Update README.md

README.md

@@ -11,17 +11,6 @@ This package provides the images generated by our approach and competing approac
 
 To support safety analysis practices, we propose SEDE, a technique that generates readable descriptions for commonalities in failure-inducing, real-world images and improves the DNN through effective retraining. SEDE leverages the availability of simulators, which are commonly used for cyber-physical systems. It relies on genetic algorithms to drive simulators towards the generation of images that are similar to failure-inducing, real-world images in the test set; it then employs rule learning algorithms to derive expressions that capture  commonalities in terms of  simulator parameter values. The derived expressions are then used to generate additional images to retrain and improve the DNN. With DNNs performing in-car sensing tasks, SEDE successfully characterized hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled retraining leading to significant improvements in DNN accuracy, up to 18 percentage points.
 
-## NOTICE on dependencies and libraries
-
-SEDE is a toolset that might be used to generate explanations for DNN errors with any simulator.
-However, for our experiments we rely on the faces simulator developed by IEE S.A. (https://iee-sensing.com)
-We refer to such simulator as IEE-Face-Simulator.
-The released implementation of SEDE invokes the IEE-Face-Simulator through a program call (i.e., not through APIs).
-SEDE makes use of two versions of IEE-Face-Simulator, v0.5, which is in directory IEE_V1, and v1.0, which is in directory IEE_V2.
-
-Both the two versions of the IEE-Face-Simulator are released with GPL v3 licence (see files 'gpl-3.0.txt' in both directories).
-The directory IEEPackage is Copyright (C) 2018-2022 IEE S.A. (https://iee-sensing.com) released with GPL v3 licence.
-
 ## Usage
 
 The package contains the python code implementation of the SEDE approach and the files used to evaluate RQ1-5 (SEDE_RQx.py) along with IEE simulators and DeepJanus adoption of our case studies. We modified the implementation of DeepJanus for their BeamNG case study with the following changes:
@@ -69,6 +58,16 @@ E.g.: 'RQ4/HPD-F/RCC-1_500_10.2%.gif' where 500 is the number of images and 10.2
 * 'RQ5' contains a folder for each case study DNN ('HPD-F', 'HPD-H', 'FLD'). Each case study DNN contains a folder with the images selected/generated by competing approaches ('HUDD', 'RBL') for the 10 runs.
 E.g.: 'RQ5/HPD-F/HUDD/RCC-1.gif' contains the images selected by HUDD for Cluster 1.
 
+## NOTICE on dependencies and libraries
+
+SEDE is a toolset that might be used to generate explanations for DNN errors with any simulator.
+However, for our experiments we rely on the faces simulator developed by IEE S.A. (https://iee-sensing.com)
+We refer to such simulator as IEE-Face-Simulator.
+The released implementation of SEDE invokes the IEE-Face-Simulator through a program call (i.e., not through APIs).
+SEDE makes use of two versions of IEE-Face-Simulator, v0.5, which is in directory IEE_V1, and v1.0, which is in directory IEE_V2.
+
+Both the two versions of the IEE-Face-Simulator are released with GPL v3 licence (see files 'gpl-3.0.txt' in both directories).
+The directory IEEPackage is Copyright (C) 2018-2022 IEE S.A. (https://iee-sensing.com) released with GPL v3 licence.
 
 ## Reference: