Embedding method has become a popular way to handle unstructured data, such as word and text. Word embedding, providing computational-friendly representations for word similarity, is almost be one of the standard solutions for various text mining tasks. Lots of recent studies focusing on word embedding try to generate a more comprehensive representation for each word that incorporating task-spe...

Standard neural machine translation (NMT) is on the assumption that document-level context independent. Most existing NMT approaches are satisfied with a smattering sense of global information, while this work focuses exploiting detailed in terms memory network. The capacity network detecting most relevant part current sentence from renders natural solution to model rich context. In work, propo...

Embedding learning is essential in various research areas, especially natural language processing (NLP). However, given the nature of unstructured data and word frequency distribution, general pre-trained embeddings, such as word2vec GloVe, are often inferior tasks for specific domains because missing or unreliable embedding. In many domain-specific tasks, pre-existing side information can be c...

introduction:in this study, ecg signals have been embedded into medical images to create a novel blind watermarking method. the embedding is done when the original image is compressed using the ezw algorithm. the extraction process is performed at the decompression time of the watermarked image. materials and methods: the multi-resolution watermarking with a secret key algorithm developed in th...

Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in fine granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent se...

Neural embedding techniques are being applied in a growing number of machine learning applications. In this work, we demonstrate a neural embedding technique to model users’ session activity. Specifically, we consider a dataset collected from Microsoft’s App Store consisting of user sessions that include sequential click actions and item purchases. Our goal is to learn a latent manifold that ca...