Reviewerly/exHarmony huggingface dataset:
The Technology of Media Culture usage in the Educational Environment: Development of Creative Self-Actualization

The article looks at the experience of building a media environment at school as a system of self-education and self-development for modern teenagers. The authors present a model of development of teenagers’ creative self-actualization in the media educational environment, the technology of media culture use in the educational environment and an original diagnostic methodology for determination of the degree of development of school students’ creative self-actualization based on assessment of independent work carried out by teenagers (media projects creation) taking into account emotional and semantic visualization of information and the symbolism of associative-semantic connections to evaluate the quality of the conducted work. As a result of the performed experiment, the authors identified personal characteristics shown by school students in the course of their work on media projects and developed a typology of dominant activities that includes four types of individual characteristics observed in school students: analytical, informational, proactive and synthetic types. Based on this typology the authors have determined the relevance and necessity for differentiation of work with teenagers of different ages in various forms of media educational environment and development of their individual educational paths. Correspondingly, a four-stage mechanism of introduction of a teenager into a media educational environment has been suggested providing for the integration of art into the educational environment. The article presents the results of a pilot project that was carried out in state budgetary educational institutions located in Moscow: School no. 1253, School no. 171, School no. 89 and in the children's creative center Trubetskoy Estate in Khamovniki. The article is intended for scientists, teachers, pedagogues and methodological experts who deal with the issues of media education.
Machine Learning for Detecting Credit Card Frauds

Credit card frauds has been a threat that has evolved as a major source of loss for the financial sectors. It has been seen in the different parts of world causing loss of billions of dollars. It is also a area which needs attention from the researchers as the task of fraud detection can be automated using the different machine learning classifiers and data science. If the frauds model encounter the fraudulent transactions it will raise an alarm to the system administrator. The paper proposes a model which uses the machine learning classifiers to detect the fraudulent transactions. The classifiers used in the paper are SVM (Support Vectore Machine ), Isolation Forest and Local Outlier. The focus of the research is to detect the fraudulent transactions to 100% and also we emphasise on the fact that no normal transaction should be detected as fraud wrongly. The process starts with preprocessing the data and then the classifers are applied. The results from each classifers is evaluated to check the one with the better performance. The performance can be increased with use of deep learning algorithms but with the rise in expennses.
MFCC Based Speech Retrieval

This paper presents an approach for speech retrieval. The feature being used in this approach is MFCC. This approach does not use any phoneme recognizer or Speech to text tool hence it can be used for other languages as well leads to the problem of speech retrieval (SR). This method retrieves ranked audio files containing spoken text in response to a given speech query. In this paper indexing methods are described which represent the contents of the spoken documents. The indexing methods, which are based on the output of phoneme recognizer, take account of speech recognition errors. While in this paper, speech documents are directly compared with the speech query based on MFCC. Thus, reduced the overhead of conversion from speech to text.
Auto-feed Hyperparameter Support Vector Regression Prediction Algorithm in Handling Missing Values in Oil and Gas Dataset

There are many types of equipment involved in the oil and gas industry.However, they have their useful lives and will degrade over time.This issue prompts to be solved using predictive analytics to predict the Remaining Useful Life (RUL) of equipment.In the historical data, however, there are missing values due to broken equipment sensors probes and different time rate sensors.This can significantly affect the prediction results and making it less accurate due to missing value and become a challenging issue.Missing values in datasets is a synonymous problem in data mining which could lead to an incomplete dataset, making inaccurate predictions results in machine learning prediction processes.This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches.SVR however is inferior in accuracy and thus this paper discusses the usage of an optimized SVR with Evolved Bat Algorithm (EBA) to handle the missing value accurately with high execution time.The paper also presents the topic of missing values in the dataset, as well as compares the performance of the optimized SVR with the original SVR in terms of accuracy and execution time while handling missing values in a large dataset.The novel optimization-based artificial intelligence algorithm proposed in this paper implies an improved way to overcome a real engineering challenge i.e. handling missing values for better RUL prediction, hence bringing great opportunities for the domain area.
An ANALYSIS OF STUDENTS’ SPEAKING ABILITY IN RETELLING STORY ON DESCRIPTIVE TEXT AT THE SECOND GRADE STUDENTS OF SMP YLPI PEKANBARU

ABSTRACT&#x0D; The aims of this research was to know students' ability in speaking and to find out which aspects are the most dominant problems for the students at the second grade students of SMP YLPI Pekanbaru. The focused of this research was to analyze the students' problems in speaking based on five components of speaking which are pronunciation, grammar, vocabulary, fluency and comprehension. The design of this research was a qualitative research. This research was conducted at SMP YLPI Pekanbaru. The population of this research was the second-grade students at SMP YLPI Pekanbaru which consists of 54 students. The researchers chose class II.2 as a sample with the total number of the students were 28 students through purposive technique sampling. The researchers used speaking test as the instrument of research. This research conducted via Whatsapp by recording the students’ performances when retell descriptive text. Then the researcher transcribed the videos into scripts and asked raters to provide the scores. From the results of this study, the researcher found that the students' speaking ability in retelling story had the average score of 3 and categorized as a good level. The pronunciation component, the students’ mean score was 2.8 points, while grammar got 3.5 points. Following that, vocabulary component was 3.2 points, conversely on fluency, it was 2.7 points. The last, the students’ average score on the component of comprehension was 2.8 points. Finally, it can be concluded that the most dominant problem faced by students in speaking was the fluency component.&#x0D; Keyword: Students’ Speaking Ability, Retelling Story, Descriptive Text
SELF ASSESSMENT IN WRITING SKILL OF INTROVERTED STUDENTS

Self assessment is one of parts in assessment process. This process helps the students evaluate their own work and learning process. This study try to implement one of self assessments, namely Type Token Ratio (TTR). It calculates word repetition by deviding the total number of words in a text (tokens) by the number of individual words. This self assessment will be used to only one type of personalities, namely introverted students. The aims of the study are describing the types of token that most used by the introverted students, describing the types of token rarely used by the introverted students, and describing the introverted students opinion about Type Token Ratio (TTR) for their writing. The descriptive qualitative method was implemented in this study. the sources of the data were taken from twenty students of first semester in English Educational Program of UNISDA Lamongan. The findings showed that types of token most used by the introverted students are: nouns appear 466 times, verbs appear 266 times, pronouns appear 266 times and prepositions appear 251 times. Whereas, they rarey used: adverbs appears 171 times, determiners appears 159 times, conjunctions appear 123 times, adjectives appear 94 times and interjections appear 7 times. Based on the result, it can be concluded that the introverted students most often use noun and most rarely use interjection in their writing. Almost all of students think that TTR is good and help them to measure their vocabulary variety.Key words: Self Assessment, Writing Skill, Introverted
Mathematical music theory of embodied acoustics of Ikoro music using beat-class theory

Through mathematical representation (beat-class theory) of embodied acoustics (psychoacoustics) the predominance of the musical tradition of the Ikoro drum with the Igbo's can be traced from the past, into the present and forecasted into the future. The Ikoro music tradition has been viewed as an integral and indispensable part of Igbo culture at large (Onwubiko and Neilsen, 2019). The major musical instruments that accompany most Igbo music are percussional, such as, ichaka (beaded-gourd rattle), okpokolo (wooden claves), and igba (membrane drum) and are characterized by successions of rhythmic interchange unlimited to interesting pitch, timbre, rhythm and meter by employing shifted accents, non-accented rhythms and syncopations. In order to understand Ikoro music located in the listener's experience (embodied psychoacoustics), we demonstrate how mathematical music theory (beat-class theory) provides the means to articulate the “mind and body” response to the stimulus of sound. By examining the aural tradition of Ikoro music of the Igbo's through visualizations and sonifications of beat-class theory using ski-hill graphs and circular cyclic graphs, “hidden” musical structures are revealed which possess significant cultural significance.
Multiple evidence combination for web site search using server log analysis

In this thesis, a novel method is proposed to improve the retrieval performance by using web server logs. Web server logs are grouped into different sessions and then terms are extracted for each page in the session, meanwhile weights of terms are calculated. A new representation of web page from user's perspective is generated after going through the entire log. The new representation and the anchor-based representation are combined with original text-based representation. Two combination methods: combination of document representations and combination of ranking scores are investigated. In the experiments, three measurements are employed to evaluate the performance and the results show that for Cosine Similarity model, the highest improvement on top-10 precision is around 38%, for Okapi model, the hightest improvement is around 13%, for TFIDF model, the highest improvement is around 48% and for Indri model, the highest improvement is around 17%.
Psychological and pedagogical aspects of education of tolerance in elementsry school

The article reveals the peculiarities of the development of cognitive, motivational and emotional spheres among junior pupils, in particular their social status, ethnic and gender identity, the formation of a picture of the world, moral representations and moral behavior as a basis for the education of the tolerance of elementary school students.
Stylistic Analysis for the EFL Classroom

Literature can be both used and useful in foreign language classrooms.Sometimes the objective is incidental learning, where learner enjoyment of the work is foremost, and where the contention is that learners acquire language incidentally while enjoying a work of literature, as is the case with extensive reading.Other times the goal is more deliberate learning where there is a conscious focus on language teaching, as when a specific language point from the text is highlighted and practiced.One type of deliberate learning which is not as much utilized in the EFL context as other methods is literary stylistics.Stylistic analysis focuses on the linguistic mechanics of a text (primarily at the lexical, phonological, grammatical, and discourse levels) not primarily as a means to give form-focused instruction in any of those areas but as a means to enhance appreciation of a work of literature by understanding how the work achieves its overall communicative effect (though foreign language competence can improve in a stylistic analysis).This paper will describe what stylistics is, give a short example of a stylistic analysis of a text, an excerpt from Charles Dickens' Bleak House, and then discuss how stylistics can be used in the EFL classroom.
Bitcoin: An Analysis of the Accounting and Finance Research Published in International Journals.

The results corroborate with the law of Bradford (many journals with few articles) and will meet with the Lotka's Law (80% of the authors are logged only one study on the theme).The majority of international studies performed, in short, comparative studies with indexes and mathematical properties, using statistical tools such as analysis of variance and regression.As contributions from research, it is expected that has contributed to a theme little explored in Latin journals, paving the way for the emergence of new studies of Brazilian researchers interested in the subject. ResultsThe research quantified a population of 337 articles with 760 authors on the theme the bitcoin;The leading journal is "Finance Research Letters and Economics Letters."The survey shows that 62% of the papers were found in journals that presented a maximum of two articles on the topic.The exploratory studies seek to highlight the phenomenon of their creation and their main characteristics.It interprets the possible impacts of the emergence of cryptocurrency and its current condition.
THE GENERALIZED VARIANCE ALGORITHM APPLICATION IN MULTIDIMENSIONAL STATISTICAL CONTROL OF THE PROCESS BASED ON INDIVIDUAL OBSERVATIONS

In practice, the use of instantaneous samples for statistical control of the process is often impossible due to technical conditions or is not economically feasible. In this case, the process is monitored by individual observations. To control the average level of a multiparametric process based on the Hotelling algorithm, appropriate methods have been developed, but when controlling multidimensional scattering based on the generalized variance algorithm, a number of problems arise. Various approaches to the estimation of the generalized variance are proposed in the article, and their comparative analysis is carried out. The practical application of the proposed algorithms is also considered.
A novel pooling method for CNN model based on discrete cosine transform

Deep learning can be used to learn huge volume of data, which will be processed through hidden layers and according to the number of hidden layers ,filter size and numbers and the required computation cost is increased because of the size of raw data, this problem can be avoided by using pooling techniques, different method s are proposed to extract the basic features of the signal instead of all signal, but unfortunately this operation may introduce some noise or omission because of elimination important data from the signal.In this paper, A novel pooling method are proposed based on discrete cosine transform , this method is utilized DCT technique to reduce spatial redundancy of image by transform the spatial domain into frequency domain , which can preserve the most significant image information from the other coefficients, which represents the other details information of the image, so discard these less important coefficients.Its effect will be slight and this can reduce the eliminated information as compared with other methods.After applying DCT, we crop the most significant coefficients to be used in the reconstructed data by applying inverse DCT .then the result is combined in different methods with Max pooling and average pooling methods, this new structure can reduce the effect of discarding most important information and reduce the drawbacks of average and Max.pooling method.‫إل‬he results are proved that our proposed methods are outperformed some standard methods and can be used in more application.
Организация информационных потоков и управление ими как неотъемлемая функция аналитика

The article is devoted to the importance of the analyst's information work in the context of considering information analytics as flowing data analytics. The authors consider the problems of studying data flows, the laws of their formation and managing them, including their fine-tuning (modification, reorganization, adaptation), and pay a special attention to the organization of new flow that meet the research goals, analyst's personal daily participation in the process of collecting, processing, indexing and primary generalization of information.
Sensitiv – Designing a Sonic Co-play Tool for Interactive Dance

In the present study a musician and a dancer explore the co-play between them through sensory technology.The main questions concern the placement and processing of motion sensors, and the choice of sound parameters that a dancer can manipulate.Results indicate that sound parameters of delay and pitch altered dancers' experience most positively and that placement of sensors on each wrist and ankle with a diagonal mapping of the sound parameters was the most suitable.
Conscientious Ant Colony Optimization Based Support Vector Machine for Text Document Classification

Document classification indicates the keyword extraction and it become a thrust research in text mining research. The main purpose of keyword extraction is to classify the documents in a more efficient manner. Misclassification of documents may lead the results to worst case. Hence, there exists a need for optimization to precede the document classification more efficiently. In this paper Conscientious Ant Colony Optimization based Support Vector Machine is proposed to classify the documents. Different keyword extraction methods are available for extracting the contents from documents. Proposed classifier is ensemble with selected keyword extraction methods to increase the classification accuracy. Results shows that the proposed classifier has got better accuracy when ensemble with different keyword extraction methods. The results show that the proposed classifier has better performance in terms of Classification Accuracy and F-Measure, than baseline classifiers.
Multi Language Application of Previously Developed Transcripts Classifier

Developing classification models and using them on another similar data-set represents a challenging task. We have adapted an existing data analysis pipeline that classified Spanish educational video transcripts from Universitat Politècnica de València (UPV) to process English video transcripts from TedTalks. Performed adaptation and experimental results were performed also in educational context as in the initial study. We found that the process needs minor adaptations of the data analysis pipeline, but the overall results is highly dependent of the size of the data-set, and especially on the class balance. Parametrisation of the pipeline may open the way of deploying the model in other similar contexts by transfer learning.
Enhanced Efficiency of Deep Learning Analysis on Digital Content

To expand the exactness of an previous face recognition acknowledgment framework on similar littler training data according to the prerequisites of present day. In particular in delicate areas. The philosophy had been embraced by consolidating greater than a one calculation. The element location capacity of the harr course alongside Ada-lift to bring to the Bi-linear CNN in which a similar little training data will create relative outcome on the greater training data.
Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Cloud computing is a delivery model of IT resources such as computing servers, storage, databases, networking and software over the Internet. It offers the resources as services based on demand with more faster, flexible and economies of scale. The major challenges in the cloud computing are resource allocation and workload management due to the scalability of the cloud users and the services deployed in it. Even though there are various approaches available to manage workload and resource allocation, unfortunately most of them fail to mange it properly. This paper proposes a Reinforcement Learning based Enhanced Resource Allocation and Workload Management (RL-ERAWM) approach to increase the performance of cloud with large number of tasks and users. It implements the Q-Learning approach which effectively considers arrival rate of the requests and workload of the virtual machine. Experimental results prove that the proposed method alleviates the performance of task scheduling and workload management process compared with other approaches in terms of response time, makespan and virtual machine utilization.
Cover

Meteoritics & Planetary ScienceVolume 54, Issue 1 p. i-i Cover Cover First published: 13 January 2019 https://doi.org/10.1111/maps.13148AboutPDF 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 onEmailFacebookTwitterLinked InRedditWechat Abstract Volume54, Issue1January 2019Pages i-i RelatedInformation
Multi-Criteria Decision Making for Risk Management of Feasibility Phase of Solar Park in India

This paper aims at application of Multi-Criteria Decision Making (MCDM) tool like fuzzy VIKOR for decision making process amongst available alternatives for a solar power plant project in India.Here the available three alternatives are "application of both preventive and corrective mitigation measures" which would involve more additional project cost but reduces the project risks in terms of time and cost overrun, "application of preventive mitigation measure", which involves less additional project cost but increases the project risks and "application of corrective mitigation measure", which also involves less additional project cost and increases the project risks.The decision makers considered for this study are client and main contractor respectively.It has been observed from the analysis of this study that after application of fuzzy VIKOR the alternative application of both preventive and corrective mitigation measures would be recommended to the project authorities.The decision makers client and the main contractor need to provide and arrange for the additional funding for implementation of both preventive and corrective mitigation measure so the successful completion of the project within stipulated time and cost frame is enhanced.