Reviewerly/exHarmony huggingface dataset:
Training High School Teachers for the Formation of Research-Oriented Student’s Personality

The objective of our paper was to highlight the roles and responsibilities of research supervisors; to reveal the structure of training teachers for the formation of research-oriented student’s personality; to identify psychological and pedagogical features of didactic technology in developing the research potential of a personality; to characterize the system of experimental models underlying this technology.&#x0D; Improving the preparation of students for research activities is extremely relevant at the present stage of socio-economic development. To ensure the effectiveness of developing research-oriented personality of a future professional is possible due to the optimization of teacher training system. It includes important scientific and practical tasks such as ensuring the scientific and creative worldview of an individual and their ability to introduce the latest achievements in science, as well as to develop and implement innovative technologies, to contribute to the accumulation of the spiritual, cultural, scientific, technical, and economical potential of the state. An analysis of the recent studies that have begun to solve this issue allows for outlining the following directions of its study:&#x0D; &#x0D; developing the methods of creating scientific creativity of students;&#x0D; covering the means of training high school teachers for implementing person-centered approach;&#x0D; disclosing the functional and methodological aspects of professional culture formation in high school teachers.&#x0D;
ADAPTASI PROGRAM MICROTEACHING BAGI CALON GURU BAHASA INDONESIA PADA MASA PANDEMI COVID-19

This study aimed to describe the implementation of a microteaching program to pre-service Bahasa Indonesia teachers during the Covid-19 pandemic, the advantages of the program, and its weaknesses. Data related to the changes in the program when facing a pandemic, advantages of program changes, and weaknesses. Data obtained from the Microteaching course in the Indonesian Language and Literature Department in The Universitas Muhammadiyah Surakarta. Microteaching in the semester of 2019/2020 amounted to 209 students. Data were from observation techniques, questionnaires, and document analysis. The sample was selected randomly. Triangulation techniques and methods validated the data, the interactive term used for data analysis. The results of this study are as follows. (1) To deal with the Covid-19, there are no changes in the core stages of Microteaching, but it needs some adaptations to the implementation, such as learning tools, teaching practices, observation activities, and assessments. (2) The advantages of adapting this program are students have flexible time and place of practice, improved information technology capabilities, and the student experience varies as they can write more stories. (3) The weaknesses of the adaptation of this program are the workload of lecturers increases, student learning burden increases, limited interaction, and network problems
Modeling the Potential Impact of Government Regulation on Cryptocurrency Prices

&lt;p&gt;&lt;big&gt;Cryptocurrencies have gained popularity over the past five to six years. Most recently, events like the FTX bankruptcy fueled the interest in regulation. Moreover, it is possible that the FTX event disrupting the cryptocurrency market was a factor in Silicon Valley Bank&amp;#39;s failure. While several countries consider regulation, from soft regulation, like Japan, to more rigid standards, like the total ban in China, we study the effect of other news or events on cryptocurrency prices. This paper looks at historical closing prices for Bitcoin, the largest of the cryptocurrencies, and how prices react to various events. Then we focus on modeling the time series considering an &amp;#39;event,&amp;#39; China&amp;#39;s ban on cryptocurrency exchanges, using intervention analysis. We find that intervention analysis provides a reliable approach to quantifying the impact regulation may have on cryptocurrency pricing.&lt;/big&gt;&lt;/p&gt;
Hrudaya Chetana Sthanam - What Modern Science Says?

Hrudaya is one of the Kostanga. Hrudaya, which resembles like Adhomukha Pundarika is situated between two Stana and is the Sthana of Satwa, Raja and Tama. Mahat and Artha are the synonyms of Hrudaya. Also, Hrudaya is the Vishesha Sthana for Chetana i.e., Atma, Shareera experiences Shukha Dhukhadi feelings because of this Chetana. Medical science says that heart and mind work together. Recent scientific research says that Consciousness emerges from the brain and heart acting together. In this paper an effort is made to explain the scientific truth regarding Hrudaya as Chetana Sthana.
Edge-assisted quantum protocol for secure multiparty logical AND its applications

Security and privacy have always been key concerns for individuals in various edge-assisted services. In this paper, we present a feasible quantum solution to an important primitive of secure multiparty computations, i.e., Secure Multiparty Logical AND (SMLA), in which n participants can securely compute logical AND of n private bits. In order to ensure perfect security and achieve good feasibility, we introduce a semi-honest edge server and two non-collusive fog nodes, and design a secure and feasible edge-assisted quantum protocol for SMLA, which cleverly utilizes Secure Multiparty XOR to implement SMLA group by group. Furthermore, we focus on applications of this quantum primitive protocol and design two quantum protocols for Multiple Private Set Intersection and Anonymous One-vote Veto. Compared with classical related protocols, our proposed quantum protocols obtain higher security, which can be guaranteed by the basic principles of quantum mechanics.
Effectiveness of the Pedagogical System of Training Future Primary School Teachers to Use Innovative Technologies: Research Results

The purpose of the article is to theoretically substantiate the peculiarities of forming the readiness of future primary school teachers to use innovative technologies. Among the research methods used, the following were decisive: analysis, generalization, experiment, questionnaires, expert evaluation, and observation. Results. In today's conditions, the driving force in the field of education is the use of innovative pedagogical technologies of learning and education, which satisfy the didactic requirements for ensuring the educational process. The training of a new generation of primary school teachers, who are able to depart from traditional forms and methods of working with students and at the same time perceive and extrapolate new pedagogical experience, new technologies, forms and methods of work in their professional activities, is aimed at realizing the need to create a positive educational environment by activating intellectual and sensory and emotional resources of each student. That is why there is a need to use appropriate pedagogical technologies in professional education. This, in turn, actualizes the need to include appropriate content in the subject-methodical training of future primary school teachers, requires rethinking and searching for new organizational forms and methods of training that would improve its quality and effectiveness, expand and deepen the content of professional training. This is due to the latest trends in the development of social, interstate, and cultural relations in the field of professional and pedagogical training of specialists and is the subject of special attention of the state, which is reflected in the main documents that determine the priorities of the educational policy of Ukraine: the laws of Ukraine «On Education», «On Higher Education» , «On the national informatization program», state programs «Information and communication technologies in education and science» for 2006-2010, Concept of implementation of state policy in the field of general secondary education reform «New Ukrainian school» for the period until 2029, Concept of media implementation of education in Ukraine (2010), Strategy for the development of the information society in Ukraine (2013), etc. So, the result of professional training of future teachers is the readiness to use pedagogical technologies in professional activities, which is determined by the actual problem of pedagogical science and practice. Conclusions. The author formulates general conclusions and recommendations for the implementation of theoretical and practical results of the study in the educational process of pedagogical universities, and identifies prospects for further research in the field of the problem under study.
ЕND-TO-END SPEECH RECOGNITION SYSTEMS FOR AGGLUTINATIVE LANGUAGES

With the improvement of intelligent systems, speech recognition technologies are being widely integrated into various aspects of human life. Speech recognition is applied to smart assistants, smart home infrastructure, the call center applications of banks, information system components for impaired people, etc. But these facilities of information systems are available only for common languages, like English, Chinese, or Russian. For low-resource language, these opportunities for information technologies are still not implemented. Most modern speech recognition approaches are still not tested on agglutinative languages, especially for the languages of Turkic group like Kazakh, Tatar, and Turkish Languages. The HMM-GMM (Hidden Markov Models - Gaussian Mixture Models) model has been the most popular in the field of Automatic Speech Recognition (ASR) for a long time. Currently, neural networks are widely used in different fields of NLP, especially in automatic speech recognition. In an enormous number of works application of neural networks within different stages of automatic speech recognition makes the quality level of this systems much better. Integral speech recognition systems based on neural networks are investigated in the article. The paper proves that the Connectionist Temporal Classification (CTC) model works precisely for agglutinative languages. The author conducted an experiment with the LSHTM neural network using an encoder-decoder model, which is based on the attention-based models. The result of the experiment showed a Character Error Rate (CER) equal to 8.01% and a Word Error Rate (WER) equal to 17.91%. This result proves the possibility of getting a good ASR model without the use of the Language Model (LM).
Exploring the Influencing Factors of Social Entrepreneurship Intention among University Volunteers

With the vigorous development of university volunteers who has achieved good social benefits in the aspects of helping people in danger and distress, community service, environmental protection, employment promotion, rescue and disaster relief, etc.The emergence of social entrepreneurship is mainly due to the further prosperity and development of non-profit sectors such as voluntary services and charitable organizations.The process of the germination, exploration and development of college students' voluntary service is the development and evolution process from the pursuit of moral experience to social benefits.This study examined how the factors are associated with social entrepreneurial intentions.Factor analyses supported that the antecedents of social entrepreneurial intentions could be divided into dimensions of moral obligation, entrepreneurial self-efficacy, attitude towards behavior and perceived social support.Through a survey, a sample of 317 university volunteers was used for analyses.Multiple regression analysis results indicated that entrepreneurial self-efficacy was the most prominent antecedent of social entrepreneurial intentions, followed by attitude towards behaviour, perceived social support and moral obligation.
Artificial digital life, creation and evolution

A.mon are computer programs analogous to biological life, but existing inside the digital environment as an executable file, able to interact with their environment, move around, evolve and hypothetically adapt to new hardware and software, through processes that resemble those found in biological life, although not identical: duplication of their own respective files (reproduction), random alteration of their own binary code (genetic mutation) and natural selection, having the digital environment as a source of selective pressure. Although one of the objectives of this project is adaptation of the digital organisms to new hardware and software autonomously, the original source code is written in the programming languages Go and Python, on Windows 10. Since complexity and diversity are expected to come from evolution, the code is simple and so is the lifespan of the specimens: once their executable files (ancestors) are ran (either by other specimen, a computer user, the computer's start up function, etc.), they access specific sections of their binary codes, randomly alter them by adding, modifying, or removing bytes, and use this mutated binary code to create new executable files (offspring), then they execute the executable files of the offspring and deactivate. The new specimens will restart the process if they're able to thrive. Furthermore, the A.mon have a visual representation in the form of animated sprites that move around on the computer screen, whose appearance, movement patterns, and speed are determined by their binary code, this is useful for easy visualization of new mutations.
Rekha: A Reference Based Machine Translation Evaluation Metric Using Linguistic Knowledge and Contextual Embeddings

Since the beginning of machine translation (MT) research, MT evaluation has been an area of interest of researchers.In literature, one can find more papers on MT evaluation than on machine translation itself.This paper describes the work done on developing our MT evaluation metric which incorporates linguistic as well as word embeddings for the evaluation of MT outputs.We have studied the performance of our metric on some English to Indian language machine translation systems.For this study, a comprehensive corpus was also developed which considered sentences based on different constructs.It was found that the proposed metric provides good results which are comparable with human (evaluation) judgments.
An introduction to visual analytics

This chapter provides an introduction to visual analytics, explaining how visualization concepts are intertwined with automated analysis approaches, such as machine learning or statistics, through interactive interfaces to support the users in their sense-making processes. In the context of medical applications, these systems are often designed to provide an interface towards exploratory, analytical, and decision-making processes on the basis of large and complex data. For example, users may get an overview of the dimensions in a data set, in the relations between pairs of dimensions, and in subgroups that share certain characteristics and differ strongly from other data sets. We explain basic machine learning techniques and how their results may be interactively explored and interpreted. Since real-world data in medicine are large, we also discuss the scalability of these methods, i.e., their appropriateness for larger, multi-variate data sets.
IoT Enabled Prenatal Health Monitoring System for Pregnant Women

Maternal and new-born well-being is a concern for underdeveloped as well as developing countries.Pregnancy problems, such as miscarriage, stillbirth and premature birth, increase the risk of maternal and new-born death.Pregnancy problems can be reduced by making lifestyle changes and focused health monitoring under supervision of trained, well equipped health care professionals during and after pregnancy.Approximately more than 40% population reside in rural areas, where medical systems are not integrated to facilitate information exchange.In the first trimester of pregnancy, most pregnant women in these countries are unable to get their normal checks, which results in a greater death rate for both the new-born and mothers in these areas.Also, it is not possible to monitor health vitals of pregnant women in the absence of physicians all the time during pregnancy due to several reasons like global availability of physicians, labs etc.The women's health is at jeopardy as a result of this predicament.Although, practitioners recommend ultrasound scans to see the growth of fetus.In underdeveloped and developing countries, access to such scanning facilities is not very common as well as expensive.To address these issues, the authors propose a solution in the form of a device which consists of various sensors like Temperature sensor, Heartbeat rate sensor, Blood Pressure sensor, Sweat sensor to measure sugar levels, oximeter to measure oxygen level, weight machine to measure weight, Accelerometer sensor and other devices like Arduino, GSM module etc. which can be further converted into compact device for accessing vital statistics of pregnant women to integrate it and to transfer the captured readings to the Thingspeak cloud so that the analytics can be monitored by physicians with the help of mobile or web application etc.The availability of IoT sensors and devices make this solution affordable with high accessibility for personalized care.
Visual analytics in clinical medicine

This chapter describes how visual analytics may be employed in clinical applications. This requires to dig into how patient data is typically represented, which information is available, and how the quality of that information can be assessed and improved. To visualize the patient history at a glance and to enable selective filtering of diagnostic results, treatments and symptoms are essential goals. The patient history is characterized by many different types of data, among which event-typed data are essential, e.g., the begin and end of a treatment or a hospital stay. Therefore the visual exploration of event-type data is discussed intensely. Whereas one complex patient history is already challenging to visualize, it is often useful to analyze a cohort of patients to identify frequent patterns during a treatment process. Thus simplification of patient histories and alignment of patient histories to make them comparable are essential topics. All the available clinical data require interactive visualization to be able to make sense of their underlying complex patterns. Here, two important applications are predictive analytics (answering the question Can we predict a severe course of a disease early?) and clinical decision-making support.
Enhancing Software Requirements Selection Process Using Genetic Algorithm

Software requirements selection is an important phase of the software development process.It is very difficult to decide the most important software requirements from a large set.Therefore, the capability of the genetic algorithm to assist in making better decisions on the software requirements selection is demonstrated in this paper using a practical case study.The objective of the genetic algorithm implemented in the software requirements selection function is to get a better decision in selecting the appropriate software requirements based on a range of criteria in various circumstances.To acquire information on assessment criteria and requirements, we used an Institute Examination System.The results obtained from experiments based on different criteria by prioritizing different software requirements have proved the capability of genetic algorithm to select the best solution.Our proposed genetic algorithm based software requirements selection approach is compatible with existing genetic algorithm based techniques and as well as the some other fuzzy based techniques also.
Introduction to Virtual Reality

This chapter provides an introduction to virtual reality, including a discussion of major terms and concepts. We discuss components of virtual worlds, advantages and potential problems of immersive environments and applications. Among the concepts discussed in more detail are avatars, which represent users in the virtual world and locomotion techniques, which enable users to navigate in the virtual world. We also consider performance issues that are essential to ensure a fluid interaction and to reduce or avoid VR sickness problems.
Visual analytics in public health

Public health aims at preventive measures to reduce health risks. In western countries, the high burden of chronic diseases causes high socio-economic costs. Public health activities aim at understanding which subpopulations and regions benefit in particular from interventions towards a healthier behavior or towards improved health care. Visual analytics solutions are employed to make sense of the large, high-dimensional data and understand spatio-temporal characteristics. Moreover, public health activities aim at limiting the risks due to infectious diseases. These more urgent activities are based on simulation models that predict the further course of epidemics or pandemic based on various assumptions. Visual analytics solutions were developed to train public health experts for a pandemic and to explain simulations during an ongoing pandemic. In this chapter, we characterize use cases, target users and their specific tasks, and provide an overview of visual analytics solutions developed for supporting public health experts.
Medical animations

Animations employ the human abilities to perceive and understand motions. Animations enables seamless transitions between different states, e.g., emphasizing different anatomical structures. Animation design is conceptually based on principles developed in the film industry and based on learning theories. Animation design involves the selection of appropriate viewpoints, the computation of camera paths that connect them. Moreover, material properties, such as color and transparency, are changed over time, e.g., to make structures visible and highlight them. The computation of fly-throughs through the colon or the bronchial tree are examples with a high relevance in clinical practice. Processes in the human body, such as digestion, breathing, pulsatile blood flow, may be captured with dynamic medical image data, e.g., perfusion data and blood flow measurements. In these settings, animations enable to directly observe changes in these dynamic data. Care has to be taken to avoid too complex animations that lead to cognitive overload.
Learning to Navigate in Turbulent Flows With Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach

Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in turbulent flows, decoupling the trajectory-tracking control from the turbulence compensation via a nested control architecture. Unlike previous works, our method does not learn to compensate for the air-flow at a specific time and space. Instead, our method learns to compensate for the flow based on its effect on the team. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture, which enables robots to achieve better wind compensation by processing the spatial-temporal correlation of wind flows across the team. Our approach scales well to large robot teams -- as each robot only uses information from its nearest neighbors -- , and generalizes well to robot teams larger than seen in training. Simulated experiments demonstrate how information sharing improves turbulence compensation in a team of aerial robots and demonstrate the flexibility of our method over different team configurations.