diff --git a/Modules/Classification/CLUtilities/test/mitkGIFCurvatureStatisticTest.cpp b/Modules/Classification/CLUtilities/test/mitkGIFCurvatureStatisticTest.cpp
index d56d087a0d..f4fc5fadf1 100644
--- a/Modules/Classification/CLUtilities/test/mitkGIFCurvatureStatisticTest.cpp
+++ b/Modules/Classification/CLUtilities/test/mitkGIFCurvatureStatisticTest.cpp
@@ -1,118 +1,114 @@
 /*============================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center (DKFZ)
 All rights reserved.
 
 Use of this source code is governed by a 3-clause BSD license that can be
 found in the LICENSE file.
 
 ============================================================================*/
 
 #include <mitkTestingMacros.h>
 #include <mitkTestFixture.h>
 #include "mitkIOUtil.h"
 #include <cmath>
 
 #include <mitkGIFCurvatureStatistic.h>
 
 class mitkGIFCurvatureStatisticTestSuite : public mitk::TestFixture
 {
   CPPUNIT_TEST_SUITE(mitkGIFCurvatureStatisticTestSuite);
 
   MITK_TEST(ImageDescription_PhantomTest);
 
   CPPUNIT_TEST_SUITE_END();
 
 private:
-  mitk::Image::Pointer m_IBSI_Phantom_Image_Small;
   mitk::Image::Pointer m_IBSI_Phantom_Image_Large;
-  mitk::Image::Pointer m_IBSI_Phantom_Mask_Small;
   mitk::Image::Pointer m_IBSI_Phantom_Mask_Large;
 
 public:
 
   void setUp(void) override
   {
-    m_IBSI_Phantom_Image_Small = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Small.nrrd"));
     m_IBSI_Phantom_Image_Large = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Large.nrrd"));
-    m_IBSI_Phantom_Mask_Small = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Small.nrrd"));
     m_IBSI_Phantom_Mask_Large = mitk::IOUtil::Load<mitk::Image>(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Large.nrrd"));
   }
 
   void ImageDescription_PhantomTest()
   {
     mitk::GIFCurvatureStatistic::Pointer featureCalculator = mitk::GIFCurvatureStatistic::New();
 
     featureCalculator->SetUseBinsize(true);
     featureCalculator->SetBinsize(1.0);
     featureCalculator->SetUseMinimumIntensity(true);
     featureCalculator->SetUseMaximumIntensity(true);
     featureCalculator->SetMinimumIntensity(0.5);
     featureCalculator->SetMaximumIntensity(6.5);
 
     auto featureList = featureCalculator->CalculateFeatures(m_IBSI_Phantom_Image_Large, m_IBSI_Phantom_Mask_Large);
 
     std::map<std::string, double> results;
     for (const auto &valuePair : featureList)
     {
       MITK_INFO << mitk::AbstractGlobalImageFeature::GenerateLegacyFeatureNameWOEncoding(valuePair.first) << " : " << valuePair.second;
       results[mitk::AbstractGlobalImageFeature::GenerateLegacyFeatureNameWOEncoding(valuePair.first)] = valuePair.second;
     }
     CPPUNIT_ASSERT_EQUAL_MESSAGE("Image Diagnostics should calculate 44 features.", std::size_t(44), featureList.size());
 
     // These values are obtained by a run of the filter.
     // The might be wrong!
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Minimum Mean Curvature with Large IBSI Phantom Image", -1.51, results["Curvature Feature::Minimum Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Maximum Mean Curvature with Large IBSI Phantom Image", 0.51, results["Curvature Feature::Maximum Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Mean Curvature with Large IBSI Phantom Image", 0.095, results["Curvature Feature::Mean Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Mean Curvature with Large IBSI Phantom Image", 0.45, results["Curvature Feature::Standard Deviation Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Mean Curvature with Large IBSI Phantom Image", -2.55, results["Curvature Feature::Skewness Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Mean Curvature with Large IBSI Phantom Image", 0.30, results["Curvature Feature::Mean Positive Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Mean Curvature with Large IBSI Phantom Image", 0.12, results["Curvature Feature::Standard Deviation Positive Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Mean Curvature with Large IBSI Phantom Image", 0.60, results["Curvature Feature::Skewness Positive Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Mean Curvature with Large IBSI Phantom Image", -0.955, results["Curvature Feature::Mean Negative Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Mean Curvature with Large IBSI Phantom Image", 0.56, results["Curvature Feature::Standard Deviation Negative Mean Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Mean Curvature with Large IBSI Phantom Image", 0.09, results["Curvature Feature::Skewness Negative Mean Curvature"], 0.01);
 
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Minimum Gaussian Curvature with Large IBSI Phantom Image", -0.211, results["Curvature Feature::Minimum Gaussian Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Maximum Gaussian Curvature with Large IBSI Phantom Image", 1.81, results["Curvature Feature::Maximum Gaussian Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Gaussian Curvature with Large IBSI Phantom Image", 0.14, results["Curvature Feature::Mean Gaussian Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Gaussian Curvature with Large IBSI Phantom Image", 0.42, results["Curvature Feature::Standard Deviation Gaussian Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Gaussian Curvature with Large IBSI Phantom Image", 3.51, results["Curvature Feature::Skewness Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Gaussian Curvature with Large IBSI Phantom Image", 0.26, results["Curvature Feature::Mean Positive Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Gaussian Curvature with Large IBSI Phantom Image", 0.53, results["Curvature Feature::Standard Deviation Positive Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Gaussian Curvature with Large IBSI Phantom Image", 2.52, results["Curvature Feature::Skewness Positive Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Gaussian Curvature with Large IBSI Phantom Image", -0.03, results["Curvature Feature::Mean Negative Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Gaussian Curvature with Large IBSI Phantom Image", 0.055, results["Curvature Feature::Standard Deviation Negative Gaussian Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Gaussian Curvature with Large IBSI Phantom Image", -1.92, results["Curvature Feature::Skewness Negative Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Gaussian Curvature with Large IBSI Phantom Image", 0.38, results["Curvature Feature::Mean Positive Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Gaussian Curvature with Large IBSI Phantom Image", 0.60, results["Curvature Feature::Standard Deviation Positive Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Gaussian Curvature with Large IBSI Phantom Image", 1.89, results["Curvature Feature::Skewness Positive Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Gaussian Curvature with Large IBSI Phantom Image", -0.11, results["Curvature Feature::Mean Negative Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Gaussian Curvature with Large IBSI Phantom Image", 0.049, results["Curvature Feature::Standard Deviation Negative Gaussian Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Gaussian Curvature with Large IBSI Phantom Image", -0.74, results["Curvature Feature::Skewness Negative Gaussian Curvature"], 0.01);
 
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Minimum Minimum Curvature with Large IBSI Phantom Image", -2.19, results["Curvature Feature::Minimum Minimum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Maximum Minimum Curvature with Large IBSI Phantom Image", 0.35, results["Curvature Feature::Maximum Minimum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Minimum Curvature with Large IBSI Phantom Image", -0.11, results["Curvature Feature::Mean Minimum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Minimum Curvature with Large IBSI Phantom Image", 0.573, results["Curvature Feature::Standard Deviation Minimum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Minimum Curvature with Large IBSI Phantom Image", -2.742, results["Curvature Feature::Skewness Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Minimum Curvature with Large IBSI Phantom Image", 0.161, results["Curvature Feature::Mean Positive Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Minimum Curvature with Large IBSI Phantom Image", 0.165, results["Curvature Feature::Standard Deviation Positive Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Minimum Curvature with Large IBSI Phantom Image", 0.108, results["Curvature Feature::Skewness Positive Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Minimum Curvature with Large IBSI Phantom Image", -0.42, results["Curvature Feature::Mean Negative Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Minimum Curvature with Large IBSI Phantom Image", 0.733, results["Curvature Feature::Standard Deviation Negative Minimum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Minimum Curvature with Large IBSI Phantom Image", -1.73, results["Curvature Feature::Skewness Negative Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Minimum Curvature with Large IBSI Phantom Image", 0.264, results["Curvature Feature::Mean Positive Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Minimum Curvature with Large IBSI Phantom Image", 0.132, results["Curvature Feature::Standard Deviation Positive Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Minimum Curvature with Large IBSI Phantom Image", -1.335, results["Curvature Feature::Skewness Positive Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Minimum Curvature with Large IBSI Phantom Image", -0.946, results["Curvature Feature::Mean Negative Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Minimum Curvature with Large IBSI Phantom Image", 0.844, results["Curvature Feature::Standard Deviation Negative Minimum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Minimum Curvature with Large IBSI Phantom Image", -0.599, results["Curvature Feature::Skewness Negative Minimum Curvature"], 0.01);
 
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Minimum Maximum Curvature with Large IBSI Phantom Image", -0.83, results["Curvature Feature::Minimum Maximum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Maximum Maximum Curvature with Large IBSI Phantom Image", 0.79, results["Curvature Feature::Maximum Maximum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Maximum Curvature with Large IBSI Phantom Image", 0.30, results["Curvature Feature::Mean Maximum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Maximum Curvature with Large IBSI Phantom Image", 0.369, results["Curvature Feature::Standard Deviation Maximum Curvature"], 0.01);
     CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Maximum Curvature with Large IBSI Phantom Image", -1.617, results["Curvature Feature::Skewness Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Maximum Curvature with Large IBSI Phantom Image", 0.419, results["Curvature Feature::Mean Positive Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Maximum Curvature with Large IBSI Phantom Image", 0.217, results["Curvature Feature::Standard Deviation Positive Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Maximum Curvature with Large IBSI Phantom Image", -0.958, results["Curvature Feature::Skewness Positive Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Maximum Curvature with Large IBSI Phantom Image", -0.44, results["Curvature Feature::Mean Negative Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Maximum Curvature with Large IBSI Phantom Image", 0.399, results["Curvature Feature::Standard Deviation Negative Maximum Curvature"], 0.01);
-    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Maximum Curvature with Large IBSI Phantom Image", 0.109, results["Curvature Feature::Skewness Negative Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Positive Maximum Curvature with Large IBSI Phantom Image", 0.513, results["Curvature Feature::Mean Positive Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Positive Maximum Curvature with Large IBSI Phantom Image", 0.094, results["Curvature Feature::Standard Deviation Positive Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Positive Maximum Curvature with Large IBSI Phantom Image", 1.548, results["Curvature Feature::Skewness Positive Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Mean Negative Maximum Curvature with Large IBSI Phantom Image", -0.47, results["Curvature Feature::Mean Negative Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Standard Deviation Negative Maximum Curvature with Large IBSI Phantom Image", 0.394, results["Curvature Feature::Standard Deviation Negative Maximum Curvature"], 0.01);
+    CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Curvature Feature::Skewness Negative Maximum Curvature with Large IBSI Phantom Image", 0.273, results["Curvature Feature::Skewness Negative Maximum Curvature"], 0.01);
   }
 
 };
 
 MITK_TEST_SUITE_REGISTRATION(mitkGIFCurvatureStatistic )
diff --git a/Modules/CppMicroServices/core/src/module/usModuleManifest.cpp b/Modules/CppMicroServices/core/src/module/usModuleManifest.cpp
index ebd5cfc108..904d538cb7 100644
--- a/Modules/CppMicroServices/core/src/module/usModuleManifest.cpp
+++ b/Modules/CppMicroServices/core/src/module/usModuleManifest.cpp
@@ -1,154 +1,154 @@
 /*============================================================================
 
   Library: CppMicroServices
 
   Copyright (c) German Cancer Research Center (DKFZ)
   All rights reserved.
 
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
 
 ============================================================================*/
 
 #include "usModuleManifest_p.h"
 
 #include "jsoncpp.h"
 
 #include <stdexcept>
 
 US_BEGIN_NAMESPACE
 
 namespace {
 
   typedef std::map<std::string, Any> AnyMap;
   typedef std::vector<Any> AnyVector;
 
   void ParseJsonObject(const Json::Value& jsonObject, AnyMap& anyMap);
   void ParseJsonArray(const Json::Value& jsonArray, AnyVector& anyVector);
 
   Any ParseJsonValue(const Json::Value& jsonValue)
   {
     if (jsonValue.isObject())
     {
       Any any = AnyMap();
       ParseJsonObject(jsonValue, ref_any_cast<AnyMap>(any));
       return any;
     }
     else if (jsonValue.isArray())
     {
       Any any = AnyVector();
       ParseJsonArray(jsonValue, ref_any_cast<AnyVector>(any));
       return any;
     }
     else if (jsonValue.isString())
     {
       return Any(jsonValue.asString());
     }
     else if (jsonValue.isBool())
     {
       return Any(jsonValue.asBool());
     }
-    else if (jsonValue.isDouble())
-    {
-      return Any(jsonValue.asDouble());
-    }
     else if (jsonValue.isIntegral())
     {
       return Any(jsonValue.asInt());
     }
+    else if (jsonValue.isDouble())
+    {
+      return Any(jsonValue.asDouble());
+    }
 
     return Any();
   }
 
   void ParseJsonObject(const Json::Value& jsonObject, AnyMap& anyMap)
   {
     for (Json::Value::const_iterator it = jsonObject.begin();
          it != jsonObject.end(); ++it)
     {
       const Json::Value& jsonValue = *it;
       Any anyValue = ParseJsonValue(jsonValue);
       if (!anyValue.Empty())
       {
         anyMap.insert(std::make_pair(it.name(), anyValue));
       }
     }
   }
 
   void ParseJsonArray(const Json::Value& jsonArray, AnyVector& anyVector)
   {
     for (Json::Value::const_iterator it = jsonArray.begin();
          it != jsonArray.end(); ++it)
     {
       const Json::Value& jsonValue = *it;
       Any anyValue = ParseJsonValue(jsonValue);
       if (!anyValue.Empty())
       {
         anyVector.push_back(anyValue);
       }
     }
   }
 
 }
 
 ModuleManifest::ModuleManifest()
 {
 }
 
 void ModuleManifest::Parse(std::istream& is)
 {
   Json::Value root;
   Json::Reader jsonReader(Json::Features::strictMode());
   if (!jsonReader.parse(is, root, false))
   {
     throw std::runtime_error(jsonReader.getFormattedErrorMessages());
   }
 
   if (!root.isObject())
   {
     throw std::runtime_error("The Json root element must be an object.");
   }
 
   ParseJsonObject(root, m_Properties);
 }
 
 bool ModuleManifest::Contains(const std::string& key) const
 {
   return m_Properties.count(key) > 0;
 }
 
 Any ModuleManifest::GetValue(const std::string& key) const
 {
   AnyMap::const_iterator iter = m_Properties.find(key);
   if (iter != m_Properties.end())
   {
     return iter->second;
   }
   return Any();
 }
 
 std::vector<std::string> ModuleManifest::GetKeys() const
 {
   std::vector<std::string> keys;
   for (AnyMap::const_iterator iter = m_Properties.begin();
        iter != m_Properties.end(); ++iter)
   {
     keys.push_back(iter->first);
   }
   return keys;
 }
 
 void ModuleManifest::SetValue(const std::string& key, const Any& value)
 {
   m_Properties[key] = value;
 }
 
 US_END_NAMESPACE