Reviewerly/exHarmony huggingface dataset:
FORMATION OF MEDIA COMPETENCE AS MEANS OF INFLUENCE ON EMOTIONAL, COGNITIVE, AND VOLITIONAL DEVELOPMENT OF PERSONALITY

The article analyzes the state of modern education, actualizes the need for its qualitative changes and priorities, new requirements for the organization of innovative educational activities of schoolchildren.Theoretical research of scientists on media competence and media socialization of students is analyzed.The essence of concepts «media competence of the student» and «media socialization» is deepened based on the reviewed literature.The article dwells on contradictions as factors of socio-psychological and educational problems, which indicate the understanding of the advantages and disadvantages of the usage of media in the learning process.It also researches the dependence of the world perception of high school students on their presentation of information.The authors highlight the advantages and disadvantages of using media in the learning process, the dependence of students' perception of the world on its representation by television, cinema, magazines, newspapers, radio and the Internet.Pedagogical approaches and conditions of successful formation of media competence of students, and which make it possible to create a favorable learning environment for emotional, cognitive, volitional development of young people are proposed.The essence and structure of media competence is
A local deviation constraint based non-rigid structure from motion approach

In many traditional non-rigid structure from motion ( NRSFM ) approaches, the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models. Aimed at solving this issue, a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation. In our work, an effective model is first constructed with two terms: the overall estimation error, which is computed by a linear subspace representation, and a constraint term, which is based on the variance of the reconstruction error for each frame. Furthermore, an augmented Lagrange multipliers ( ALM ) iterative algorithm is presented to optimize the proposed model. Moreover, a convergence analysis is performed with three steps for the optimization process. As both the overall estimation error and the local deviation are utilized, the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points. Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
Inverse-Computational Approach in the Problem of Dynamic Correction of Measurement Systems with Application of Integral Models

The development of modern systems of measurement, control, management and diagnostics is characterized by an increase in requirements for quality indicators, an increase in functionality, an increase in the complexity of research and design tasks in their creation. As a rule, the basis for the functioning of these systems are the methods and means of processing experimental data and computer implementation of mathematical models. In many practically important tasks, prompt assessment of the processes and phenomena under testimony of measuring instruments is of paramount importance. Similar tasks are known in practice as signal recovery tasksThe use of computer equipment for signal recovery makes it possible to use low-cost primary measuring transducers with low resolution in systems of measurement and control. The efficiency of the use of computing devices in measurement and control systems is primarily due to the low cost of their development in comparison with the improvement of the physical design of certain elements of these systems. Moreover, in some cases, such a possibility is limited not just by physical boundaries, but more so by economic factors.The development of methods of operational processing of information is characterized by an increase in the proportion of inverted tasks in the total list of solved problems by systems of this class. This implies that in principle, all the problems of mathematical modeling can be divided into two groups: direct problems, i.e. problems of analysis, when the known causes of certain processes and one has to find the consequences, and inverse problems, when the consequences are by physical boundaries and one has to find the reasons. One of the most important tasks of signal recovery is the task of structural correction of the characteristics of dynamic systems, which is constructing and using in the conversion channel or circuit of the system such blocks, which, due to their specially formed dynamic properties, transform in a necessary way the general dynamic properties of the system.
Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global dependencies. Prior researches have indicated that attention-based methods can capture long-range dependencies and further reconstruct the feature maps for better representation. Nevertheless, limited by the mere perspective of spacial and channel attention and huge computation complexity of self-attention mechanism, it is unlikely to model the effective semantic interdependencies between each pixel-pair of remote sensing data of complex spectra. In this work, we propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations from the perspective of space, channel and category in a more effective and efficient manner. Concretely, a class augmented attention (CAA) module embedded with a class channel attention (CCA) module can be used to compute category-based correlation and recalibrate the class-level information. Additionally, we introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism via region-wise representations. Extensive experimental results on the ISPRS Vaihingen and Potsdam benchmark demonstrate the effectiveness and efficiency of our HMANet over other state-of-the-art methods.
The use of online forums in the class of English Literature

A hands-on approach to the use of the online forums, formerly known as bulletin boards, in the teaching of literature at both BA and MA levels.Advantages, pitfalls, caveats and a few tips and tricks inspired by the author's over ten years' experience managing three such forums.
WhatsApp Goes To Classroom : Using WhatsApp to Foster Students’ Speaking Skill in Speech

Language learning by using technology has a great impact to improve language skills due to teenagers have close relationship with social network. Social network has opened up new opportunities of interaction and collaboration between teachers and learners. One of trends using social networking is WhatsApp.  WhatsApp offers various features including audio and video enable the users to take benefit for learning. WhatsApp is not only sending text message but files and message broadcasting. Even with WhatsApp, the learning and information sharing happen at the comfort when students are bad especially in Speaking Skill on Speech Subject. Students often feel not comfortable when they are delivering speech in front of many people due to some of them  are not accostomed to speak on stage. In this case, they can begin to show their performance in speech by using whatsApp through WhatsApp group. In addition it can save teacher’s time in assesment. This article aims to study the use of WhatsApp in English language classroom to optimize the function of the mobile phone for students’ speech. It can be said that the integration of WhatsApp into their education will be easy, fun, and useful.
Face Attribute Invertion

Manipulating human facial images between two domains is an important and interesting problem. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs. In this paper, we proposed a novel self-perception method based on GANs for automatical face attribute inverse. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images.
Detecting bot behaviour in social media using digital DNA compression

This publication has emanated from research supportedin part by a research grant from Science Foundation Ireland (SFI) under GrantNumber SFI/12/RC/2289P2.
Enterprise Knowledge Modeling, UML vs Ontology: Formal Evaluation

Everyday activities in enterprises rely heavily on the experts' know-how. Due to experts departure, the loss of expertise and knowledge is a reoccurring problem in these enterprises. Recently, in order to capture experts knowledge into intelligent systems, formal knowledge representation methods, such as ontologies, are being studied and have caught up with non-formal or semi-formal representation, such as UML. The similarities and differences between UML class diagram and computational ontology have for long raised questions about the possibility of synthesizing them in a common representation (usually an ontology). Indeed, the problem of migrating knowledge encoded in UML into an ontology is an active research domain. This paper outlines our approach, which is based on semantic matching between existing ontologies and a UML class diagram, to support UML driven ontology refactoring and engineering.
Review on Machine Translation Post-Editing of Science and Technology Texts in China

Nowadays, with the popularization of machine translation software, new progress has been made in the development of machine translation post-editing of science and technology texts.However, researches on science and technology text post-editing in China has not been developed correspondingly.This paper collates and summarizes all the literatures on science and technology text post-editing in China, makes them into four categories based on the type of source texts and provides the corresponding features and analyses respectively, sorts out three major problems of current researches in this field, and points out targeted future research directions, so as to make a detailed analysis of problems and promote progress in this field.
Interdisciplinary data bank in oncoendocrinology. Medullary thyroid cancer and type 2 multiple endocrine neoplasia syndromes

Medullary thyroid cancer (MTC) is about 5% of all thyroid carcinomas and is hereditary in 2030% of cases. Multiple endocrine neoplasia syndrome type 2 (MEN2), in addition to medullary thyroid cancer, may include pheochromocytoma, hyperparathyroidism, and some other manifestations.&#x0D; The multidisciplinary data bank (MDB) developed by us is a specialized resource for storing, accumulating and subsequent analysis of data on patients with MTC and MEN2 syndromes. The MDB allows you to collect data from any sources (primary care, medical institutions at various levels, federal centers) and provides internal automated control of the quality and quantity of input data. It is necessary to keep a record of such data to improve the efficiency of diagnostics, treatment and rehabilitation of patients.&#x0D; MDB is an integrated digital platform containing reference materials for doctors, which allows combining evidence-based clinical experience in patients, and provides the opportunity for expert support of medical decisions. In the long run, it will help us organize digitally stored data for machine learning and lays the groundwork for the development of artificial neural networks.
ISLAM AND GENETICS: RELIGIOUS, ETHICAL AND LEGAL ISSUES

The rapid development and achievements of science and technology provides people to improve their lives. Over the past 10 years, genetic researches have grown significantly. Today they are the subject of debate not only by doctors, lawyers, but also theologians. Currently, legislation of countries in Middle East regulates genomics and genetic research differently. Countries are having orient towards religion and therefore pay more attention in these countries to the ethical regulators of Islam besides only legal regulation of genomics (humans, animals, plants, i.e. all living things). Ethical standards are gradually becoming legal norms. In some countries of the Middle East, there are draft laws on the legal regulation of genetics; in some countries given attention in the legal acts of executive authorities in genomics sphere, and in some, have been developed local acts of leading medical centers. A number of eastern countries are also highlighted, in the legislation of which some aspects of the legal regulation of genetic research act as legislative novels. In the most economically developed Middle Eastern countries, genetics is one of the priority programs of the state, in particular, there are national strategic programs for the development of countries already use of modern methods of genome sequencing, bioinformatics, and validation methods. Many Middle Eastern countries have ratified international acts in genetic research and on issues related to their regulation. In addition, Islamic states have developed an independent concept of genomics regulation, taking into account the attitudes of the fundamental sources of Islamic law. Based on the read material of the article, the reader learns about the legal, ethical and legal regulation in the field of genomics of Middle Eastern Islamic states.
Analyzing the Concept of Speech Act of Thanking in the English Textbooks of Primary Schools

Teaching politeness speech is an important step toward teaching foreign language. Speech act of thanking is the first thing that must be taught at primary schools. The study aims to reveal the degree of existence of the concept of the speech act of thanking in the English for Iraq 6th primary textbook. The researchers used theme, subject, and illustrations as units to analyze the data of the study. The sample of the study was the English for Iraq 6th primary English textbook. The research tries to answer the following questions " To what degree do themes, subject, and illustrations in English for Iraq 6th primary textbook include concept of speech act of thanking? To answer the question, a percentage was used. The results revealed that the concept of speech act of thanking existed with very low percentage in the themes and subject, and there was not existed in illustrations in the 6th primary English textbook.
The Optimization of Crop Pattern for Maximizing The Irrigation Yield in Jatikulon Mojokerto

There were problems to meet water demand in 431-hectare Jatikulon irrigation area in Mojokerto District from Brantas river.The purpose of this study was to obtain maximum irrigation yield in limited field under the dependable discharge constraint by Linear Programming.The variables are area of paddy, corn, and sugarcane.The constraints are dependable discharge and available irrigation area.The objective is to maximize irrigation yields.The optimization analysis reveals that the largest dependable discharge is 712 l/sec.The highest water demand for paddy, corn, and sugarcane are 2.487, 1.180, and 1.282 l/sec/ha respectively.The designed crop plants are 62 ha paddy, 369ha corn, and 0 sugarcane in season I; 0 paddy, 356ha corn and 0 sugarcane in season II; and 0 paddies, 194ha corn, and 0 sugarcane in season III.The maximum profit in season I, II, and III are Rp.6,730,554,448,Rp 5,118,677,651, and Rp.2,790,580,427 respectively.
Indicator Patterns as Features for Argument Mining

The paper discusses methods for creating dictionaries of indicators represented by specific speech constructions that signal the presence of an argument in a text written in Russian. The indicators are described using lexical-grammatical patterns. The initial filling of the dictionary is carried out by an expert based on the analysis of a non-annotated collection of popular science articles. The work of the expert is supported by software tools for multi-purpose study of lexical characteristics of text. Significant attention is paid to the replenishment of indicator dictionaries by forming samples with variables and implementing iterative search in collections of popular science and scientific texts, in order to enrich the lexical composition of the constructed patterns.
Android-Based Physics Learning-Media Apps for High School Students

Abstract The use of Android smartphones has been emerging quite fast. It distracts most of the students’ attention from their studies. While at one side, it brings some negative impacts on students, on the other hand, it opens some opportunity to use the students’ deep engagement with Android smartphones as a source of excitement to promote physics learning-media. We have conducted research in Surabaya (Indonesia) and its vicinity to address the use of Android smartphones as student physics learning source. Applying the 4-D model, we have developed Android mobile-apps based physics learning-media on several topics of introductory high school physics. The physics learning-media have been tried out by several groups of students and have received positive comments from the users.
Through the looking glass [Cyberbullying]

In a world driven by social media, with cases of hate crime, can virtual reality and artificial intelligence help people experience life through another person's eyes?
The Current Situation and Future Development Trend of Educational Live Broadcast Platform in Mobile Live Broadcast Environment

Nowadays, with the rapid development of the Internet, education live broadcast has become the latest online education model under the Internet + Education model. This paper takes the mobile education live broadcast as the research object, and analyzes the characteristics of the education live broadcast platform based on the present, and looks forward to the future development trend of the educational live broadcast platform through the existing problems and development limitations of the live broadcast platform.
Stratified Meta Structure Based Similarity Measure in Heterogeneous Information Networks for Medical Diagnosis

Electronic Health Records (EHR) offers point by point documentation on various clinical occasions that crop up amid a patient’s stay in the emergency clinic. Clinical occasions can be spoken to as Heterogeneous Information Networks (HIN) which comprises of multi-composed and interconnected articles. A focal issue in HINs is that of estimating the likenesses between articles by means of basic and semantic information. The proposed strategy utilizes a Stratified Meta Structure-centred Resemblance measure named SMSS in heterogeneous data systems. The stratified meta-structure can be made consequently and seizure opulent semantics. At that point, the com-quieting framework of the stratified meta-edifice is characterized by the prudence of the transforming matrix of meta-ways and meta-structures. The primary point is to decipher EHR information and its rich connections into a heterogeneous data for robust therapeutic analysis. This demonstrating approach takes into account the direct handling of missing qualities and heterogeneity of information.
The Logic of AGM Learning from Partial Observations

We present a dynamic logic for inductive learning from partial observations by a “rational” learner, that obeys AGM postulates for belief revision. We apply our logic to an example, showing how various concrete properties can be learnt with certainty or inductively by such an AGM learner. We present a sound and complete axiomatization, based on a combination of relational and neighbourhood version of the canonical model method.
Embedded Entropy-Based Registration of CT and MR Images

In this paper, a novel approach has been developed for registration of noisy MR images with CT images. Noisy MR image is considered as floating image and CT image as the reference image. Here, the structural information of the images has been presented for both the images. The structural representation of the images has been computed by embedding Arimoto entropy and Tsallis entropy. MR image is the noisy one, and hence, Gaussian kernel filter is applied to estimate the pixel for structural representation. These structural images are considered for image registration. The joint histogram of structural images has been computed and then is used to compute mutual information (MI) which is considered as the similarity metric for registration framework. Our proposed method has been analyzed for different noise (SNR) conditions and also with other methods from literature.
A Sentiment Analysis Model for Customer Reviews Considering Sentence Location

Consumers use online user reviews as important decision-making tools when purchasing products or services, and companies analyze consumer opinions on their products and services to derive business insights. Thus, analyzing online user reviews effectively and intelligently using text mining techniques like sentiment analysis has been a popular research topic. This study aims to enhance the accuracy of sentiment analysis on online user reviews by considering the constitutive characteristics of writing through an integrated approach combining machine learning and vocabulary-based approaches. As the Internet and mobile technology evolves, communication habits are changing in online, and people’s writing behavior is also changing. Especially, recent writing structure is changing into the writing form of deductive or inductive composition reflecting the characteristics of speaking. Thus, we tried to improve the prediction accuracy of sentiment analysis by amplifying the weights of the words included in the first sentence or the last sentence in our study. Specifically, we applied 1.5 to 10 times weight to the first sentence and the last sentence to find the optimal weights with the highest accuracy. As a result, we found that the proposed algorithm improved the prediction accuracy when the weight of the first sentence is doubled, and the weight of the last sentence is three times. We also found that the performance of the proposed algorithm was higher in neutral reviews rather than in positive or negative reviews.