Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

Perfect secret sharing scheme based on vertex domination set

Due to the fast development in data communication systems and computer networks in recent years, the necessity to protect the secret data has become extremely imperative. Several methods have been proposed to protect the secret data; one of them is the secret sharing scheme. It is a method of distributing a secret K among a finite set of participants, in such a way that only predefined subset of participant is enabled to reconstruct a secret from their shares. A secret sharing scheme realizing uniform access structure described by a graph has received a considerable attention. In this scheme, each vertex represents a participant and each edge represents a minimum authorized subset. In this paper, an independent dominating set of vertices in a graph G is introduced and applied as a novel idea to construct a secret sharing scheme such that the vertices of the graph represent the participants and the dominating set of vertices in G represents the minimal authorized set. While most of the previous schemes were based on the principle of adjacent vertices, the proposed scheme is based upon the principle of non-adjacent vertices. We prove that the scheme is perfect, and the lower bound of the information rate of this new construction is improved when compared to some well-known previous constructions. We include an experiment involving security threats to demonstrate the effectiveness of the proposed scheme.     

Are U.K. Citizens Satisfied With E-Government Services? Identifying and Testing Antecedents of Satisfaction

Citizens’ satisfaction is acknowledged as one of the most significant influences for e-government adoption and diffusion. This study examines the impact of information quality, system quality, trust, and cost on user satisfaction of e-government services. Using a survey, this study collected 1518 valid responses from e-government service adopters across the United Kingdom. Our empirical outcomes show the five factors identified in this study have a significant impact on U.K. citizens’ satisfaction with e-government services.     

DCT statistics-based digital dropout detection in degraded archived media

With the rapid development of visual digital media, the demand for better quality of service has increased the pressure on broadcasters to automate their error detection and restoration activities for preserving their archives. Digital dropout is one of the defects that affect archived visual materials and tends to occur in block by block basis (size of 8 × 8). It is well established that human visual system (HVS) is highly adapted to the statistics of its visual natural environment. Consequently, in this paper, we have formulated digital dropout detection as a classification problem which predicts block label based on statistical features. These statistical features are indicative of perceptual quality relevant to human visual perception, and allow pristine images to be distinguished from distorted ones. Here, the idea is to extract discriminant block statistical features based on discrete cosine transform (DCT) coefficients and determine an optimal neighborhood sampling strategy to enhance the discrimination ability of block representation. Since this spatial frame based approach is free from any motion computation dependency, it works perfectly in the presence of fast moving objects. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.     

Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD

This paper introduces a robust voiced/non-voiced (VnV) speech classification method using bivariate empirical mode decomposition (bEMD). Fractional Gaussian noise (fGn) is employed as the reference signal to derive a data adaptive threshold for VnV discrimination. The analyzing speech signal and fGn are combined to generate a complex signal which is decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) by using bEMD. The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. The log-energies of both types of IMFs are calculated. There exist similarities between the IMF log-energy representation of fGn and unvoiced speech signals. Hence, the upper confidence limit from IMF log-energies of fGn is used as data adaptive threshold for VnV classification. If the subband log-energy of speech segment exceeds the threshold, the segment is classified as voiced and unvoiced otherwise. The experimental results show that the proposed algorithm performs better than the recently reported methods without requiring any training data for a wide range of SNRs.     

Privacy-enhanced architecture for smart metering

The recent deployment of smart grids promises to bring numerous advantages in terms of energy consumption reduction in both homes and businesses. A more transparent and instantaneous measurement of electricity consumption through smart meters utilization leads to an enhancement in the ability of monitoring, controlling and predicting energy usage. Nevertheless, it also has associated drawbacks related to the privacy of customers, since such management might reveal their personal habits, which electrical appliances they are using at each moment, whether they are at home or not, etc. In this work, we present a privacy-enhanced architecture for smart metering aimed at tackling this threat by means of encrypting individual measurements while allowing the electricity supplier to access the aggregation of the corresponding decrypted values.     

ESPAR array design for the UHF RFID wireless sensor communication system

In this paper, in order to improve the received signal strength (RSS) and signal quality, three arrays of electronically steerable parasitic array radiator (ESPAR) antennas are suggested for the ultra-high frequency (UHF) radio frequency identification (RFID) communication and sensing system applications. Instead of the single antenna, the array antennas have recently been widely used in many communication systems because of their peak gains, better radiation patterns, and higher radiation efficiency. Also, there are some important issues to use the antenna array like high data rates in wireless communication systems and to better understand the many targets or sensors. In this article, a wireless sensor network (WSN) is being investigated to overcome multipath fading and interference by antenna nulling technology that can be achieved through beam control ESPAR array antennas. The proposed ESPAR array antennas exhibit higher gains like 9.63, 10.2, and 12 dBi and proper radiation patterns from one array to another. Moreover, we investigate the mutual coupling effect on the performance of array antennas with different spacing (0.5λ, 0.75λ, λ) and configurations. It is found that the worst mutual coupling reduced by −28 to −34 dB for 2 × 2 array, −3 to −43 dB for 2 × 3 array, and finally −42 dB to −51 dB due to the antenna spacing from 0.5λ to λ. Thus, these suggested antennas could effectively be applied in the WSN communication systems, internet of things (IoT) networks, and massive wireless and backscatter communication systems.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...