Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

Engineering Bimetallic Ag–Cu Nanoalloys for Highly Efficient Oxygen Reduction Catalysts: A Guideline for Designing Ag‐Based Electrocatalysts with Activity Comparable to Pt/C‐20%

Development of highly active and stable Pt‐free oxygen reduction reaction catalysts from earth‐abundant elements remains a grand challenge for highly demanded metal–air batteries. Ag‐based alloys have many advantages over platinum group catalysts due to their low cost, high stability, and acceptable oxygen reduction reaction (ORR) performance in alkaline solutions. Nevertheless, compared to commercial Pt/C‐20%, their catalytic activity still cannot meet the demand of commercialization. In this study, a kind of catalysts screening strategy on Ag_x Cu_100?x nanoalloys is reported, containing the surface modification method, studies of activity enhancement mechanism, and applied research on zinc–air batteries. The results exhibit that the role of selective dealloying (DE) or galvanic displacement (GD) is limited by the “parting limitation”, and this “parting limitation” determines the surface topography, position of d‐band center, and ORR performance of Ag_x Cu_100?x alloys. The GD‐Ag₅₅Cu₄₅ and DE‐Ag₂₅Cu₇₅ catalysts alloys present excellent ORR performance that is comparable to Pt/C‐20%. The relationship between electronic perturbation and specific activity demonstrates that positive shift of the d‐band center (≈0.12 eV, relative to Ag) for GD‐Ag₅₅Cu₄₅ is beneficial for ORR, which is contrary to Pt‐based alloys (negative shift, ≈0.1 eV). Meanwhile, extensive electrochemical and electronic structure characterization indicates that the high work function of GD‐Ag₅₅Cu₄₅ (4.8 eV) is the reason behind their excellent durability for zinc–air batteries.     

Multi-scale computational model for failure analysis of metal frames that includes softening and local buckling

In this work we present a new modelling paradigm for computing the complete failure of metal frames by combining the stress-resultant beam model and the shell model. The shell model is used to compute the material parameters that are needed by an inelastic stress-resultant beam model; therefore, we consider the shell model as the meso-scale model and the beam model as the macro-scale model. The shell model takes into account elastoplasticity with strain-hardening and strain-softening, as well as geometrical nonlinearity (including local buckling of a part of a beam). By using results of the shell model, the stress-resultant inelastic beam model is derived that takes into account elastoplasticity with hardening, as well as softening effects (of material and geometric type). The beam softening effects are numerically modelled in a localized failure point by using beam finite element with embedded discontinuity. The original feature of the proposed multi-scale (i.e. shell-beam) computational model is its ability to incorporate both material and geometrical instability contributions into the stress-resultant beam model softening response. Several representative numerical simulations are presented to illustrate a very satisfying performance of the proposed approach.     

Stable splitting scheme for general form of associated plasticity including different scales of space and time

An efficient implementation of the operator split procedure for boundary value solution with plastic flow computation is presented for a general form of associated plasticity model. We start with the general form of phenomenological model of plasticity where the yield criterion is not restricted to a simple (quadratic) form, and the elasticity tensor does not have constant entries. We then turn to the multi-scale model of plasticity which employs the fine scale representation of the plastic deformation along with the homogenization procedure for stress computation. We also visit the plasticity model with rate sensitive plastic response where plastic flow computation is carried out at fine scale in time. We proved herein the sufficient and necessary conditions for the proposed operator split procedure to converge, for any such general form of associated plasticity. Moreover, we presented a systematic manner for constructing the main ingredients for the plastic flow computation and the global Newton’s iteration, such as the consistent elastoplastic tangent.     

Exploiting information extraction techniques for automatic semantic video indexing with an application to Turkish news videos

This paper targets at the problem of automatic semantic indexing of news videos by presenting a video annotation and retrieval system which is able to perform automatic semantic annotation of news video archives and provide access to the archives via these annotations. The presented system relies on the video texts as the information source and exploits several information extraction techniques on these texts to arrive at representative semantic information regarding the underlying videos. These techniques include named entity recognition, person entity extraction, coreference resolution, and semantic event extraction. Apart from the information extraction components, the proposed system also encompasses modules for news story segmentation, text extraction, and video retrieval along with a news video database to make it a full-fledged system to be employed in practical settings. The proposed system is a generic one employing a wide range of techniques to automate the semantic video indexing process and to bridge the semantic gap between what can be automatically extracted from videos and what people perceive as the video semantics. Based on the proposed system, a novel automatic semantic annotation and retrieval system is built for Turkish and evaluated on a broadcast news video collection, providing evidence for its feasibility and convenience for news videos with a satisfactory overall performance.     

Modelling of residual stresses in the shot peened material C-1020 by artificial neural network

This study consists of two cases: (i) The experimental analysis: Shot peening is a method to improve the resistance of metal pieces to fatigue by creating regions of residual stress. In this study, the residual stresses induced in steel specimen type C-1020 by applying various strengths of shot peening, are investigated using the electrochemical layer removal method. The best result is obtained using 0.26 mm A peening strength and the stress encountered in the shot peened material is ?276 MPa, while the maximum residual stress obtained is ?363 MPa at a peening strength of 0.43 mm A. (ii) The mathematical modelling analysis: The use of ANN has been proposed to determine the residual stresses based on various strengths of shot peening using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R² values are 0.996112 and 0.99896 for annealed before peening and shot peened only, respectively. Similarly, these values for testing data are 0.995858 and 0.999143, respectively. As seen from the results of mathematical modelling, the calculated residual stresses are obviously within acceptable uncertainties.     

A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.     

Wire antennas optimized using genetic algorithm

In this work, wire antennas are designed to jam GSM frequencies using genetic algorithms. These antennas are designed to block communication at 3-band GSM frequencies. They are planned to be mounted on a vehicle and therefore are modeled on a ground plane. Jammer antennas designed in this work are composed of wires, placed on two square dielectric frames, perpendicular to each other. Genetic optimization routines are developed on MATLAB environment for the designs carried out in this work. Electromagnetic simulation program called SuperNEC, which analyses antennas by the method of moments, is used to determine the antenna performances and is called by the developed GA routines. The purpose of the antenna optimization is to obtain low VSWR values and omni-directional radiation pattern near ground at θ = [70° 80° 90°] planes at all GSM frequencies, since the targets will be near ground for this application. Original and interesting antenna designs are obtained as the result of genetic optimization, and are presented in the paper.