Update README.md

README.md

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 ![Framework](./picture/framework.jpg)
 # Automated Spatial-Temporal Graph Contrastive Learning for Region Representation (AutoST) #
-This is the implementation of Automated Spatial-Temporal Graph Contrastive Learning for Region Representation (AutoST) in the following paper:
+This is the implementation of Automated Spatio-Temporal Graph Contrastive Learning (AutoST) in the following paper:
 
 ## Requirements ##
 Pytorch = 1.7.0 and Tensorflow = 1.15.3 (crime prediction task (ST-SHN))
@@ -21,7 +21,7 @@ Also, we provide the process data and processing code for data preprocessing.
     pre_s6_dataloader.py # obtaining the dataloder (dataset.pt file) for next step to get region embeddings
 
 ## Hyperparameters ##
-The dimensionality d of region representation is set as 96 . The depth of convolutional layers in GCN is set as 3. The learning rate is initialized as 0.0005 with the weight decay of 0.01. For the crime prediction backbone model, ST-SHN is configured with the learning rate of 0.001 and the weight decay of 0.96. The depth of the spatial path aggregation layers is set as 2. For the traffic prediction backbone model ST-GCN, the historical time window of all tests are set as 60 minutes with 12 observed data points that are utilized to forecast traffic conditions in the next 15, 30, 45 minutes. The baselines are implemented with the source code released by their original papers. We further apply the grid search strategy for parameter tuning in baselines to achieve their best performance. 
+The dimensionality d of region representation is set as 96 . The depth of convolutional layers in GCN is set as 3. The learning rate is initialized as 0.0005 with the weight decay of 0.01. For the crime prediction backbone model, ST-SHN is configured with the learning rate of 0.001 and the weight decay of 0.96. The depth of the spatial path aggregation layers is set as 2. For the traffic prediction backbone model ST-GCN, the historical time window of all tests are set as 60 minutes with 12 observed data points that are utilized to forecast traffic conditions in the next 15, 30, 45 minutes. The baselines are implemented with the source code released by their original papers.
 
 ## Obtaining region representations ##
     cd data_augmentation