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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]

The intermittent, random and uncontrollable power generation characteristics of renewable energy pose challenges for the full utilization of green energy. The high energy consumption feature of the virtual power plant data center makes it an efficient absorption and regulation strategy for the intermittent (non-dispatchable) power in renewable energy. This paper proposes a method to predict the "source-load" dual-state of the virtual power plant using a long short-term memory network that incorporates time-embedded encoding. The results indicate that using the model presented in this paper can achieve proactive alerts for "power shortages" at 15-minute intervals, creating ample buffer time windows for container suspension and backup. Combined with container technology, it realizes dynamic energy consumption management in data centers, thereby enhancing the robustness of the virtual power plant data center against power supply-demand imbalances. This technology is of great significance for stabilizing grid operations, accelerating the application of green clean energy, constructing a service pattern for the energy ecosystem and speeding up the digital transformation of the grid. [ABSTRACT FROM AUTHOR]

The management of safety hazards in water conservancy engineering construction is transitioning towards informatization and intelligence. In order to efficiently mine valuable potential information from a large amount of unstructured construction safety hazard data, an intelligent recommendation system for construction safety hazard rectification based on deep learning is proposed. This paper is based on the TF-IDF algorithm to extract feature words of hidden danger, construct a safety hazard association Sankey diagram and display the information flow characteristics among construction sections, hazard features and hazard types. Then, this paper mines association rules in historical data based on the FP-Growth algorithm. In addition, the process of case retrieval recommendation is optimized by combining the sequence similarity matching algorithm and the Doc2Vec model. This paper uses 80 953 construction safety hazard information as the data source, which is recorded in the water resources allocation project of Pearl River Delta from 2019 to 2023. Example verification shows that the proposed method can match accurate rectification measures for current construction safety hazards, effectively assisting construction safety managers to identify and address hidden danger. [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]

In recent years, the technology of digital content generation has been greatly developed, and the detection and forensic technology of digital content are facing new challenges. This paper firstly introduced digital content generation technology from three aspects: large natural language model, visual generation technology, and multimodal generation technology. Secondly, it introduced digital content detection technology from three aspects: generated text detection, generated image detection, and generated audio and video detection. Thirdly, it introduced digital content forensics technology from two aspects: utilizing fact ual information and forging traces. Then, this paper introduced the application scenarios of these techniques. Finally, it prospected the future work in this research field, and pointed out several directions that need to be focused on. [ABSTRACT FROM AUTHOR]

The demand for scientific data utilization is increasingly urgent, and in the open environment brought by the new scientific research paradigms such as “Fourth Paradigm” and “Convergence Science”, the data utilization shows the characteristics of cross-the-boundary, end-to-end, dynamic and collaborative. As products of the “era of data repository”, the FAIR and TRUST principles can no longer provide in-depth guidance for the efficient use of scientific data in the open environment. This paper analyzed the typical scenarios of scientific data utilization in detail. Then, it presented the PARIS principles to promote scientific data utilization: processable, askable, reliable, incorporable, and suppliable. Finally, this paper given a technical practice path that the PARIS principles can refer to. As beneficial extensions of the FAIR and TRUST principles, it is expected that the PARIS principles can effectively improve the usability of scientific data. [ABSTRACT FROM AUTHOR]

Industrial control system (ICS) is the brain of national industrial manufacturing and civil infrastructure. However, the security risks associated with ICS have become increasingly prominent, making it a significant target for cybersecurity protection. This paper proposed a solution for the issues associated with ICS security data dispersion and delayed threat perception. Specifically, the paper presented a multi-source data-driven ICS security situational awareness system named Argus, which incorporated an awareness chain for ICS security. Furthermore, the paper developed autonomous situational awareness technologies for ICS security, such as stateless high-speed device scanning, precise threat intelligence extraction, and suspicious attack behavior detection, to achieve multi-channel and three-dimensional ICS security monitoring and situational awareness. The experimental results indicated that, compared with conventional ICS situational awareness methods, the perception accuracy of the Argus system has improved by over 10%, with efficiency improvements by two orders of magnitude. Additionally, Argus allows for proactive warning and mitigation of potential security risks. [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]

Based on the current development status of privacy computing both domestically and internationally, this paper summarizes the research progress in the field of privacy computing interconnectivity. Utilizing a systemic architecture perspective, it elaborates on the implementation pathways of interconnectivity technology across three levels: the "application layer, protocol layer, and communication layer". Considering the complexities arising from the current characteristics of interconnectivity platforms, including intricate computational principles and diverse architectures, an innovative Adaptation mechanism is proposed as an interconnectivity framework. Through the design and implementation of key technologies, this framework not only ensures the realization of existing functionalities but also addresses the compatibility issues posed by different architectures. By means of experiments conducted in various scenarios encompassing traditional machine learning, horizontal federated learning, and vertical federated learning, and considering dimensions such as data volume and feature distribution, the effectiveness and viability of the trustworthy interconnectivity gateway under the Adaptation framework have been demonstrated. [ABSTRACT FROM AUTHOR]

Facing the challenges of data security governance in the new situation and following the technological development trends in the field of data security, in response to the prominent issue of identifying key authorized personnel in the practical application of data security governance in large state-owned enterprises, this article proposes a key authorized personnel identification technology based on graph algorithm, which can discover potential authorization influencing factors in the system and measure the weight influence of different meanings from multiple perspectives, The recognition results have strong interpretability. Aiming at the problems of user and entity behavior anomaly detection in data security governance, this paper proposes a user and entity behavior anomaly detection method based on the generative adversarial network. The experimental results show that the accuracy, recall rate and average F1- score of the proposed method are better than the comparison baseline model method. A data security platform has been designed and developed. The platform has played an important role in reducing data security risks, assisting enterprise compliance construction, promoting data development and utilization, and has been applied in multiple data centralized management projects. It can meet the needs of big data processing in security scenarios, and has good application and promotion value. [ABSTRACT FROM AUTHOR]

With the launch of the "Channel Computing Resources from the East to the West" project, Beijing-Tianjin-Hebei region, as a national integrated big-data center system, has established a Zhangjiakou data center cluster and continuously optimized the regional computing power layout. Beijing has been accelerating the construction of a global digital economy benchmark city since 2021, committed to forming a collaborative development pattern of computing power integration, and achieving a globally leading density of data centers. The core industry of Beijing's digital economy has strong fundamental advantages, but as a high-energy consuming industry, data centers cannot fully meet real-time computing power needs solely relying on the local infrastructure construction in Beijing. Therefore, this paper explored the promoting role of the Beijing-Tianjin-Hebei national integrated big-data center system in the construction of a global digital economy benchmark city in Beijing through channels such as computing power resource sharing, green energy consumption support, and industrial development collaboration. Based on this, it analyzed the challenges that still exist in current regional collaboration. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta is the source of national innovation with the most concentrated advanced manufacturing industry, the solidest development foundation, and the most active and potential innovative economy. The construction of a national computing hub node in the "Channel Computing Resources from the East to the West" project is great significance to accelerate the upgrading and transformation of the advanced manufacturing industry in the Yangtze River Delta. Therefore, this paper explored the logical mechanism, practical challenges and development path of the digital transformation of manufacturing industry enabled by the national computing hub node in the Yangtze River Delta based on the current difficulties and problems encountered in the digital transformation of the manufacturing industry, with a view to promoting the transformation and upgrading of manufacturing industry and the green and high-quality development of the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

In the era of the digitaleconomic, computing power has become an important indicator to measure the degree of national economic development. With analyzing the two national strategies of "Channel Computing Resources from the East to the West" progect and "Construction of Chengdu-Chongqing Economic Circle", and based on the construction of Chengdu-Chongqing Hub Node, the reasons that why Chengdu-Chongqing hub node has been laid out as one of the eastern hub nodes were analyzed. It due to four aspects, namely, strategic location advantage of computing, data integration system guarantee, computing infrastructure compaction, and maturity of computing application scenarios. Further, the operations in the advanced data center of Western (Chongqing) Science City demonstrate that the nationalcomputing hub built in Chengdu-Chongqing economic circle boosts the project that channels computing resources from the east to the west. Then the paper discussed the coordinated development between Tianfu data center cluster and Chongqing data center cluster, as well as the coordinated development between national computing hub built in Chengdu-Chongqing economic circle and national computing hub built in Guizhou province. It suggested that Tianfu data center cluster and Chongqing data center cluster should build coordination mechanisms from six aspects, including the network, data, computing power, industry, algorithm and energy. The neighboring national computing hubs can implement coordination based on the allocation of computing power business, the cross-domain circulation of data elements, and new business formats for digital cooperation. [ABSTRACT FROM AUTHOR]

"Channel Computing Resources from the East to the West" project is a scientific planning by comprehensively considering national strategy, regional coordination, energy optimization, and industrial technology development. It aims to reinforce the effect of scale and to realize cross-region scheduling of computing power resources across the nation. Besides, it helps to optimize geographic layout of computing power resources nationally. Based on the perspective of digital economy development and digital industry ecosystem, this paper analyzed Guizhou's foundation of digital economy, and put forward suggestions to develop computing infrastructure, to support data exchange and sharing securely, and to innovate data applications. [ABSTRACT FROM AUTHOR]

The western region is a destination for the transfer of data center computing capacity. It is rich in natural resources and must take full advantage of its energy and climate advantages. In the context of "Channel Computing Resources from the East to the West", data centers urgently need to quantify the carbon reduction benefits during the process of transferring computing capacity. Therefore, this paper constructed a carbon emission accounting model for computing capacity transfer by considering three influencing factors: renewable energy, climate factor, and transfer process. To illustrate the effectiveness, a case of eight nodes of "Channel Computing Resources from the East to the West" was presented. The results show that compared with the carbon emission reduction from renewable energy and climate factors, the additional carbon emission caused by the transmission process is minimal, and for each 1 kW·h of computing capacity, 0.053~0.344kg of carbon emission can be reduced if only the first two factors are considered. To improve the carbon emission benefits from load shifting, the western region should guide data centers to gather in resource-intensive places, vigorously develop clean energy industries, increase the development of clean energy, and promote the degree of clean energy consumption, while grasping this opportunity to attract digital industries to land and promote the digital transformation of traditional industries. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to report on the development status of the Qingyang cluster of the Gansu Hub of the "Channel Computing Resources from the East to the West", to find out the existing problems, to look forward to the development prospects, and to accelerate the pace of the construction of the Qingyang cluster of the Gansu Hub. Literature research and field research methods were used to investigate and analyze the characteristic advantages, achievements, challenges and problems of the Qingyang cluster, and finally to look forward to the prospects for the construction of the Qingyang cluster. Suggestions were made: deploy from the national, provincial and cluster levels, through the implementation of industrial innovation and development period, industrial ecological enhancement period, industrial highland casting period three-step strategy, to complete the "seven strategies" tasks, focusing on the development of the "Channel Computing Resources from the East to the West" core, derivatives, empowering the "three industries". It will complete the tasks of "seven strategies", focus on the development of "Channel Computing Resources from the East to the West" core, derivation and empowerment industries, the formation of "three systems" of smart computing, intelligence and smart production, and the establishment of a "green, scientific and technological, and safe" smart park. [ABSTRACT FROM AUTHOR]

Digital humanities aims to use modern computer network technology to help traditional humanities research. Classical Chinese historical books are the important basis for historical research and learning, but since their writing language is classical Chinese, it is quite different from the vernacular Chinese in grammar and meaning, so it is not easy to read and understand. In view of the above problems, the solution to extract entities and relations in historical books based on pre-trained models was proposed to obtain the rich information contained in historical texts effectively. The model usedmulti-level pre-training tasks instead of BERT's original pre-training tasks to fully capture semantic information. And the model added some structures such as convolutional layers and sentence-level aggregations on the basis of the BERT model to optimize the generated word representation further. Then, in view of the scarcity of classical Chinese annotation data, a crowdsourcing system for the task of labeling historical classics was constructed, high-quality, large-scale entity and relation data was obtained and the classical Chinese knowledge extraction dataset was constructed. So it helped to evaluate the performance of the model and fine-tune the model. Experiments on the dataset constructed in this paper and on the GulianNER dataset demonstrated the effectiveness of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

The paradigm of near-data computing has driven banks and securities firms to build multiple data centers globally or nationally. In the traditional business model, transactions focused on accessing data within a single data center. With the changing business model, distributed transactions across data centers have become common, such as transferring money between bank accounts or exchanging equipment between game accounts, with data stored in different data centers in different regions. Distributed transaction processing requires the two-phase commit protocol to ensure the atomicity of the sub-transactions submitted by each participating node. In processing cross-domain transactions, traditional transaction processing technology needs to be expanded to ensure that the system can provide higher throughput due to the longer and more varied network latency between nodes. After analyzing the problems and optimizing space for crossdomain distributed transactions, this paper proposes a new distributed transaction processing algorithm called Harp. Harp delays the execution of some sub-transactions based on the difference in network latency while ensuring serializable isolation level, reducing the duration of transaction lock contention, and improving system concurrency and throughput. Experiments show that Harp improves the performance by 1.39 times compared with the traditional algorithm under YCSB workload. [ABSTRACT FROM AUTHOR]

The market is the process of establishing product prices, and the methods of establishing prices in different markets are different. The pricing model of product is an abstraction of the process of establishing product prices in the market. At present, the market demand for data has been formed, but an effective data market has not yet been formed and data pricing is still in the state of exploration. Most of the existing data pricing models are designed for some specific data transaction scenarios, rather than for specific types of data markets. This paper considers the economic market types of the data market, and divides the current data market into five data market types from the perspective of economics: monopoly market, monopsony market, oligopoly market, centralized perfect competition market and decentralized perfect competition market. Existing data pricing models are summarized into corresponding data market types. By analyzing the dependence relationship between data market types and data pricing models, the "market type principle" of data pricing is proposed to provide theoretical guidance for the construction of data element markets and its data pricing. [ABSTRACT FROM AUTHOR]

Intelligence data, as a high-value data asset, is stored on different platforms and held by different subjects, and has the characteristics of high dispersion and low availability. Due to the different structural forms and storage methods, it is non-trivial to achieve efficient aggregation and sharing of multi-source heterogeneous intelligence data, and there are great difficulties in the fusion and comprehensive analysis and utilization of intelligence information among multiple subjects. Therefore, there is an urgent need to establish a secure and trustworthy sharing and interoperability mechanism among cross-domain intelligence subjects to meet management requirements such as data validation and security auditing, while realizing correlation analysis and cross-validation of intelligence information and deeply mining the intelligence value therein. To address the needs and application characteristics of cross-domain intelligence data trustworthy sharing, this paper proposes a data object-based intelligence management method and adopts the digital object architecture and blockchain trusted access control technology to build a cross-domain intelligence data trustworthy sharing system, realizing the view unification and cross-domain trustworthy sharing of multisource heterogeneous intelligence data, providing technical support for intelligence data fusion and convergence and intelligence information intelligent analysis, and fully exploiting the huge potential of intelligence information. This will provide technical support for intelligence data convergence and intelligent analysis, and fully exploit the huge potential of intelligence information. [ABSTRACT FROM AUTHOR]

With the development of big data and cloud computing, data management is breaking down "data silos" and developing from isolated services for single domain to cross-domain data sharing and collaborative services. Based on the public data authorization and operation framework, this paper presented the full-link structure of cross-domain data authorization and operation, and discussed the challenges of data privacy and efficiency in the cross-domain data processing. In response to these challenges, two data processing models, centralized and privacy computing, were proposed, which could improve data processing efficiency while protecting data privacy. Finally, an application case of a cross-domain data authorization operation platform in a practical scenario was given. [ABSTRACT FROM AUTHOR]

k-dominant skyline is a prevailing skyline query which has widespread applications in multi-criteria decision making and recommendation. As these applications continuously scale up, there is an increasing demand to support k-dominant skyline over a data federation which consists of multiple data silos, each holding disjoint columns of the entire dataset. Yet it is challenging to support k-dominant skyline over a data federation. This is because strict security constraints are often imposed to query processing over data federations, whereas naively adopting security techniques leads to unacceptably inefficient queries. In this paper, we presented an efficient and secure k-dominant skyline for a data federation. Specifically, we devised a novel private vector aggregation-based solution with ciphertext compressionbased optimization for efficient k-dominant skyline query processing while providing security guarantees. Extensive evaluations on both synthetic and real datasets showed the superiority of our method. [ABSTRACT FROM AUTHOR]

The development of information technology has promoted the generation of a new scientific research paradigm. Social sciences and humanities have gradually developed a data-driven research method in recent years. From the perspective of visualization, the current status of visualization applications in digital humanities was summarized from three levels, task, data, and application, through the analysis of papers at the digital humantites conference organized by alliance of digital humanities organizations. By analyzing different projects setup by different experts with different backgrounds, i.e., humanities, visualization, and art. The great potential of multidisciplinary cooperation to improve the quality of digital humanities plus visualization projects was revealed. The practice of Peking University in exploring this new paradigm of multidisciplinary cooperation in digital humanities plus visualization was shared, which included education in multidisciplinary, practice experience on promotion, and research experience in intelligent visualization. Finally, two ways for the future development of the interdiscipline between digital humanity and visualization were revealed, the cooperation between experts and the collaboration between humans and computers. [ABSTRACT FROM AUTHOR]

Scientific topic prediction is central to scientific research and can substantially advance the allocation of scientific resources. Machine learning and data mining approaches have been widely applied to scientific topic prediction, including paper content-based topic model and citation prediction models. A novel scientific topic prediction algorithm PKGM (Poincare keywords graph embedding) was proposed, which utilized keywords and their relations to build a keyword network, and calculated the distance between two nodes in this network to predict the probability that an edge existed. The result of comparing PKGM with seven baselines showed that PKGM obtained a 7.3% improvement by using AUROC and 5.8% improving by using AP in comparison to the best method in Euclidean space, and 10.8% improvement by using AUROC and 7.2% improving by using AP over the best approach in hyperbolic space. The results demonstrated the effectiveness of PKGM. [ABSTRACT FROM AUTHOR]