Reviewerly/exHarmony huggingface dataset:
The Student-Self Oriented Learning Model as an Effective Paradigm for Education in Knowledge Society

Proceeding from broadly accepted role of emotional intelligence (EI) in professional and personal life, the paper suggests a new learning model (LM) called Student-Self Oriented LM (SSOL-model). It is defined as the model being beneficial for self-cognition and self-construction through the prism of the acquired knowledge and life experience. Two successful implementations of the SSOL-model are shortly described: the system of emotional-imaginative teaching (the EIT-system) developed by the authors in the 1990s and expanded in the 2000s and art cognitonics. The EIT-system is underpinned by the authors’ Theory of Dynamic Conceptual Mappings (the DCM-theory). The EIT-system includes an original method of developing figurative thinking and creativity at the lessons of second language (English for Russian children), literature and poetry in English and Russian, symbolic language of painting, and communication culture. It is stated that this method may be used as an effective starting framework of STEAM education. It is mentioned that the DCM-theory and the EIT-system became the starting point for developing the foundations of a new scientific discipline called cognitonics. The basic principles of art cognitonics – a well developed branch of cognitonics and the concept of cognitive engagement are described. The significance of art cognitonics for helping the learners to answer the encountered moral questions is explained. The broad possibilities of designing intelligent tutoring systems (ITSs) of new generation are indicated. These are culture and moral experience oriented systems aimed at developing figurative thinking, creativity, early socialization and helping the students to find in the outstanding art works the answers to emerging moral questions. An original, detailed script of an ITS contributing to early socialization of the learners is described, its literary source is the fairy-tale "Sleeping Beauty". Since the sphere of using the SSOL-model is not restricted, it is suggested to interpret this model as an effective paradigm for education in knowledge society and a strong support to anthropocentric approach to education in the digital age.
A comparison of text versus audio for information comprehension with future uses for smart speakers

Abstract Objective Audio is increasingly used to access information on the Internet through virtual assistants and smart speakers. Our objective is to evaluate the distribution of health information through audio. Materials and Methods We conducted 2 studies to compare comprehension after reading or listening to information using a new corpus containing short text snippets from Cochrane (N = 50) and Wikipedia (N = 50). In study 1, the snippets were first presented as audio or text followed by a multiple-choice question. Then, the same information was presented as text and the question was repeated in addition to questions about perceived difficulty, severity and the likelihood of encountering the disease. In study 2, the first multiple-choice question was replaced with a free recall question. Results Study 1 showed that information comprehension is very similar in both presentation modes (53% accuracy for text and 55% for audio). Study 2 showed that information retention is higher with text, but similar comprehension. Both studies show improvement in performance with repeated information presentation. Discussion Audio presentation of information is effective and the format novel. Performance was slightly lower with audio when asked to repeat information, but comparable to text for answering questions. Additional studies are needed with different types of information and presentation combinations. Conclusion The use of audio to provide health information is a promising field and will become increasingly important with the popularity of smart speakers and virtual assistants, particularly for consumers who do not use computers, for example minority groups, or those with limited sight or motor control.
Saliency Detection Using Global and Local Information Under Multilayer Cellular Automata

To detect the salient object in natural images with low contrast and complex backgrounds, a saliency detection method that fuses global and local information under multilayer cellular automata is proposed. First, a global saliency map was obtained by the iteratively trained convolutional neural network (CNN)-based encoder-decoder model. Moreover, to transmit high-level information to the lower-level layers and further reinforce the object edge, the skip connections and edge penalty term were added to the network. Second, the foreground and background codebooks were generated by the global saliency map, and sparse coding was subsequently obtained by the locality-constrained linear coding model. Thus, a local saliency map was generated. Finally, the final saliency map was obtained by fusing the global and local saliency maps under the multilayer cellular automata framework. The experimental results show that the average F-measure of our method on the MSRA 10K, ECSSD, DUT-OMRON, HKU-IS, THUR 15K, and XPIE datasets is 93.4%, 89.5%, 79.4%, 88.7%, 73.6%, and 85.2%, respectively, and the MAE is 0.046, 0.067, 0.054, 0.044, 0.072, and 0.049. Ultimately, these findings prove that our method has both high saliency detection accuracies and strong generalization abilities. In particular, our method can effectively detect the salient object of natural images with low contrast and complex backgrounds.
New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Semantic web technologies are increasingly used in different domains.The core technology of the Semantic Web is the RDF standard.Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex requests at the join level, Several RDF data processing systems have been proposed, but are not dedicated to handling complex SPARQL queries. in this paper we present a new approach based on model driven engineering for processing complex SPARQL queries using one of the big data processing tools named Hive.We evaluate our system using three datasets from LUBM Benchmark.The results of this evaluation show the performance, and the scalability of our approach, also give very interesting results when it is compared with existing works.
Are we who we cite?

The #metoo movement reaching academia and Applied Linguistics creates a need for discussion on how we as scholars react to oppressive ideologies and behaviors in our community. However, this is not merely a question of processing cases of sexual harassment and assault. More deeply, we need conversations on who we do and do not know, read, and cite, and how to make our field more epistemologically equitable. This article hopes to elicit comments, reactions, and dialogue.
THE PHILOSOPHICAL FOUNDATIONS OF THE RESEARCHES OF THE INCLUSIVE EDUCATION

In the article on the basis of primary sources the philosophical foundations of the modern inclusive education are examined. The content description of the idea “inclusive education” is presented, the necessity of its implementation is proved. It is focused on two basic philosophical foundations of its development: personalism and intuitionism. Such an approach is caused by the fact that in modern studies on the above-mentioned range of problems are mainly dominated by rationalist methodological approaches, in contrast with the irrationalistic theories are often out of the attention. In our analysis and interpretation, the philosophy of personalism plays an important methodological role because it helps clarify the essence of the uniqueness and originality of each child with special needs and to determine the real ways of her spiritual and psychophysical development. The philosophy of intuitionalism focuses on the study of issues related to the formation of consciousness of a person with special needs. Therefore, the authors proved the necessity of using the intuitive method of research, which allows to promote the communication of subjects of interaction actively, to reveal their spiritual potential, to perform the search for ways of forming thinking in children with special needs, use the language, culture and art at the same time and in an active way.
Weighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> Norm Sparse Error Constraint Based ADMM for Image Denoising

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.
Student Presence Using RFID and Telegram Messenger Application: A Study in SMK Unggulan Terpadu Pgii Bandung, Indonesia

Students in Indonesia have been cutting classes for some time now and it has become a bad habit. They leave their house toward their educational institutions, either school or college, but in fact, they go somewhere else. The issue was supported by the lack of communication between the schools and parents related to the student attendance. Dealing with the problems mentioned above, it is required to create a Telegram Messenger application for student presence as a solution. Application-based student presence Telegram Messenger was made to ensure that students attend classes, by steps as follow: firstly, students must hand in the ID card to the teacher on duty, then the teacher will tap the card, and finally the data will be recorded and be saved in the database automatically. The data will be saved automatically every day, and then it will be sent personally to their parents who had their mobile phones registered to receive information regarding their children presence at school on the particular date and hour. In addition, this application provides service for the parents to find the information on daily and monthly basis. This application can also be a part of consideration in decision making for the principals by downloading student attendance data in Microsoft Excel file format. The implementation of the applications based on Telegram Messenger for students’ presence at school was expected to be a solution for the problems of student absence due to skipping school. This application has been tested in SMK Unggulan Terpadu PGII Bandung with very satisfying results and the level of student absence was able to be fixed.
Evaluation of the “Quality of Education” in higher educational institutions (on the example of the Batumi State Maritime Academy)

The article addresses the problem of higher education - improving the quality of graduates' professional training, provides a brief analysis of research results, Recommendations on how to use the results “Quality of education” is a complex characteristic of education, the task of which is to train specialists who are competitive in the world market. It is impossible to develop a single, scientifically based system of indicators for the quality of student training, since the education system is different in all countries of the World, moreover, a different system of training students, which is one of the influencing factors on the quality of Higher Education. It is necessary to take into account the specifics of the university.&#x0D; One of the elements of the quality assessment system is monitoring, which is considered to be a comprehensive system for diagnosing the quantitative and qualitative characteristics of the effectiveness of an educational system. The assessment of the quality of education should be formed as a cumulative indicator of all the qualities that influence the educational process. During the work we used the results of sociological research of the subjects of the educational process: students, teachers, monitoring of the educational process (academic performance of students).
Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.
Facial Expression Recognition Based on Local Features and Monogenic Binary Coding

Fast developing facial expression reciognition is one of the significant recognition technologies for biological features with high applied value.In this study, a monogenic binary coding algorithm was considered to illustrate a good matching with local features through the analysis of monogenic signal theory and monogenic binary algorithm.Then, the results of the facial expression recognition simulation experiment of monogenic based classical facial expression database, the Japanese Female Facial Expression (JAFFE) Database, and the results of traditional Local Binary Patterns-Sparse Representation-based Classification (LBP-SRC) residual fusion method were compared to illustrate the efficiency of the monogenic binary coding algorithm in the aspect of facial recognition and to provide a basis for the application of the monogenic signal theory in facial expression.
Loitering Detection Based on Pedestrian Activity Area Classification

Loitering detection can help recognize vulnerable people needing attention and potential suspects harmful to public security. The existing loitering detection methods used time or target trajectories as assessment criteria, and only handled some simple loitering circumstances because of complex track. To solve these problems, this paper proposes a loitering detection method based on pedestrian activity area classification. The paper first gave loitering definition from a new perspective using the size of pedestrian activity area. The pedestrian loitering behaviors were divided into three categories. The proposed algorithms dynamically calculate enclosing rectangle, ellipse, and sector of pedestrian activity areas through curve fitting based on trajectory coordinates within given staying time threshold. The loitering is recognized if the pedestrian activity is detected to be constrained in an area within a certain period of time. The algorithm does not need to calculate complex trajectories. The PETS2007 dataset and our own self-collected simulated test videos were tested. The experimental results show that the proposed method accurately detected the pedestrian loitering, not only detected some loitering that the existing methods could not detect, but also distinguishing different types of loitering. The proposed method is effectiveness, robust, and simplified in implementation.
Development of Educational Games for The Introduction of Fruits and Vitamins

Indonesia is one of the developing countries with the world's most abundant fruit and vegetable production. Indonesia is supported by geographical conditions that have two seasons, namely summer and rainy season. This condition causes fruits and vegetables to flourish in Indonesia. Fruit is a healthy food source for humans. Fruit is very beneficial for humans and contains lots of vitamins. The introduction of fruit benefits must be started early on from children. From the results of the pre-test, it found that there were still 32% of elementary school students who knew of the vitamins contained in the fruit. To overcome this, researchers made an educational game application for the introduction of fruit and vitamins based on Android. From the results of the post-test, 72% of students understood the type of vitamin on the fruit. From the results of this post-test, the application developed can help improve children's abilities by 39% from the prior state. In terms of application usability, get an average rating of 82%. Appraisal of application the five factors: understandability, learnability, operability, attractiveness, and usability compliance with the percentage values of 81%, 77%, 76%, 84%, and 93% respectively.
A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Discovering association rules is a useful and common technique for data mining in which relations and co-dependencies between datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the Genetic Algorithm, and there is no need there is no need to determine the extent of the threshold for the support and confidence criteria. By proposing a multi-criteria method, the useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discrete numerical values and the determination of the minimum support threshold and minimum confidence threshold. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness, and cosine2. The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets and the results show the performance superiority of the proposed algorithm compared to similar algorithms.
Ways of migration of ecotoxicants in agro-ecosystems

The fundamental property of ecosystems is the ability to accumulate and contain substances that come to them. The study of problems, in particular the impact of ecotoxicants on agroecosystems, is extremely important for understanding the environmental hazard for the environment biota and humans. Purpose of the research is to consider ways of migrating ecotoxicants with agroecosystems and to offer a unified model of their movement, which can be used to develop measures for preventing or minimizing negative environmental impacts. Paper presents the scheme of ecotoxicants migration by agroecosystems. The main attention is paid to the mathematical assessment of the accumulation of ecotoxicants by plants due to the intake of substances by the endocrine and root paths. In addition, in irrigated agriculture, the need to take into account also the share of ecotoxicants that pass into plants from contaminated irrigation water is shown. Migration of ecotoxicants by agro ecosystems is a rather complicated process, which depends on the physical and chemical properties of substances, soil, water, air and biotic factors. The chamber models method can be used to evaluate the migration of ecotoxicants by ecosystem. It is used to describe the migration of substances by chains of ecosystems, which are conventionally divided into boxes, and the transition between boxes is determined by the coefficients of transition. It is assumed that the substance content in each box is uniform, and between them there is a statistical equilibrium, as well as transition factors are constant for chamber models method. The chamber method is a simple and adequate mathematical description of migration processes in ecosystems of varying complexity. The possibility of forecasting the migration capacity of ecotoxicants by agroecosystems is demonstrated on the basis of the use of chamber model using transition coefficients. We suggest the approach, based on the results of experimental research, which provides an opportunity to assess adequately the ecological hazard of ecotoxicants and develop tools for preventing their negative effects.
Low-resource neural character-based noisy text normalization

User generated data in social networks is often not written in its standard form. This kind of text can lead to large dispersion in the datasets and can lead to inconsistent data. Therefore, normalization of such kind of texts is a crucial preprocessing step for common Natural Language Processing tools. In this paper we explore the state-of-the-art of the machine translation approach to normalize text under low-resource conditions. We also propose an auxiliary task for the sequence-to-sequence (seq2seq) neural architecture novel to the text normalization task, that improves the base seq2seq model up to 5%. This increase of performance closes the gap between statistical machine translation approaches and neural ones for low-resource text normalization.