In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.
Linguistic steganography is used for hiding information in the multimedia data in a traditional approach. For that, we are taking an image and converting the text into bits like 0's and 1's. In this paper, we are using both image and text dataset. Nowadays, providing secure data with confidentiality and integrity has become a big challenge in the present world. To improve the security of these existing parameters, we have proposed a methodology by referring through various reference papers and existing methodologies that can provide and attain confidentiality and integrity by utilizing various algorithms to provide a collective result. We have used algorithms like support vector machine (SVM), recurrent neural network (RNN), and convolutional neural network (CNN). For higher security, steganography algorithm is employed to mix with this algorithm. By applying the above algorithms, we will be doing encoding for providing more security. Image quality will not be loosed and there will be efficiency in decoding the information by doing encoding. If any users attack the code, due to the techniques that we used in our encoding, enhancing more security will be provided.
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