Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Research and application of key technologies for data delivery in railway engineering design based on metadata

In view of the problems of inconsistent data semantics,inconsistent data formats,and difficult data quality assurance between the railway engineering design phase and the construction and operation phase,as well as the difficulty in fully realizing the value of design results,this paper proposes a design and implementation scheme for a railway engineering collaborative design platform.The railway engineering collaborative design platform mainly includes functional modules such as metadata management,design collaboration,design delivery management,model component library,model rendering services,and Building Information Modeling(BIM)application services.Based on this,research is conducted on multi-disciplinary parameterized collaborative design technology for railway engineering,infrastructure data management and delivery technology,and design multi-source data fusion and application technology.The railway engineering collaborative design platform is compared with other railway design software to further validate its advantages and advanced features.The platform has been widely applied in multiple railway construction projects,greatly improving the design and project management efficiency.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

A Metadata Reconstruction Algorithm Based on Heterogeneous Sensor Data for Marine Observations

Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of Things(IoT)sensor data.However,the use of an inefficient data management strategy during the data storage process can lead to missing metadata;thus,part of the sensor data cannot be indexed and utilized(i.e.,‘data swamp’).Researchers have focused on optimizing storage procedures to prevent such disasters,but few have attempted to restore the missing metadata.In this study,we propose an AI-based algorithm to reconstruct the metadata of heterogeneous marine data in data swamps to solve the above problems.First,a MapReduce algorithm is proposed to preprocess raw marine data and extract its feature tensors in parallel.Second,load the feature tensors are loaded into a machine learning algorithm and clustering operation is implemented.The similarities between the incoming data and the trained clustering results in terms of clustering results are also calculated.Finally,metadata reconstruction is performed based on existing marine observa-tion data processing results.The experiments are designed using existing datasets obtained from ocean observing systems,thus verifying the effectiveness of the algorithms.The results demonstrate the excellent performance of our proposed algorithm for the metadata recon-struction of heterogenous marine observation data.

A Novel Metadata Based Multi-Label Document Classification Technique

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

Real-Time Spammers Detection Based on Metadata Features with Machine Learning

Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).

《中医药数据资源元数据》规范

《中医药数据资源元数据》的发布可为中医药数据资源提供元数据著录规范。通过对国内外相关元数据标准的调研及研究,结合中医药数据资源实际情况,在广泛征求专家意见基础上,制定本规范。本规范规定了中医药数据资源元数据结构,包括元数据子集、元数据实体及元数据元素,用于完整描述中医药数据资源,适用于中医药数据资源的编目、归档、建库、发布、共享、交换与检索等,可以帮助用户从海量数据中发现符合需求的资源,以促进中医药领域数据资源描述的规范化和标准化。

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

多源地理产品数据的元数据统一设计与采集管理技术

面向多源地理产品数据管理应用的需求,明确了本文讨论的地理产品数据的范畴,分析了地理产品数据的存储组织及应用特点。基于存储组织需求,统一设计了数据大类小类两级分类目录及类别编码,设计了地理数据产品的元数据采集规范。提出了数据集-数据单元两级元数据的设计理念以便满足不同数据粒度的管理需求,分别设计了数据集元数据和数据单元元数据的详细组成,统一设计了空间地理产品数据与非空间地理产品数据的元数据,明确了数据集目录和数据单元的命名原则。设计并实现了能够自动采集多源地理产品元数据的软件,实现了基于统一元数据设计的数据管理软件。结果表明,这种方法能够满足用户对多源地理产品数据的统一存储组织、多粒度管理、高效便捷检索等需求。

知识图谱赋能下的历史人物数据平台构建实践——以宋庆龄文献数据中心平台建设为例

线上数据平台已成为历史人物文献史料研究的重要载体,其借助数字化、知识化充分保护文献、挖掘史料的内在价值,能为广大研究者和历史爱好者提供强有力的数据支撑和便捷的文献服务。本研究在梳理历史人物数据库和数字人文下知识图谱的相关研究基础之上,提出并分析了构建历史人物数据平台的两大关键问题:多源异构文献数据的结构化和多模态信息下的知识图谱构建,并通过数据分类统计、元数据结构化设计、Schema构建、知识抽取、实体融合5个环节解决了上述问题,实现了文献聚合、文献检索、文献可视化等功能,并以宋庆龄为研究对象进行实证研究,所设计的数据平台构建流程为图书馆智慧化领域研究提供了切实可行的方案实例。

基于智能化矿山数据分类与编码规范的元数据标准构建方法

目前智能化矿山建设面临数据标准不健全、多源异构数据集成难度大、共享机制有待完善等问题。建立统一的智能化矿山元数据标准,对形成多源异构数据融合共享机制具有重要意义。基于智能化矿山数据分类与编码规范,研究了智能化矿山元数据标准构建方法,明确了元数据标准构建是对数据分类与编码的延伸研究与价值挖掘。定义了元数据实体与元数据属性的内涵,设计了包含基础描述框架与扩展描述框架的智能化矿山元数据描述框架,给出了反映智能化矿山业务场景特征的基础元数据属性描述项和智能化矿山数据分类与编码规范中基础类、生产类、安全类、管理类4大主题域组的拓展元数据属性描述项,从而构成元数据标准。以生产主题域组中采煤机惯导系统的惯导传感器为例,介绍了元数据标准的构建过程。基于智能化矿山数据分类与编码规范构建元数据标准,可以解决多源异构数据的融合与共享问题,促使矿山企业更加高效地管理、分析和应用数据,提高矿山数据治理的智能化水平。

基于MDR2023的元数据值域语义约束注册标准化模型研究

元数据注册(MetadataRegistry,MDR)是数据治理中元数据精确表达语义的必要前提。通过全面系统地分析国内外的MDR系统,发现国内外MDR更多关注基本数据元素,数据语义约束注册方面的研究缺乏。因此,首先基于ISO/IEC11179:2023(MDR2023)系列标准提出元数据语义约束外延分类模型,明确元数据语义约束范围,并选取其中的值域语义约束详细研究;其次,基于MDR2023标准提出元数据值域语义约束注册元模型,为元数据语义约束注册提供标准化且完整的注册算法流程,从而为元数据值域语义约束注册提供解决方案;最后,以石油领域著名的POSC标准为需求背景,对其中的值域语义约束进行注册,据此实现石油领域元数据值域语义约束的标准化,验证提出的元数据值域语义约束注册元模型的合理性和可行性。提出的元模型对于其他领域数据治理具有普适性。

基于语义的地方志资源聚合与可视化研究

论文应用语义网相关技术开展地方志资源聚合研究,挖掘海量、分布式、异构资源间的潜在关联,系统分析了基于语义的三种信息资源深度聚合模式特点,并以东北师范大学图书馆“东北地方志”馆藏资源作为数据源,开展基于元数据的地方志资源聚合实证研究。构建元数据视角下地方志资源聚合的理论框架和可视化服务平台,为地方志资源的深度关联和揭示提供从理论到实践的实现路径。

国外高校图书馆元数据馆员调查研究

在智慧图书馆开展知识服务的背景下,以编目为核心的信息组织工作面临巨大的挑战。国外图书馆通过引导编目馆员向元数据馆员转变,实现了图书馆信息组织的增值效应,元数据馆员在图书馆中的作用日益凸显,设置元数据馆员岗位、开展馆藏资源的合理建设以及开展知识服务是值得深入考虑的问题。通过文献调研法和网络调研法,选取ALA、ARL、Choronicle等3个图情领域的权威网站,在其招聘板块中对元数据馆员岗位招聘信息中的岗位名称、所属部门、工作职责和知识技能等数据进行梳理和分析。从国外元数据馆员岗位的现状以及基本任职资质和首选资质上,得出推进编目馆员向元数据馆员角色转变、制定元数据馆员培养体系、适应元数据标准发展趋势和重视本馆特藏元数据馆员培养4个方面的元数据馆员岗位设置相关启示。

《数据论文出版元数据》国家标准研制与实践

2023年8月,《GB/T 42813-2023数据论文出版元数据》国家标准正式发布,该标准规定了数据论文出版元数据的内容与结构,包括数据集元数据、数据论文元数据及其描述方法、扩展原则与方法。适用于数据论文及其关联数据集的描述、保存、在线出版和共享服务等。该标准的发布标志着首次从国家层面对数据论文出版这一科学数据开放共享新模式进行了总体约定,解决了数据出版领域缺少标准规范指导的问题。有助于数据出版机构快速地发布数据论文和关联数据,为科学数据确权、产权保护提供支撑,对于推动科学数据开放共享生态建设具有重要意义。自标准发布以后,一些数据期刊或科学数据出版平台开始使用标准规定的元素与定义,开展较为广泛的实践。

WebGIS中地理空间Metadata管理系统研究与设计

为实现 Web GIS中地理空间信息的有效管理和网络共享 ,结合国际地理空间信息共享的标准规范 ,对Web GIS中地理空间 Metadata的定义、作用、内容、技术实现及其应用进行了详细的研究和设计 ,提出了与Web GIS集成的地理空间 Metadata管理系统解决方案 ,并给出了体现具体技术实现的原型系统 ,为国家空间信息基础设施和数字地球的建设提供了理论基础和技术原型 ,在当今信息化与数字地球时代 。

数字地球建设中的地理空间Metadata研究

为解决数字地球建设中现有海量地理空间信息的共建共享、有效管理和利用 ,结合国际地理空间Metadata标准规范 ,对数字地球建设中所需的地理空间Metadata的定义、作用、分类、内容、实现模型与实例及其应用进行详细研究和设计 ,提出用于数字地球建设的地理空间Metadata网络管理系统解决方案 ,并设计实现了原型系统 ,为数字地球建设中现有海量地理空间信息的有效管理和共建共享提供了理论基础和切实可行的技术支撑。对国家空间信息基础设施建设与数字地球建设具有重要的推动作用和深远的社会意义。