期刊论文支撑数据FAIR原则的应用评估与案例分析

[目的/意义]FAIR原则在科技期刊的应用有助于增强期刊论文支撑数据的可发现、交互、共享与重用。评估FAIR实施情况有利于其推广实施,以期为我国期刊论文的支撑数据共享与重用提供有益参考。[方法/过程]本文在国外FAIR原则评估模型的基础上,综合考虑各模型的优势和指标设计特点,结合《数据分析与知识发现》期刊论文相关的科学数据特征,构建FAIR原则指标评价体系,基于该体系从4个维度分析调研结果,最后对中文期刊论文的支撑数据FAIR应用提出合理化建议与优化策略。[结果/结论]FAIR原则在期刊论文支撑数据的应用仍需进一步完善,科研人员的数据共享意识及对于FAIR原则的认知度远远不够,建议从宏观和微观两个层面推广FAIR原则及其实施,推动数据更加开放和可重用。

政府开放数据平台FAIR原则评估指标体系及实证研究

[目的/意义]评估政府开放数据平台FAIR原则的实施效果有助于平台规范标识符、统一元数据标准等,增强政府数据的发现、共享和重用。[方法/过程]采用网络调研法,借鉴国际代表性评估框架,结合调研构建面向政府开放数据平台的FAIR原则评估指标体系,并选取国内外8个样本平台作为案例,从可发现、可访问等4个维度展开评估分析,比较FAIR原则实施状况和差异。[结果/结论]从永久性标识符方案、元数据标准规范、数据使用许可等方面提出相应改进策略,完善国内政府开放数据平台数据管理规范,为后续相关平台的建设与利用提供理论指导和实践借鉴。

基于FAIR原则的数据期刊开放共享与关联模型研究

[目的/意义]为揭示数据论文与期刊论文关联出版的新形态,对目前数据期刊的开放共享、数据论文与期刊论文之间的关联进行研究,有助于推动科学数据的开放共享发展,促进科学数据的高效流通,使科学数据在多层维度释放数据价值。[方法/过程]基于FAIR原则,从元数据元素、文献服务等角度出发,构建数据流向视角下数据论文与期刊论文之间的互关联模型,分析数据论文与期刊论文之间的关联过程,并选取代表性数据期刊Data in Brief的数据论文为实例展开模型验证与实践参照。[结果/结论]本文基于“可访问”“可发现”对“开放共享”展开研究;基于“可互操作”和“可重用”对“关联”展开研究。通过构建理论模型、实例验证,厘清数据论文与期刊论文之间的关联模式以及验证理论模型的可行性与合理性。

A Formal Model for Analyzing Fair Exchange Protocols Based on Event Logic

Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure manner.These protocols are widely used in electronic payment systems and electronic contract signing,ensuring the reliability and security of network transactions.In order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols,this paper proposes a formal model for analyzing such protocols.The proposed model begins with a thorough analysis of fair exchange protocols,followed by the formal definition of fairness.This definition accurately captures the inherent requirements of fair exchange protocols.Building upon event logic,the model incorporates the time factor into predicates and introduces knowledge set axioms.This enhancement empowers the improved logic to effectively describe the state and knowledge of protocol participants at different time points,facilitating reasoning about their acquired knowledge.To maximize the intruder’s capabilities,channel errors are translated into the behaviors of the intruder.The participants are further categorized into honest participants and malicious participants,enabling a comprehensive evaluation of the intruder’s potential impact.By employing a typical fair exchange protocol as an illustrative example,this paper demonstrates the detailed steps of utilizing the proposed model for protocol analysis.The entire process of protocol execution under attack scenarios is presented,shedding light on the underlying reasons for the attacks and proposing corresponding countermeasures.The developedmodel enhances the ability to reason about and evaluate the security properties of fair exchange protocols,thereby contributing to the advancement of secure network transactions.

政策文本视角下我国国家科学数据中心FAIR化特征初探

[目的/意义]对我国国家科学数据中心数据政策的FAIR化特征进行探索,为我国数据中心的数据管理政策制定和工作优化提供初步参考。[方法/过程]综合运用网络调研和文本挖掘的方法,使用KH Coder内容挖掘软件对20家数据中心的79部数据政策进行量化文本分析。通过对FAIR原则在政策文本中的出现频次和高相似词汇的分析,揭示FAIR原则在各个数据中心、不同类型政策文本中表现出的关注度差异与语义特征。[结果/结论]数据中心的数据政策已体现了一定的FAIR原则理念,但对每项FAIR原则的关注度不均衡;不同类型的数据政策关注FAIR原则的不同方面,共性在于对可发现原则和可互操作原则比较关注;对元数据给予了重点关注。[创新/价值]建议数据中心在数据政策制定中突出“元数据”在数据全生命周期管理中的作用,推动“数据增值驱动”的数据政策体系构建,并立足我国科学数据管理实际,适度引入FAIR原则。

Aerodynamic Analysis and Optimization of Pantograph Streamline Fairing for High-Speed Trains

A pantograph serves as a vital device for the collection of electricity in trains.However,its aerodynamic resistance can limit the train’s running speed.As installing fairings around the pantograph is known to effectively reduce the resistance,in this study,different fairing lengths are considered and the related aerodynamic performances of pantograph are assessed.In particular,this is accomplished through numerical simulations based on the k-ωShear Stress Transport(SST)two-equation turbulence model.The results indicate that the fairing diminishes the direct impact of high-speed airflow on the pantograph,thereby reducing its aerodynamic resistance.However,it also induces interferences in the flow field around the train,leading to variations in the aerodynamic resistance and lift of train components.It is shown that a maximum reduction of 56.52%in pantograph aerodynamic resistance and a peak decrease of 3.38%in total train aerodynamic resistance can be achieved.

Ada-FFL:Adaptive computing fairness federated learning

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

Two“Immoral”Ladies in the Kaleidoscope of Victorian Age-Comparison of Jane Eyre in Jane Eyre and Becky Sharp in Vanity Fair

Jane Eyre and Vanity Fair are both masterpieces in the late 1840s in English literature.The two“immoral”female images,Jane Eyre in Jane Eyre and Becky Sharp in Vanity Fair,are“rebellious”women against Victorian ideals,but there still exist differences between them.

Dilemmas and Optimization of Third-Party Assessment System in Chinese Fair Competition Review

The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, characterized by independence and specialization, is designed to mitigate these shortcomings. However, the implementation of third-party assessment faces challenges too. This paper intends to improve the third-party assessment system and to realize the legislative purpose of the system. Based on social research, discussions and exchanges with relevant parties, and the existing research results, this paper analyzes the challenges and possible optimization measures for the third-party assessment. The challenges include repulsion from policy-making bodies, insufficient independence of assessment bodies, disparity of assessment quality, and limited application of assessment outcomes. Possible optimization measures include promoting fair competition culture, increasing the acceptance of third-party assessment from policy-making bodies, enhancing the quality of third-party assessment, clarifying the relationship between policy-making bodies and assessment bodies, ensuring the independence of third-party assessments, and promoting the application of assessment results.

The Impact of Corporate Governance and Fair Value Accounting on Debt Contracts

This paper investigates the theoretical relationship between corporate governance,fair value accounting,and debt contracts.It primarily examines the individual impacts of corporate governance and fair value accounting on debt contracts,while also exploring the influence of corporate governance on fair value accounting.The study emphasizes the importance of considering the interests and legal status of creditors in the context of debt contracts.The findings indicate that strong corporate governance can reduce the likelihood of debt default and that the company’s restructuring costs in the event of a default determine whether improved corporate governance will increase or decrease debt costs.Additionally,the study reveals that the strength of corporate governance affects the value relevance of fair value accounting.However,the impact of fair value accounting on debt contracts is not inherently positive or negative;for instance,companies may use fair value adjustments with manipulative intent to enhance performance.Ultimately,the research highlights that discussions about corporate governance should not prioritize shareholder interests exclusively but also consider the legitimate position of creditors.

FAIR原则评估指标与评估工具研究

FAIR原则自发布以来引发国际社会的广泛关注,一些机构和项目制定FAIR评估指标、开发FAIR评估工具。对这些评估指标和工具进行调研,揭示FAIR评估发展的现状,总结经验,以促进FAIR原则的发展。采用文献研究、网络调研的方法,介绍FAIR原则的发展背景,并针对研究数据联盟、FAIRsFAIR、欧洲开放科学云提出的FAIR评估指标体系进行对比和分析,此外还介绍目前国际上主要的评估工具类型、原理及评价维度。研究结果显示:相较于其他组织,欧盟更为积极主动地推动FAIR评估;当前主要的评估指标和评估工具存在认知差异,但对于FAIR的落地起到一定的积极作用;目前FAIR评估体系需提高标准化水平、与可信认证体系紧密结合、积极融入国际环境、结合社区的发展需求与特点开展互动,并与更多类型的科研成果管理模式结合。

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

基于Fair函数神经网络的厚度传感器输出特性分析

针对最小二乘法的BP学习算法对训练数据准确性要求较高的问题,提出将Fair估计函数应用于神经网络,优化神经网络的计算,当拟合数据中含有过失误差和随机误差时,基于Fair估计函数学习算法拟合效果要明显优于传统基于最小二乘法BP学习算法,具有较高的鲁棒性。将该算法应用于厚度传感器输出特性拟合,实验结果表明,厚度传感器能准确测量纸张厚度,测量相对误差小于1.5%。

基于FAIR原则的中国科学数据资源现状分析及启示

为推动开放科学实践,促进科学数据共享与重用,选取re3data、FAIRsharing和Data Citation Index这3个国际主要科学数据仓储(SDR)注册和发现平台作为主要数据源,开展中国科学数据资源现状与国外的对比分析,发现我国有国际影响力的SDR数量与科学数据集的体量不匹配,包括我国在内的各国科学数据集的传播共享主要依靠欧美国家的SDR;基于FAIR原则分析我国典型SDR面临的挑战,结果表明,我国的国家科学数据中心在数据和元数据的可发现性、可访问性方面受到数据标识符分配不足和无法正常解析的影响。在数据和元数据的可交互性和可重用性方面,存在引用信息和使用说明不完整、不清晰等问题,阻碍数据的传播和共享。国际组织和机构的典型经验与举措为我国的科学数据管理提供了启示。基于此,未来我国可以通过开展对FAIR原则的政策支持和规范实施,提高我国科学数据资源的开放共享水平。

领域数据库的FAIR原则实践——以生物信息学为例

文章深化国内外领域数据库的FAIR原则实践研究,奠定进一步开展科学数据的管理工作布局与实践基础,为不同学科领域的数据开放路径提供支持。采用文本分析法对国内外11个生物信息学领域数据库的介绍文献内容进行编码,探索其FAIR原则实践进展。通过分析生物信息学领域数据库的可发现、可访问、可互操作和可重用的FAIR原则实践,发现FAIR原则在生物信息学领域的数据实践研究中存在管理与应用两方面的不足。建议在后续FAIR原则实践中,生物信息学领域数据库应创新设计和规范管理标准,丰富领域数据相关描述,遵循通用且符合FAIR原则的数据库规定,实现领域中各数据库平台的互联互通,提高数据开放共享水平。

我国国家科学数据中心FAIR原则的实践现状调查与分析

FAIR原则是规范科学数据管理和促进数据共享的指导原则,调查分析我国国家科学数据中心FAIR原则的实践现状,对推动科学数据的发现、访问、获取和重用具有重要意义。文章通过分析FAIR原则的主要内容,把握国际科学数据开放共享原则;调研20个国家科学数据中心对FAIR原则的实践现状,从元数据标准和用户授权两个层面分析结果并提出改进建议。研究发现:我国国家科学数据中心对FAIR原则的应用还有待提升,需要在持久性标识符的采用、元数据标准的公开访问、数据许可声明的明确等方面加强规范化、标准化管理。

Blockchain-Based Secure and Fair IoT Data Trading System with Bilateral Authorization

These days,data is regarded as a valuable asset in the era of the data economy,which demands a trading platform for buying and selling data.However,online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other.Therefore,in this paper,a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption.The proposed system enables bilateral authorization,where data trading between a seller and a buyer is accomplished only if their policies,required by each other,are satisfied simultaneously.This can be achieved by exploiting the security features of the matchmaking encryption.To guarantee non-repudiation and fairness between trading parties,the proposed system leverages a smart contract to ensure that the parties honestly carry out the data trading protocol.However,the smart contract in the proposed system does not include complex cryptographic operations for the efficiency of onchain processes.Instead,these operations are carried out by off-chain parties and their results are used as input for the on-chain procedure.The system also uses an arbitration protocol to resolve disputes based on the trading proof recorded on the blockchain.The performance of the protocol is evaluated in terms of off-chain computation overhead and on-chain gas consumption.The results of the experiments demonstrate that the proposed protocols can enable the implementation of a cost-effective data trading system.

Joint User Association and Fair Scheduling for Dual Connectivity Heterogeneous Networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks.Dual connectivity(DC)can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks.In this paper,we propose a joint user association and fair scheduling algorithm(JUAFS)to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks.Considering different coverage sizes,numbers of users,and quality of experience characteristics of heterogeneous cells,we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability.Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.