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Conventional recurrent neural networks (RNNs) have difficulties in learning long-term dependencies. To tackle this problem, we propose an architecture called segmented-memory recurrent neural network (SMRNN). A symbolic sequence is broken into segments and then presented as inputs to the SMRNN one symbol per cycle. The SMRNN uses separate internal states to(More)
As Short Message Service (SMS) is now widely used as business tool, its security has become a major concern for business organizations and customers. However, their security is a critical issue cumbering their application and development. This paper analyses the most popular digital signature algorithms such as DSA, RSA and ECDSA and compared these(More)
The Evolved Packet System-based Authentication and Key Agreement (EPS-AKA) protocol of the long-term evolution (LTE) network does not support Internet of Things (IoT) objects and has several security limitations, including transmission of the object’s (user/device) identity and key set identifier in plaintext over the network, synchronization, large(More)
In theoretical computer science, it is well-established fact that Turing model (and its equivalent models like Chomsky's phrase structured grammar model, or recursively enumerable functions) is a most general model for computation. Many biologically inspired computing paradigms such as traditional neural networks, genetic algorithms, evolutionary computing(More)
Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range(More)
The formation of protein secondary structure especially the regions of β-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called Segmented-Memory Recurrent Neural Network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neu-ral networks on(More)
We carry out a comprehensive study of long-range interactions on a large data set of non-homologous proteins. Our study reveals that the long-range interactions between amino acids far apart are common in protein folding, and play an important role on the formation of secondary structure. Using residue-wise contact order(RWCO) to describe long-range(More)