The emergence of big data technology continues to innovate and change the world, bringing opportunities and challenges to all walks of life. Against the background of this era, traditional entrepreneurship education requires reform and innovation. This research attempts to explore the ways and practices of applying big data technology to entrepreneurship education so as to improve and perfect traditional entrepreneurship education and achieve its sustainable development. Based on classic theories, such as entrepreneurial theory, strategic management theory, and leadership theory, this paper develops a relatively systematic attribute system of entrepreneurship education under big data technology, comprehensively uses Fuzzy-DEMATEL and ISM methods to explore the relationship between different attributes and their importance, and finally constructs a hierarchical framework for the application of big data technology in entrepreneurship education. The results show that the attributes of entrepreneurship education under big data technology can be divided into four levels, each with different priorities and degrees of importance, and there are complex interactions and constraints among them. This study provides important guidance and suggestions for the development of entrepreneurship education and multiattribute decision-making management under the given resources, which is conducive to the sustainable development of entrepreneurs and new ventures.

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Constructing a Hierarchical Framework for Assessing the Application of Big Data Technology in Entrepreneurship Education.

With the growth of Industrial Internet, various types of data show explosive growth. Data is being moved from the cloud to the edge for computing more frequently and edge computing becomes an important factor affecting the deep application of Industrial Internet platforms. However, the security issue of data transmission sharing is not addressed. Therefore, this paper proposes a security scheme based on timedrelease, multi-dimensional virtual permutation, and proxy reencryption(PRE), to protect the confidentiality of the data during the transmitter; symmetric cryptography is employed to encrypt the transmission data. At the same time, a time server is used, making it impossible for data receivers to get the information about the data before the specified time arrives, it solves the application scenario of data transmission and sharing with timed-release requirements and the efficiency of a large number of data is solved, and the security of data is improved. Finally, the security of the scheme was proved theoretically. Compared to existing PRE schemes, this scheme adds timed-release controlled access, has resistance to ciphertext attacks and end-to-end security features, and uses fewer bilinear pair operations in the algorithm. The performance was tested experimentally and the results show that the scheme improves efficiency while ensuring security and has significant advantages in terms of data security and private data protection. Keywords—Timed-release; edge-computing; multi-dimensional virtual permutation; proxy re-encryption; symmetric encryption

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Proxy Re-encryption Scheme based on the Timed-release in Edge Computing

This paper, mainly from the background of big data, the conception and characteristics of big data, combined with the reformed direction of financial information system in the era of big data, puts forward the strategic significance of accounting personnel’s positioning transition and functional promotion, and emphasizes the following three research directions, that is, Great data of financial data, accounting standardization and the wisdom of the investment decision. Finally, this paper conclude that the solution of assisting businesses to take advantage of the financial management of big data is a worth pondering question. Meanwhile, financial officers should keep with the times to enhance to help companies maximize value through their professional skills, integrated management capacity and the security of data storage and analysis. Key words—Big data; Accounting personnel; Opportunities; Challenges In China, the accounting computerization has gained popularity in recent years. Most enterprises have got rid of the tedious manual bookkeeping and stepped into the informatization palace. Big data era and financial informatization will be full of new life. A lot of work having difficulty in information collection, processing and analysis, such as enterprises’ investment decisions and comprehensive budget management, etc. will no longer be difficult problems; the financial information system will better help enterprises realize management promotion in enterprise investment decisions, accounting management specification, comprehensive budget and cost reduction, etc.

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A Brief Analysis of Opportunities and Challenges for Accounting Personnel in the Big Data Era

Industrial internet security is a critical component of cyberspace safety. Furthermore, the encryption protocol is a critical component of cyberspace security. Due to the rapid development of industrial internet and edge computing, increasingly more devices are outsourcing their data to cloud servers to save costs. Edge devices should have a secure session key to reduce communication costs and share information. However, most key generation and storage are completed by a centralized third-party organization, which carries some security risks. In this context, this paper will propose a lightweight multi-dimensional virtual iteration of the group key agreement protocol. Group key agreement protocol allows for one-at-a-time encryption and timely key updates without the involvement of a trusted third party, and each device in the network can agreement a large number of keys. According to the analysis of this protocol, it has high security, rapid computation speed, and little storage space.

Group key agreement protocol for edge computing in industrial internet.

ABSTRACT Remote sensing image retrieval (RSIR) refers to finding images from an image database that contain the same instance as the query image, which is an essential task in remote sensing applications. Traditional depth-based hashing algorithms usually convert the image library into a hash matrix with a specified number of digits. On the one hand, the quality of hash matrices generated by traditional methods is low and cannot guarantee good clustering between pictures of the same class. On the other hand, the ability to extract features using backbone networks must be improved. This paper proposes a deep hashing model called adaptive hash code balancing (AHCB) to solve two existing problems. The model introduces a balanced binary method to maximize the hash value entropy so that the generated hash has better clustering. Graph convolutional networks(GCNs) are introduced to automatically detect relevant data points in the graph, perform back-propagation, and propagate the updated network feedback to the feature extraction layer to improve the ability to extract features. It enables the model to learn the intrinsic data structure between remote sensing images. Experimental results on three public datasets show that the proposed method outperforms the current state-of-the-art deep hashing remote sensing image retrieval algorithms by a large margin.

Adaptive hash code balancing for remote sensing image retrieval

Travel time estimation is a crucial task in practical transportation applications, while providing the reliability of estimation is important in many working scenarios. Most existing studies do not consider the dynamics of traffic status for different road segments in real time, thus yielding unsatisfactory results. To address the problem, we propose to formulate the traffic network as a temporal attributed graph and perform node representation learning on it. The learned representation is capable of jointly exploiting the dynamic traffic conditions and the topology of the road network, which is then fed into a route-based spatio-temporal dependence learning module to estimate the travel time. By incorporating a distribution loss function, our proposed model is able to predict the distribution of travel time. In the meantime, we design an auxiliary local task of predicting the congestion status of each road segment, which further enhances the generalization performance of the representation learning. Extensive experiments on real-world large-scale datasets demonstrated the superiority of our method compared with the state-of-the-arts.

Wanyi Zhou

Papers

A Brief Analysis of Opportunities and Challenges for Accounting Personnel in the Big Data Era

Group key agreement protocol for edge computing in industrial internet.

Adaptive hash code balancing for remote sensing image retrieval

Proxy Re-encryption Scheme based on the Timed-release in Edge Computing

Travel Time Distribution Estimation by Learning Representations Over Temporal Attributed Graphs