An efficient fragile watermarking scheme with multilevel tamper detection and recovery based on dynamic domain selection

Self-recoverable fragile watermarking is meant for accurate tamper localization as well as image recovery with superior visual quality. However, most of the existing state of art approaches perform authentication and recovery on block basis owing to which the entire block is categorized as tampered in case of alteration of one or more pixels of it. This, results in staircase formation of tamper detected regions, hence lacking in accuracy. Furthermore, the visual quality of the recovered image also deteriorates as an approximate value is assigned to all the block pixels corresponding to the altered region. The proposed watermarking scheme performs both authentication and recovery pixelwise. The authentication of each pixel is done via multi level tamper detection(MLTD) through three authentication bits based on value, location and neighbourhood information. The domain for image recovery is chosen dynamically based on the content of the block, may it be in spatial domain for smooth blocks or frequency domain for the rough ones. This provides high accuracy in recovery. As the embedding of recovery information is done in the frequency domain, the imperceptibility of the watermarked image scheme remains high. Also, embedding of authentication information in the spatial domain maintains its fragile nature. Even for higher tampering ratios, the lost content is rebuilt with high peak signal to noise ratio(PSNR) of the recovered image. The probabilities of false rejection and false acceptance head towards the ideal value for most of the empirical analysis. Comparative study via metric evaluation of probability of false rejection (PFR), probability of false acceptance (PFA) and PSNR of recovered image for different standard test cover images demonstrate the efficacy of the proposed scheme over other existing state of art approaches. Further, the security of the proposed scheme remains high due to usage of multi-layered secret keys and chaos based random mapping handling worst tamper scenarios.     

Viscoelastic flow in an obstructed microchannel at high Weissenberg number

Detailed measurements of the flow instability of dilute shear-thinning viscoelastic aqueous solutions, with relatively low zero-shear viscosities, in an obstructed microchannel flow are reported. We examine the flow behaviour resulting from a 100 μm post placed in the channel centreline over a range of Reynolds numbers (\(5<Re<300\)) and Weissenberg numbers (\(20<Wi<10^3\)). Micro-particle image velocimetry measurements show the onset of an upstream instability within a Reynolds number range and at a critical elasticity number corresponding to polymer concentrations above 25 ppm of long-chain polyacrylamide. The instability results in significant local fluctuations in the flow field approaching 30 % of the mean velocity. The magnitude of the local viscosity ratio in the region upstream of the post is proposed as a driving mechanism for the instability which resembles a buckling flow. Additionally, the classical instability owing to separation and vortex formation downstream of the post in Newtonian flow is suppressed and a very long stable wake is observed extending over 10 post-diameters downstream.     

(Approximate) Uncertain Skylines

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

A Multi-Criteria Route Selection Method for Vehicles Using Genetic Algorithms Based on Driver’s Preference

Wireless Personal Communications - Finding the best path between a given source and a destination in a road network is an important problem. It has its applications in various map services and...     

Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...     

Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.