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Data classification and grading is the foundation for ensuring the safe circulation of data and promoting the release of data value. This paper focuses on the key task of government data classification and grading in digital reform. Using a theoretical case study method and based on publicly released plans by various provincial governments and ministries, the implementation of government data classification and grading in China is systematically sorted and quantitatively analyzed. This paper summarizes four key processes and five characteristics of the implementation of government data classification and classification in China. Based on the special complexity of the classification and grading of government data, this paper puts forward four problems corresponding solutions in the implementation of the classification and grading of government data in China, such as unclear overall target positioning, different classification and grading objects, separated classification and grading relations, and different security grading standards. Based on the practice of classification and grading government data of a national ministry, this paper verifies the scientificity and effectiveness of the solutions, and provides a reference for constructing a unified national government data classification and grading system. [ABSTRACT FROM AUTHOR]

Building a high-quality big data resource platform for the government is an important basic project to realize the integration of government information across departments, regions and levels, accelerate the construction of digital government and improve the digital intelligence level of public services and social governance. Firstly, this paper reviews the development of China's E-government and summarizes the three construction mode and main problems of traditional big data platforms, i.e., low data freshness, poor data consistency, difficult business collaborative management, weak basic support and high overall investment. Secondly, this paper conducts a case study on the credit information resource management platform in Jiangsu and explains the reasons for using Jiangsu case as an example. This paper proposes corresponding solutions and overall architecture design for the problems existing in traditional construction models as well as four reference value of Jiangsu case. Finally, based on summarizing the research and practical experience in Jiangsu, five suggestions are summarized to provide a reference for various provinces to study and formulate policy documents, such as provincial government big data platform construction guidelines. [ABSTRACT FROM AUTHOR]

News recommendation system plays an important role in news dissemination of new media. This paper proposed a recommendation system based on deep reinforcement learning, which aimed to combine the representation ability of neural network and the strategy selection ability of reinforcement learning to improve the effect of news recommendation. This paper used dynamic action masks to enhance the ability of judging the short-term interests of users, used the optimization cache mechanism to improve the efficiency of experience cache use, and accelerated model training through the reward design of regional masking nature to improve the performance of the recommendation system in the field of news recommendation. Experimental results show that the accuracy of the proposed model in news data sets is comparable to the current mainstream neural network recommendation methods, and its ranking performance is better than others. [ABSTRACT FROM AUTHOR]

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 high-performance 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. [ABSTRACT FROM AUTHOR]

A sequential implementation of Bootstrap sampling and Bagging ensemble learning is computationally inefficient and not scalable to build large Bagging ensemble models with a large number of component models. Inspired by distributed big data computing, a new Bootstrap sample partition (BSP) big data model and a distributed ensemble learning method for large-scale distributed ensemble learning were proposed. The BSP data model extended a dataset as a set of Bootstrap samples stored in Hadoop distributed file system. Our distributed ensemble learning method randomly selected a subset of samples from the BSP data model and read them into Java virtual machines of the cluster. Following this, a serial algorithm was executed in each virtual machine to process each sample data and build a machine learningmodel on each sample data independently and in parallel with other virtual machines. Eventually, allsub-results were collected and processed in the master node to produce the ensemble result, optionally adding a sample preferences trategy for the BSP data blocks. The BSP data model generation and the component model building were computed using a non-MapReduce computing paradigm. All component models were computed in parallel without data communication among the nodes. The algorithms proposed in this paper were implemented in spark as internal operators that can be utilized in Spark applications. Experiments have demonstrated that BSP data model of a dataset can be generated efficiently through the new distributed algorithm. It improves the reusability of data samples and increases computational efficiency by over 50% in large-scale Bagging ensemble learning, while also increasing prediction accuracy by approximately 2%. [ABSTRACT FROM AUTHOR]

Voice conversion is a research topic in the fields of speech and artificial intelligence. The goal of voice conversion is to change the timbre of speech while preserving the content of the source speech, making it sounds like spoken by the target speaker. It is essential to ensure both the quality and naturalness of the converted speech. Voice conversion based on nonparallel data gains much attention currently, where models are trained using non-parallel multilingual speaker datasets, enabling many-to-many and any-to-any voice conversions. This paper provides a comprehensive summary and analysis of recent developments in non-parallel voice conversion. Firstly, we outline the early voice conversion techniques based on parallel corpus and their limitations. Then, we introduce and compare various approaches to voice conversion based on nonparallel data, providing a thorough analysis. Finally, a summary and outlook on voice conversion technology is provided. [ABSTRACT FROM AUTHOR]

At present, the construction of digital government has entered a deep water area. The government big data platform, as a data base, supports various government information applications. The security and compliance of its private data has been widely concerned by the industry. Federated learning is an important method to effectively solve data silos, and the application of government big data platforms based on federated learning has high research value. Firstly, the current status of government big data platforms and its federated learning application were introduced. Then this paper analyzed three major management challenges involved in the collection, classification and grading and sharing of privacy data on government big data platforms. Further, the problem-solving methods of federated learning based recommendation algorithms and privacy intersection techniques were explored. Finally, summaries and prospects were made for the future application of privacy data on government big data platforms. [ABSTRACT FROM AUTHOR]

In promoting the development of China's digital economy, relevant departments have given full play to the underpinning role of the rule of law, actively promoted the formulation of laws and regulations in the field of data, and put in place a foundational legal system for data that combines vertical and horizontal aspects. Firstly, this paper introduces the basic overview of China's data legal system, including both horizontal system and vertical system. Secondly, it analyzes the vertical system, including data legislation at central and local levels, and covering laws, administrative regulations, departmental rules, local regulations and local administrative rules. Thirdly, it analyzes the horizontal system, including legal systems in the fields of data security and development, personal information protection, commercial data circulation and government data management. Finally, this paper summarizes the achievements of China's data legislation and deficiencies of the current data law system, and puts forward suggestions for improvement. [ABSTRACT FROM AUTHOR]

XAI(explainable AI) is an important component of trusted AI. In-depth research on the technology points of XAI has been carried out in the current industry, but systematic research on engineering implementation is lacking. This paper proposed a general XAI technical architecture, which started from the follow four aspects: atomic interpretation generation, core competence enhancement, business component embedding and trusted interpretation application. We designed four levels: XAI foundation layer, XAI core competence layer, XAI business component layer and XAI application layer. Through the division of labor and cooperation among the technical layers, the implementation of XAI engineering was guaranteed throughout the whole process. Based on the XAI architecture presented in this paper, new technical modules can be introduced flexibly to support the industrialization application of XAI, providing certain reference for the promotion of XAI in the industry. [ABSTRACT FROM AUTHOR]

Entity extraction is an essential task in information extraction. In recent years, under the trend of training model with big data, deep learning has achieved success in entity extraction. However, in the fields such as natural environment, there are very few entity samples or labeled samples of terrain, disasters and other types, and labeling those unlabeled samples is time-consuming and laborious. Therefore, entity extraction for low-resource scenarios has gradually attracted more and more attention, which is called low-resource entity extraction or few-shot entity extraction. This paper systematically combs the current approaches of low-resource entity extraction. It introduces the research status of three types of methods: meta-learning based, multi-task learning based, and prompt learning based. Next, the paper summarizes the low-resource entity extraction datasets and the experimental results of the representative models on these datasets. In the following, the current low-resource entity extraction approaches are analysed. Finally, this paper summarizes the challenges of low-resource entity extraction and discusses the future research direction in this field. [ABSTRACT FROM AUTHOR]

In recent years, applications such as 360 video, augmented reality, virtual reality, etc. have been rapidly developed and gradually formed a new mode of immersive metaverse experience. These new services and applications have common features such as high fidelity and immersive interaction. As a new paradigm, edge computing is increasingly ready to support these features. This paper firstly elucidates that the key to releasing the power of edge computing to support immersive metaverse experience is the processing of AI models and data for content generation. Then, this paper presents a generic framework to realize adaptive deep learning model deployment and data flow to support metaverse services and applications. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence continues to heat up. Large language models have attracted much attention and set off a wave of enthusiasm around the world. The success of artificial intelligence is not essentially a "miracle" of large computing power, but a change in computing models. Firstly, this paper affirms the fundamental role of data in AI, and points out that synthetic data will be the main source of data in the future. Then, this paper reviews the development of computing models, highlights the historic competition between neural network models and Turing models. We points out that the important hallmark of large language models is the emergence of intelligence in machines, emphasizes that the essence of large language models is "compression", and analyzes the reasons for the "illusion" of large language models. Finally, we call on the scientific community to attach importance to large scientific models in "AI for research(AI4R)". [ABSTRACT FROM AUTHOR]

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 & utilization type and platform & data problem type. The research results are helpful to provide a reference prototype for the research and practice of tourism data opening evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 "five-stage, six-dimensional" 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. [ABSTRACT FROM AUTHOR]

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 decision-driven 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. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and the disclosure of massive judicial data, the LJP task for "smart justice" 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. [ABSTRACT FROM AUTHOR]

With the increasing diversity and complexity of material requirements for underground water conservancy and water pipeline networks, the efficient and convenient design of special materials to meet individual needs through machine learning has become a hot topic of concern. Traditional supervised learning methods are all based on a large dataset to train models, but obtaining large datasets for special materials required in deeply buried underground water pipeline networks and high-end military equipment, such as rare and high-entropy alloys, etc. requires extremely high cost and a long period. To solve this problem, we propose a small sample expansion model-RX-SMOGN, using XGBoost and RFECV algorithms for feature screening. We enrich the dataset with the SMOGN algorithm. In this paper, the phase structure of high-entropy alloys is used as the research object, and traditional machine learning models are trained to predict them to verify the effectiveness of the RX-SMOGN model. From the results of 5-fold cross-verification and 4 evaluation indicators, it can be seen that the RX-SMOGN model fully improves the performance of the machine learning model, provides a more convenient method for alloy material design, and fully improves the efficiency of alloy material design. [ABSTRACT FROM AUTHOR]

Federated learning uses data from multiple participants to collaboratively train global models and has played an increasingly important role in recent years in facilitating inter-firm data collaboration. On the other hand, the federal learning training paradigm often faces the dilemma of insufficient data, so it is important to provide assurance of fairness to motivate more participants to contribute their valuable resources. This paper illustrates the issue of fairness in federated learning. Firstly, three classifications of fairness based on different equity goals, from model performance balance, contribution assessment equity, and elimination of group discrimination are proposed, and then we provide indepth introduction and comparison of existing fairness promotion methods, aiming to help researchers develop new fairness promotion methods. Finally, by dissecting the needs in the process of federal learning implementation, five directions for future federated learning fairness research are proposed. [ABSTRACT FROM AUTHOR]