increases linearly. The experimental data show that the designed spatiotemporal information cloud platform model can store spatial big data, and the storage method is very accurate. By establishing a spatiotemporal information cloud platform, cloud computing technology can provide higher spatial information services. [ABSTRACT FROM AUTHOR]

Blockchain is a distributed time-series database. Based on the blockchain platform, this paper designs the framework model of cloud storage and designs the cloud storage model based on homomorphic encryption based on the operation process of the model. According to the applicability of underlying blockchain storage, the HElib library is established as the algorithm for data privacy protection. The BGV homomorphic encryption algorithm is used as the bottom layer of the algorithm, and the efficiency of the BGV homomorphic encryption algorithm is compared with that of the Gentry's bootstrap-based homomorphic encryption algorithm. It is proved that the BGV algorithm is more suitable for big data. [ABSTRACT FROM AUTHOR]

Various novel Internet rumors have also emerged. This article mainly researches the audience dissemination response law and the mechanism of action. This paper constructs a model of network rumor spreading. In the homogeneous network, we know that for the rumors that the initial infection rate is large, the increase caused by the herd phenomenon is not very large. For heterogeneous networks, we need to separately examine the increase of nodes and sparse nodes in the network under different initial infection rates. With the change of time, the number of nodes in each state will change due to interaction. When an ignorant person contacts a communicator, the ignorant person will be converted into a rumor communicator with a certain probability. Affected by the memory ability of individual user nodes, each online rumor support node and online rumor opposition node becomes an online ordinary user node with a constant probability f. Affected by the rumor support node, an online ordinary user node is transformed into a receiving unread node by a rumor support node with probability β at time t. Experimental data shows that when the herd effect is not considered, it is found that the transmission peak is 0.4328 at t = 10. When the herd effect is introduced, the spread of rumors reaches the transmission peak 0.5431 at t = 8. After the introduction of the herd effect, the peak value increased by 25.5%, and the time to reach the peak was shortened by 20%. The results show that computer big data technology has a significant inhibitory effect on the spread of Internet rumors. [ABSTRACT FROM AUTHOR]

Whether minors can grow up healthily is the key to the happiness and peace of hundreds of millions of families, the great rejuvenation of the Chinese nation, and the development of the country. For better legal protection of minors' sexual autonomy, China has put forward the application and analysis of minors' sexual autonomy under legal protection under big data. This paper analyzes and compares the applied conventional technology and big data technology. Through the analysis of the impact of different technology applications on minors' sexual autonomy and the legal protection of minors' sexual autonomy, it can be seen that the protection and application of laws on minors' sexual autonomy under big data technology can better safeguard the legitimate rights and interests of minors' sexual autonomy. This is more conducive to protecting the healthy growth of minors and implementing the idea of socialist harmony and development. We should establish a good network environment and network order, effectively reduce the infringement of minors' sexual rights, better protect minors' sexual rights, build a good social environment, promote the stable development of society, and maintain the peace and happiness of families. [ABSTRACT FROM AUTHOR]

The order system plays the role of the central nervous system in the integrated logistics operation. Theoretically speaking, the whole operation process of logistics service is to complete the order, the order information runs through all the links of the whole logistics, and the order processing efficiency affects the logistics process. This paper studies the high-concurrency and high-performance applications of the microservice order system based on big data, which prove that on the basis of big data technology, the performance of the microservice order system will be greatly improved. To this end, the analysis provides an overview of big data technologies and related content of microservices. It describes their application scenarios and then combines the development of the order system with a comprehensive analysis to prove its feasibility. At the same time, we compared multiple algorithms applied in microservices and found the most suitable algorithm for the order system and applied it, so that the order system can achieve high concurrency and high performance. The big data-based microservices control system proved to be useful for reasonable resource planning and for improving system performance in experiments. The final experiment found that in the microservice order system using big data, its transaction average response time was 0.01 s, and the success rate was 100%. [ABSTRACT FROM AUTHOR]

In order to enlarge the scale of multimedia communication, based on the design of cloud computing platform, this paper makes an in-depth study on the design of multimedia communication platform. Firstly, we study from three methods: extracting high-quality content, broadening multimedia profit channels, and enhancing copyright awareness. Among them, the big data diversion system under the cloud computing platform is designed to optimize data clustering and pattern recognition. The traditional big data shunting system under the cloud computing platform adopts the open-source embedded system design method. The shunting system is connected to the switch of the cloud computing platform through the coupling coil, resulting in missing points and wrong points in the data in the shunting system. Using this method, it is concluded that the cloud computing platform cannot achieve the optimal allocation in terms of SLA violation rate, resource consumption, and power consumption. After experiments and research, the current development trend of media shows a deviation from this phenomenon. The development of media itself, that is, the self-appreciation of media, is gradually becoming the main purpose of media development. A power simulation cloud computing platform architecture with dynamic expansion, efficient computing, and convenient resource scheduling should be given to face large-scale multimedia communication. [ABSTRACT FROM AUTHOR]

The aim of this study is to help financial enterprises establish a solid foundation in a big data ecosystem (BDE) and fully play their competitive edges in the fierce business competition. This paper innovatively conducts research studies on the reform of enterprise financial management. First, it expounds on the relevant concepts of big data technology (BDT) alongside financial management. Afterward, the impact of BDT on enterprise financial management is examined. Consequently, a financial enterprise-oriented economic management model is proposed by integrating BDT, enterprise economic management (EEM), and enterprise performance evaluation methods. Further, an experiment is designed to verify the proposed model on estimating the annual enterprise operating income (OI) and human resource (HR) structure. The research findings show that the predicted and actual OI error is within 6%. The predicted enterprise personnel deployment only deviates slightly from actual personnel deployment. Thus, the proposed economic management model can accurately predict OI and cost. It provides a reliable reference for enterprise innovation management, optimizing recruitment conditions, sales strategy, and development strategy. Lastly, the research deficiencies are pointed out alongside corresponding suggestions. [ABSTRACT FROM AUTHOR]

With the continuous advancement of big data technology, there are many drawbacks in the past urban greenway design. The environment, population distribution, geographic location, spatial distribution, and other factors affect the greenway design. At the same time, a large amount of historical data is mixed. The systematic arrangement has indirectly led to the fact that urban greenway design does not know how to analyze and use past data. There is always a waste of various resources. While greenways bring many positive impacts, they also have negative impacts: it is difficult for the wild animals in the greenways to move along the predetermined passages, and most animals spread in the greenways in a disorderly manner, so that the greenways accelerate the invasion of alien species and diseases to a certain extent. Using big data technology can enable us to allocate resources more reasonably and accurately. The purpose of this article is to study the use of big data technology to rationalize our urban greenway design. This paper adopts a calculation method that closely combines quantitative and qualitative features to study its features. The local feature analysis algorithm can extract and analyze urban spatial data. Spatial remote sensing image technology can deepen our knowledge and understanding of urban spatial characteristics, conduct data mining, provide all the information of urban space, analyze the content and type, and classify it; then filter and integrate the big data in the greenway planning and design information. The information about greenway planning and design in the big data of Dao Design is digitized. This information can be composed of traditional data and dynamic data. Finally, after scientific analysis, the data is subjected to scenario assumptions, modeling, and output, we applied various analysis results to actual operations, and we obtain the final inspection. In fact, this is also going out from actual work, summing up the experience we have done, and again starting from the perspective of specific practice. Compared with our traditional field investigation before, with the guidance of big data analysis, the experimental results of this article show that under the use of big data technology, the wrong resolution of the SegNet semantic classification algorithm for buildings is 9.6%, and the others are 10.9%, 16%, 20%, etc., which are all greater than 9.6%, so the segNet model has a higher image acquisition accuracy. It can solve the problems caused by the lag of the past questionnaires and the small sample size, improve the accuracy of data extraction through algorithms, find some features and problems of greenway design, and complete the urban greening rate through computer algorithms. Calculation and analysis can better provide the most reasonable method for greenway design site selection and route selection. [ABSTRACT FROM AUTHOR]

The Internet's growth has opened up new avenues for educational reform, and educational informatization has shifted the traditional classroom-teaching model. Teachers' teaching techniques have changed dramatically because of the introduction of a variety of new mobile intelligent terminals to the campus and classroom. This paper examines the design content of intelligent teaching methods against the backdrop of big data, using a cross-cultural business communication course as an example to reform the classroom-teaching mode, with the goal of resolving the current classroom-teaching dilemma and improving teaching effectiveness through the new intelligent teaching mode. [ABSTRACT FROM AUTHOR]

Homomorphic encryption is widely used in the scenarios of big data and cloud computing for supporting calculations on ciphertexts without leaking plaintexts. Recently, Li et al. designed a symmetric homomorphic encryption scheme for outsourced databases. Wang et al. proposed a successful key-recovery attack on the homomorphic encryption scheme but required the adversary to know some plaintext/ciphertext pairs. In this paper, we propose a new ciphertext-only attack on the symmetric fully homomorphic encryption scheme. Our attack improves the previous Wang et al.'s attack by eliminating the assumption of known plaintext/ciphertext pairs. We show that the secret key of the user can be recovered by running lattice reduction algorithms twice. Experiments show that the attack successfully and efficiently recovers the secret key of the randomly generated instances with an overwhelming probability. [ABSTRACT FROM AUTHOR]

In view of the poor effect of badminton players' physical fitness and response training, this paper puts forward the construction method of badminton players' pace training model based on big data, constructs badminton players' pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players' pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete's pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements. [ABSTRACT FROM AUTHOR]

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods. [ABSTRACT FROM AUTHOR]

Histograms are important tools for data mining and analysis. Several differentially private publishing schemes for histograms have been proposed recently. Existing differentially private histogram publication schemes have shown that histogram reconstruction is a promising idea for the improvement of publication histograms' accuracy. However, none of these have properly considered the problem outliers in the original histogram, which can cause significant reconstruction errors. Based on the problem, the publication of histogram outliers under differential privacy, this paper puts forward a publication method for histograms with outliers under differential privacy: Outlier-HistoPub. Our method deals with the count sequence of the original histogram first, using a 'global sort' to reduce the degree of alternative distribution (a concept proposed in this paper), which may eliminate the influence of outliers during reconstruction. To avoid individual privacy leakage in the reconstruction process, an exponential mechanism is used to select the most similar adjacent bins of the uniformity distribution histogram to merge each time, and the Laplace mechanism is utilized to generate noisy data to perturb the count sequence of the reconstruction histogram. Experiments prove that the method proposed in this paper can improve the efficiency and accuracy of histogram publication. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An introduction is presented in whihc the editor discusess topics within the issue including privacy of Big Data mining; data privacy problems in third-party auditors; and privacy disclosure problems in the online image sharing.