Reviewerly/exHarmony huggingface dataset:
Stream Sediments Geochemistry of the Nyambaka Drainage System Northern Cameroon (Central Africa): A Target for Mining Exploration

Mining exploration in the Nyambaka area Northern Cameroon still at reconnaissance stage.In this study, ten active stream sediments samples were collected for geochemical survey.These stream sediments were analyzed by inductively coupled Plasma/ Mass Spectometry (ICP/MS), the data set obtained was transformed into a standard formation an excel database, and was subject to statistical treatment using IBM SPSS statistics 21 for 33 chemical elements to highlight the relationship between the stream sediments geochemistry, the region lithology, the geological processes and eventual primary mineralization.The data were analyzed using multivariate statistics.R-mode analysis produced a five-factor model behind multi-elements associations which account for 93.40% of the total variance in the data with the following metals associations: Sc-Mo-V-In-Ga-Cu-Cr, Ba-Sr-Ag-Cr-Cu-Co-Be-Ni-Y-Zn-V, Ga-Hf-Zr, Sn-Au, As-Cd.Sn-Au association indicates that Au mineralization is link to Sn mineralization.As-Cd, Cu Ga, In, Mo suggested that the paragenesis represent others sulphidation events that is barren with respect to Au.The spatial distribution of Sn-Au and As-Cd factors show that these factors are mores express in the centre part of the study area; they can be link to granitic rocks, defining a primary gold target.A detail mining investigation have to me carry out in that area to highlight the primary mineralizations.
Correlation Analysis Among Innovation, Risk-taking, Competitive Aggressiveness, and Proactiveness on Company Performance in the Covid-19 Situation

This research originated from the Covid-19 pandemic that began in February 2020.Since the Covid-19 pandemic, the Indonesian economy has experienced a significant decline, especially the MSMEs in Indonesia, so the authors are interested in examining the correlation among innovation, risk-taking, competitive aggressiveness, and proactiveness on company performance.This study uses a sample of MSMEs domiciled in Surabaya.This research method was carried out using purposive sampling by distributing questionnaires through the Google form application.This study uses classical assumption tests (normality test, multicollinearity test, and heteroscedasticity test) and paired sample test.This study's results are that innovation, risk-taking, competitive aggressiveness, and proactiveness variables have a strong effect on Company Performance.Research results are in line with previous research (
Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

&lt;span&gt;Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.&lt;/span&gt;
Spatially Resolved Experimental Modal Analysis on High-Speed Composite Rotors Using a Non-Contact, Non-Rotating Sensor

Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 μrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.
MEDIA MANIPULATION DETECTION SYSTEM USING PASSIVE AGGRESSIVE

Due to the extreme growing use of social media and online news media, there has been a rise in fake news recently.It has become much easier to spread fake news than it was before.This type of fake news, if widely circulated, could have a significant impact.As a result, it is necessary to take steps to reduce or distinguish between true and false news.We design a system to verifying such type of news and extract correct news or provide correct news corresponding to the fake news.On a text-based dataset, we give an overview of false news detection using various classifiers such as Passive Aggressive Classifier, Random forest, Logistic regression and decision tree classifier gets better results, as seen by the work done.Also top ten recommendations corresponding to the real news is displayed through our proposed model.
Developing Interactive Digital Mathematics Book with Multi Representation Approach for Deaf Students

Mathematics learning objectives for deaf students are not different from normal students, but their learning approach is not the same. Learning materials for deaf students should be made simple and equipped with multi representation. With the development of ICT, multi representation has become easy and inexpensive. Therefore, the purpose of this study was to develop an interactive digital book with a multi representation approach for deaf students. This study was carried out using 3 initial stages of 4-D research and development design (define, design, develop, and disseminate). Media feasibility is determined based on the results of expert validation, practicality test, and a passing grade. The average score of book validation by the material expert was 2.92 (97.3%), learning design expert was 2.97 (99%), media expert was 2.99 (99.7%), and language expert was 2.78 (92.7%), so it can be said that the book met the validity criteria. The average score of the practicality test by students was 4.24 (84.8%) and teachers were 4.35 (87%), so it can be said that the book met the practicality criteria. The average passing grade was 83.34%, so the book met the effectiveness criteria. By meeting validity, practicality, and effectiveness criteria, it can be concluded that the digital book met the feasibility criteria as mathematical learning materials for VII-grade deaf students.
Framework to Manage Software Quality on IIoT Apps

Abstract The research presents a Framework based on CMMI and ITIL methodology. It complemented with an ASQ management type, which allows incorporating metrics that directly involve both the development process of embedded systems based on IoT architectures and oriented to industry 4.0 or IIoT. The methodology is based on the development of software for Embedded Systems using SysML modeling. The scheme to allows incorporates necessary mechanisms that enable the management of the System related to service continuity and easy adaptation to changes, considering service connectivity and compatible with an industrial environment. This scheme allows quantifying through indicators each one of the quality levels, providing at the same time the practicality and the systematization of the processes, which in the end translates into a consistent mapping for the standardization of the quality related to the lines of business that imply the treatment of IoT data at industrial level. The methodology permits are simplifying the need to deal with rapid software changes, one of the essential tasks of software systems development and maintenance teams. Due to the lack of standards and regulated work schemes for the development of IoT architectures; therefore, it is intended to establish implicitly the metrics and an essential work scheme to regulate the design, progress, and development of IIoT architectures, until defining an optimal state of implementation.
Behavioural Evidence Analysis

Recent developments in digital forensics (DF) have emphasized that along with inspection of digital evidence, the study of behavioural clues based on behavioural evidence analysis (BEA) is vital for accurate and complete criminal investigation. This paper reviews the existing BEA approaches and process models and concludes the lack of standardisation in the BEA process. The research comprehends that existing BEA methodologies are restricted to specific characteristics of the forensic domain in question. To address these limitations, the paper proposes a standardised approach detailing the step-by-step implementation of BEA in the DF process. The proposed model presents a homogenous technique that can be practically applied to real-life cases. This standard BEA framework classifies digital evidence into categories to decipher associated offender characteristics. Unlike existing models, this new approach collects evidence from diverse sources and leaves no aspect unattended while probing criminal behavioural cues, thus facilitating its applicability across varied forensic domains.
A Novel High-order Linguistic Time Series Forecasting Model with the Growth of Declared Word-set

The existing researches have shown that the fuzzy forecasting methods based on the high-order fuzzy time series are better than the fuzzy forecasting methods based on the firstorder fuzzy time series.The linguistic forecasting method based on the first-order linguistic time series which can handle directly the word-set of the linguistic variable have been examined by Hieu et al.This paper examines a novel model of high-order linguistic time series with the growth of declared word-set.A procedure for forecasting the enrollments of University of Alabama and Lahi crop production of India is developed based on the proposed model.In the proposed forecasting method, the high-order linguistic logical relationship groups will be established and utilized to calculate the forecasted values based on the quantitative semantics of the used words generated by the hedge algebras structure.The experimental results show that the forecasting accuracy of the proposed high-order forecasting method is better than their counterparts and the growth of the word-set of the linguistic variable is significant in increasing the accuracy of the forecasted results.
The Student Perspective: The Impact of Podcasts on EFL Students Listening Comprehension

Technological developments are very beneficial for foreign language learning, even for teachers and students themselves. One of them is the podcast media which has proven to be an easy-to-use and effective means of learning English. The research sample was taken from 30 high school students in Indonesia by using a questionnaire to explore students' perceptions about learning to listen to foreign languages ​​using podcasts. The data collection of this research uses descriptive techniques, namely describing and explaining a phenomenon or case study in schools. Most of the students taken from the questionnaire strongly agreed that podcasts are something that is effective in helping improve listening skills because they are considered to have interesting and fun features that stimulate student enthusiasm and motivate students to continue learning. However, podcasts can prove to be a tool to improve students' listening skills in EFL classes and students recommend teachers to use this media.
Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.
Impact of Technology on Higher Education

We are pleased to publish the first regular issue of &lt;em&gt;Higher Learning Research Communications (HLRC)&lt;/em&gt;, which follows the Special Issue, &lt;em&gt;Implications of COVID-19 on Higher Education&lt;/em&gt;. The pandemic continues to impact higher education; in many parts of the world, it is still resulting in classes being held remotely, while in other regions, classrooms are slowly reopening to face-to-face or hybrid instruction. As the manuscripts in the special issue, as well as those in the current issue, reflect, the implications of the pandemic on higher education will be far-reaching. We continue to encourage authors to submit empirical research and essays that document ways that higher education will be different as we emerge from the pandemic. The focus of the HLRC is on issues whose importance has been magnified by the pandemic, including digital teaching and learning, higher education and the public good, and the preparation of students in key 21st-century employability skills.
Facial Emotion Recognition with Sparse Coding Descriptor

With the Corona Virus Disease 2019 (COVID-19) global pandemic ravaging the world, all sectors of life were affected including education. This led to many schools taking distance learning through the use of computer as a safer option. Facial emotion means a lot to teacher’s assessment of his performance and relation to his students. Researchers has been working on improving the face monitoring and human machine interface. In this paper we presented different types of face recognition methods which include: Principal component analysis (PCA); Speeded Up Robust Features (SURF); Local binary pattern (LBP); Gray-Level Co-occurrence Matrix (GLCM) and also the group sparse coding (GSC) and come up with the fusion of LBP, PCA, SURF GLCM with GSC. Linear Kernel Support Vector Machine (LSVM) Classifier out-performed Polynomial, RBF and Sigmoid kernels SVM in the emotion classification. Results obtained from experiments indicated that, the new fusion method is capable of differentiating different types of face emotions with higher accuracy compare with the state-of-the-art methods currently available.Keywords— Face Recognition, Principal Component Analysis, Speeded Up Robust Features, Grey Level Co-Occurrence Matrix
Formalization of the meaning. Part 1

Every area of knowledge builds its descriptions using concepts. At the same time, the definition of concepts given through their characteristic features is widespread. On this basis, both basic mathematical and many philosophical concepts are built. The concepts which a person uses are subject to similar properties, and their nature is the nature of definitions. Numerous attempts to create strong artificial intelligence are based on the corresponding paradigm. The article attempts to substantiate the need to use the contextual-semantic paradigm to explain the work of the natural brain and to create a strong artificial intelligence. A formal model describing the meaning is presented, and its connection with the known data on the functioning of the brain is given. It is shown that a context can be created around each concept, which can be the bearer of the concept's meaning. The context allows one to move away from using a set of features to recognize the phenomenon behind a concept. The context turns out to be a point of view associated with the concept, in which the description of the surrounding world changes. Knowing the rules of these changes, one can not only model different points of view, but also determine which of them create adequate interpretations. At the same time, the presence of an adequate interpretation in the context of the phenomenon serves as a criterion for the presence of this phenomenon.
A MATLAB App for calculating the age-dependent degree of erosion of monogenetic scoria cones from DEM data

We present a MATLAB App aimed to characterize the degree of erosion of monogenetic scoria cones from two independent sets of morphometric parameters. One quantifies the shape and size of undulations in DEM level contours through a novel parameter, the Average Erosion Index (AEI), using Elliptical Fourier Descriptors to calculate the wavelength spectra of undulations in selected contours over an ample range of resolutions of the database. The other is a more conventional set of parameters characterizing the vertical profiles of cones. This algorithm permits correcting those parameters for terrain inclination. These methods aim for a consistent assessment of the erosion-related relative ages of volcanoes in extensive monogenetic fields, from DEM data. The age-related degree of erosion is revealed by two main parameters: The Average Erosion Index (AEI), calculated from the Elliptic Fourier Descriptor spectrum of a scoria cone contour lines; and the aspect ratio of a scoria cone vertical profile, considering terrain inclinations, if it is assumed that all cones acquired the same morphology at the time of their formation eruptions. The Method provides erosion metrics from a sizable range of DEM resolutions. For age-related applications, it is recommended to use resolutions better than 12 m.
Peningkatan Ketrampilan Kreativitas Desain Grafis Digital Siswa SMU Menggunakan Aplikasi CANVA pada Ponsel Pintar

During the COVID-19 pandemic, teaching and learning activities must be carried out online from home. The development of technology today really helps the online teaching and learning process, there are many tools / software that can be used. Tools / software commonly used in online teaching and learning activities are Ms. Word, Ms. Power Point, and Virtual Lab. Another impact of technological developments coupled with pandemic conditions has led to more interactions between humans being carried out online through internet intermediaries on cellphones or computers. Currently cellphones do not only function as a medium of communication, some work that was previously completed using a computer / laptop can now be completed using a smart phone. So that cellphones can be used for more positive activities and support the teaching and learning process, sharing is carried out with students through sharpening design creativity through mobile applications on smart phones. In this activity, students hone graphic design skills using the CANVA application to support the teaching and learning process. The enthusiasm of students is quite high, as evidenced by the work produced in the form of personal profile designs, extracurricular activities and logos. Students can practice well the material presented. The school welcomes this activity, and wants to form an extracurricular Graphic Design at SMA Dharma Wanita so that it becomes a forum for student creativity in graphic design.
Generating E-commerce Product Titles in Portuguese

This paper explores how Natural Language Processing techniques can be integrated to solve real-world problems in the e-commerce scenario. We address the issue of having high quality information products offered to customers in a marketplace platform, composed by thousands of sellers producing original content in multiple languages, following different SEO and cultural assumptions. We propose an NLP pipeline to generate high quality titles products in Portuguese.
Verifying the Audio Evidence to Assist Forensic Investigation

Audio forensics is a field in forensics that is used to authenticate, enhance, and analyze audio files to aid in solving different crime investigations. Audio as a forensic evidence must be enhanced and analyzed to be admissible in courts of law. But more importantly, it must be authenticated in order to prove that it is authentic and no manipulations were done to it. In this paper, an overview on audio forensics is presented, previous related work to this topic is shown, and methodologies for audio enhancement and authentication are explained along with audio tampering ways and signatures presentation.
What Can We Actually See Using Computer Aided Detection in Mammography?

The main goal of this study was to compare the results of computer aided detection (CAD) analysis in screening mammography with the results independently obtained by two radiologists for the same samples and to determine the sensitivity and specificity of CAD for breast lesions. A total of 436 mammograms were analyzed with CAD. For each screening mammogram, the changes in breast tissue recognized by CAD were compared to the interpretations of two radiologists. The sensitivity and specificity of CAD for breast lesions were calculated using contingency table. The sensitivity of CAD for all lesions was 54% and specificity 16%. CAD sensitivity for suspicious lesions only was 86%. CAD sensitivity for microcalcifications was 100% and specificity 45%. CAD mainly 'mistook' glandular parenchyma, connective tissue and blood vessels for breast lesions, and blood vessel calcifications and axillary folds for microcalcifications. In this study, we confirmed CAD as an excellent tool for recognizing microcalcifications with 100% sensitivity. However, it should not be used as a stand-alone tool in breast screening mammography due to the high rate of false-positive results.