Reviewerly/exHarmony huggingface dataset:
Effective Factors on the Productivity of Human Capital via an Interpretive Structural Modeling (Case Study: General Office of Sports and Youth in Khorasan Razavi)

The purpose of this study is to explore the effective factors on the productivity of human capital via an interpretive structural modeling (ISM) in General office of Sports and Youth in Khorasan Razavi Province. The present study is a qualitative research with an analytical nature and it is also an applied one that was conducted via interpretive structural modeling. Literature review, interviews and ISM questionnaires were used for data collection. The research population included professors and experts in sports management, managers, deputies and staff of the General office of Sports and Youth in Khorasan Razavi, of which 35 people were totally selected through purposeful sampling in three stages of this study. According to the research findings, 59 effective indicators were identified under seven major dimensions playing role in improving the productivity of human capital, which were classified into five levels. Motivational factors, organizational culture and work environment, the relationship between managers and employees along with individual factors, the quality of labor force, and finally the technology factor were identified under five levels of the model. Finally, it can be concluded that managers in sports organizations should pay special attention to the technology factor and its indicators, which have been identified as the foundation of improving employees’ productivity, which then provides the ground for exploiting other dimensions at higher levels.
The Influence of Teacher Training on the Moodle Platform and ESL Language Learning on Students at a Latin American University

Objective: To accurately know the influence of teacher training on the Moodle platform and English learning in ESL students at a Latin American university. Materials and Methods: For this pre-experimental study, the pre- and post-tests were applied to a sample of 29 students and then processed with the SPSS (Statistical Analysis Software) program. Results: The pre-test reached an average score of 13.86 compared to the post-test, which reached an average score of 17.83. This indicates a positive influence with an increase of 3.97 points, P =000. Therefore, the hypothesis stating that ESL students are able to learn in a virtual class is accepted. In the four specific hypotheses, increases of 4.00,3.50, 3.74 and 4.20 points between the pre- and post-tests, respectively, verify the improvements. Conclusion: We can conclude that there is a positive influence with the Moodle platform teacher training in the learning of English as a second language. Clear evidence is shown in the increase of 3.97 points between the pre- and post-tests, which indicate an academic progress of the students of a regular average to very good. Objectivo: Conocer la influencia de la capacitación de docentes en la plataforma Moodle y aprendizaje del inglés básico en estudiantes de ingles basico de una universidad. Materiales y Método: Para este estudio pre experimental para ello se aplicó el pre y pos test a una muestra de 29 estudiantes, que será procesada con el programa SPSS. Resultados: El pretest alcanzó un promedio de 13.86, a comparación del postest con un puntaje promedio de 17.83, con ello una influencia positiva con un incremento de 3.97 puntos. P = .000, se acepta la hipótesis planteada. En las hipótesis específicas también se comprobó un incremento de 4.00 , 3.50 , 3.74 y 4.20 puntos, respectivamente. Conclusión: Existe una influencia positiva de la Capacitación docente plataforma Moodle en el aprendizaje del inglés básico, ya que entre la prueba pre y pos test se experimentaron un aumento de 3.97 puntos, lo que permite señalar un progreso académico de los estudiantes de un promedio regular a muy bueno.
A causal learning framework for the analysis and interpretation of COVID-19 clinical data

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient’s outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.
Classification of Persian News Articles using Machine Learning Techniques

Automatic text classification, which is defined as the process of automatically classifying texts into predefined categories, has many applications in our everyday life and it has recently gained much attention due to the in-creased number of text documents available in electronic form. Classifying News articles is one of the applications of text classification. Automatic classification is a subset of machine learning techniques in which a classifier is built by learning from some pre-classified documents. Naive Bayes and k-Nearest Neighbor are among the most common algorithms of machine learning for text classification. In this paper, we suggest a way to improve the performance of a text classifier using Mutual information and Chi-square feature selection algorithms. We have observed that MI feature selection method can improve the accuracy of Naive Bayes classifier up to 10%. Experimental results show that the proposed model achieves an average accuracy of 80% and an average F1-measure of 80%.
From Clause to Function: Texts Analysis Using Systemic Functional Linguistics Theory and Its Pedagogical Implication in Language Teaching

Using Systemic Functional Linguistics (SFL) theory, this study was an attempt to investigate the metafunctions of language which are realized by register category of field, tenor, and mode existing simultaneously in texts. Employing qualitative approach with the use of discourse semantics analysis, two different selected newspaper texts were purposefully chosen as the source of data. Following the orientation of SFL, the writer modified the texts into some sentences and clauses which were subsequently analysed and compared regarding the objectives in question. The result of analysis shows that the field of texts was mostly concerned with physical actions and events employing both direct and indirect speech for the sake of information. Meanwhile, in terms of mode, Text I was more rhetoric and more detail in describing the event than text II. Yet, the level of modality in both texts was low and low in which there were not many actions of getting other people to do things. Aside from those two, this analysis revealed that the mode of both texts mostly used anaphoric types of referential cohesion indicating written mode. In the meantime, Text I had more number of conjunctions implying more explanative in giving information and cause and effect relation of the information. Of greater importance of this finding was that a paradigmatic description of context implying a meaning, system, and metafunctions of language and its pedagogical implication in language teaching.
Impact of Domestic Public Borrowing on Financial Development: Evidence from EU Transition Economies

Improvements inthefinancial sector have been suggested as a significant factor of economic growth. For that reason, it is crucial to reveal the determinants of financial sector development to ensure appropriatepolicymaking. In this regard, this paper explores the influence of public borrowing from domestic money banks together with FDI inflows and remittances on the development ofthefinancial sector over the period 1996–2017 in 11 EU transition economies with second-generation cointegration and causality analysis. The causality analysis disclosesthat domestic public borrowing had a significant influence on financial development. On the other side, the cointegration analysis revealed findings supporting both the safe asset view andthelazy bank view. Furthermore, a positive weak influence of FDI inflows and remittances on financial sector development was revealed in the long run.
DECISION MAKING IN DYNAMIC ENVIRONMENTS AN APPLICATION OF MACHINE LEARNING TO THE ANALYTICAL HIERARCHY PROCESS

The purpose of this work is to propose a method of algorithmic decision making that builds on the Analytical Hierarchy Process by applying reinforcement learning. Decision making in dynamic environments requires adaptability as new information becomes available. The Analytical Hierarchy Process (AHP) provides a method for comparative decision making but is insufficient to handle information that becomes available over time. Using the opinions of one or many subject matter experts and the AHP, the relative importance of evidence can be quantified. However, the ability to explicitly measure the interdependencies is more challenging. The interdependency between the different evidence can be exploited to improve the model accuracy, particularly when information is missing or uncertain. To establish this ability within a decision-making tool, the AHP method can be optimized through a stochastic gradient descent algorithm. To illustrate the effectiveness of the proposed method, an experiment was conducted on air target threat classification in time series developing scenarios.
Gesture recognition for sign language Video Stream Translation

Due to the complexity of camera viewing angle, background and different execution speed, the difference between hand movements is low, which makes it difficult to extract features in the process of sign language recognition. In order to solve this problem, we propose a sign language recognition framework based on attention mechanism, which enables the network to pay attention to the areas that contain objects related to the activities under consideration. We use Gradient-weighted Class Activation Mapping in the pre-trained CNN for general image recognition to learn the attention map of each frame, and use convolutional LSTM to encode the video in time and space. In view of the fact that the convolution structure in LSTM has no obvious contribution to spatio-temporal feature fusion, the LSTM is adjusted to make it possible to effectively fuse spatiotemporal features with less parameters and computation.
COVID-19 NO AMAZONAS E A VULNERABILIDADE DA SAÚDE E DA EDUCAÇÃO INDÍGENA

This article will deal with the pandemic of the new Coronavirus , as consequences for the indigenous peoples of Amazonas.It is important to highlight that the Special Secretariat for Indigenous Health (Sesai) recognizes that indigenous peoples are more vulnerable to viruses, specifically respiratory infections such as Covid-19, causing mainly the death of children and the elderly.Named by the Brazilian Institute of Geography and Statistics (IBGE) as the municipality with the most indigenous people in the country, São Gabriel da Cachoeira, in the northwest of Amazonas, recorded a rapid advance of the disease among peoples, in addition, it is the city with the largest number of contamination among Indians in the State: there are 3,018 cases and 51 deaths, until August 25, 2020, whose indigenous people were of the following ethnic groups: Baré, Baniwa, Tukano, Wanano, Piratapuia, Tariano and Dessano.Across the Amazon, data from the first week of October 2020 reveal that the number of cases exceeds 144,000, with more than 4,200 deaths.Of this total, there are 836 deaths of indigenous people in Brazil, of which 182 are from Amazonas, according to data from the Articulation of Indigenous Peoples of Brazil (Apib) and Coordination of Indigenous Organizations in the Amazon (Coiab).Given this scenario, social
Bayes-Optimal Set-Valued Tracking of Single Point Targets

Conventional single-point-target tracking algorithms are recursive point estimators with point-measurement input data. In less well-known approaches, the tracking algorithm is a recursive set estimator with point-measurement or set-measurement input data. This paper provides a very general, systematic, and theoretically rigorous foundation for Bayes-optimal single-target trackers of the latter kind; as well as specific trackers arising from that foundation. This foundation is intuitive and conceptually simple and, in particular, requires no measure-theoretic complexities.
CONTEMPORARY THEATER ARTS IN THE EDUCATIONAL ACTIVITY OF THE TEACHER

Contemporary theatrical art is considered in the article in two aspects: as a way to solve the problems of educating students (education by means of theatrical art) and as a means of preparing future teachers to solve educational problems (theatrical pedagogy). At the same time, educational activity is understood from the standpoint of a culturological approach as a purposeful activity of a teacher acting as an intermediary between a child and culture, to include students in modern culture context. The ideas about the peculiarities of contemporary theatrical art that influence the educational tasks solution are concretized: brightness and originality, which create motivation for moral problems discussion; minimalism, allowing you to focus on moral conflict; a large selection of genres, taking into account the students individual capabilities; interactive nature, allowing students to formulate, substantiate and defend their own moral position; combination of artistic and creative activity and computer technology, adequate to modern schoolchildren worldview. Theatrical art, removing the contradictions between the emotional-figurative and the emotional-logical, contributes to the personal holistic development, individuality and subjectivity of the child, allows him to experience the various situations of modern world. The scientific knowledge about the possibilities of theatrical pedagogy in the process of preparing future teachers for educational activities has been clarified: the use of theatrical pedagogy elements contributes to the students' communicative abilities development, the rejection of the stereotypical solution of professional tasks, the ability for improvisation, emotional self-regulation, flexible and productive conflict resolution. The preparation of future teachers for educational activities by means of theatrical pedagogy is considered as one of the conditions for the effectiveness of their professional activities in solving education problems. The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 20-013-00809.
Analisis Keuntungan Maksimum Dengan Konsep Turunan Pada Industri Percetakan

In a business, profit is a must to reckoned with. Many factors affect the profits of a business, among them, the cost of production, operating costs, selling price, the number of penjulan and others. This study aims to analyze the maximum profit on a business theoretically. This research is a qualitative research using case study method. In this case, the researcher takes the sales data of Cahaya Bintang which is a business in the field of printing. The object of research is the advantage of selling business card and invoice book. Maximum profit will be analyzed by using derivative concept. The steps taken in determining the maximum benefit using the concept of derivatives are: 1) Determine the function of cost, demand function, acceptance function and profit function; 2) Define the first derivative of the profit function; 3) Determining the critical point of the first derivative of the profit function; 4) Analysis of critical point by using second derivative. Based on the results of the analysis using the concept of derivatives, sales of business card and invoice book will achieve maximum profits if the company is able to sell 309 boxes per month and for invoice book of 199 rim per month. Sales of business cards and invoice book in the previous month were below the maximum value determined based on the analysis results. Therefore the profit earned in the previous month has not reached the maximum profit.
Image super-resolution reconstruction algorithm based on channel shuffle

Convolutional neural networks have achieved good results in single image super-resolution. Nowadays, image super-resolution model based on deep learning are gradually becoming lightweight, and lightweight networks use grouped convolution stacking, which hinders the channel Information flow between the two, and weaken the feature representation. Aiming at the existing problems of the existing image super-resolution lightweight model, this paper proposes a super-resolution reconstruction algorithm based on channel shuffle. This paper introduces a channel shuffle mechanism after grouped convolution. Channel shuffle disrupts the grouping order and allows group convolution to obtain input data from different groups, which allows the fusion of feature information between different channels. At the same time, this paper also introduces the dynamic activation function DY-RELV-B, which enhances the feature representation ability of the network model and further enhances the image reconstruction effect. Experimental results show that compared with LapSRN, VDSR, traditional interpolation method, etc., the method in this paper is superior to other methods in PSNR on x2 scale and x4 scale.
A user task design notation for improved software design

System design is recognized as one of the most critical components of a software system that bridges system requirements and coding. System design also has a significant impact on testing and maintenance activities, and on further improvements during the lifespan of the software system. Software design should reflect all necessary components of the requirements in a clear and understandable manner by all stakeholders of the software system. To distinguish system elements, separation of concerns in software design is suggested. In this respect, identification of the user tasks, i.e., the tasks that need to be performed by the user, is not currently reflected explicitly in system design documents. Our main assumption in this study is that software quality can be improved significantly by clearly identifying the user tasks from those that need to be performed by the computer system itself. Additionally, what we propose has the potential to better reflect the user requirements and main objectives of the system on the software design and thereby to improve software quality. The main aim of this study is to introduce a novel notation for software developers in the frame of UML Activity Diagram (UML-AD) that enables designers to identify the user tasks and define them separately from the system tasks. For this purpose, an extension of UML-AD, named UML-ADE (UML-Activity Diagram Extended) was proposed. Afterwards, it was implemented in a serious game case for which the specification of user tasks is extremely important. Finally, its effectiveness was analyzed and compared to UML-AD experimentally with 72 participants. The defect detection performance of the participants on both diagrams with two real-life serious game scenarios was evaluated. Results show a higher level of understandability for those using UML-ADE, which in turn may indicate a better design and higher software quality. The results encourage researchers to develop specific design representations dedicated to task design to improve system quality and to conduct further evaluations of the impact of these design on each of the above mentioned potential benefits for the software systems.
Refining Transitive and Pseudo-Transitive Relations at Web Scale

The publication of knowledge graphs on the Web in the form of RDF datasets, and the subsequent integration of such knowledge graphs are both essential to the idea of Linked Open Data. Combining such knowledge graphs can result in undesirable graph structures and even in logical inconsistencies. Refinement methods that can detect and repair such undesirable graph structures are therefore of crucial importance. Existing refinement methods for knowledge graphs are often domain-specific, are limited to single relations (e.g. owl:sameAs), or are limited in scale. We present a challenge consisting of a number of datasets of transitive and pseudo-transitive relations and hand-labeled gold standards, as well as baselines. We introduce an efficient web-scale knowledge graph refinement algorithm that works for such relations. Our algorithm analyses the graph structure, and allows the use of weighting schemes to heuristically determine which possibly erroneous edges should be removed to make the graph cycle free. When compared against general-purpose graph algorithms that perform the same task, our algorithm removes the least amount of edges to make the graph of transitive relations cycle-free while maintaining a better precision in identifying erroneous edges as measured against a human gold-standard.
Green Technology: A Must or a Need in TVET Education in Malaysia?

Green Technology is seen as the optimal solution in addressing most of the environmental issues affecting our society today. The movement towards green technology will undoubtedly change the labour market. Although, there were numerous studies been done in regards to green technology but the question remains unanswered when it comes to Vocational Colleges in Malaysia. Therefore, the main goal of this current study is to investigate and develop an in-depth exploration of the need of incorporating Green Technology in one of the most prominent TVET institutions in Malaysia which is the Vocational Colleges. The study uses exploratory sequential mixed method as the research design but only the qualitative approach is being selected to be discussed in this paper. Hence, we report the findings conducted through semi-structured interview by interviewing 7 industrial players in TVET, associated with Vocational Colleges in Malaysia. In relation to the main findings from this study, we found that all 7 participants believe that there is a need to incorporate green technology in the curriculum of Vocational Colleges as to upgrade and fulfill the industry’s needs. Taken together, this finding suggests that educational reform may be the most important tool in the future of green technology in Vocational Colleges and by all means it is defined as a need rather than a must.