The assetization of data and the provision of high-quality data elements have amplifying, cumulative and multiplicative effects on empowering the real economy.Current research primarily focuses on theoretical frameworks and critical links of data assetization and lacks systematic thinking and systematic guidance in practical application.This paper introduced a practical framework for operational data assets, termed as the ＆quot;five-stage, six-dimensional＆quot; data assetization model, aiming to explore the scientific problem of “how can enterprises effectively configure and optimize enabling factors of various dimensions to systematically advance the process of data assetization”.By investigating the data products of enterprises in typical industries in Shanghai, the high-quality supply strategy of enterprise data elements was condensed.The applicability of this model in the process of enterprise data capitalization was explored and verified in practice, which provided practical basis for enterprise data capitalization.The formulation and instantiation of the model provide reference to promote the supply of high-quality data elements.

It has become a social consensus that data is the core strategic asset for enterprises.In the wave of digital transformation, businesses drive digitization through data resource utilization and commercialization to fully unleash data asset value.This paper examines how to establish a practical management system by effective data asset operations from the supply side, so as to enhance data quality and security.It also explores how to connect internal and external data by data asset operations from the demand side, so as to foster deep integration with business and enriching data asset application scenarios.The data asset operation platform supports the operational loop of data assets, enabling online, standardized, and configurable processes, meeting the needs for automated and intelligent operations.

To estimate changes in assets value is a crucial task of asset management and accountancy.The calculation of changes in the value of data assets is an urgent question in the current process of including data assets on the financial statements.Differing from traditional assets, data assets exhibit distinctive patterns in value fluctuation.To address this, it is essential to understand the factors contributing to the appreciation or impairment of data assets.This paper starts with the difference between data assets and traditional assets, analyzing different factors causing impairment or appreciation for assets value.Four factors of asset impairment were concluded, including the data timeliness factor, usage timeliness factor, management cost factor, and authorization deadline factor.Additionally, three factors of asset appreciation were analyzed, comprising the data integrity improvement factor, data usage discovery factor, and the factor of cost reduction caused by technology improvement.This exploration lays the foundation for the design of relevant calculation methods.

At present, data trading venues have been built all over the country, and listed data products have hit new highs, but onsite data trading is still not active.After comprehensive analysis of data products listed in the current major data trading venues, it was proposed that the lack of asset characteristics of data products is the main reason for the current ＆quot;more listed data goods but less trading＆quot;.Then, three requirements of asset characteristics of data products were discussed, and a basic framework for data product assetization was proposed, including the path of realizing value, the listing principle of＆quot;assets are listed＆quot;, the asset verification framework of data product, the infrastructure of development-utilization-tradingcirculation, operational trading logic of ＆quot;listing is assets＆quot; and the regular mechanism of evaluation, review and warning.This provides a reference for data product transactions and boom the construction of data market.

With the explosive growth of data, the digital economy, where data serves as a crucial element, continues to advance.In the context of data markets, establishing a fair and efficient pricing and trading system becomes paramount.Addressing fairness within data markets, this study introduces a data market model based on data quality.Firstly, aiming at user demands, a comprehensive pricing strategy based on data quality is formulated.Secondly, to mitigate malicious fraudulent behaviors from users, a market mechanism ensuring fair data transactions is designed.Lastly, building upon primary data transactions, data cleaning services related to data quality are discussed.A multi-user value allocation mechanism for cleaning is designed using principles from game theory.Experimental results demonstrate that constructing systems according to this model ensures both efficiency and fairness within data markets.

As state-owned enterprises advance in digital transformation and intelligent renovation in China, the transfer of data assets has gradually emerged in traditional corporate property rights transfers and capital increase transactions, leading to various risk events.Firstly, the paper analyzes the deficiencies in compliance management of data asset transactions in state-owned enterprises.It then elaborates on the differences between data assets and traditional asset transactions of state-owned enterprises, including technological advancements that have derived new rights, and the two-tier transaction risks brought about by these new rights.Subsequently, it summarizes the new characteristics of data assets in state-owned enterprises: public welfare and benefits, sensitivity and competitiveness.Finally, it suggests that under the leadership of the enterprise party committee, efforts should be made to explore the establishment of a specialized legal and compliance review mechanism for data asset transactions.

Although multimodal text classification techniques have potential when applied to specific scenarios, there are still some limitations.Existing multimodal fusion models require modal alignment in the input data, resulting in a large amount of incomplete multimodal data being directly discarded, thus limiting the scale and flexibility of available data for inference.To address this problem, we proposed a text classification model based on multimodal fusion enhancement and an insufficient multimodal resource training method.Compared with traditional methods, our model had shown an improved performance of an average of 4.25% on a standard dataset.Furthermore, when the missing rate of other modalities except for text input was 50%, using the insufficient multimodal resource training method improved the performance by about 4% compared with traditional multi-route strategies.The experimental results demonstrate the effectiveness of the proposed model and training method.

Cross-domain computing power networks wish to integrate computational and data resources from multiple computing centers, but existing methods do not pay enough attention to cross-domain file and data management.In this paper, a lightweight data access scheme for cross-domain computing power networks was proposed: (1) accessing parallel file system storage resources of remote computing centers through file system protocol conversion; (2) local caching as a supplement to cope with high IOPS applications; and (3) mounting remote or local storage to specified directories through container binding technology.The prototype system based on this scheme had been deployed on highperformance computing centers in multiple universities.The measured data and user experience showed that the scheme in this paper could meet the requirements of common high-performance computing applications.

With the development of artificial intelligence technology and the disclosure of massive judicial data, the LJP task for ＆quot;smart justice＆quot; services has received widespread attention from academia and industry.The task aims to predict the charges, laws, and sentences of a case based on limited factual descriptions of the text.However, existing work lacks research on intelligent decision-making in easily confusing judicial cases, and related models often lack interpretability, which leads to heavy reliance on domain experts for model predictions and hinders the application of LJP in different legal systems.To this end, this article proposes a judicial judgment prediction method CGLJ based on causal graph analysis.Firstly, the causal relationships among elements are mined from unstructured legal fact description texts.Then a causal graph is constructed using a composition method of easily confused accusation clustering.It not only considers the difference among similar fact descriptions, but also enhances the interaction between fact descriptions and laws and regulations.Finally, the constructed causality diagram is integrated into a deep neural network for joint inference to obtain the decision prediction result.In addition, this paper also visualizes the causal diagram inference process in the model prediction, providing better interpretability for the judgment result.The experimental result on the CAIL2018 judicial judgment prediction dataset shows that the proposed method achieves better result than the baseline models.

Unbalanced medical and educational resources, low intelligence of the medical system, and reliance on individual experience in surgical operations are common in medical trade.The metaverse with immersive and interactive features is an effective tool to solve the problem.However, most of the existing solutions are based on a specific technology of virtual reality or artificial intelligence or a specific operation, and there is little systematic research on the multifunctional and multi-scenario medical metaverse.Therefore, a multi-scenario artificial intelligent medical model based on metaverse (MetaMed) was proposed, which elaborated the bottom-up implementations from four layers, including the access layer, data layer, technology layer and application layer.MetaMed was mathematically applied in five medical scenarios, i.e., intelligent surgery, online consultation, medical education, robotic surgery and outpatient registration scenarios, which provides references for the construction of medical metaverse in the future.

In the new era, government data has undergone tremendous changes in terms of form and generation mechanism, presenting multi-modality, huge volume, complex structure, and time series evolution, which has brought new challenges to traditional governance.This paper takes provincial government big data governance as the starting point, and mainly includes three aspects: (1) analyzed the technical framework and bottlenecks of traditional provincial government big data governance; (2) based on the government decision-making scenario, proposed a decisiondriven provincial government big data governance technology architecture (called Fengxiang model), introduced its components, design principles, overall architecture, and demonstrated its feasibility; (3) aiming at the four key business issues and key technical issues in the technical framework of Fengxiang model, further proposed targeted technical implementation ideas to provide a reference for the construction of follow-up governance projects in various provinces and cities and the research and selection of related technical routes.

In the context of the information age and smart tourism, the government’s initiative to open up internal tourism data is conducive to releasing the commercial and social value of tourism data resources.This paper used the coefficient of variation method and the entropy weight method for combined weighting, constructed an evaluation index system for government tourism data opening, calculated the scores of 21 provincial administrative regions, and used fsQCA to compare high and low evaluation values with configuration analysis.The results showed that the index system included 4 first-level indicators, 17 second-level indicators and 51 third-level indicators, which effectively evaluated the performance of tourism data opening in provincial administrative regions; the utilization layer and data layer were the first-level indicators with higher weights, legal policy efficiency, content, platform relationship, etc.were second-level indicators with higher weights;high evaluation values were configured as comprehensive development type and data-assisted application type, and low evaluation values were configured as insufficient policy ＆amp; utilization type and platform ＆amp; data problem type.The research results are helpful to provide a reference prototype for the research and practice of tourism data opening evaluation.

Firstly, the basic principles of graph database and graph algorithms are introduced, including the data model of graph database, query language, and common graph algorithms.Then, the method of constructing the graph model of the power system is elaborated, where system components are represented as nodes and component relationships are represented as edges.Finally, the graph algorithm process of carbon flow calculation is designed, using the AtlasGraph graph database and graph computing components to perform carbon flow iterative calculation.This method makes full use of the advantages of graph database and graph algorithms, achieving accurate and efficient calculation of power system carbon flow.This research provides strong support for monitoring, analyzing, and optimizing carbon emissions in power systems, and is of great significance for promoting the green and low-carbon development of power systems.