minor reviews

043_DynamicErrorRobustness/DynamicErrorRobustness.tex

@@ -272,7 +272,7 @@ However, different network topologies with different number of neurons in each l
 To effectively train and validate the \ac{nn}, firstly the cases corresponding to three video sequence were left out, to be used in the performance evaluation of the proposed method.
 The remaining data set was randomly partitioned into three different groups: (i) 65\% of the test cases are used for training, (ii) 15\% for validation and (iii) 20\% for testing the \ac{nn} performance. 
 \HIGHLIGHT{These ratios were chosen to use a relevant number of samples for testing \ac{nn} performance (i.e., 20\%), and the remaining were used mainly for training, leaving a 15\% samples for validation and stop criteria of the supervised training.
-The \ac{nn} supervised training uses 557 test conditions and a scaled conjugate gradient algorithm~\cite{NNScaledConjugateTraining_1993} and stop when the cross-entropy obtained with the validation samples increases, revealing that the training starts to over-fit the training samples and is not being general enough to predict new data.
+The \ac{nn} supervised training uses 557 test conditions, a scaled conjugate gradient algorithm~\cite{NNScaledConjugateTraining_1993} and stopped when the cross-entropy obtained for the validation samples increases, revealing that the training starts to over-fit the training samples and is not being able to generalise to new data.
 Therefore, 129 test conditions were used for \ac{nn} validation.}
 Finally, 172 conditions are used to evaluate the accuracy of the proposed method after training with different conditions.
 
@@ -370,7 +370,7 @@ The quality obtained with the proposed optimal error robustness scheme (\PROP) i
 	\centering
 	\input{043_DynamicErrorRobustness/Results/PlotQualty_vs_PLR_Predicted.tex}
 	\vspace{-2mm}
-	\caption{Average video quality using different error resilience techniques.}
+	\caption{Average video quality obtained for the reference HEVC (\REF), the optimal stream (\BEST) and the one selected by the proposed approach (\PROP).}
 	\label{Figure6:ProposedQuality}
 \end{figure}