Data property rights are of great significance in the data foundational institution system.Data property rights are structurally separated into three rights, namely, the right of data resource possessing, the right of data processing and the right of data product managing.Existing theories are not practice-oriented, and the responsive way of right-granting can hardly fix the market failures as well.Based on the data factor circulation practice, two typical logics of data factor circulation were proposed, namely, architecture logic and market logic.Based on market logic, the data factor circulation value chain model was constructed.The generated way of right-granting could provide an explanation of the structural separation of data property rights.

At present, the theory and practice of authorized data operation are still chaotic and controversial.Based on the clarification of what open data means in the Chinese context, the current theory that authorized data operation is either complementary to open data or part of open data was challenged, and authorized data operation that implemented open data through a market-oriented delegation mechanism was repositioned.The need was presented to unpack the definitions of two key elements of authorized data operation: data product and data operation, and a novel interpretation of authorized data operation was proposed as it was a social-technical system serving the purpose of open data and mainly producing primary data products for reuse rather than just using with support from a rich tiered data operation service ecosystem.

Based on the conceptual and historical analysis of data and production factors, it was proposed that the data factor was the computer data and its derivative that was gathered, sorted and processed according to specific production needs and participated in social production and operation activities.It should focus on the value of data factor as a new driving force for economic growth and production development.It was summarized the three value release ways of data factor, namely, business operation by data, intelligent analysis by data and external effects by data exchange, and it should be given full attention in the process of promoting the development of data factor.

Data products are the implementation path of ＆quot;data resources for external transactions＆quot; in the accounting classification.Research on the development and circulation of data products can lay the foundation for accounting treatment related to enterprise data resources.Through case analysis, the development and circulation process of data products of bank A were investigated and summarized, and the process from development to external provision of data products was extracted.It was found that as data product development was often based on the data and data capabilities accumulated in the past, the cost calculation of a single data product often faced significant difficulties.In addition, the unit of development and circulation of data products was often based on the application scenarios of data resources, and the value of data products dependedon their application in the scenarios.Therefore, the collection, processing, and sharing of data for data product transactions could play a significant role in the accounting treatment of data resources.

Based on the development status of the domestic and foreign data trading market and platform thinking perspective, the economic overview and prospect of data trading in China were analyzed.Combined with the platform economy and the characteristics of data trading platform market development, the transaction mode, platform types, data source method, and characteristics of data trading platform were discussed in depth respectively.In addition, a analysis of typical domestic government-led data trading platforms was conducted.The study of data trading platform models and characteristics presented reference starting points for the government in the policy formulation of data trading platform cultivation and development.Also, it put forward positive suggestions for cultivating the data trading platform market.

Data trust is regarded as a new and credible data transaction model.Data trust is not only an organizational structure to guarantee information security, but also an innovative institutional design to enhance the trustworthiness of data factor market.A data trust operation mechanism was designed, and the organizational structure, characteristics, functions, and regulatory scheme of the data trust under this mechanism were discussed.That data trust contained two independent legal relationships were pointed out.Its most important feature was the risk segregation among participants, which was the basis of data trust as a credible data transaction model.Data trusts could play an important function in the data value chain, specifically in terms of data value addition, data escrow, and data commons.Finally, the unique advantages of data trust based on the institutional guidelines of “20 measures to build basic systems for data” were demonstrated, and that personal data trust and public data trust were feasible practical paths was pointd out.

The security risks existing in the process of circulation and use of data factors were analyzed systematically.On the basis of summarizing the statue of researches, practices and relevant national standards and norms of data circulation at home and abroad, a countermeasure before, during and after the whole process of circulation and use of data factors for the security risks was proposed, and amanagement and technology synergistic solution to construct a secure and trusted system for circulation and use of data factors was presented.It provided reference for the realizing trusted, controllable data trading that “data sources can be confirmed, scope of use can be defined, circulation process can be traced, and security risks can be prevented”, and promoted the stable and sustainable development of data trading market.

Intelligent text generation is one of the advanced research directions in the fields of artificial intelligence and natural language processing.It is one of key technologies for making AIGC successful, and it has been highly concerned by both academia and industry in recent years.The technology has been deployed and used in many application areas including media publishing and e-commerce, and it can much improve the efficiency of text content production.The systematic overview of the applications of intelligent text generation and the mainstream ways of text generation were given.And the recent deep learning techniques for text generation were introduced.Lastly, the challenges faced by neural text generation were summarized.

Dataspace is the transformation of cyberspace from ＆quot;computing centric＆quot; to ＆quot;data centric＆quot;, which contains great technological issues and innovative opportunities.Similar to the internet, which is the main infrastructure of cyberspace, dataspace also needs a new ＆quot;data-centric＆quot; infrastructure, whose core function is to realize the first-class entity of data.From the perspective of dataspace, the supports and shortcomings of mainstream technologies such as the internet, the World Wide Web, and the digital object architecture for the first-class entity of data were analyzed and summarized, and then the basic connotations and technical challenges of dataspace infrastructure were given.Finally, a first-class data substantialization method based on data pragmatics was proposed.Based on this method, a solution called the internet of data by integrating digital object architecture, distributed ledger, smart contract, and other technologies was proposed to support the construction and operation of internetscale dataspace infrastructure.

With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use client data for modeling.Because users have different habits, there are significant differences between different client data.How to solve the statistical challenge caused by the data imbalance becomes an important topic in federated learning research.Using the fast learning ability of meta learning, it becomes an important way to train different personalized models for different clients to solve the problem of data imbalance in federated learning.The definition and classification of federated learning, as well as the main problems of federated learning were introduced systematically based on the background of federated learning.The main problems included privacy protection, data heterogeneity and limited communication.The research work of federated metalearning in solving the heterogeneous data, the limited communication environment, and improving the robustness against malicious attacks were introduced systematically starting from the background of federated meta learning.Finally, the summary and prospect of federated meta learning were proposed.

The tourism point service is an important support for promoting the development of the tourism industry, but the existing point systems generally suffer from insufficient credibility, poor liquidity, and high maintenance costs, which have not achieved the expected scale effect yet.The research status of the tourism points industry at home and abroad was analyzed, and a blockchain-based tourism points exchange system was proposed, which utilized the decentralized, immutable and secure features of the blockchain technology to realize the free circulation of points among users.The architecture and functional modules design of the system were explained, and a demo system was implemented based on the framework.Privacy analysis and experimental results showed that the system designed had better feasibility, security and privacy.Finally, the current challenges of the system were analyzed combined with the existing technology and business environment.