Research on decision-making behavior of multi-agent alliance in cross-border electricity market environment: an evolutionary game

Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets.This study considers multiple neighboring countries in the form of alliances,introduces neighboring countries’exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory.To this end,this study uses three national agents as examples,constructs a tripartite evolutionary game model,and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value,cost of payment,and additional revenue of the alliance.This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.

Development of a risk index for cross-border data movement

Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.

Digital Development Rights in Developing Countries:Where the Governance Rules for Cross-Border Data Flows

The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.

Application of Big Data Analysis Technology in Cross-Border E-Commerce

Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.

Data Flow&Transaction Mode Classification and An Explorative Estimation on Data Storage&Transaction Volume

The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.

Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.

Securing Stock Transactions Using Blockchain Technology: Architecture for Identifying and Reducing Vulnerabilities Linked to the Web Applications Used (MAHV-BC)

This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.

A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

Data is regarded as a valuable asset,and sharing data is a prerequisite for fully exploiting the value of data.However,the current medical data sharing scheme lacks a fair incentive mechanism,and the authenticity of data cannot be guaranteed,resulting in low enthusiasm of participants.A fair and trusted medical data trading scheme based on smart contracts is proposed,which aims to encourage participants to be honest and improve their enthusiasm for participation.The scheme uses zero-knowledge range proof for trusted verification,verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,and realizes privacy protection.At the same time,the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price.The smart contract is used to complete the verification and game bargaining process,and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial.Finally,by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations,it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment.The experimental results show that the model improves the credibility and fairness of medical data transactions,maximizes social benefits,encourages more patients and medical institutions to participate in the circulation of medical data,and more fully taps the potential value of medical data.

Are Chinese Acquirers Discriminated against in Cross-border Mergers and Acquisitions?An Analysis Based on Covered Transactions Filed with CFIUS

This study employs a sample of covered transactions filed with the Committee on Foreign Investment in the United States(CFIUS)of cross-border mergers and acquisitions targeting American companies based on publicly available news reports and announcements to examine the heterogeneity hypothesis in the CFIUS review process.A comparative analysis of case studies and an empirical analysis of logit regressions are performed.We also explore how CFIUS reviews may have an impact on corporations'future mergers and acquisitions decisions in a panel data model setting.The results show that both unfair treatment in CFIUS review and the deterrent effect of CFIUS interventions have contributed to the recent sharp decline in China s direct investment in the US.

MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis

DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

RESEARCH ON DATA CONSISTENCY IN SPATIAL DATABASE SYSTEM

In this paper,two cases of data consistency problems are discussed in Multi_user Geographical Information System.When a user updates some spatial data that have been accessed by other users in database,or executes an UNDO/REDO operation,data consistency will probably be destroyed.To solve the problem caused by the first case,a “Notification_Reread Method" is introduced.As for the second case,there are many problems to be studied.

Consumers'privacy data sharing between the seller and the e-commerce platform

Due to the fact that consumers'privacy data sharing has multifaceted and complex effects on the e-commerce platform and its two sided agents,consumers and sellers,a game-theoretic model in a monopoly e-market is set up to study the equilibrium strategies of the three agents(the platform,the seller on it and consumers)under privacy data sharing.Equilibrium decisions show that after sharing consumers'privacy data once,the platform can collect more privacy data from consumers.Meanwhile,privacy data sharing pushes the seller to reduce the product price.Moreover,the platform will increase the transaction fee if the privacy data sharing value is high.It is also indicated that privacy data sharing always benefits consumers and the seller.However,the platform's profit decreases if the privacy data sharing value is low and the privacy data sharing level is high.Finally,an extended model considering an incomplete information game among the agents is discussed.The results show that both the platform and the seller cannot obtain a high profit from privacy data sharing.Factors including the seller's possibility to buy privacy data,the privacy data sharing value and privacy data sharing level affect the two agents'payoffs.If the platform wishes to benefit from privacy data sharing,it should increase the possibility of the seller to buy privacy data or increase the privacy data sharing value.

公共数据进入数据要素市场模式研究

公共数据是数据要素市场的重要组成部分,其如何进入数据要素市场以及其参与数据要素市场的模式分为几类,学界对此少有研究。通过梳理和比较,深入挖掘国内外公共数据参与数据要素市场的模式类型,归纳得出公共数据进入数据要素市场有两大类、五种模式,包括一级市场(授权运营)+二级市场(场内交易)模式,公共数据开发利用+场外交易模式,依托数据平台构建公共数据流通市场模式,借助数据经纪商、数据中介的交易模式,以及通过(公共)数据信托参与数据市场模式等。在此归纳基础上,演绎出公共数据进入数据要素市场的理想模式,这一理想模式分为数据获取、数据产品生产、数据产品交易三个各有其独特特征的环节,可为我国公共数据进入数据要素市场的策略选择提供参考。

数据交易安全刑事合规的“软法”治理及清单模式

数据交易安全合规指参与数据交易活动的企业内外部各方主体进行的维护数据安全合规的治理活动。刑事治理是由各方主体参与的惩治和预防犯罪的过程,具有过程性。数据交易安全刑事合规是刑事治理的重要方面,可分为事前合规和事后合规两个阶段。《信息安全技术数据交易服务安全要求(征求意见稿)》和《上海数据交易所数据交易安全合规指引》及配套清单采取正面清单和负面清单的管理模式,列举了数据交易主体、数据安全管理体系、数据来源、数据产品可交易性等方面的合规要求。数据交易安全合规清单具有“软法”性质,“软法”与“硬法”相结合,形成企业刑事合规的规范体系。刑法根据数据交易主体及行为场景的差异,以不同的罪刑规范保护数据交易行为所涉及的多元法益。数据犯罪的空白罪状具有定罪指引作用,也为企业合规的开展留下了空间。在实践中应注重数据安全关联罪名适用与刑行衔接,将企业刑事合规融入刑事司法过程并与行政监管相协调。同时,积极发挥企业合规清单的刑事治理机能,构建和完善激励机制、滤罪机制、评估机制,实现合规清单治理的出罪机能与治本效果。

我国数据要素市场治理的模式、现状与对策

要推动数据要素市场高质量发展,首先需要构建科学高效的市场治理机制。本文对数据要素市场治理素现状进行了梳理,提出市场化治理、垂直型治理和关系型治理等三种治理模式,并比较其特点,分析其现状与瓶颈。在此基础上,本文指出我国需要同时采用多种治理模式,形成以市场化治理为主、垂直型治理为辅、关系型治理作为重要补充的数据要素市场治理格局,并就三种治理模式的进一步完善提出了对策建议。

数据概念的体系性建构

在推动数据合规交易的背景下,统一数据法律制度与数据交易实践中的数据概念愈发迫切。基于明确数据交易阶段的核心功能,可从历史视角建构以“行为—结果”为依据,以“数据要素—数据产品(数据商品或数据资产)—数据资本”为顺序的数据概念体系。在数据生产阶段,数据分级子概念对应的不同市场流通度决定了核心数据、重要数据、一般数据三级概念的选用;数据分类子概念所能引发的不同加工规则要求个人数据、企业数据、公共数据应以数据内容为分类标准。在数据交易阶段,以原有信息为内容的数据资源和以再生信息为内容的数据服务对应了不同的风险防范要求。

金融科技:研究现状与展望

本研究认为金融科技是指借助移动互联、区块链、人工智能、大数据等科技创新方式,通过数字化、智能化、安全化等手段把金融服务与应用场景紧密互联,实现金融服务提供者流动性、营利性、安全性等目标,满足金融服务需求者支付、融资、投资和交易等功能需求,并且促进金融服务向便利、便宜、普惠、安全等方向发展的综合金融活动总称.本研究从分析移动互联、大数据、人工智能、区块链等新兴技术及其所推动的金融创新入手,以金融服务三大业务流程为切入点,从支付与结算、信贷与融资、资产管理服务三个方面进行文献梳理.此外,从监管理念变革、监管内容扩展、监管机制重构、监管技术创新四个方面对金融监管进行思考.最后对未来相关研究提出一些展望.

海洋环境数据交易定价的关键基础与相关机制

海洋环境数据作为发展海洋经济的重要基础性要素,其市场化机制的相关理论与实践均处于探索起步阶段。数据交易定价是在交易过程中对数据资源及其通过加工形成的数据产品和服务进行定价,作为数据交易开展的重点和前提,数据交易定价是数据要素市场化的关键环节。文章基于海洋环境数据特征分析了其交易定价的部分关键基础,包括相关的基本概念、数据确权和价值评估,进而讨论了交易定价的相关机制,包括定价主体、定价方法以及定价策略,最后基于上述分析在海洋环境数据的权益分配和交易平台建设等方面提出了建议。