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简介：三星堆遗址是四川境内目前所知范围最广、延续时间最长、文化内涵最为丰富的古蜀文化遗址，被誉为“世界第九大奇迹”。

简介：Theneedforwritingsoftwarepackagesthatdonotdependexplicitlyontheservercode,anddonotneedtobemodified,whennewserverinterfacesarise,leadtothedevelopmentofthehiddenadapterpattern.Wewillshowitsworkings,andgiveanexampleofhowcompletedecouplingbetweendepended-onanddependentcodecanbeachievedusingthehiddenadapterpattern.

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简介：最近在写一套ASP同学录程序，随着页面的增加，编写过程中出现了一些疏漏，犯了许多错误，特别是hidden方式下的注入，觉得很有必要跟大家说明一下。

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简介：Anaudiorecoveringmethodofspread-spectrumhiddeninformationisproposedbasedongeneticalgorithm.Inthismethodtheembeddedsequencelengthisconfirmedfirstly,thenthebestestimatedsequencewiththeconfirmedlengthisgotbygeneticalgorithm,finallytheconfidentialmessagehiddeninstego-audiocanberecovered.Usingthisapproach,thehiddeninformationcanberecoveredwithoutanyinformationfromthetransmitter.ThepresentedmethodhasbeenimplementedonPC,andtheexperimentalresultsshowthattheaveragerecoveringcorrectrateishigherthan90%.

简介：Amingatwaterconservancyprojectvisualization,ahidden-removalmethodofdamperspectivedrawingsisrealizedbybuildingahidden-removalmathematicalmodelforoverlappingpointslocationtosetupthehiddenrelationshipamongpointandplane,planeandplaneinspace.Onthisbasis,asanexampleofpanelrockfilldam,adamhidden-removalperspectivedrawingisgeneratedindifferentdirectionsanddifferentvisualanglesthroughadaptingVC++andOpenGLvisualizingtechnology.Theresultsshowthatthedataconstructionofthemodelissimplewhichcanovercomethedisadvantagesofconsiderableandcomplicatedcalculation.Thismethodalsoprovidesthenewmeanstodrawhidden-removalperspectivedrawingsforthoselandformsandgroundobjects.

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简介：ThispaperexaminesrecentdevelopmentsandapplicationsofHiddenMarkovModels(HMMs)tovariousproblemsincomputationalbiology,includingmultiplesequencealignment,homologydetection,proteinsequencesclassification,andgenomicannotation.

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简介：Videoobjectsegmentationisimportantforvideosurveillance,objecttracking,videoobjectrecognitionandvideoediting.Anadaptivevideosegmentationalgorithmbasedonhiddenconditionalrandomfields(HCRFs)isproposed,whichmodelsspatio-temporalconstraintsofvideosequence.Inordertoimprovethesegmentationquality,theweightsofspatio-temporalcon-straintsareadaptivelyupdatedbyon-linelearningforHCRFs.Shadowsarethefactorsaffectingsegmentationquality.Toseparateforegroundobjectsfromtheshadowstheycast,lineartransformforGaussiandistributionofthebackgroundisadoptedtomodeltheshadow.Theexperimentalresultsdemonstratedthattheerrorratioofouralgorithmisreducedby23%and19%respectively,comparedwiththeGaussianmixturemodel(GMM)andspatio-temporalMarkovrandomfields(MRFs).

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简介：隐藏的Markov建模的侧面(HMMs)广泛地基于古典HMMs被申请了蛋白质顺序鉴定。在侧面HMMs的前面、向后的变量的明确的表达在概率理论的统计独立假设下面被做。我们建议模糊侧面唔定序克服那个假设的限制并且为蛋白质完成改进排列属于一个给定的家庭。建议模型fuzzifies由合并Sugeno的前面、向后的变量模糊措施和Choquet积分，进一步因此延长概括唔。把前面、向后的变量基于fuzzified，我们为侧面建议一个模糊Baum-Welch参数评价算法。强壮的关联和涉及结构使这模糊体系结构基于的蛋白质的顺序偏爱作为造一个给定的家庭的侧面的一个合适的候选人当模特儿，自从模糊集合能比古典方法更好处理无常。

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