Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

The Industrial Internet of Things(IIoT)consists of massive devices in different management domains,and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange.To address the above challenges,a viable solution that combines Certificateless Public Key Cryptography(CL-PKC)with blockchain technology can be utilized.However,as many existing schemes rely on a single Key Generation Center(KGC),they are prone to problems such as single points of failure and high computational overhead.In this case,this paper proposes a novel blockchain-based certificateless cross-domain authentication scheme,that integrates the threshold secret sharing mechanism without a trusted center,meanwhile,adopts blockchain technology to enable cross-domain entities to authenticate with each other and to negotiate session keys securely.This scheme also supports the dynamic joining and removing of multiple KGCs,ensuring secure and efficient cross-domain authentication and key negotiation.Comparative analysiswith other protocols demonstrates that the proposed cross-domain authentication protocol can achieve high security with relatively lowcomputational overhead.Moreover,this paper evaluates the scheme based on Hyperledger Fabric blockchain environment and simulates the performance of the certificateless scheme under different threshold parameters,and the simulation results show that the scheme has high performance.

A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

The Internet of Vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network.However,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain interactions.During such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication process.Additionally,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency issues.To mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for IoV.This scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree structures.It divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and security.Finally,we evaluate the performance of CAIoV.Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

A Post-Quantum Cross-Domain Authentication Scheme Based on Multi-Chain Architecture

Due to the rapid advancements in network technology,blockchain is being employed for distributed data storage.In the Internet of Things(IoT)scenario,different participants manage multiple blockchains located in different trust domains,which has resulted in the extensive development of cross-domain authentication techniques.However,the emergence of many attackers equipped with quantum computers has the potential to launch quantum computing attacks against cross-domain authentication schemes based on traditional cryptography,posing a significant security threat.In response to the aforementioned challenges,our paper demonstrates a post-quantum cross-domain identity authentication scheme to negotiate the session key used in the cross-chain asset exchange process.Firstly,our paper designs the hiding and recovery process of user identity index based on lattice cryptography and introduces the identity-based signature from lattice to construct a post-quantum cross-domain authentication scheme.Secondly,our paper utilizes the hashed time-locked contract to achieves the cross-chain asset exchange of blockchain nodes in different trust domains.Furthermore,the security analysis reduces the security of the identity index and signature to Learning With Errors(LWE)and Short Integer Solution(SIS)assumption,respectively,indicating that our scheme has post-quantum security.Last but not least,through comparison analysis,we display that our scheme is efficient compared with the cross-domain authentication scheme based on traditional cryptography.

FSL05N2C触力传感器原理及在分娩监护仪中的应用

简要介绍了母婴监护仪中宫缩压力探头传感器的选择。介绍了霍尼韦尔公司生产的 FSL 0 5 N2 C触力传感器的原理及其在分娩监护仪中的应用。

一种与领域无关的基于框架的表示工具-FSL

ＦＳＬ是作者在微机上实现的一种与领域无关的基于框架的表示工具，它适用于控制调度，该文叙述了ＦＳＬ的组成及工作原理，并对ＦＳＬ的特性进行了分析。

临床实验室APTT ACTIN FSL试剂建立活化部分凝血活酶时间参考范围

目的建立临床实验室血浆活化部分凝血活酶时间(aPTT)的参考范围。方法选取昆明医科大学第二附属医院,体检中心2018年10月至2018年12月的244份健康汉族成人作为研究对象,用Sysmex CS5100全自动血凝分析仪,APTT ACTIN FSL试剂,检测244例体检健康汉族成年人aPTT,建立参考区间。结果男女比例1∶1,男性组年龄中位数44,女性组年龄中位数41。男性组均值25.99,标准差1.58,参考范围24.0 s-30.4 s;女性组均值25.90,标准差1.72,参考范围22.4 s-30.4 s,不同性别健康汉族成年人APTT均值没有统计学上的差异。aPTT的参考范围为23.2 s-30.4 s。结论临床实验室APTT ACTIN FSL试剂建立的aPTT参考范围为23.2 s-30.4 s,不同临床实验室应根据自身情况建立合适的参考范围。

猪单核源性巨噬细胞受FSL-1刺激后lncRNAs的鉴定与特征分析

lncRNA是新近发现的一种具有重要功能的RNA分子,在各种生理活动中发挥着重要作用。为鉴定猪单核源性巨噬细胞受FSL-1刺激后相关的lncRNA,利用二代测序技术结合生物信息学对lncRNA进行了组装与特征分析。结果显示FSL-1刺激成功地构建了细胞炎症模型,试验组与对照组共鉴定到1 056个lncRNA转录本,其对应于831个基因座,这些lncRNA的平均长度为1 643 bp,平均外显子数为2.4个。相对于对照组,试验组有51个lncRNA上调表达,44个lncRNA下调表达。在上调lncRNA两侧相邻的蛋白质编码基因参与免疫反应、炎症反应、病毒反应等通路,而在下调lncRNA两侧相邻的蛋白质编码基因参与的通路较少,不参与上述通路。定量PCR结果显示挑选的4个lncRNA的差异倍数与RNA-Seq的结果相似,其中上调的lncRNA呈现一定的组织特异表达,而下调的lncRNA呈现多组织表达。这些结果为进一步研究lncRNA在FSL-1介导的炎症反应中的作用奠定了基础。

APTT ACTIN FSL试剂对APTT及Ⅷ因子检测的影响

目的探讨Siemens公司活化部分凝血活酶时间(APTT)ACTIN FSL试剂对APTT及Ⅷ因子检测的影响。方法选取20例标本分别使用APTT ACTIN和APTT ACTIN FSL 2种试剂检测APTT,比较阴阳性符合率。选取70例标本分别使用以上2种试剂检测Ⅷ因子,比较阴阳性符合率。选取120例体检者建立APTT及Ⅷ因子新的生物参考区间。选取20例体检者及20例确诊静脉血栓或血友病的患者验证新建参考区间的适用性。结果改用APTT ACTIN FSL试剂后重新建立APTT和Ⅷ因子的生物参考区间分别为25.2~32.6 s和60%~189%。使用新的生物参考区间,明显提高了APTT及Ⅷ因子使用2种试剂检测结果的阴阳性符合率,分别为95.0%和90.0%。分别采用体检者及患者APTT及Ⅷ因子的结果验证新参考区间均为合格。结论将Siemens公司的APTT ACTIN试剂升级为APTT ACTIN FSL试剂后,对APTT及Ⅷ因子的检测影响较大,实验室应修改或重新建立新的生物参考区间,并进行验证。

雾与能见度数值预报诊断SW方案和FSL方案改进的研究及评估

为了提高雾与能见度的预报水平,对业务上常用的两种能见度诊断方案,即Stoelinga and Warner(SW)方案与Forecast Systems Laboratory(FSL)方案的改进进行预报试验,SW方案基于Gultepe方案考虑了液态水粒子数浓度对能见度的影响,FSL改进方案中利用了递减平均法对公式中用到的温度与露点温度进行订正,并用其重新计算公式中的相对湿度。基于山东省气象科学研究所逐时更新循环(hourly update cycle,HUC)业务模式输出结果,从2015—2016年选取10次雾天气过程,并详细分析了2015年11月13—14日这次雾天气过程的预报结果,比较了改进前后各方案对雾与能见度的预报效果,结果显示:在模式预报的雨水含量占总液态含水量比例较大的预报时效,改进后的SW方案对雾与能见度预报效果优于原始方案,在模式预报液态含水量接近0的预报时效,改进前后的SW方案对雾与能见度的预报效果相当;利用订正的温度与露点温度重新计算相对湿度,其平均绝对误差(mean absolute error,MAE)降低明显的预报时段,改进后的FSL方案对雾与能见度的预报效果大大提升。将两种改进后的方案相融合并进行预报试验,结果显示,综合对能见度与雾的预报效果,Combined Visibility(CVIS)方案要优于其他两种改进方案。

基于FSL总线的JPEG解码协处理器

介绍一种面向嵌入式应用,能与Micrblaze处理器较好地异步协同工作,不需要专用双口RAM的JPEG解码协处理器。该JPEG解码器采用Verilog语言实现,能很好地处理JFIF格式的JPEG压缩文件,并且在只有少量缓冲空间的FIFO上能正确工作。验证实验结果表明,该处理器在xilinx公司的XUP VirtexⅡ Pro开发板上,采用FSL总线与Microblaze处理器相连,工作效率较高。

FSL总线IP核及其在MicroBlaze系统中的应用

MicroBlaze是基于Xilinx公司新一代FPGA器件的软处理器核。其FSL总线是FIFO单向链路,可以实现用户自定义IP核与MicroBlaze内部通用寄存器的高速直连。本文对MicroBlaze的几种主要总线接口进行比较,详细分析、介绍FSL总线的结构、特点、工作原理和配置方法。通过一个矢量汉字还原的应用实例,具体描述在FPGA片上系统设计中利用FSL高速总线整合用户自定义IP核,实现软处理器系统硬件加速的方法及步骤。

FSL-SP的研究

在机器学习领域,特征选择对于提高学习机器的性能和效率具有重要意义,但是当前特征选择算法普遍存在着具体实现独立性强、可扩展性差的问题,使得对多种算法性能的统一对比评估实施困难,算法的替换和扩展比较复杂。以面向对象的设计理念为指导,基于设计模式中的策略模式,提出特征选择算法工具库FSL的设计构想,通过将一些常用的特征选择算法按照策略模式进行包装,以便机器学习算法用户的使用,同时确保其较强的可扩展性。

基于框架的混合式知识表示工具—FSL

ＦＳＬ是我们在微机上实现的一种与领域无关的基于框架的表示工具，它适用于控制调度，本文要领了ＦＳＬ的组成及工作原理，并对ＦＬＳ的特性进行了分析。

FSL在DTI大脑图像分析中的应用现状

随着弥散张量成像(diffusion tensor imaging,DTI)技术在大脑结构方面应用研究的增多,与之相关的数据分析软件也在增多,其中FMRIB Software Library(FSL)在示踪时使用的是基于概率性示踪的算法,在进行交叉纤维示踪和灰质连接上有很大优势。本文主要根据数据分析方式的不同对FSL包含的两个子工具(tract-based spatial statistics,TBSS和FMRIB’s diffusion toolbox,FDT)在DTI大脑图像分析方面的应用现状进行分类介绍,主要涉及老年抑郁症、精神分裂症以及认知功能等领域。本文首先简要介绍DTI原理,然后根据数据分析方式的不同,在TBSS部分分为全脑分析和感兴趣区域(region of interest,ROI)分析两方面,在FDT部分分为全脑示踪、ROI互连分析、基于连通度的分类三方面进行介绍,最后对FSL的局限性以及发展状况进行总结。

基于FSL总线的UART外设IP核设计

介绍基于MicroBlaze的SOPC系统中FSL总线的结构特点,并对FSL总线和OPB总线加以比较;给出了基于FSL总线的UART外设IP核的硬件设计和驱动设计,并通过实验加以验证。实验证明,设计的UART外设IP核可以集成到SOPC系统中正常工作。

基于FSL206MR的离线式开关电源设计

介绍了一种基于集成PWM和SenseFET的开关电源芯片FSL206MR的离线式开关电源设计,详细分析了反馈电路和降压拓扑的工作过程及设计方案。采用NPN晶体三极管MMBT2222A和稳压管BZX84C13组成反馈网络。该开关电源性能优良,具有结构简单、稳压效果好、易封装等优点,具有很高的生产应用价值。

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer,termed as SwinFusion.On the one hand,an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction.More specifically,the proposed method involves an intra-domain fusion unit based on self-attention and an interdomain fusion unit based on cross-attention,which mine and integrate long dependencies within the same domain and across domains.Through long-range dependency modeling,the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective.In particular,we introduce the shifted windows mechanism into the self-attention and cross-attention,which allows our model to receive images with arbitrary sizes.On the other hand,the multi-scene image fusion problems are generalized to a unified framework with structure maintenance,detail preservation,and proper intensity control.Moreover,an elaborate loss function,consisting of SSIM loss,texture loss,and intensity loss,drives the network to preserve abundant texture details and structural information,as well as presenting optimal apparent intensity.Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-theart unified image fusion algorithms and task-specific alternatives.Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.

Context Information and Fragments Based Cross-Domain Word Segmentation

A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.

Ad Hoc的FSLS路由算法的实现和仿真

无线Ad Hoc网络是目前国际上前沿和热点的研究领域,具有分布式的体系结构。其组织网络灵活,支持随时随地通信。该文介绍了移动Ad Hoc网络的特点,重点介绍了FSLS路由算法的基本原理,并对FSLS路由算法进行了仿真,对仿真结果进行了分类,对比和分析。

FedCDR:Privacy-preserving federated cross-domain recommendation

Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.However,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive.To this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called FedCDR.In our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central server.The central server then aggregates the weights and distributes them to each client for updating.Furthermore,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy.To distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model.Extensive experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.