Reviewerly/exHarmony huggingface dataset:
Fractal control with anti-windup effect applied on the design of a 2000W power flyback source

This paper presents detailed information for the optimal design of a 2000w flyback source, together with the study of the application of a PID fractal control based on a PID control implemented with operational amplifiers under the assumption of capacitive effects of a fractional nature. Several strategies are proposed for obtaining a simplified second-order model of a flyback source operating in open loop. Besides, a simplified model, based on trans-conductance and transresistance of non-linear elements for the implementation of the behavior of the diode, is presented. In addition, the anti-windup effect of electronic type is incorporated, which acts upon the source inability to limit the voltage in the face of sudden increases in load resistance. This same strategy is implemented by software to validate the fractal controller avoiding the voltage boundary violation.
Machine Learning in Authentication of Digital Audio Recordings

The field of audio forensics has seen a huge advancement in recent years with an increasing number of techniques used for the analysis of the audio recordings submitted as evidence in legal investigations. Audio forensics involves authentication of the evidentiary audio recordings, which is an important procedure to verify the integrity of audio recordings. This chapter focuses two audio authentication procedures, namely acoustic environment identification and tampering detection. The authors provide a framework for the above-mentioned procedures discussing in detail the methodology and feature sets used in the two tasks. The main objective of this chapter is to introduce the readers to different machine learning algorithms that can be used for environment identification and forgery detection. The authors also provide some promising results that prove the utility of machine learning algorithms in this interesting field.
Structural Optimization Analysis and Design of Telescopic Boom for Bridge Inspection Vehicle

In this paper, it is used to simulate and analyze the telescopic boom structure of a bridge inspection vehicle by HyperWorks software, to solve the problem that the bridge inspection vehicle is heavier and the stability is slightly insufficient, due to the increase of operating parameters. According to the analysis results, it is carried out to optimize the boom by using topological optimization. Compared with the previous, the weight of the telescopic boom is reduced by 16%, the stiffness is increased by 5%, the strength is increased by 10%, and the stability of the whole vehicle is increased by 2.7%. The simulation results are in good agreement with the test results, which proves the safety and reliability of the optimization scheme.
The Future of Money: Central Bank Issued Electronic Money

The modern day society driven by a variety of electronic devices and high-speed internet is changing its perception and practice of paper currency, mode of economic and financial transactions, and so on. The usage of cash is increasingly reducing because of the ease of payments facilitated by cards, mobile phone apps and contact-less chips, online payment systems, etc. Furthermore, cryptotokens (cryptocurrencies), such as bitcoin, have fueled the interest of society and policymakers in investigating the usefulness and limitations of a central bank-backed electronic fiat currency. Also, blockchain/distributed ledger technology, the technology enabling the cryptotokens, has gained a lot of attention from almost all the sections of the society for its ability in providing a decentralized transaction verification process while maintaining the features similar to the traditional cash currency. This chapter presents an overview of the concepts and potential features, potential primary models of issuing electronic fiat currency, as well as the key design principles from a technical perspective and a high-level architecture of a central bank-backed electronic fiat currency. We present the key aspects of currency in modern society, the advancements in the field of financial technologies and how could these be harnessed to launch a central bank-backed electronic fiat currency. We also discuss the position of various central banks and governments on cryptocurrencies, blockchain technology, and the initiatives related to issuing electronic fiat currency.
Enhanced blocking block area method for segmentation of continuous speech

Abstract Segmentation is one of the important stages in the speech recognition in the type of continuous speech. The segmentation phase serves to break the sentences into words that can be recognized by the computer. The quality of segmentation results can affect the results of the recognition that is done. This research examines the dynamic threshold used in the process of continuous speech segmentation and also proposes an Enhanced Blocking Block Area method in the Indonesian language domain. Three algorithms were compared (K-Means, Fuzzy C-Means, and Otsu) to find the best dynamic threshold and add morphological operations and overlapping to the blocking block area method to obtain the best segmentation accuracy. Based on the results of the research, the Fuzzy C-Means algorithm provides the best threshold results compared to the other two algorithms. By using the Fuzzy C-Means algorithm with the addition of morphology and overlapping, this study can improve the accuracy of continuous speech segmentation in the Indonesian Language from 24% to 90%.
Certificate-Based Encryption Resilient to Continual Leakage in the Standard Model

The security for many certificate-based encryption schemes was considered under the ideal condition, where the attackers rarely have the secret state for the solutions. However, with a side-channel attack, attackers can obtain partial secret values of the schemes. In order to make the scheme more practical, the security model for the certificate-based encryption which is resilient to continual leakage is first formalized. The attackers in the security model are permitted to get some secret information continuously through the side-channel attack. Based on the certificate-based key encapsulation scheme, a novel certificate-based encryption scheme is proposed, which is resilient to the continual leakage. In the standard model, the new scheme we propose is proved to be secure under the decisional truncated q -augmented bilinear Diffie–Hellman exponent hard problem and the decisional 1-bilinear Diffie–Hellman inversion hard problem. Additionally, the new scheme can resist the chosen-ciphertext attack. Moreover, a comparison is performed with other related schemes, where the proposed solution further considers the continual leakage-resilient property and exhibits less computation cost.
Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P2P News Tagging

Peer-to-Peer news portals allow Internet users to write news articles and make them available online to interested readers. Despite the fact that authors are free in their choice of topics, there are a number of quality characteristics that an article must meet before it is published. In addition to meaningful titles, comprehensibly written texts and meaningful images, relevant tags are an important criteria for the quality of such news. In this case study, we discuss the challenges and common mistakes that Peer-to-Peer reporters face when tagging news and how incorrect information can be corrected through the orchestration of existing Natural Language Processing services. Lastly, we use this illustrative example to give insight into the challenges of dealing with bottom-up taxonomies.
New Evidence of the Influence of Post-materialism Orientations on the Financial Markets Mechanisms

The study comes with new empirical evidence of the impact of the sociocultural values on the financial market mechanisms. The need to test materialism and post-materialism cultural theories is leading us to a study that takes into consideration an analysis period, from 2000 to 2014, for a sample of 14 countries with markets having socio-mature economies. The data used for our study is grouped into three waves. The objective of the study is to determine the post-materialism degree in 14 mature markets having direct connection with the cultural dimensions within the same markets. We intend to observe whether the mechanisms of the mature markets are under the influence of post-materialism factors and if yes, to what extent. As dependent variables, we will use different cultural dimensions offered by the World Bank. The results lead us to conclude that the presence of post-materialism is positively associated with socio-mature economies, furthermore, citizens with a predominance of post-materialism values have better skills to invest in financial markets. The results show that the cultural determinants behave as evidence of post-materialism and the post-materialism has the ability to influence the change of values and believes.
Image Compression Using Deep Learning Based Multi-structure Feature Map and K-Means Clustering

Image compression play significant role in the data transfer and storage. Recently, deep learning has achieved tremendous success in various domain of image processing. In this paper, we propose a multi-structure Feature map-based Deep Learning approach with K-means Clustering for image compression. We first use a modified CNN to select a multi-structured region of interest MS-ROI feature map by using several stacked of convolution layers then compress the image by integrating MS-ROI map with K-means. We can establish through experimental results that the proposed approach perform better compared to traditional K-means clustering approach.
Proposal of an educational strategy to bring students in telecommunications and networks back to the fundamentals of the discipline

In this article, we propose a strategy based on the introduction of the Internet of Things in the training of technicians and engineers in telecommunications and networks. This strategy aims to make the learner aware of the fundamental concepts of IoT from the start of their training. Indeed, an IoT network is subdivided into three layers called perception, network and application. The perception layer consists of electronic components such as temperature sensors, cameras, presence detectors, actuators, etc. The network layer is the heart of IoT. It makes it possible to transmit the information collected by the perception layer to the entities of the application layer. Finally, the application layer groups together the hardware and software elements used to save and process all of the data collected. Thus, the IoT becomes a more dynamic personalized method for developing skills in electronics, data transmission and programming among new learners.
ESYNDIAG: A Fuzzy Expert System for Eight Syndrome Diagnosis in Traditional Vietnamese Medicine

A fuzzy rule—based expert system ESYNDIAG is presented for eight syndrome diagnosis in Traditional Vietnamese Medicine combining positive and negative rules. After designing and building a suitable inference engine for this system, efforts have been committed to create effective knowledge base consisting of more 800 positive rules for confirmation of conclusion and of more 100 negative rules for exclusion of the same conclusion. How the rule base is constructed, managed and used are focussed on for diagnosis of eight syndromes in Traditional Vietnamese medicine such as Yin syndrome, Yang syndrome, Superficial syndrome, deep syndrome, Cold syndrome, Hot syndrome, Deficiency syndrome, Excess syndrome. The inference engine shows how to combine positive and negative rules. The first evaluation of ESYNDIAG is presented by the traditional medicine expert’s group in Vietnam and confirmed that ESYNDIAG diagnoses with a high accuracy.
PROMISE: PRoposing an Ontological Model for developing collaboratIve SystEms

Currently, there is a great necessity in the organizations to support communication, collaboration, and coordination — important aspects that characterize collaborative systems—between its workers and enterprises; in order to simplify and improve their production processes. However, the development and maintenance of these systems are very complex. Although several proposals to develop them have been made, they usually lack theoretical models, which allow specifying and creating both group and interactive activities in a conceptual and formal way to sustain the requirements of group work. Therefore, this paper PRoposes an Ontological Model for developing collaboratIve SystEms (PROMISE), it tries to be a guide for the analysis, design, and implementation of such systems in a formal, explicit manner. This model is based on an ontology, created using OWL (Web Ontology Language), providing a model of knowledge about in what way entities should be used and combined to control the execution of a set of orderly steps to develop these systems. Furthermore, this ontology has been validated through a set of academic’s projects, showing be great usefulness to developers.
The Effect of Chart Height and Color Saturation and Lightness to Graph Comprehension

Color is an important property of visualizations especially in the comprehension of complex line charts while chart size measured as distance between data points, is significant when dealing with numerous data points that are used for comparison. Multiple time-series line (MTSL) graphs are a common tool in visual representation of data but only a few studies explored the effect of visual properties on its comprehension. This study evaluated the effect of color properties (saturation and lightness), and vertical distance (chart height) in the comprehension of multiple time-series line graphs. It was posited that color palettes with higher saturation levels and charts with higher vertical distance improve comprehension. Comprehension was measured in terms of accuracy and response time using a discrimination task that required participants to find which time series possessed the highest value at a point. Six sets of multi-hued palettes with varying lightness and saturation were developed using colors equidistant in the color wheel. Two levels of chart heights were used to evaluate vertical distance. A total of ninety-two (92) subjects took part in the experiment. Results showed that when color saturation alone was considered, the highest saturation level was established to have the best user performance. Nevertheless, when the interaction of saturation with lightness was factored in the study, findings show that when colors with high level of lightness are used, they should have lower saturation levels and vice versa. This implies that there should be a balance between lightness and saturation when using colors in data visualizations. Vertical distance did not have a significant effect on comprehension, but a larger size generally resulted in better performance.
A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

Internet made a bigRathi,R. revolution in the real world and thus poses so many challenges to the researchers by generating an enormous amountAcharjya,D. P. of data. The data generated contains an enormous amount of unwanted information. Before processing with such a dataset, the important features present in the dataset must be retrieved. The feature selection process is important because the performance of a model built for the purpose of classification, prediction or clustering depends mainly on the number of relevant features present in the dataset. In this proposed work, the real coded genetic algorithm is used to find the important features by considering the fuzzy rough degree of dependency as its fitness function for finding out optimum features for agricultural dataset, iris dataset and Pima Indian diabetes dataset. The experimental results show that the proposed work produces relevant features by maintaining classification accuracy.
A Multiagent Approach To Web Service Composition Based On TROPOS Methodology

Web services are nowadays providing users with a diverse range of services. They are mostly delivered through web applications. It is often the case that a single atomic web service cannot fulfill the demands of users. Rather, many simple atomic web services may have to be composed and form a complex one in order to handle users' growing requests properly. Regarding the overall structure of web services as passive software components, they might fail to succeed properly, facing new types of requests. Recently, the concept of multiagent systems inspired many solutions in various research fields. In the web service composition domain, using smart agents so as to composite web services appropriately, leads to a complex, dynamic, and flexible service that meets different quality metrics altogether. In this study, a multiagent-based solution to web service composition is proposed using the “TROPOS” methodology that handles incoming requests based on constructing task dependency graphs.
Modified RC-4 Algorithm Against FMS Attack

RC4 is the simplest and most widely adopted stream cipher which finds its application in Wired Equivalent Privacy Protocol (WEP) and Wi-Fi-protected access (WPA). Being the simplest available cipher, RC4 is vulnerable to a number of security threats and attacks. In this paper, we have focused on one such vulnerability of RC4, which makes the cipher prone to Fluhrer, Mantin, and Shamir (FMS) attack and proves the insecurity of RC4 algorithm. To overcome FMS attack, a new variant of RC4 named RC4FMS has been proposed and compared with the existing RC4 variants. Performance analysis has been done in terms of processing intricacy (PI), processor cycles, run time, throughput, energy consumed, and security. Modification in RC4 has been made aiming to remove the trade-off between security and performance. It is observed that the proposed algorithm does not increase the computational complexity as increased with existing RC4+ and other variants, but provides better security. Encryption time of the proposed scheme RC4FMS is 0.16 s (PRGA) which is comparable to the conventional RC4 and less than the other existing RC4 variants. The proposed modified algorithm removes the initial byte bias and makes the algorithm resistant to the FMS attack.
Clustering Standard Errors at the "Session" Level1

Session-specific features of a laboratory experiment, if those exist, do not disappear by clustering standard errors at the session level. Randomly ordering or counterbalancing sessions to deal with sampling issues, cannot justify clustering the standard errors at the session level. The experimental design, reflected on the researchers’ intention, should primarily determine the clustering level. In a typical controlled laboratory experiment where subjects make choices in the same environment repeatedly, clustering at a participant level is intended by the experimental design, and standard errors could be larger (that is, a statistical inference could be more conservative) when clustered at the individual or decision-group level than the session level. It implies that clustering standard errors at the session level can lead to false-positive treatment effects if it is mistakenly chosen. Having a small per-session sample to increase the number of sessions could yield undesirable heterogeneities that are hard for the experimenter to control or observe.
Time-Varying Perfect Control Algorithm for LTI Multivariable Systems

In this paper, a new approach to perfect control design is proposed. The novel application of polynomial degrees of freedom in calculation of generalized matrix inverse has enabled a new branch of perfect control-oriented robust scenarios. Crucially, the obtained closed-loop system has revealed to be internally time-varying even though the general input-output behavior is still time-invariant. Simulation instances made in Matlab/Simulink environment show some interesting peculiarities covering the perfect control speed and energy.
An Analysis of Machine Learning Approach for Detecting Automated Spammer in Twitter

With the evolution of social media, information sharing in online is turning into ubiquitous each and every day. Various facts are propagating thru online social networks along with each the wonderful and negative. In this paper, our essential focal point is to resolve terrible or incorrect data such as rumors. Rumor is a serious problem in online social media. The wrong rumors can create many problems in society; hence, it is important to block these rumors. First in this paper, we have compared Naive Bayes algorithm and logistic regression for deciding which algorithm is better for spam classification. Then, we have proposed a model for detecting spammers in twitters and automatically blocking those rumors by hindering an assured subset of the nodes. In order to decrease the influence of the rumors, nodes are allotted an acceptance time threshold. If a user posts the rumor more than a particular threshold, the user gets automatically blocked. We have created a Java-based Web application using tomcat SQL server, JavaScript html and CSS, like Facebook where a user can search for friends, accept friend requests, chat with them, change profile pictures and post messages. In this paper, we used a supervised learning technique known as Naive Bayes Algorithm for blocking of the users. The analysis of users blocked per month can be also done which will help in study of users and rumor details.
Predicting grade-tonnage characteristics of undiscovered mineralisation: application of the USGS Three-part Undiscovered Mineral Resource Assessment to the Sandstone Greenstone Belt of the Yilgarn Block, Western Australia

The United States Geological Survey (USGS) Three-part Undiscovered Mineral Resource Assessment provides a framework for estimating undiscovered mineral endowment. Previous studies that applied the Three-part Assessment to estimate the undiscovered orogenic gold endowment of the Sandstone Greenstone Belt, Western Australia, have relied upon dated or expert-derived grade-tonnage models. Here, several assessments are conducted using local grade-tonnage models, comprising known orogenic gold deposits within the entire Yilgarn Block and several individual terranes with contrasting lithosphere- to terrane-scale characteristics. These models are generated through comprehensive review of historical exploration, resource and production data. Based on these models, the Sandstone Greenstone Belt is estimated to contain significant undiscovered gold mineralisation, with a median total endowment of between 166 and 298 t gold, and mean of 167–319 t gold. Although these updated grade-tonnage models provide an approximately 80 per cent variation in predicted gold endowment, it is still evident that the belt remains an under-explored region within the Yilgarn Block, Western Australia.