Reviewerly/exHarmony huggingface dataset:
Implementation of Steganography in Speech Signal Using Spread Spectrum Modulation and Differential Method in Wireless Communication Systems

In this paper, two steganography techniques to improve speech quality and clarity in communication systems and bandwidth expansion factor of the signal are designed and evaluated through simulation study. The two methods are: direct sequence modulation and differential methods. In order to achieve the desired speech quality, the speech signal parameters such as speech power ratio and the number of segments in a message were changed. Direct sequence modulation was performed by multiplying the source signal into a pseudo-random sequence. The resolution of the image depends on the size of the segment and the signal strength ratio. The second method is using differential methods. Similar to the first method, speech signal is divided into specified number of segments and for each one, one bit of message is embedded. Comparative and experimental studies are performed to evaluate the effectiveness of the proposed methods, where peak signal to noise ratio(PSNR) is used to assess the speech quality. Results of the study suggested that in the direct sequence method, the higher the speech power ratio, the clearer the hidden image in the signal. The result is better speech quality in communication system receivers. In the differential method, the higher number of segments results in a higher PSNR value. The proposed method are implemented and simulated in MATLAB.
Tsunami and tsunami warning

This chapter is an overview of tsunami excitation and propagation and a review of ongoing efforts to extend and improve tsunami warning systems. Devastating tsunami from large earthquakes in the last 20 years provide examples of the power of these destructive hazards. We review the historical catalogs of tsunami, which identify earthquakes as the source for most tsunami. We discuss the excitation of tsunami by shallow subduction-zone earthquakes and the physics of tsunami propagation across ocean basins. The chapter includes a discussion of tsunami observation technology, which is important for the reliability of tsunami warnings, under development since the mid-20th century. The chapter closes with a list of giant (magnitude greater than 8.8) earthquakes since the 1500s.
Human Activity Recognition Based on Wavelet-CNN Architecture

Human activity recognition (HAR) has become an important research field in pervasive computing and has attracted extensive attention from researchers at home and abroad. However, traditional recognition methods rely heavily on artificial feature extraction, which greatly affects the generalization ability of the model. Therefore, this paper designs a deep learning model based on wavelet transform and convolution neural networks. Firstly, the waveform data of multi-channel sensor is decomposed into low-frequency and high-frequency components by wavelet transform (WT) after window sliding segmentation, which are combined as the input data of network model. Then, convolution neural networks with different convolution kernels are used to extract multidimensional features efficiently, and the max-pooling layers are used to filter the interference noise caused by human unconscious jitter. Finally, through the output classification of full connection layer, the accurate recognition of human activity state is realized. In order to verify the effectiveness of the designed model, we evaluate the performance of the model from the convergence speed, loss and accuracy of the model, and compare it with the more advanced recognition model on the public dataset of OPPORTUNITY. Finally, the proposed architecture Wavelet-CNN achieves 91.65% F-measure and has higher activity recognition ability.
A Gaussian-inverse Gamma mixture Distributions and Expectation-Maximization Based Robust Kalman Filter

In the study of the state estimation for the systems with unknown time-varying non-Gaussian noises, the existing robust Kalman filters (RKFs) perform well. However, the calculation loads of these RKFs usually are large and their performance is easily affected by the roughly preselected initial process noise covariance matrix (PNCM). To solve the problems, a new RKF is proposed. Firstly, a Gaussian-inverse Gamma mixture distribution is developed to model the inaccurate noises and a simple hierarchical Gaussian (HG) model is constructed. Then, the expectation-maximization (EM) method is applied to realize the adaptive adjustment of the prior scale matrix of the prediction error covariance. Based on the HG model and EM, a robust KF is derived, where the variational Bayesian (VB) approach is used to jointly estimate model parameters and an alternate iteration method is employed to reduce the computation time. Finally, our filter performance is tested. Compared with the existing state-of-the-art robust filters, the proposed filter has slightly better estimation accuracy and significantly less computation load. Meanwhile, the filter performance is almost not affected by the selection accuracy of initial PNCM.
Literature Review

The analysis of sentiments has been a popular research topic towards social media data processing (Dashtipour et al. in Cogn Comput 8(4):757–771, 2016, [1]). The majority of sentiment analysis research is using the English language, but there is a gradual increase towards the multilingual aspect.
Big Data Mining Using Collaborative Filtering

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.
Digital Forensics

Digital forensics is the science of recovery and investigation of material found on digital devices. It started in correlation to computer / cybercrimes. In reality, digital forensics use is not limited to criminal investigation in the strict sense of the term, it can proactively encompass the investigative element of every facet of our digital life. The very nature of technology by way of strict and infallible documentation allows for its all pervasiveness. It has the potential to provide an intelligence of the functional type, which generally seems to be missing besides adding a new dimension to criminal investigation.
A Tale of Two Agile Requirements Engineering Practices

There are a number of concerns in agile software development, including requirements engineering. There are different types of agile requirement, of which currently the most common forms are use cases and user stories. The use cases and user stories have different origins, both in space and in time, but by being among the practices of scenario-oriented requirements engineering (SORE), they are not entirely unrelated. The purpose of this chapter is to situate use cases and user stories in the context of each other. This is done by means of a conceptual framework for systematically comparing use cases and user stories. The understanding of similarities and differences between use cases and user stories have pedagogical as well as practical implications.
E-Vote: Online Voting System for Campus Election

E-voting means the voting process in elections by using electronic devices. E-voting promises the possibility of a convenient, easy and safe way to capture and count the votes in an election. This project provides the specification and requirements for e-voting using the CodeIgniter platform. The CodeIgniter platform is used to develop e-voting software. A voter may need to register for a particular election, and after successful registration an OTP will be generated and sent to the voter’s registered mobile number or email id. If OTP entered is valid, the voter is directed to the login page. Else if OTP entered is invalid, voters will be instructed to re-enter OTP. In this work, a new e-voting system is proposed that ensures voter confidentiality and accuracy of voting, thereby providing an important framework based on unique identification, which is the admission number of the voter and his/her password used at the time of registration. The database of eligible voters is already kept in the software to avoid malpractice at the time of voter registration. Hence the software provides security at multiple levels. An online solution is very useful as the information about the voters and the election committee is also made available to the people in this system.
Presentation Format

In the depictions of the selected works, there were a variety of presentation formats including screen-based, installation, performance, and musical compositions.
Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.
Real Time Object Detection and Recognition

In this paper, we are Aiming at the most unfortunate viable profound learning segment on little article identification. Here we present a strategy for recognition utilizing a organization. Also, characterizing the article utilizing classifiers and give a likelihood of conviction which coordinates the preinformational collection That is available in frozen_inference_graph and Coco. Names. In SSD the item is examined without a moment's delay and contrasted and informational index which are available as of now and afterward we scale up the pixel of picture and by changing it over to dark scale picture and coordinating the credits. At the forecast time, the profound neural organization creates a container and score for each article. Boxes of deferent proportions to various item. To make it closest to constant we are Using Single identification we give the info picture of 520*520 of size. This approach accomplished a 75.8% beating that is superior to YOLO and RCNN. After the article location part is done, we recognize the item and give the yield to a client. Until this time the discovery and acknowledgment are a must for each robotization task. We Are utilizing this innovation in computerized vehicles and in robots to distinguish and perceive objects in close to continuous. We gather the measurement and match it with the trait that is now prepared with managed learning.
Exploring Socio-Cultural Elements in Three Commercial English Language Learning Apps

Second language teaching and learning has experienced a change towards the use of mobile technologies inside and outside the classroom. The goal of this article is twofold: to compare and evaluate three different commercial English language learning apps (Duolingo, Babbel, and Busuu) that cover the four skills (speaking, writing, listening, and reading), and to analyze the learning theory supporting their design. These applications include in their homepage the option of interactive learning with friends. However, as this article shows, each app offers a different level of interaction and collaboration. The theoretical framework for this analysis is grounded on Vygotsky's Socio-cultural theory.
First and Second Order Similarity Learning for Clustering on Grassmann Manifolds

It is challenging to cluster multidimensional data (such as image sets or videos) with complex intrinsic relationships and nonlinear manifolds structure. Inspired by the self-expression models for clustering in the linear space, researchers proposed several effective clustering methods in Grassmann manifolds space. However, these methods in the manifolds space obtain the affinity matrix by the self-expression coefficient, which may fail to fully consider the local structural relationships of data points on Grassmann manifolds. To address these issues, a first and second order similarity learning model for clustering on Grassmann manifolds (GFSOSL) is proposed. To consider the local structural relationships of data points and their neighborhood structural relationships, we learn the first order similarity (FOS) and second order similarity (SOS) simultaneously. Further, to constrain the rank of affinity matrix properly, the nuclear norm and Frobenius norm simultaneous constraint is proposed. The experiments have been constructed on some common image datasets and video datasets and the results demonstrate that the performance of our proposed algorithm outperforms the state-of-the-art baselines.
Introduction

We live in a world that is built upon patterns. What is a pattern? The Oxford dictionary defines a pattern as a repeated decorative design. In the language of pattern recognition, however, a pattern has been described as an entity that could be given a name [36]. Thus, the bird, boat, buildings, and people that we see in Fig. 1.1 are all examples of patterns. Recognizing patterns in the environment is one of the fundamental signs of intelligent behavior.
Distance-Texture Signature Duo for Determination of Human Emotion

Previous Chaps. 2 , 3 and 4 consist of three feature descriptors individually distance signature, shape signature and texture signature and Chap. 5 considered as combined descriptor such as distance and shape signature (D-S). In course of Distance-Texture (S-T) signature, respective stability indices and statistical measures supplement the signature features with a view to enhance the performance task of facial expression classification. Incorporation of these supplementary features is duly justified through extensive study and analysis of results obtained thereon.
Character Segmentation

This chapter presents in detail an enhanced character segmentation algorithm that improves the detection efficiency of image-based emails using the hybrid approach of DWT and Hough transform methods with pixel count analysis technique; in addition, the researcher used spatial frequency cross-correlation to improve the processes of segmentation wherein text is segmented from the image-based email efficiently. The component of the algorithm and their functions are discussed. The experimental results of this algorithm are also presented and compared with related methods of the literature in terms of segmentation accuracy wherein the performance of the algorithm used is assessed.
Peacock: a Benchmarks Generation Framework for High-Level Information Fusion Evaluation

This work presents Peacock, a framework that aims at generating benchmarks for High level information fusion. Peacock makes it possible to generate several structured information sets, that are representative, coherent, diversified and controlled. The principle of Peacock lies in the generation of several information sets from one scenario. The scenario contains on the one hand, a storyboard of perfectly described events and a chronology of perfectly structured information. On the other hand, it contains communicating entities, organized in a network. These entities will alter the scenario events as well as the information exchanged. The modifications on the information consist in the introduction of imperfections (over-precision, imprecision, incompleteness, uncertainty, irrelevance, incomprehension) according to the entities communication behaviors. In this paper, we present the principles under the Peacock framework. We detail its characteristics and describe its implementation.
Measurement of Hysteresis Energy Using Digital Image Correlation With Application to Energy-Based Fatigue Life Prediction

A modified experimental method using digital image correlation (DIC), a noncontact optical method for measuring full-field displacements and strains, is used to interrogate accumulated fatigue damage for low and high cycle fatigue at continuum scales. Previous energy-based fatigue life prediction methods have shown that cyclic strain energy dissipated during fatigue acts as a key damage parameter for accurate determination of total and remaining fatigue life. DIC enables the collection of accurate strain energy measurements or damaging energy of complex geometries that would otherwise be exceedingly difficult and time consuming using traditional strain measurement techniques. Thus, the use of DIC to obtain strain energy measurements of gas turbine engine (GTE) components is highly advantageous for energy-based fatigue life prediction methods. Presented in this study is the experimental characterization of the cyclic strain energy dissipation as a means of predicting fatigue performance and assessment of damage progression of Aluminum 6061 subjected to fully reversed axial fatigue loading utilizing DIC. Validation of total and cyclic strain energy dissipation DIC measurements is accomplished with the simultaneous use of axial extensometery for direct comparison and implementation to strain energy-based life prediction methods.
Basic Inference

This chapter considers some basic problems of statistical inference that underly the rest of the problems addressed in this volume. Namely, it is shown how to construct consistent estimates of a distance between stationary ergodic process distributions. As an easy application of this construction, an algorithm for solving the so-called three-sample problem for this class of processes is presented. On the other hand, it is demonstrated that there is no asymptotically consistent test for the problem of discrimination (homogeneity testing) for stationary ergodic processes, even if one only considers binary-valued processes.
A Feature and Deep Learning Model Recommendation System for Mobile Application

Deep learning is becoming more and more popular in mobile applications. We also find apps using deep learning are more popular. For app designers and developers, it is particularly important to filter out certain features of corresponding scenarios and target user groups effectively from complex and diverse deep learning techniques, which is hard for developers. To solve this problem, we design a mobile deep learning application features and models recommendation system for app designers in this paper. Designers only need to input the description information of their app to acquire some appropriate features and models advice. Firstly, we analyze the feasibility and necessity of mobile application feature and model recommendation. Then the database of app details is built and sorted out to provide the basis for the recommendation system. Finally, we give out a new recommendation system specially designed for mobile apps on Google App Store, which mainly includes Labeled-LDA text classification and BERT text similarity matching combined with recommendation correction. The demonstration shows that our system has some reference value for mobile application developers.
COMPARATIVE ANALYSIS OOF PEDAGOGICAL TECHNOLOGIES OF PROFESSIONAL TRAINING OF GRAPHIC DESIGNERS IN UNIVERSITIES OF LEADING COUNTRIES OF THE WORLD

The worldwide trend in education is to train a professional who is able to respond quickly to the rapid development of information technologies that extend the graphic designer from the master of illustration, visual systems, advertising, and exhibition design to such areas of graphic design as moving graphics, interaction design, design animation, interactive design. The study found that the peculiarity of such training is the collaboration of educational institutions with enterprises, firms and institutions to make sure that the educational programs offered for students training meet the needs of production, and students really acquire the necessary knowledge, skills and competencies that are modern requirements for competitive graphic design specialist.
Online Automatic Service Composition for Mobile and Pervasive Computing

In the mobile and pervasive computing environment, automatic service composition faces the challenges of the highly dynamic network structure and limited computation resources. Existing approaches create service flows under the assumption that network context changes are slower than the time required to plan the composite service which is no longer set up in the mobile and pervasive computing environment. In this paper, we propose an online automatic service composition approach to dynamically construct service flows and interleave the planning and executing processes. Particularly, it introduces a novel distributed heuristic searching algorithm to determine solutions with limited knowledge bases and execute them in real time. Simulation results show that our proposed online approach reduces the composition time and performs faster composition time and higher composition success rates than state-of-the-art approaches in mobile computing environments.
Implementation and Testing Details of Document Classification

It is trivial to achieve a recall of 100% by returning all documents in response to any query. Therefore, recall alone is not enough, but one needs to measure the number of non-relevant, for example by computing the precision. The analysis was performed for 30 documents to ensure the stability of precision and recall values. It is observed that the precision of large documents is less than a moderate length document, in the sense that some unimportant keywords get extracted. The reason for this may be attributed to the frequent occurrence and its unimportant role in the sentence.
Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

Software evolution control mostly relies on the better structure of the inherent software artifacts and the evaluation of different qualitative factors like maintainability. The attributes of changeability are commonly used to measure the capability of the software to change with minimal side effects. This article describes the use of the design of experiments method to evaluate the influence of variations of software metrics on the change impact in developed software. The coupling metrics are considered to analyze their degree of contribution to cause a change impact. The data from participant software metrics are expressed in the form of mathematical models. These models are then validated on different versions of software to estimate the correlation of coupling metrics with the change impact. The proposed approach is evaluated with the help of a set of experiences which are conducted using statistical analysis tools. It may serve as a measurement tool to qualify the significant indicators that can be included in a Software Maintenance dashboard.