Reviewerly/exHarmony huggingface dataset:
유아 무용교사의 직무환경이 직무만족도와 행복감에 미치는 영향

The purpose of this study is to examine direct pathways by which the work environment affects the happiness of the child dance teacher and the indirect pathway through the job satisfaction, and also to provide basic data for high quality educational development while systematically nurturing child dance teachers. To accomplish the purpose of the study, a total of 202 teachers having a experience of child dance were selected as the subject of the study and the questionnaire was used for the measurement tool. To verify the correlation between the child dance teacher''s work environment effecting job satisfaction and sense of happiness, the data collected through the questionnaire were analyzed by the SPSS program to construct validity and reliability of the questionnaire. Pearson''s correlation analysis was performed to analyze the correlation of each factor, and to learn about the mediated effect, the regression coefficients estimated by the hierarchical regression analysis were used.
The possibilities of theatrical art in the devel-opment of the negotiation competence of the teacher-mediator

The purpose of the article is the theoretical substantiation of the didactic potential of the means of theatrical art in the development of the negotiation competence of the future teacher-mediator. The main idea of the research is to consider the problem of organizing the training of negotiators for the field of education at the interdisciplinary level, which ensures the complex nature of the educational activities of students-future teachers-mediators. More results consist in the presentation of an innovative approach to the development of the negotiation competence of the teacher-mediator, in particular, in development of the idea to Supplement the structure of the negotiation competence of acting component, the formulation of the scientific definition of acting competence of the teacher-mediator, development and justification of the conditions of realization of educational activity in the University by means of theatrical art, contributing to the successful professionalization of the future teacher-mediator. The theoretical significance of the research is to expand the understanding of the phenomenon of negotiation competence in relation to acting creativity, in particular, with acting technique, and to identify opportunities for developing the negotiation competence of a future teacher-mediator based on the methodology of theatrical art (on the example of theatricalization, forum theater and the use of acting and directing memoirs in professional training).
Acts of Architecture: In Conversation with Andrew Hinton

Andrew Hinton, the author of Understanding Context: Environment, Language, and Information Architecture, is interviewed about embodiment—how language, physicality and the environment all affect design in a digital realm, the importance of taking time to work on ethical and research aspects of the field of information architecture, and how service design models can support information architecture activities.
Camera placement optimization for sports filming

With the rise of high-quality and affordable video cameras, it has never been more accessible to record and broadcast live amateur sports games. However, to maintain a decent viewing angle of the field and its players, one must continuously adjust their camera's orientation to follow the players. While there have been several commercial products that give sports teams equipment and software to record their games with higher quality and convenience, these technologies are usually proprietary and quite expensive. Therefore, we propose a solution to utilize the existing lighting poles around a sports field for mounting cameras. In this way, sports teams and field managers can utilize low-cost hardware to record games. Additionally, this offers the opportunity for a permanent fixture of cameras around the field that can be readily available whenever a game takes place. To this end, this paper investigates how to find the ideal placement of cameras on lighting poles to best capture a nearby field in the following two scenarios. (i) Single-camera scenario: when the properties of a pinhole camera such as focal distance, sensor dimensions, and image resolution are known, the camera's projection quadrilateral on the field can be calculated. Based on this, we optimize the camera's height and elevation angle based on a desired ground sampling distance. (ii) Multi-camera scenario: when the properties of multiple cameras are known, we optimize camera placement to either capture the same field area from different views or to capture different areas of the field for greater coverage. To showcase the potential application of these optimization methods, we have implemented them into a web-based simulator that can run in almost any browser (source code available at https://github.com/figuedj1/SportsFilmingOptimization). When a user inputs their camera and field specifications, they are not only given both a 2D and 3D view of the field and environment but also methods for optimizing each camera's orientation and placement.
Comparative analysis of distance learning systems

The article is devoted to the actual problem-the introduction of distance learning systems in the educational process.The most common distance learning platforms, such as Moodle, Skype, Zoom, and Google meet, are considered.A comparative analysis is performed, and positive and negative aspects are identified for each platform.
Learning Sampling Policy for Faster Derivative Free Optimization

Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad applications in machine learning community. The two function evaluations are normally generated with random perturbations from standard Gaussian distribution. To speed up ZO methods, many methods, such as variance reduced stochastic ZO gradients and learning an adaptive Gaussian distribution, have recently been proposed to reduce the variances of ZO gradients. However, it is still an open problem whether there is a space to further improve the convergence of ZO methods. To explore this problem, in this paper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. To find the optimal policy, an actor-critic RL algorithm called deep deterministic policy gradient (DDPG) with two neural network function approximators is adopted. The learned sampling policy guides the perturbed points in the parameter space to estimate a more accurate ZO gradient. To the best of our knowledge, our ZO-RL is the first algorithm to learn the sampling policy using reinforcement learning for ZO optimization which is parallel to the existing methods. Especially, our ZO-RL can be combined with existing ZO algorithms that could further accelerate the algorithms. Experimental results for different ZO optimization problems show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
The Effects of perceived trust in the company on the customer loyalty based on the companys website contents(case study of Aval-Option startup)

In our modern world, the customers are at the center of attention for companies and customer loyalty is the mainfactorin creating organizational competitive advantage. In recent years, various economical firms, from the startupcompanies and small businesses to the international companies , have recognized the importance of customer loyalty.Those companies are well aware that maintaining the existing customers is more profitable than attracting new ones.The present study aims to examine the effect of perceived trust in the company on the levels of customer loyaltybased on the Aval-Option companys website contents. This is an applied study in terms of purpose and adescriptive survey study in terms of the data collection method. The statistical population of the studyconsisted of allthe customers of the Aval-Option startup website. We followed the rule of five times of the number of questions tomake a decision about the sample. 175 customers were selected for the sample. The findings showed that a positiveand significant relationship exists between the quality of information and the user satisfaction from the previousexperiences, and the trust in the website; meanwhile a positive and significant relationship was detected between theperceived trust in the companys website as a variable and the intention of repeating purchase, recommendingthe purchase to other people and rejecting the other options .
Near-Ideal Quantum Efficiency with a Floquet Mode Traveling Wave Parametric Amplfier

Broadband quantum-limited amplifiers would advance applications in quantum information processing, metrology, and astronomy. However, conventional traveling-wave parametric amplifiers (TWPAs) support broadband amplification at the cost of increased added noise. In this work, we develop and apply a multi-mode, quantum input-output theory to quantitatively identify the sidebands as a primary noise mechanism in all conventional TWPAs. We then propose an adiabatic Floquet mode scheme that effectively eliminates the sideband-induced noise and subsequently overcomes the trade-off between quantum efficiency (QE) and bandwidth. We then show that a Floquet mode Josephson traveling-wave parametric amplifier implementation can simultaneously achieve $>20\,$dB gain and a QE of $\eta/\eta_{\mathrm{ideal}}> 99.9\%$ of the quantum limit over more than an octave of bandwidth. Crucially, Floquet mode TWPAs also strongly suppress the nonlinear forward-backward wave coupling and are therefore genuinely directional. Floquet mode TWPAs can thus be directly integrated on-chip without isolators, making near-perfect measurement efficiency possible. The proposed Floquet scheme is also widely applicable to other platforms such as kinetic inductance traveling-wave amplifiers and optical parametric amplifiers.
Age-Invariant Facial Expression Classification Method Using Deep Learning

This paper proposes a method for facial expression classification as a solution to some of the challenges caused by age variations in image classification tasks. The proposed method utilizes deep learning neural networks pre-trained for image classification tasks which are then fine-tuned using a training dataset composed of mixed age facial expression images. The experimental results obtained after testing the model trained on a mixed age dataset, and another model trained on a standard dataset, using new images from a mixed age test dataset, show that the proposed model has better performance at facial expression classification task involving age variations in the input images.
근대 이전 소학 교육의 구조와 문자 학습 교재의 국어교육사적 의미

This article started with the goal of analyzing the structure of SOHAK(小學) before the modern era and textbooks for children, and to find out the meaning of the Korean language education history of the letters and vocabulary study books. This goal is to show that Korean language education existed even before modern times by examining the characteristics of elementary school education through literature review, and analyzing textbooks for children in the presence of considerable prejudice against the concept of SOHAK(小學) or the object of elementary school education. It originated from the intention to clarify.Prior to the modern era, schools existed in a form of relatively autonomy based on the basic ideology of SOOK SANG SEO HAK(塾庠序學) mentioned in Yegi(禮記). Although a system such as HYANGGYO(鄕校) of the county(州 郡), SABUHAKDANG(四部學堂) in the center of nation, or SEONGGYUNGWAN(成均館) was established at the national level, the SEODANG(書堂) and SEOWON(書院) in the small town, were established around individual or family group.Prior to the modern era, the concept of SOHAK(小學) was divided into JAHAK(字學, study for letters) and JUJA SOHAK(朱子小學, the principals of morality established by JUJA), but in the case of Korea, the former was not included in concept of SOHAK. However, in that character comprehension, reading, writing, and counting form the basis of elementary education, it can be seen that in pre-modern Korea, learning of letters and vocabulary formed the basis of education for children. Pre-modern letters and vocabulary study books can find the meaning of Korean language education history in that they tried to test not only the sound and meaning of the letters, but also the correct pronunciation and luck. In addition, it was attempted to reveal that the use of Korean in the process of classifying vocabulary by item and organizing and organizing the names of objects and knowledge of everyday life has contributed to the maintenance and development of Korean language.
Investigating Opportunities to Support Kids' Agency and Well-being: A Review of Kids' Wearables

Wearable devices hold great potential for promoting children's health and well-being. However, research on kids' wearables is sparse and often focuses on their use in the context of parental surveillance. To gain insight into the current landscape of kids' wearables, we surveyed 47 wearable devices marketed for children. We collected rich data on the functionality of these devices and assessed how different features satisfy parents' information needs, and identified opportunities for wearables to support children's needs and interests. We found that many kids' wearables are technologically sophisticated devices that focus on parents' ability to communicate with their children and keep them safe, as well as encourage physical activity and nurture good habits. We discuss how our findings could inform the design of wearables that serve as more than monitoring devices, and instead support children and parents as equal stakeholders, providing implications for kids' agency, long-term development, and overall well-being. Finally, we identify future research efforts related to designing for kids' self-tracking and collaborative tracking with parents.
Understanding Apache Spark

Apache Spark is a data analytics platform that has made big data accessible and brings large-scale data processing into the reach of every developer. With Apache Spark, it is as easy to read from a single CSV file on your local machine as it is to read from a million CSV files in a data lake.
A Detailed Review On Medical Image Compression

The current article is a scientometric analysis of the research articles on the topic “Medical Image Compression”. Scopus and WoS databases have been used for downloading the papers related to the above discussed topic. PRISMA guideline have been used for the selection of the articles. A total 884 articles have been downloaded and 397 have been selected and analyzed with VOS viewer.
OPTIMETA – Strengthening the Open Access publishing system through open citations and spatiotemporal metadata 

The BMBF project OPTIMETA aims to strengthen the Open Access publishing system by connecting open citations and spatiotemporal metadata from open access journals with openly accessible data sources. For this purpose, we will extend Open Journal Systems (OJS) to give it functionalities for collecting and distributing open data by developing two OJS plugins for capturing citation networks and articles' spatial and temporal properties as machine-readable and accessible metadata. We will ensure the target group-orientated design of the plugins by performing a comprehensive needs analysis for key stakeholders: the editors or operators of OA journals and the researchers, as authors and readers of articles. The developments will be designed and tested in cooperation with several independent journals and OA publishers. Overall, OPTIMETA supports the attraction of independent OA journals as publication venues by substantially improving the discoverability and visibility of OA publications through enrichment and interlinking of article metadata.
Challenges on Teaching Online English Subject in SMK Negeri 1 Nawangan

This study describes of the challenges of teachers in teaching online English subject in SMK Negeri 1 Nawangan. This study aimed at identifying and describing the challenges faced by teachers in teaching online English subject in SMK Negeri 1 Nawangan. The subjects of this study were 2 English teachers in SMK 1 Nawangan. This study applied a qualitative method. The research method used by researchers is by interviewing and providing online questionnaires. The interview was conducted at the home of Mrs. Rn. Whereas online questionnaires were given via WhatsApp contact Mr. S. This study found that poor internet access experienced by students of SMK Negeri 1 Nawangan is a major problem. Students are often slow to respond to the process of learning activities carried out online. In addition, teachers also cannot directly measure students' ability. To reduce internet access problems experienced by students, the school facilitates internet data packages for teachers and students. In the end, this research is expected to be useful for English language teachers, readers, and schools to obtain information and be able to develop the quality of education.
Hierarchical Transformer Networks for Longitudinal Clinical Document Classification.

We present the Hierarchical Transformer Networks for modeling long-term dependencies across clinical notes for the purpose of patient-level prediction. The network is equipped with three levels of Transformer-based encoders to learn progressively from words to sentences, sentences to notes, and finally notes to patients. The first level from word to sentence directly applies a pre-trained BERT model, and the second and third levels both implement a stack of 2-layer encoders before the final patient representation is fed into the classification layer for clinical predictions. Compared to traditional BERT models, our model increases the maximum input length from 512 words to much longer sequences that are appropriate for long sequences of clinical notes. We empirically examine and experiment with different parameters to identify an optimal trade-off given computational resource limits. Our experimental results on the MIMIC-III dataset for different prediction tasks demonstrate that our proposed hierarchical model outperforms previous state-of-the-art hierarchical neural networks.
Towards adaptive and curious artificial agents: learning, perception, and planning in dynamic uncertain environments

In both military and commercial domains, tasks are increasingly entrusted to autonomous systems and robots. These artificial intelligence (AI) systems are expected to be safe and intelligent, adapt to changing environments, and interact with other actors, both automated and human. In this paper, we present a framework and corresponding analytics for developing AI agents that possess (1) cognitive skills, including the ability to perform counter-factual reasoning and self-assessment and to exhibit human-like curiosity, biases, and errors; (2) the ability to learn complex tasks quickly with limited feedback; (3) the ability to coordinate and co-learn with human or AI teammates; and (4) the ability to function well over long time horizons (e.g. hours or days). Our framework is based on the theory of adaptive behavior called active inference, which was developed in computational neuroscience and psychology. Together with learnable deep factorized representations, the active inference provides the objective function, high-capacity predictions, and scalable computational mechanisms that enable AI agents to execute four processes fundamental to human cognition: learning, perception, planning, and simulation. We demonstrate the advantages of our AI solution in the domain of planning multi-agent maneuvers for executing area control missions. Our model achieves faster learning compared to the reinforcement learning baseline, producing faster point accumulation and game win rate.
Design and Application of Web based College English Online Learning System

With the development of modern network technology, people begin to use the relevant network technology to carry out teaching activities, which is no exception in the field of College English teaching, and this gradually formed the concept of online learning system. The system can effectively connect with the offline learning system, make up for the defects of the offline system, optimize the learning environment and behavior, and ensure the quality of students' learning. Therefore, the system has attracted much attention in the field of education. But how to realize the system is another big problem. This paper will put forward a design scheme of College English online learning system based on Web, and discuss its application performance.
Diagnosa Resiko Penyakit Jantung Menggunakan Logika Fuzzy Metode Tsukamoto

The heart is one of the most vital organs in the body and its a very important role for humans. Therefore, it is very important to pay attention to the risk of heart disease from an early age. This disease can be detected early with routine examinations. Based on WHO data (2011), heart disease is the number one cause of death in the world and at least 17.5 million or the equivalent of 30% of deaths worldwide are caused by heart disease. From these problems, the researchers created an expert system using the Fuzzy Tsukamoto method to diagnose the risk of heart disease. The benefit of this research is that it can help make it easier for the general public to check the level of risk for heart disease. The input from the system is blood sugar, cholesterol, blood pressure, and body mass index (BMI), while the output is a risk rating for heart disease with 3 categories, namely small, medium, and large. The stages of the fuzzy method Tsukamoto include fuzzification, formation of IF-THEN rules, inference engine, and finally defuzzification. From the application of the fuzzy Tsukamoto produces an expert system that can diagnose heart disease with three risk categories and based on 30 test data, an accuracy value of 83 percent is generated based on a comparison of the system results with expert results.
Binary ensemble machine learning for confident decisions

Folk wisdom on the subject of human knowledge holds that it is “better to know nothing than to know what ain’t so<sup>1</sup>”. In some circumstances that precept may be particularly important. If the negative consequences of false knowledge are sufficiently severe, we may be willing to forgo the benefits of knowing some facts to avoid the dangers of believing “facts” that are incorrect. This is the foundation of the “innocent until proven guilty” system of justice. According to English jurist William Blackstone, “it is better that ten guilty persons escape than that one innocent suffer<sup>2</sup>”. Similar principles apply wherever it is especially harmful to act upon false beliefs. If we wish to employ machine learning as an aid to human judgment, it may in some cases be advisable to insist upon near-certainty from the machine’s reported results. One way of working towards this goal is to use an ensemble of different agents. If their results are consistent with each other, we can have greater confidence in their overall reliability. We can also set a threshold value for the average confidence of the agents themselves. This paper explores the decision-making process for developing an ensemble of classifiers, and evaluates the results in the context of an example set. This set is appropriately categorized by a hierarchical structure, which permits less-specific judgments to be made if confidence falls below our predetermined threshold. We examine the tradeoffs to be made when setting parameters, and discuss aligning them with overarching requirements.