Reviewerly/exHarmony huggingface dataset:
Scheelite of the Bystrinsky skarn-porphyry Cu-Au-Fe deposit (Eastern Transbaikalia, Russia): genetic implications

The first results of the study (methods of mineragraphy, cathodoluminescence (CL), electron-probe microanalysis (EPMA) and laser-ablation inductively-coupled plasma-mass spectrometry (LA-ICPMS)) of scheelite from quartz-molybdenite and quartz-carbonate-sulfide vein-veinlet (porphyry type) are considered, as well as the magnetite-sulfide massive, veinlets and disseminated (skarn type) mineralization of the skarn-porphyry Cu-Au-Fe deposit of Bystrinskoye (East Transbaikalia) one of the largest gold-copper porphyry ore-fields in Russia. It has been established that scheelite, being not the main mineral of the deposit ores, is found almost everywhere, which makes it possible to identify its key features reflecting the specific features of the genesis, both of different types of mineralization and the deposit as a whole. It is shown that scheelite from different types of ore mineralization has clearly determined individual characteristics, differing in prevalence, ensembles of associated minerals, color of CL and fluorescence in the UV-light, composition and concentrations of macro- and microelements, as well as the nature of REE-spectra. These distinctive features testify to a significant difference in the conditions for the formation of the studied ore types and reveal the dependence on the physicochemical and compositional parameters of the mineral-forming medium, which makes it possible to consider scheelite as a fundamentally important genetic indicator of the evolution of mineral formation patterns. Concentrations of Mo in scheelite, the type and form of REE-spectra, which are generally, determined both by the inheritance of the mineral-forming fluid chemistry and the peculiarities of isomorphic occurrence of REE in the structure of the mineral, and variations in the redox properties of the mineral-forming fluid, are of key importance.
Shape Memory and Superelasticity Authors Presented at IMAT—The Virtual Edition

the difficult decision to cancel due to the COVID-19 pandemic.
Grey Wolf Optimization on Modeling Saliency Detection in Stereoscopic 3D Images

Till now, several saliency detection models have been introduced for numerous applications in multimedia developments. Anyhow, specific appliances of stereoscopic imaging require improvements in saliency detection schemes for extracting the salient regions in a precise manner. The saliency detection (SD) model faces numerous shortcomings like intricacy in natural images and minor-scale patterns on salient objects. Hence, this paper endeavors to attain the SD model in two levels; Feature extraction (FE), for which Gaussian kernel model is utilized to extort the features and depth SD, for which Gabor Filter (GF) is exploited to attain the depth of saliency map. Accordingly, the adopted scheme optimizes ‘2’ coefficients such as, feature difference betwixt image patches H in feature evaluation and also fine scale c from which the accurate detection is attained. For optimization purpose, a well-known optimization termed Grey Wolf Optimization (GWO) is exploited and evaluated for varying values of a , and the results are attained with respect to ROC (i.e. Receiver Operating Characteristic), and statistical analysis.
Basic Equivalence of the Four Formalizations

The four formalizations presented in Chap. 1 are shown to be basically equivalent.
Selling Records

Third-party data brokers collate and synthesize governmental information, create markets of criminal record consumers, and sell criminal record data as commodity. These companies contribute to digital punishment through delivering consumer-friendly alternatives to bureaucratic governmental sources of data. These databases are populated into web search results and downloadable reports and then sold to an anxious public, made ever more anxious by creative marketing tactics. At the same time, these companies embrace the glossy, self-assured culture of tech start-ups to further convince the public that not only do they have extremely important information but also they have the best means of delivering it.
Application of Agile methodologies for software development

In this work explored evolution of software lifecycle models firstly to lightweight and then to agile software development methodologies, and factors that have led to a search for ways to improve approaches to software development. Also compared "outdated" development designing approaches with modern flexible and made conclusions whether the advantage of the latter over the firsts is absolute and whether or not they and only they should be used in practice or maybe older approaches still have their advantages and it is too early to exclude them.
Wlamc Scheme To Monomodal Brain Icon Registering

An advanced method to get the better of the challenges of medical icon registering is anticipated, using Wang Landau Adaptive Monte Carlo (WLAMC) approach. The Wang-Landau (WL) technique is a distinct Adaptive Monte Carlo (AMC) procedure that has engendered a lot of curiosity in the medical icon registering (IR) technique owing to some remarkable simulation implementations. The multiresolution centered process for registering multimodal imageries by using AMC structure is the efficient existing approach for icon registering anywhere arbitrary finding aspirants are caused as of an all dimensional outcome spread out of probable geometric conversions on each of the repetition by using a testing approach. The remedy applicants generated are appraised instituted upon the Pearson category-Seven inaccuracy amongst the stage instants of the pictures in deciding the key contender by the deepest faulty left over. Even the AMC approach is efficient, it has some drawbacks and it can be eliminated with this proposed WLAMC approach. The experiments are performed on the real time medical imageries and the comparison of results illustrates that the approach put forward performs considerably well for monomodal brain images than the previous icon registering techniques.
Lightwave Driven Valleytronic Qubit Flip

We demonstrate a flip of the valley pseudo-spin in a tungsten-diselenide monolayer on a sub-5fs timescale using strong terahertz fields. The resulting sideband emission reveals Coulombic many-body effects.
Brachiopods classification based on fusion of contour and region based descriptors

In this paper, we propose a contour-region based shape descriptor for Brachiopods classification by using a combinations of Fourier descriptors and R-transform extracted from Radon transform. Fourier descriptors is supported by the well-developed and well-understood Fourier theory and are a powerful features for the recognition of two-dimensional connected shapes. We used the stable and complete Fourier descriptors proposed by Ghorbel to present the contour information. To depict shape interior content we used the R-transform. It is advantages lies in its low computational complexity and geometric invariance. We compared the proposed descriptor with curvature scale space, R-transform and Ghorbel descriptors using city block distance measure and our Brachiopods database. We present the experimental results to reveal the performance of the proposed descriptor which is independent on the starting points and efficient.
ICMLANT 2020 Preface

shares his greetings wishes to congress.The congress report was read out by Dr. Ajay Mousmi Chorasia, SMIEEE and she share that conference attracts 183 submissions from various part of country.The TPC after the three reviewer reports identify only 26 manuscripts for the presentation and recommended submission to IEEE xplore library.In her vote of thanks she appreciates the two days schedule and wish warm greeting to one and all.The two days virtual congress was divided into the four sessions.The four keynotes from four countries e.g., Mexico, El Salvador, Vietnam & India who were invited to share their knoweldeful insight.Day one two keynote speaker Dr. Jose Sebastián Gutiérrez from Mexico and Dr. Nguyen Ho Quang from Vietnam speak on IOT and Industry 4.0 and their speech were appreciated by participants.Total 14 oral presentations were completed on day one.Day two started by Session and total 12 paper were presented in two
CONTEXT BASED SENTIMENT ANALYSIS APPROACH USING N-GRAM AND WORD VECTORIZATION METHODS

Consumer reviews are key indicators for product credibility and central to almost all product manufacturing companies to align and alter the products to the needs of customers.Using Sentiment analysis approach, these reviews can be analyzed for positive, negative and neutral feedback.There are many techniques designed to do Sentiment analysis and opinion mining in the past on drug reviews to study their effectiveness and side-effects on the people.In this paper, an approach is presented which is a combination of context-based sentiment analysis using Ngram and tf-idf word vectorization method to find the sentiment class -positive, negative, neutral and use this sentiment class in Naïve Bayes and Random Classifiers to predict user review emotion.Our validation process involved measuring the model performance using quality metrics.The results showed that the proposed solution outperformed conventional sentimental analysis techniques with an overall accuracy of 89%.
São Tomé e Príncipe – New Banknotes

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 11 p. 22799B-22799B Policy and Practice São Tomé e Príncipe – New Banknotes First published: 02 January 2020 https://doi.org/10.1111/j.1467-6346.2019.09259.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue11January 2020Pages 22799B-22799B RelatedInformation
Topics and Subjects in German Newspaper Editorials: A Corpus Study

AbstractWe discuss an approach to identifying aboutness topics in authentic text, viz. a corpus of German newspaper editorials. On the basis of dedicated annotation guidelines, 176 texts have been labelled with topics, and this layer has been added to the corpus, which we made available in the search engine ANNIS3. Since syntactic annotation is also available, aboutness topics can now be correlated with grammatical subjects, an element that is generally taken to often coincide with topic. As expected, the two notions mostly coincide, but we found 26 % of cases where topics are not subjects. In a qualitative analysis, we suggest a classification of the reasons for these mismatches. Furthermore, we present experiments on automatically classifying aboutness topics given various features of the noun phrases and their context, and we find that it is difficult to beat a simple “subject implies topic”, but we do so by 2 points in the F1-measure.
Certifiable, Mobile Configurations for the Look aside Buffer

The investigation of randomized calculations has imitated hash tables, and current patterns propose that the hypothetical unification of connection level affirmations and model checking will soon rise. We forget these calculations until the point when future work. In this position paper, we contend the reenactment of postfix trees. In this position paper we utilize homogeneous correspondence to discredit that the notable nuclear calculation for the combination of wide-territory organizes by Maruyama is Turing finished.
Image Restoration by Linear Regression for Gaussian Noise Removal from Natural Images

Image restoration improves the features information of degraded or corrupted image. The degradation of image because of addition of noise when acquiring the image. Many algorithms are developed by many researches. In this paper image is corrupted by Gaussian noise to generate degraded image. The image is restored from this degraded image by supervised learning based algorithm. Few images are considered for training the dictionary with each element of size 9x9. The degraded image is considered patch by patch for restoring the patch from the trained set of images by support vector machine. The quality assessment of the image done by comparing the quality matrices like mean square error, root mean square error, peak signal to noise ratio, structural similarity index measure and feature similarity index measure. In this paper the images are considered are cameraman, house, Lena, Barbara and Parrot
E-Books

Although much of the e-book market and world is in flux, one thing is safe to say: the success of e-books is difficult for publishers and resellers to predict, and it has been from the beginning. If there is one thing the publishing establishment...
Electric Conductivity in Silicone-Carbon Black nanocomposites: percolation and variable range hopping on a fractaL

The acoustoelectric effect is the generation of a acoustoelectric current in a non-biased device by a piezoactive acoustic wave.This effect has recently attracted the attention of researchers as possible method to control the motion of massive objects in the quantum regime.We suggest use this effect for development of high sensitive nanoelectronic sensors.The sensitivity could be increase through the use of modulation mode of operation based on the acoustic pilot signal and the transition from quasi-stationary measurement to measurement at the modulation frequency.The acoustic delay line based on shear-horizontal acoustic wave of zero order (SH 0 ) with resonant frequency 2.77 MHz in Y-X LiNbO 3 plate was produced.The nanostructure consisting of nanowire with size 20nm x 180nm was placed in the center between IDTs.The volt-ampere characteristics of this nanowire in presence and at the absence of the acoustic wave were meazured by picoampermeter.The analysis has shown that presence of piezoactive acoustic wave is influenced on electric current in a nanowire.
Peramalan Jumlah Kasus Infeksi Saluran Pernafasan Akut (ISPA) pada Laki-Laki Tahun 2019 dengan Metode ARIMA

The ARIMA method is an approach that forms the most powerful model in analyzing time series data, and the studies given are very thorough. This method can be modeling data stationary or not stationary, it can be seen from sine wave shape of the plot ACF. This method is used because obtained the results are better and more accurate. According to WHO, Acute Respiratory Infection (ARI) is an infectious disease that causes can be morbidity and mortality. A four million people die each year. This study used secondary data so that it is categorized as non reactive research. The population were cases of Acute Respiratory Infections (ARI) at Jagir Health Center Surabaya which were recorded in 2013 to 2018 (monthly). The dependent variable is the cases of Acute Respiratory Infection (ARI), while the independent variable is time. The model that was obtained from the ARIMA method is a model (2.0,1). The forecasting result is 354 cases in 2019, the forecasting has increased from 2018 to only 313 cases. It was a suggestion that the forecasting result can be a reference for developing a policy and a new program or improvement in previous program so that the number cases of ARI at the Jagir Health Center can be resolved properly.
Term Categorization Using Latent Semantic Analysis for Intelligent Query Processing

With the rapid improvement in the field of social networks, a huge amount of small size texts are generated within a fraction of a second. Understanding and categorizing these texts for effective query processing is considered as one of the vital defy in the field of Natural Language Processing. The objective is to retrieve only relevant documents by categorizing the short texts. In the proposed method, terms are categorized by means of Latent Semantic Analysis (LSA). Our novel method focuses on applying the semantic enrichment for term categorization with the target of augmenting the unstructured data items for achieving faster and intelligent query processing in the big data environment. Therefore, retrieval of documents can be made effective with the flexibility of query term mapping
Development of technologies for reducing the error of traditional algorithms of correlation analysis of noisy signals

The authors analyze the factors affecting the errors in the estimates of the correlation functions of the noisy signals when using traditional calculation algorithms. It is shown that the sum noise of the noisy signal in many cases consists of the noise caused by external factors and the noise caused by the initiation of various defects during the operation of control objects. For this reason, in order to eliminate the error in the results of the correlation analysis of noisy signals, it is necessary to create algorithms and technologies for determining the estimate of the noise variance and the cross-correlation functions between the useful signal and the noise. For this purpose, appropriate algorithms and technologies are proposed that open up the possibility of reducing the error of traditional technologies for determining the estimates of correlation functions. With the purpose of reducing the error of the results of correlation analysis, a technology is proposed for determining the approximate equivalent samples of the noise of the noisy signals. It is shown that using the equivalent noise samples, it is possible to obtain results that are identical to the results of using real samples of the noise in the correlation analysis of the same signals. Moreover, by extracting the equivalent noise samples from the noisy signal, the equivalent samples of the useful signal are also determined, which allow determining the estimates equivalent to the estimates of the correlation functions of the useful signal. At the same time, having equivalent noise samples and useful signal samples, the estimates of the cross-correlation function between the useful signal and the noise are determined. The study have shown that despite certain errors in the equivalent samples compared to the true samples, with a sufficient observation time using equivalent samples, the error of traditional technologies for the correlation analysis of noisy signals can be significantly reduced. These technologies can also be used to correct errors in the results of the analysis of experimental data in information-measuring and other measuring complexes and systems, which will significantly improve their metrological characteristics.
Resurrecting a lost gem in the desert

Visitors to the World Heritage Site Mada'in Saleh in Saudi Arabia will need a reliable water infrastructure. ANDRITZ Ritz, a subsidiary of the international technology group ANDRITZ, was chosen to supply the necessary pumps for this endeavour in 2014.
Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments

After years of experience of
A Visualization Recommendation Approach based on Machine Learning

As an important means of data analysis, data visualization is used by more and more people.For most people who don't have visualization technology expertise, data visualization has some Visualization recommendation aims to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select.This paper proposes a visual recommendation method based on machine learning, which can learn the most meaningful visualization results from many visualization practice datasets and mark them.Firstly, 22 data features and corresponding meaningful visualization types are extracted from 30 real visualization datasets.Then, binary classifiers are used to train the classification model, from which we can learn meaningful visualization and use crowdsourced testsets to test the accuracy.Finally, the results of multiple classifiers are fused to vote for multiple meaningful charts in the datasets.Experiments show that this method can effectively learn the meaningful visualization types in datasets, mark and recommend them to users.
Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Indoor place recognition is a crucial and challenging field of computer science. It is widely used in robotics and computer vision for various applications. The challenges in indoor place recognition comes from the fact that recognizing localized places like office, corridor, and others may fall under various environmental effects of weather, illumination and others. In this paper, an indoor place recognition and localization system is proposed. The system utilizes the great recognition capabilities of Convolutional Neural Network (CNN) and AlexNet with the use of feature image for training. The feature images are constructed using Histogram of Gradient (HOG). The main contribution of this work is the use of 2D feature constructed image from HOG instead of the scene image used with CNN. The proposed system was compared to other previous systems, in which, it achieved better recognition accuracy when tested on COLD and IDOL standard indoor image datasets.
Virtual Reality based Classroom

In today’s world, we come across a number of technologies. We have acquired majorities of technology as we come across them. In the same way, Virtual real ity is also a computer based technology that accumulates various input and output devices by allowing the user to interact and experience with the virtual environment which seems to be real but not reel. After taking inspiration from the technology, we ar e eager to present Virtual Reality based Classroom. In this virtual classroom, the user can do all the things as done in the real classroom. Virtual Reality Based classroom will be working for the user like it stimulate the reality of its presence. The us er’s will be present physically via VR device with all other users to interact. When users will mount the VR device on their head, they will be redirected to their default dashboard (3D environment, which will be customizable) which will be constituting of different menu and that will be controlled via Pensil (3d mouse, used for interaction in the virtual world). When user will be online via server connection, then the connected users will be notified and hence, they all will be presented in a particular en vironment which will be different and all of them will be present their physically (their avatar). The designing will be done according to the requirements of the users. This will be the future of education that will mainly erase the problems of the stude nt comprising distance learning, economical and deprivation of the practical aspect of knowledge.
Systems with Assistance Capabilities

This chapter considers competence assessment and competence improvement by a meta-system as somewhat like process control, which consists of the generic tasks dynamic analysis, interpretation, diagnosis, and repair of the processes running in the object system if required. Knowledge-based systems are used to solve difficult problems. The assumptions are very abstract and totally implementation independent. The description at the meta-level is modular and can be far better understood than by a direct extension of the object system. By basing the meta-components on abstract descriptions of the object system, the knowledge model, they become immediately reusable for all those object systems that correspond to this model. One may argue that the implementation of a meta-system is less efficient than the direct extension of the object system; or the competence specialists may executable in the new target environment. Because the meta-component, too, is a knowledge-based problem solver, the same modeling technique can also be used at meta level.