Hi Young,
I think the problem is with the data itself and not the training parameters. I analysed the data file attached and it is extremely sparse, meaning the data ranges from values of the order 
to 
(200). This sparsity inherently hinders the network to learn useful features. Even on scaling/normalizing the data, the values are so small as compared to the values in positive orders of magnitude, they are scaled to the same value due to precision limits.
to (200). This sparsity inherently hinders the network to learn useful features. Even on scaling/normalizing the data, the values are so small as compared to the values in positive orders of magnitude, they are scaled to the same value due to precision limits.An improvement on this is to ignore the first feature and all features after the first 13, as they do not contain valuable information for the network to learn. Doing this and scaling the 12 features from (2:13) will improve the results slightly(make sure to introduce a dropout layer with some fraction say 0.25 to avoid overfitting) and the accuracy will reach somewhere around 50%, but the learning still saturates there(see the image attached).
At this point, its not the limitation of the network but the inherent knowledge contained in the dataset; it is not sufficient for the network to learn a generalized model which can provide good validation accuracy. I would suggest you to maybe re-calculate the feature vectors and experiment with more feature engineering to extract useful features for training.
