敏捷项目管理(第二版),amazon上五星级评价AddisonWesley,2009Bestpracticesformanagingprojectsinagileenvironments—nowupdatedwithnewtechniquesforlargerprojectsToday,thepaceofprojectmanagementmovesfaster.Projectmanagementneedstobecomemoreflexibleandfarmoreresponsivetocustomers.UsingAgileProjectManagement(APM),projectmanagerscanachieveallthesegoalswithoutcompromisingvalue,quality,orbusinessdiscipline.InAgileProjectManagement,SecondEdition,renownedagilepioneerJimHighsmiththoroughlyupdateshisclassicguidetoAPM,extendingandrefiningittosupporteventhelargestprojectsandorganizations.Writingforprojectleaders,managers,andexecutivesatalllevels,Highsmithintegratesthebestprojectmanagement,productmanagement,andsoftwaredevelopmentpracticesintoanoverallframeworkdesignedtosupportunprecedentedspeedandmobility.Themanytopicsaddedinthisneweditionincludeincorporatingagilevalues,scalingagileprojects,releaseplanning,portfoliogovernance,andenhancingorganizationalagility.ProjectandbusinessleaderswillespeciallyappreciateHighsmith’snewcoverageofpromotingagilitythroughperformancemeasurementsbasedonvalue,quality,andconstraints.Thisedition’scoverageincludes:*Understandingtheagilerevolution’simpactonproductdevelopment*Recognizingwhenagilemethodswillworkinprojectmanagement,andwhentheywon’t*SettingrealisticbusinessobjectivesforAgileProjectManagement*Promotingagilevaluesandprinciplesacrosstheorganization*UtilizingaprovenAgileEnterpriseFrameworkthatencompassesgovernance,projectanditerationmanagement,andtechnicalpractices*Optimizingallfivestagesoftheagileproject:Envision,Speculate,Explore,Adapt,andClose*Organizationalandproduct-relatedprocessesforscalingagiletothelargestprojectsandteams*A

IQmeasure仪器操作API介绍说明

基于HOG特征提取的图像分类器，HOG的核心思想是所检测的局部物体外形能够被光强梯度或边缘方向的分布所描述。
通过将整幅图像分割成小的连接区域称为cells，每个cell生成一个方向梯度直方图或者cell中pixel的边缘方向，这些直方图的组合可表示出所检测目标的目标）描述子。
为改善准确率，局部直方图可以通过计算图像中一个较大区域称为block的光强作为measure被对比标准化，然后用这个measure归一化这个block中的所有cells.这个归一化过程完成了更好的照射/阴影不变性。

传统K-modes聚类算法，并包含F1-measures指标、聚类准确率指标和聚类纯度指标，含有UCI数据集进行测试，直接运行main即可

Toconfirmtheexistenceandpropertiesofhumanmeridians,theopticaltransportpropertiesalongthepericardiummeridianandtissuesaroundthepericardiummeridianarestudiednoninvasivelyontwentyhealthyvolunteersinvivoandthencomparedwitheachother.Ourstudyshowsthatthelightpro

exam-az-304-microsoft-azure-architect-design-skills-measured.pdf

1.Supposethatadatawarehouseconsistsoffourdimensions,date,spectator,location,andgame,andtwomeasures,countandcharge,wherechargeisthefarethataspectatorpayswhenwatchingagameonagivendate.Spectatorsmaybestudents,adults,orseniors,witheachcategoryhavingitsownchargerate.(a)Drawastarschemadiagramforthedatawarehouse.(b)Startingwiththebasecuboid[date,spectator,location,game]，whatspecificOLAPoperationsshouldoneperforminordertolistthetotalchargepaidbystudentspectatorsinLosAngeles?(c)Bitmapindexingisaveryusefuloptimizationtechnique.Pleasepresenttheprosandconsofusingbitmapindexinginthisgivendatawarehouse.

Matlabcodeforcomputingandvisualization:ConfusionMatrix,Precision/Recall,ROC,Accuracy,F-Measureetc.forClassification.Matlab通过分类的label计算混淆矩阵ConfusionMatrix并且显示的函数只要一句代码就行了，方便。
[confusion_matrix]=compute_confusion_matrix(predict_label,num_in_class,name_class);

PartIMetricSearchinginaNutshellOverview31.FOUNDATIONSOFMETRICSPACESEARCHING51TheDistanceSearchingProblem62TheMetricSpace83DistanceMeasures93.1MinkowskiDistances103.2QuadraticFormDistance113.3EditDistance123.4TreeEditDistance133.5Jaccard’sCoefficient133.6HausdorffDistance143.7TimeComplexity144SimilarityQueries154.1RangeQuery154.2NearestNeighborQuery164.3ReverseNearestNeighborQuery174.4SimilarityJoin174.5CombinationsofQueries184.6ComplexSimilarityQueries185BasicPartitioningPrinciples205.1BallPartitioning205.2GeneralizedHyperplanePartitioning215.3ExcludedMiddlePartitioning215.4Extensions216PrinciplesofSimilarityQueryExecution226.1BasicStrategies226.2IncrementalSimilaritySearch257PoliciesforAvoidingDistanceComputations267.1ExplanatoryExample277.2Object-PivotDistanceConstraint287.3Range-PivotDistanceConstraint307.4Pivot-PivotDistanceConstraint317.5Double-PivotDistanceConstraint337.6PivotFiltering348MetricSpaceTransformations358.1MetricHierarchies368.1.1Lower-BoundingFunctions368.2User-DefinedMetricFunctions388.2.1SearchingUsingLower-BoundingFunctions388.3EmbeddingMetricSpace398.3.1EmbeddingExamples398.3.2ReducingDimensionality409ApproximateSimilaritySearch419.1Principles419.2GenericAlgorithms449.3MeasuresofPerformance469.3.1ImprovementinEfficiency469.3.2PrecisionandRecall469.3.3RelativeErroronDistances489.3.4PositionError4910AdvancedIssues5010.1StatisticsonMetricDatasets5110.1.1DistributionandDensityFunctions5110.1.2DistanceDistributionandDensity5210.1.3HomogeneityofViewpoints5410.2ProximityofBallRegions5510.3PerformancePrediction58Contentsix10.4TreeQualityMeasures6010.5ChoosingReferencePoints632.SURVEYOFEXISTINGAPPROACHES671BallPartitioningMethods671.1Burkhard-KellerTree6

Teamcenter认证考试资料（含题库）TheTeamcentercertificationisameasurementofanindividual'sbasicunderstandingandapplicationofTeamcenterintheareasofUsage,InstallationandAdministration.ThecertificationwillbeexecutedonlineandcanbetakeninmostSiemensPLMSoftware

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。