Restricted common data in anonymous E-cash system

Discuss the problem of infinite increasing coin list in anonymous E-cash systems, which reduce the efficiency of whole system greatly. Though some methods are suggested, no one can solve the problem with high efficiency and flexibility. Here, we use the technique of adding information in blind signatures to deal with this problem. Through adding timestamp in signatures, we can separate the valid period of all used coins into pieces. Only the coins in the last stage are recorded. So the scale of the coins list is controlled. We also analyze the anonymity of these data, and add some indispensable restrictions to them. These restrictions can ensure that the imported data don’t break the anonymity of the customers. In order to fulfill these qualifications, we lead to the concept of restricted common data (RCD). Furthermore, we propose two schemes to add RCD in the blind signature. The simple one is easy to implement, while the complex one can note the value of the coin. The usage of RCD leads to little additional cost, as well as maintaining the anonymity of customers. This method fits for most kinds of anonymous E-cash systems.

国外Data Commons对我国图书馆数据共享空间建设的启示

[目的/意义]国外的数据共享空间已在各个领域得到了广泛实践和应用,尤其在辅助科学研究、提升用户服务和改善数据管理方面表现突出,对我国图书馆数据共享空间建设具有重要的启示。[方法/过程]文章梳理并分析了国内外图书馆数据共享空间的理论和实践现状,以美国国家农业图书馆数据共享空间为例,对其平台建设、数据管理和用户服务模式等方面进行剖析,最后得到我国图书馆数据共享空间建设的启示。[结果/结论]从强化数据对等意识、鼓励数据发现及复用和创新融合新方法等方面论述我国图书馆数据共享空间建设的价值,并提出包括总体计划、平台建设、数据管理和用户服务等建设策略。

Inter-Data Commonality Detection for Spectrum Monitoring in Wireless Sensor Networks

Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.

XML-based integration data model and schema mappingin multidatabase systems

Multidatabase systems are designed to achieve schema integration and data interoperation among distributed and heterogeneous database systems. But data model heterogeneity and schema heterogeneity make this a challenging task. A multidatabase common data model is firstly introduced based on XML, named XML-based Integration Data Model (XIDM), which is suitable for integrating different types of schemas. Then an approach of schema mappings based on XIDM in multidatabase systems has been presented. The mappings include global mappings, dealing with horizontal and vertical partitioning between global schemas and export schemas, and local mappings, processing the transformation between export schemas and local schemas. Finally, the illustration and implementation of schema mappings in a multidatabase prototype - Panorama system are also discussed. The implementation results demonstrate that the XIDM is an efficient model for managing multiple heterogeneous data sources and the approaches of schema mapping based on XIDM behave very well when integrating relational, object-oriented database systems and other file systems.

Machine Learning for Selecting Important Clinical Markers of Imaging Subgroups of Cerebral Small Vessel Disease Based on a Common Data Model

Differences in the imaging subgroups of cerebral small vessel disease(CSVD)need to be further explored.First,we use propensity score matching to obtain balanced datasets.Then random forest(RF)is adopted to classify the subgroups compared with support vector machine(SVM)and extreme gradient boosting(XGBoost),and to select the features.The top 10 important features are included in the stepwise logistic regression,and the odds ratio(OR)and 95%confidence interval(CI)are obtained.There are 41290 adult inpatient records diagnosed with CSVD.Accuracy and area under curve(AUC)of RF are close to 0.7,which performs best in classification compared to SVM and XGBoost.OR and 95%CI of hematocrit for white matter lesions(WMLs),lacunes,microbleeds,atrophy,and enlarged perivascular space(EPVS)are 0.9875(0.9857−0.9893),0.9728(0.9705−0.9752),0.9782(0.9740−0.9824),1.0093(1.0081−1.0106),and 0.9716(0.9597−0.9832).OR and 95%CI of red cell distribution width for WMLs,lacunes,atrophy,and EPVS are 0.9600(0.9538−0.9662),0.9630(0.9559−0.9702),1.0751(1.0686−1.0817),and 0.9304(0.8864−0.9755).OR and 95%CI of platelet distribution width for WMLs,lacunes,and microbleeds are 1.1796(1.1636−1.1958),1.1663(1.1476−1.1853),and 1.0416(1.0152−1.0687).This study proposes a new analytical framework to select important clinical markers for CSVD with machine learning based on a common data model,which has low cost,fast speed,large sample size,and continuous data sources.

An integrated analysis platform merging SuperDARN data within the THEMIS tool developed by ERG-Science Center (ERG-SC)

The Energization and Radiation in Geospace （ERG） mission seeks to explore the dynamics of the radiation belts in the Earth＇s inner magnetosphere with a space-borne probe （ERG satellite） in coordination with related ground observations and simulations/modeling studies. For this mission, the Science Center of the ERG project （ERG-SC） will provide a useful data analysis platform based on the THEMIS Data Analysis software Suite （TDAS）, which has been widely used by researchers in many conjunction studies of the Time History of Events and Macroscale Interactions during Substorms （THEMIS） spacecraft and ground data. To import SuperDARN data to this highly useful platform, ERG-SC, in close collaboration with SuperDARN groups, developed the Common Data Format （CDF） design suitable for fitacf data and has prepared an open database of SuperDARN data archived in CDE ERG-SC has also been developing programs written in Interactive Data Language （IDL） to load fltacf CDF files and to generate various kinds of plots-not only range-time-intensity-type plots but also two-dimensional map plots that can be superposed with other data, such as all-sky images of THEMIS-GBO and orbital footprints of various satellites. The CDF-TDAS scheme developed by ERG-SC will make it easier for researchers who are not familiar with SuperDARN data to access and analyze SuperDARN data and thereby facilitate collaborative studies with satellite data, such as the inner magnetosphere data pro- vided by the ERG （Japan）-RBSP （USA）-THEMIS （USA） fleet.

基于Geodatabase极地NetCDF验潮数据的存储与分析

针对MATLAB读取南极验潮数据所出现的问题以及南极验潮数据本身的特点,结合ArcGIS9.2推出的Multidimension Tools和Geodatabase,设计出NetCDF格式的南极验潮站数据存储和处理的一般流程,并利用MATLAB混合编程的数值计算和分析功能,有效地对南极验潮数据进行存储管理和分析,为NetCDF格式的地理数据的读取、存储提供一种解决的思路。

New approach to determine common weights in DEA efficiency evaluation model

Data envelopment analysis（DEA） is a mathematical programming approach to appraise the relative efficiencies of peer decision-making unit（DMU）,which is widely used in ranking DMUs.However,almost all DEA-related ranking approaches are based on the self-evaluation efficiencies.In other words,each DMU chooses the weights it prefers to most,so the resulted efficiencies are not suitable to be used as ranking criteria.Therefore this paper proposes a new approach to determine a bundle of common weights in DEA efficiency evaluation model by introducing a multi-objective integer programming.The paper also gives the solving process of this multi-objective integer programming,and the solution is proven a Pareto efficient solution.The solving process ensures that the obtained common weight bundle is acceptable by a great number of DMUs.Finally a numeral example is given to demonstrate the approach.

基于IFC标准的机场道面结构信息模型存储与传递方法

为解决机场跑道系统在全生命周期建筑信息模型(BIM)应用中的信息传递和数据交互问题,将工业基础类(IFC)标准在机场跑道系统进行扩展,建立机场道面结构的IFC实体架构,在此基础上创建面向运维应用的刚性道面板实体,并进行几何信息与属性信息的扩展;提出基于IFC的属性信息添加与传递方法,对信息传递过程中的属性信息丢失率进行分析。研究表明:通过建立IFC标准模型,可以实现机场跑道系统的信息传递和数据交互;采用基础模型族扩展法进行机场道面模型传递的属性信息丢失率高达84.21%;采用IFC解析扩展法首次传递可保证信息不丢失,但二次传递信息存在丢失问题;族模型IFC解析扩展法可实现信息首次传递不丢失,二次传递属性信息丢失率低至16.7%,并且采用不同IFC标准版本对模型传递过程中的属性信息丢失率产生较大影响。应用IFC标准进行机场领域扩展具有较高的可行性,所提的信息传递方法可实现机场道面全生命周期信息传递。研究成果可为机场飞行区新IFC实体的扩展提供支持,为机场工程数字化提供技术参考。

论医疗数据的权属配置 ——基于公共信托理论的视角

医疗数据作为典型的公共数据资源,不愿利用、不敢利用、不知利用的现象成为制约医疗行业数字化和医疗数据科学发展的主要瓶颈,由此引发的“反公地悲剧”危机广泛存在于各地医疗机构以及卫生行政机关之中。当前,医疗数据权属存在的法律争议主要集中在两个方面,包括法律规范的不完善和数据权属的不明确。因此,有必要借鉴“公共信托理论”的观点,用以回应医疗数据的权属争议,在以公民和国家为法律主体的公共信托关系中,合理配置双方的权利义务关系,平衡医疗数据之上的个人利益和公共利益,从而解决医疗数据开发利用权属不清的问题。在公共信托理论下,政府在医疗数据收集、保管和开发利用阶段,承担不同的公共服务责任,从而保障公民数据权利的实现。为了有效监督政府信托义务,需要引入外部监督来保护公共信托权益不受侵犯。通过建构个人信息保护的行政公益诉讼制度、适当扩展行政诉讼受案范围、健全行政复议制度和行政内部举报机制等方式,有助于完善公共信托权益的行政救济制度,促进医疗数据资源在法治轨道下被有效开发和利用。

共建富裕共享成果:数据要素赋能共同富裕的实现路径

数据要素发展与共同富裕的共建共享理念相契合,在赋能共同富裕过程中彰显出独特功效。基于共建富裕和共享成果双重视角,探讨数据要素从财富创造、结构优化、普惠均衡和绿色协同4个维度赋能共同富裕的机理。针对数据要素发展存在的产权界定复杂、收入分配不均、资本配置失衡和数实融合程度不深等诸多问题,提出健全数据要素产权划分规则、调节数据要素收入分配、平衡数据要素资本配置水平、全面提升数实融合应用等实践路径,以释放数据要素在推动共同富裕、进而实现经济社会高质量发展中的积极效应。

省域数字经济对共同富裕的影响研究--基于要素流动视角的空间计量检验

在信息化时代,数字经济如何促进共同富裕具有重要的现实意义。基于2011―2021年31个省份面板数据,测算数字经济指数和共同富裕指数,选取人才和资本要素流动为中介变量,数据要素流动环境为调节变量,运用空间杜宾模型(SDM)实证检验数字经济发展对共同富裕的影响。结果表明:数字经济发展能明显促进共同富裕,对邻边地区存在显著的正向的空间溢出效应,且在地理分布上呈现明显的区域异质性。机制分析发现,数字经济能够通过人才和资本要素流动影响共同富裕;数字经济对共同富裕存在倒“U”型的非线性效应,且人才和资本要素流动对数字经济影响共同富裕起到“边际效应递增”作用;数据要素流动环境能合理调节数字经济对共同富裕的影响。据此,应大力发展数字经济,促进人才与资本要素的流动和精准匹配,形成优势互补、区域协调发展的新格局。

美欧数据跨境流动治理政策共性内容要素分析与中国启示研究

[目的/意义]通过对美欧数据跨境流动治理政策进行深入调研和内容分析,旨在为我国数据跨境流动治理政策制定和管理实践提供启示与借鉴。[方法/过程]采用文献调研、网络调查和质性文本分析法,基于数据生命周期理论,梳理并分析美欧数据跨境流动治理政策内容,通过构建政策文本分析类目体系,提炼政策共性内容要素。[结果/结论]研究发现,美欧已将数据生命周期思想融入数据跨境流动治理政策,且二者存在诸多共性,体现出数据跨境流动政策制定的总体遵循。基于此,文章结合美欧政策内容共性要素,立足我国国情,围绕数据跨境流动中的“三难选择”问题,分别从保障个人数据权利、平衡数据跨境流动与数据安全、维护数据主权等方面提出对策建议,为我国数据跨境流动治理政策的制定与完善提供思路。

数据公地悲剧的法理破解——迈向整体主义的个人数据规制模式

大数据技术催生的个人数据外部性之泛在使得个人数据所承载的个体利益与整体利益呈现越来越明显的异质性特征,且其同质性结构也发生了明显变化。此种转变摧毁了个体主义个人数据规制模式的前提预设,通过保护个体权益以实现社会整体利益并对数据安全风险进行有效化解的解题思路已然难以为继,且诱发了“数据公地悲剧”这一社会结构性问题。大数据时代的个人数据规制模式应实现从个体主义向整体主义的转型:在本体论层面,整体主义个人数据规制模式应确立“个体利益+整体利益”的双重保护客体;在方法论层面,应构建起对个人数据承载之整体利益的有效保护机制,通过对个人数据承载的个体利益与整体利益作出有效整合,在社会层面塑造良好的个人数据处理秩序,使人类社会避免数据公地悲剧。

Land Response to Atmosphere at Different Resolutions in the Common Land Model over East Asia

Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.

多采样频率合并单元设计与实现

国内智能变电站推广使用的合并单元装置普遍采用4 kHz采样频率,可以满足间隔层保护测控的需求;电能质量或PMU等对采样频率有更高的要求,通常要求12.8 kHz或者更高频率。对此,提出一种共口传输多采样频率数据的方案,采用一个合并单元装置满足变电站不同专业的采样需求。测试表明,该方案适用于组网方式,对于帧间隔及延时要求不高的场合,减少了采集装置数量,提高了采样数据的共享性。

老年常见病一体化监测平台建设与应用探索

目的:通过智能技术应用辅助老年常见病的监测、治疗和研究,以解决人口老龄化进程加速带来的老年医疗问题。方法:借助医疗物联网智能设备实时监测并整合老年人的必要院外健康数据,形成老年常见病健康数据采集的闭环,建立了老年常见病一体化云监测平台和多维健康数据中心平台。结合大数据治理技术和人工智能技术,探索老年病临床智能推荐决策模型研究。结果:一体化云监测平台已在部分医院试点应用,同时初步建成老年常见病多维健康数据中心平台,有效弥补了以往老年健康数据中心的不足。利用22万余份老年健康数据和医院临床知识库,构建了老年常见病知识图谱,并初步形成了临床智能推荐决策规则引擎;基于知识图谱、智能规则引擎和智能推荐决策模型,借助CDSS的集成应用初步实现老年常见病临床智能推荐决策支持。结论:本次探索在促进区域老年医学信息化、分级诊疗、临床决策支持和慢病管理等方面有积极影响。

临床专病数据库建设现状与思考

目的/意义为我国专病数据库建设、临床科研和医院信息互联互通等提供新思路。方法/过程结合国内外医疗体系和医疗信息化发展历程阐述专病数据库发展现状,总结国内两种主流专病数据库构建模式,提出基于SQL集成技术和通用数据模型的专病数据库构建方案。结果/结论该方案可快速构建专病数据库,并在数据层面实现互联互通。

大型综合性医院生物样本信息资源大数据科研平台的建设与应用

目的/意义通过整合临床与生物样本信息,构建生物样本信息资源大数据科研平台,为科研人员提供一站式数据检索、集成、分析服务,建立数据治理体系,提升医院临床科研基础设施建设水平。方法/过程采用通用数据模型和数据治理技术,通过抽取、转换、加载等步骤整合来自不同厂商的数据源,提供统一数据访问入口。结果/结论该生物样本信息资源大数据科研平台具备多维数据筛查和快速集成分析的优势,可为临床科研提供支撑。

海量数据的分布式主成分分析算法及其在共同富裕测度中的应用

基于两轮型方法的分布式PCA算法(TR-DPCA),每台局部机器计算出和向量,并将它们传输到中央机器计算全样本数据的均值向量,再将它们传输给每台局部机器;然后,每台局部机器计算出散度矩阵,并将它们传输到中央机器计算全样本数据的协方差矩阵;最后根据协方差矩阵进行特征分解获得特征向量。通过数值模拟发现,TR-DPCA算法的表现与全样本PCA一致,且优于基于单轮型方法的分布式PCA算法。此外,将TR-DPCA算法应用到中国共同富裕测度中发现,中国的共同富裕水平呈现上升趋势,且个体差距在不断缩小。