Themultiparty comparisonallows to compare two integers$x$and$y$blindly, where a set of players hold the shares of the elements$x and y, x, y \in \mathbb {F}_{p}$, a prime field. The existingmultiparty comparisonprotocols execute in constant rounds, but the number of multiplications depends on the size of the prime$p$, i.e., the communication complexity will be high for large prime$p$. In this paper, we present amultiparty comparisonprotocol with constant rounds in which the number of multiplications depends on the number of players rather than the prime$p$itself. Thismultiparty comparisonprotocol is further extended to design amultiparty equality-testprotocol. An equality-test protocol computes the equality of shares in constant rounds and its number of multiplications depends on the number of players. Our proposed protocolsmultiparty comparisonand equality-test are unconditionally secure against the active and passive attacks and have$O(n)$communication complexity, where$n$is the number of players. We also present an efficient technique forfault detectionthat can verify the correctness of various protocols.

With the rapid growth of video streaming across the world, a great need has emerged for the advancement of video analytics and streaming platforms. In this paper, we introduce ViStA, a video streaming and analytics benchmark that can be used to measure the performance of systems running these workloads. We hereby present the design of our benchmark along with select initial studies regarding this benchmark.

Abstract Music is the art of ‘language of emotions’. Recently, music mood recognition is an emerging task. An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression (SVR) model is developed for the music emotion recognition. Our main intention is to increase the accuracy of emotion classification of music by considering text‐dependent and non‐text‐dependent features. For the high level feature representation, stacked autoencoder is used with two hidden layers. Modified K‐Medoid‐based brain storm optimisation‐based support vector regression (SVR_KMBSO) model is utilised for the emotion classification. Using the K‐Medoid‐based brain storm algorithm, the optimal parameters of the SVR are selected. The proposed framework utilises ISMIR2012 dataset and NJU_V1 dataset for English and for Hindi; online songs are also gathered and used for the music mood recognition. All the three datasets include songs based on four emotions like happy, angry, relax and sad. The experimental results are evaluated and compared with the existing classifiers including SVR, deep belief network (DBN) and Recurrent neural network (RNN). The proposed method SVR_KMBSO achieved high accuracy using three different datasets.

Telemedicine is getting a lot of popularity. It is because of the way that the medical images are sent over the Internet to share the restorative state of a patient, making the analysis simple for the medical staff. There is, however, a good possibility that the contents of a medical image may be altered due to the openness of the Internet. The watermarking techniques are utilised to keep the integrity of medical images intact. These techniques, however, need some additional information (watermark) to be embedded into the medical images, which may deteriorate their visual quality, leading to false diagnosis. In this study, a non‐intrusive procedure for verification of the medical images is proposed in which the polar cosine transform is used for feature extraction and the PatchMatch algorithm to locate the forged regions. Experimentally, the authors demonstrate that the proposed technique is robust against the noise, blurring, scaling, rotation and JPEG compression.

Text summarization is the process of transfiguring a large documental information into a clear and concise form. In this article, we present a detailed comparative study of various extractive methods for automatic text summarization on Hindi and English text datasets of news articles. We consider 13 different summarization techniques, namely, TextRank, LexRank, Luhn, LSA, Edmundson, ChunkRank, TGraph, UniRank, NN-ED, NN-SE, FE-SE, SummaRuNNer, and MMR-SE, and we evaluate their performance using various performance metrics, such as precision, recall,F1, cohesion, non-redundancy, readability, and significance. A thorough analysis is done in eight different parts that exhibits the strengths and limitations of these methods, effect of performance over the summary length, impact of language of a document, and other factors as well. A standard summary evaluation tool (ROUGE) and extensive programmatic evaluation using Python 3.5 in Anaconda environment are used to evaluate their outcome.

Among all the data broadcasting schemes, the harmonic scheme has been reported to provide the best performance for user latency; however, it does not always deliver the video data to the users in time. To provide the video data reliably, its two main variants, i.e., cautious and quasi-harmonic schemes have been proposed. These variants require a video to be divided into large number of subsegments that leads to a problem of management complexity. Besides the manageability problem, these variants require more bandwidth than the original harmonic scheme. The cautious and the quasi-harmonic schemes need, respectively, 0.50*b and 0.1771*b more bandwidth than the original harmonic scheme in limiting case. In this paper, we propose a new broadcasting scheme that is named as the geometrico-harmonic scheme. This scheme provides the video data to the users in time unlike the original harmonic scheme, and also its bandwidth requirement is very much same as that of the original harmonic scheme. In comparison to the cautious and the quasi-harmonic schemes, it has better performance for the user latency and the buffer space. For disk transfer rate, its performance is same as that of the cautious harmonic scheme, but in comparison the quasi-harmonic scheme, it performs better

The harmonic broadcasting scheme has the best performance for the user latency. It, however, does not always provide the video data in time to the users. To provide the video data reliably, its two main variants - cautious and quasi-harmonic schemes have been proposed. They require more bandwidth than the harmonic scheme. The cautious and quasi-harmonic schemes need 0.50 b and 0.1771 b more bandwidth, respectively, than the harmonic scheme in limiting case. In this paper, a new broadcasting scheme: geometrico-harmonic scheme with continuous redundancy is proposed. This scheme provides the video data in time unlike the harmonic scheme, and its bandwidth requirement can be had as close to that of the harmonic scheme as we please. Moreover, the extra bandwidth required in this scheme can be evaluated at any point of time, that is, it can be estimated for a fractional size of a segment and/or subsegment. The bandwidth is a scarce resource and its requirement for any fractional size of segments (or subsegments) may be helpful while dealing with the variable bandwidth rate-encoded videos. In the proposed scheme, the user latency can be made arbitrarily close to that of the harmonic scheme. In comparison to the cautious and the quasi-harmonic schemes, it has better performance for the user latency as well as the buffer storage. For disk transfer rate, it performs better than the quasi-harmonic scheme and in comparison to the cautious harmonic scheme its performance is same