网络漏洞扫描原理分析.。
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两个工具Citrix的dneupdate64.msi和winfix.exe。

企业4G灾备设计方案，4G网络的链路带宽，网络稳定性有了很大的提高，目前国内主流运营商均大力打造4G网络并支持了4G商用，为4G作为网点备份链路使用提供了必要条件，4G网络采购的是按流量收费的模式，如果在主线路正常的情况下，4G作为备份线路使用的资费较低，可以节约网点线路部署的成本。

axis1.4jdk8下ConcurrentModificationExceptionbug处理，使用方式，替换原有axis.jar对应class类

TL_WR703N_官方固件包括如下3个文件：TL-WR703NV1_120925标准版.rarTL-WR703N_V1_121204.rarTL-WR703NV1.0升级软件20140120.rar推荐用于openwrt恢复官方固件

本书是华为ICT学院路由与交换技术官方教材，旨在帮助初级阶段的学生进一步学习网络技术的常用协议和对应的配置方法。
本书的写作顺序为先交换后路由。
本书首先对交换网络进行了概述，以便读者学习本书后文的内容。
接下来，用两章介绍了VLAN和STP这两项交换网络中常用的技术。
后面的6章内容均与路由技术有关，路由技术是互联网络中必不可少的核心技术。

用于excel计算MD5码可以计算32位、16位的方便批量制作MD5

ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea

ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea

ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea