It has been observed from the literature that speech is the most natural means of communication between humans. Human beings start speaking without any tool or any explicit education. The environment surrounding them helps them to learn the art of speaking. From the existing literature, it is found that the existing speaker classification techniques suffer from over-fitting and parameter tuning issues. An efficient tuning of machine learning techniques can improve the classification accuracy of speaker classification. To overcome this issue, in this paper, an efficient particle swarm optimization-based support vector machine is proposed. The proposed and the competitive speaker classification techniques are tested on the speaker classification data of Punjabi persons. The comparative analysis of the proposed technique reveals that it outperforms existing techniques in terms of accuracy, [Formula: see text]-measure, specificity and sensitivity.

Binary decision diagram (BDD) is a state‐of‐the‐art data structure for representing and manipulating Boolean functions. In 2002, Krause proposed theoretical framework for BDD‐based cryptanalysis of stream ciphers. Since then not much work have been reported in this area. In this study, the authors propose a practical approach for cryptanalysis of stream cipher using reduced ordered BDD (ROBDD). They propose various methods for ANDing operation on ROBDDs, required during process of cryptanalysis. Out of these proposed methods, ‘recursive symmetric ANDing’ gives optimal order of ANDing. They use their approach to demonstrate cryptanalysis of E 0 stream cipher. They also discuss some implementation results. The attack can recover 39 unknown key bits in 5 s on regular personal computer. BuDDy‐2.4 library is used for performing operations on BDDs.

The concept of region incrementing in visual cryptography was introduced to encrypt an image into multiple secrecy levels. But, it suffers from the pixel expansion increasing exponentially as the number of participants grows. In this paper, we propose a region incrementing visual secret sharing (RIVSS) scheme based on random grids. The proposed scheme is a general (k, n)-RIVSS scheme, in which any t (k ≤ t ≤ n) shares can be used to reconstruct the secret regions up to t − k + 1 levels. However, no information about the input image can be revealed by any k − 1 or fewer shares. With a nice property of region incrementing, the proposed scheme benefits by sharing an image without any pixel expansion and codebook requirement. We give formal proofs and experimental results to confirm both correctness and feasibility of our scheme.

WordNet is an electronic lexical database available on-line as a powerful resource to the researchers in the area of computational linguistics, text processing and other related areas.WordNet for Hindi language has already been developed by IIT, Bombay.The Indian languages WordNets are being created using expansion approach from Hindi Word-Net under IndoWordNet project.In expansion approach, semantic relations are borrowed from the reference language, while the lexical relations need to be created for each language, as these relations are language dependent.This paper describes the process of creation of lexical relations like antonym, compounding, conjunction and gradation for IndoWordNet.A lexical creation tool has been presented in this paper with provision to create lexical relations in target language on the basis of relations created in Hindi Word-Net and with another provision to create lexical relations in target language without referring to Hindi WordNet.It has been observed that lexical relations for target language can be created easily on the basis of relations created in Hindi WordNet for Hindi in-family languages, while for the languages that do not fall in the same family provision of creation of lexical relation without referring to Hindi WordNet can be used.

ABSTRACT We design a new (k,n)‐threshold visual secret sharing scheme on the basis of Boolean operations. We propose two different algorithms to encrypt a secret image into n meaningless shares such that no secret information can be obtained by having any k − 1 or fewer shares. However, the secret image can be reconstructed easily by XOR of any k (≤n) or more shares. Both the algorithms have used simple Boolean operations such as OR and XOR. The proposed scheme broadens the potential applicability of Boolean operation‐based visual secret sharing by generating the shares of size same as that of the original secret image. Formal proofs, security analysis, and the experimental results are given to demonstrate the correctness and feasibility of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a post processor for accuracy of character recognition of real-time online Gurmukhi script has been developed. Our analysis is based on dataset consisting of 184 samples of each 45 characters of Gurmukhi script collected from four different categories of writers. Based on this extensive study, we propose an efficient algorithm for online handwritten Gurmukhi character recognition that achieves promising recognition accuracy of 95.6% for single character stroke sequencing. Beside character recognition the contribution in this paper is summarized in two folds as (i) the proposed scheme resolves stroke sequencing, (ii) overwritten strokes are identified and resolved. Moreover, for every stroke, complexity of adding new stroke for Gurmukhi character formation has been computed to be O(n).

DeConverter is core software in a Universal Networking Language (UNL) system. A UNL system has EnConverter and DeConverter as its two major components. EnConverter is used to convert a natural language sentence into an equivalent UNL expression, and DeConverter is used to generate a natural language sentence from an input UNL expression. This paper presents design and development of a Punjabi DeConverter. It describes five phases of the proposed Punjabi DeConverter, i.e., UNL parser, lexeme selection, morphology generation, function word insertion, and syntactic linearization. This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter. Syntactic linearization is the process of defining arrangements of words in generated output. The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph, a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper. Special cases of syntactic linearization with respect to Punjabi language for UNL relations like ‘and’, ‘or’, ‘fmt’, ‘cnt’, and ‘seq’ have also been presented in this paper. This paper also provides implementation results of the proposed Punjabi DeConverter. The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology (IIT), Bombay, India, as gold-standards. The proposed system generates 89.0% grammatically correct sentences, 92.0% faithful sentences to the original sentences, and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale. The system is also able to achieve a bilingual evaluation understudy (BLEU) score of 0.72.