Reviewerly/exHarmony huggingface dataset:
Developing digital competency in future masters of vocational training

The use of modern digital technologies is a prerequisite for the development of more effective approaches to learning and the improvement of teaching methods, which saves time and is faster to achieve this goal. The high educational potential of modern digital technologies and the pace of their development, and modernization of software, determine the requirement for the improvement of training of masters of industrial training. In these circumstances, it is important for graduates of pre-professional higher education to have the knowledge, skills and experience to solve educational tasks, above all, digital tools. The aim of this study is to substantiate and experimentally test the levels of digital competence development of future masters of industrial training using the following methods: theoretical (analysis, synthesis, generalization); empirical (testing); statistical (results processing, charting).
ELT LEARNING MEDIA FOR YOUNG LEARNERS: FAMILY-THEMED PICTURE STORIES

The study aims at investigating whether the family-themed picture stories for young learners were valid as learning media for English Language Teaching (ELT). The researchers developed three picture stories related to family as prototypes. The research procedure was proposed by Akker, which comprised analysis, design, evaluation, and revision. The validity was reviewed by the experts of content, instructional design and media or product design. The mean of content validity of three prototypes is 4.26 which is at very highly valid level. In term of construct validity, the average score of construct validity of three stories is 4.23 which is at very highly valid level. The average score of media or product design validity of the prototypes is 4.36 which is at very highly valid level. Thus, the study result shows that, overall validities of content, construct, and media or product design of the three picture stories are 4.28, which is at very highly valid level. In conclusion, the set of family-themed picture stories as learning media are valid and categorized as very highly valid level; consequently, they are likely to facilitate Teaching English to Young Learners (TEYL). Keywords: learning media; TEYL; picture stories
특수 목적 중국어 수업 설계에 관하여 ― 4년제 대학의 항공서비스 중국어 중심으로

This study was started in the Hopeful Vocational Survey of the Chinese of B University in Busan. Depending on the results of the Learner Needs analysis, various necessary materials were presented for the Air Service Chinese class design. The change in jobs following the fourth industrial revolution informed the Chinese majors at universities that they needed CSP that could be used in specific situations and tasks. For the design of CSP classes, a lecture plan was presented to enable the use of various tasks and capability units of the NCS to demonstrate the competence of the Chinese. Therefore, for the opening and development of Chinese language courses in aviation services, the proposal is as follows: First, various teaching methods and learning methods should be developed for the development of research related to special-purpose Chinese in Korea. Chinese language courses are needed to cope with future changes in jobs and jobs. Second, Chinese-related textbooks using NCS should be developed. In developing textbooks, the knowledge and experience of the most important task experts should be reflected along with the needs of learners. Finally, class design based on various duties is also needed, including in-air operations management services, air logistics services and air ground services, not limited to air crews.
Civil Liability for Artificial Intelligence: What Should its Basis Be?

While there has already been much discussion among lawyers about liability for artificial intelligence (AI), comparatively little attention has been devoted to the actual basis of such liability. It is often taken for granted that product liability will apply to AI, but such liability is based in all legal systems on the product’s defect, and, as the article purports to demonstrate, proving an algorithm’s defect can be a daunting, if not impossible task for plaintiffs. Other liability bases should therefore be sought, which do not make compensation for harm caused by AI illusory, while preserving the legitimate interests of those who design or operate AI. As the article suggests, there is no one-size-fits-all in this respect and different liability regimes should apply, each with its own basis, depending on the field in which AI is being used. Harm caused by autonomous vehicles, for example, is probably better addressed through strict liability regimes for traffic accidents that through a general liability for AI regime.
An Empirical Study on the Effect of Serendipity in a Recommendation System

In this study, we tried to empirically analyze the effect of each recommendation method and attribute in recommendation system on satisfaction. In particular, we tried to verify the effect of serendipity, which conveys admiration through unexpected items, apart from the accuracy-based recommendation, which is gradually decreasing in satisfaction recently. As a result of regression analysis, it was found that the preference to items by the collaborative filtering method did not affect the satisfaction, while the Serendipity 2 recommendation method, which randomly manipulates the taste of a specific customer by 40%, had a significant effect on the site satisfaction. Overall, although the preference to items by serendipity 2 recommendation method was low, it was able to verify the effect of Serendipity as it satisfies precision and unexpectedness at the same time and significantly affects the satisfaction of the site. As a result of verifying the effects of precision, diversity, and serendipity in recommendation system, on satisfaction, all three factors were found to have a significant influence on satisfaction.
Student's Worksheet According to Problem Based Learning Reviewed from Understanding Mathematical Concept

Due to the limited Student Worksheets (SW) that are specific to the aspect of understanding concepts encourage the need for breakthroughs to develop them.This study aims to produce teaching materials as student worksheets with Problem Based Learning (PBL) on the understanding of mathematics concepts in grade VII students and describe the quality of SW based on aspects of validity, practicality, and effectiveness.The SW development model uses the ADDIE (Analysis, Design, Development, Implementation, and Evaluation).The SW that is developed must meet the following criteria: valid, practical, and effective.The results of the validity assessment of the material and media experts and the assessment of teachers in schools obtained an average score of 4.23 (maximum score of 5.0) with excellent criteria.The practicality aspect is stated based on the questionnaire of students' responses when learning takes place with an average score obtained at 3.30 (maximum score of 4.0) with a good category.Then, the effective criteria are shown from the results of the students' mathematical concept ability tests through the problem description, which consists of seven indicators of understanding mathematical concepts.Obtained a score of each indicator 64.5; 82.0; 75.0; 92.7; 86.4; 65.6; and 63.5 shows that each indicator has a good category above.So based on the results of aspects of validity, practicality, and effectiveness shows that SW with PBL in terms of understanding mathematical concepts can be applied in students' learning at school.This SW is expected to have a positive contribution to improving students' understanding of mathematical concepts in school.
Representable Autometrized Semialgebra

In this paper, two new concepts, namely, semilattice ordered Autometrized Algebra and Representable Autometrized Semialgebra are introduced. We characterized the Representable Autometrized Algebras among the Representable Autometrized Semialgebras.
EМПIРИЧНE ДOCЛIДЖEННЯ ПCИXOЛOГIЧНИX ЧИННИКIВ EМOЦIЙНOГO ВИГOРAННЯ ВIЙCЬКOВOCЛУЖБOВЦIВ IНЖEНEРНИX ВIЙCЬК

Introduction. The action of numerous emotional factors, both objective and subjective, causing a growing sense of dissatisfaction, deterioration of health and mood, accumulation of fatigue, reduced physiological indicators that characterize the intensity of work, leads to occupational crises, exhaustion and burnout. The result of these processes is a decrease in the effectiveness of professional activities of professionals, they cease to cope with their responsibilities, lose their creative mood on the subject and product of their work, distort their professional relationships, roles and communications.Purpose. Aacquaintance with the results of empirical research of psychological factors of emotional burnout of servicemen of engineering troops and their author's interpretation.Methods. Empirical methods such as testing and questioning were used to achieve the goal and realization of the research task - to study the psychological factors that determine the emergence of research.Originality. Emotional stability as a component of the emotional sphere of the individual determines the development of emotional burnout. The results of the observational experiment among a sample of young servicemen were as follows: about a third of those surveyed were diagnosed with an emotional burnout syndrome ("emotionally burned out") or one or another phase of emotional burnout is in the process of formation ("emotionally burnout"); a quarter do not show the expressiveness of emotional burnout. According to the results of diagnostics of self-regulation skills formation, the following regulatory skills were revealed: goal setting, action programming, evaluation of results, caution in actions, awareness of actions, focus on evaluation score, flexibility, involvement of useful habits in action regulation.Conclusion. The results of the empirical study revealed the dependence of the severity of the syndrome of emotional burnout of servicemen of engineering troops on individual psychological, organizational and socio-demographic factors, as well as psychological features of this process, which are associated with the peculiarities of their professional activities. The results obtained with the help of personal questionnaires indicate a low level of their emotional stability. The results of the observational experiment showed the presence of such regulatory skills as goal-setting, action programming, evaluation of results, caution in actions, awareness of actions, focus on evaluation, flexibility, involvement of useful habits in the regulation of actions. Qualitative analysis of the results indicates the limited nature of regulatory skills.
How Useful are Hand-crafted Data? Making Cases for Anomaly Detection Methods

While the importance of small data has been admitted in principle, they have not been widely adopted as a necessity in current machine learning or data mining research. Most predominantly, machine learning methods were typically evaluated under a “bigger is better” presumption. The more (and the more complex) data we could pour at a method, the better we thought we were at estimating its performance. We deem this mindset detrimental to interpretability, explainability, and the sustained development of the field. For example, despite that new outlier detection methods were often inspired by small, low dimensional samples, their performance has been exclusively evaluated by large, high-dimensional datasets resembling real-world use cases. With these “big data” we miss the chance to gain insights from close looks at how exactly the algorithms perform, as we mere humans cannot really comprehend the samples. In this work, we explore in the exactly opposite direction. We run several classical anomaly detection methods against small, mindfully crafted cases on which the results can be examined in detail. In addition to better understanding of these classical algorithms, our exploration has actually led to the discovery of some novel uses of classical anomaly detection methods to our surprise.
A Traffic and Resource Aware Online Storm Scheduler

Streaming applications have become widespread with the advent of big data and IoT. They are latency-sensitive applications that aim to process vast amounts of data in near real time. They are usually modelled as directed graphs and their deployment and orchestration in a cluster of nodes are managed by distributed stream processing systems. These systems are responsible for placing the graph components within the cluster nodes, which determines the application’s communication overhead and ultimately affects performance metrics such as latency and throughput. This work presents an adaptive, heuristic-based, scheduling algorithm for distributed stream processing systems that aims to minimize the latency and maximize the throughput of streaming applications deployed in heterogeneous clusters, while considering the resource constraints of the available nodes. The proposed approach uses a graph partitioning algorithm and real time traffic data monitored from a deployed application to derive a near-optimal operator placement plan that minimizes inter-node communication and balances the overall communication load distribution. We evaluated our approach using three micro-benchmark and two practical applications, and the results demonstrate that our scheduler outperforms the default scheduler of a popular stream processing system and a state-of-the-art algorithm, improving throughput by up to 106% and reducing complete latency by up to 58% for most applications.
IMPROVING STUDENTS’ VOCABULARY MASTERY THROUGH CONTEXT CLUES IN UNDERSTANDING READING TEXTS BY USING GOOGLE CLASSROOM APPLICATION AT THE GRADE XII MS 2 OF SMA NEGERI PLUS OF RIAU PROVINCE

This article reports on the purpose of the research was to explain whether context clues gives improvement toward students’ vocabulary mastery in understanding reading texts by using google classroom application at grade XII MS 2 of SMAN Plus of Riau Province, with collecting the data, checklists, field notes, and tests were used. This classroom action research consisted of two cycles constituting 3 meetings for each cycle. The result of this research shows that the students’ vocabulary mastery in understanding reading texts by using google classroom application increased in each cycles. In the first cycle, the percentage of students’ vocabulary mastery by using google classroom application was 80.60%. But, the result was not satisfactory yet. In the second cycle, the increase of students’ vocabulary mastery by using google classroom application was 90.08%. It means that there is the increase of students’ vocabulary mastery in understanding reading texts by using google classroom application in second cycles. In conclusion, the use of context clues could increase the students’ vocabulary mastery in understanding reading texts by using google classroom application.
Model agnostic meta-learning on trees

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related, and sharing information between unrelated tasks might hurt performance. A fruitful approach is to share gradients across similar tasks during training, and recent work suggests that the gradients themselves can be used as a measure of task similarity. We study the case in which datasets associated to different tasks have a hierarchical, tree structure. While a few methods have been proposed for hierarchical meta-learning in the past, we propose the first algorithm that is model-agnostic, a simple extension of MAML. As in MAML, our algorithm adapts the model to each task with a few gradient steps, but the adaptation follows the tree structure: in each step, gradients are pooled across task clusters, and subsequent steps follow down the tree. We test the algorithm on linear and non-linear regression on synthetic data, and show that the algorithm significantly improves over MAML. Interestingly, the algorithm performs best when it does not know in advance the tree structure of the data.
Analisis Mekanisme Pembelajaran Daring Secara Sysnchronous dan Asysnchronous dimasa Pandemic Covid-19 di STMIK Sinar Nusantara

Circular Number 05/SEK/STMIK-SN/IV/2020 in STMIK Sinar Nusantara Surakarta regulates implementation of learning, midterm exams, and even thesis / final exams. Hence, STMIK Sinar Nusantara took a policy for the implementation of synchronous learning and asynchronous learning. Synchronous learning (one time: teleconference, chatting) applies (1) Google Meeting by sinus.ac.id account, (2) Virtual meeting as Skype and Zoom, and (3) Discussion in Classroom. Meanwhile, asynchronous learning (time difference) uses (1) handouts, (2) tutorials, (3) videos, and (4) simple exercises. The purpose of this study was to determine the mechanisms in online learning (E-Learning) during Covid-19 Pandemic at STMIK Sinar Nusantara. This research only focuses on Synchronous and Asynchronous E-learning mechanism at STMIK Sinar Nusantara in even semester of 2019/2020. This research was conducted using 49 lecturers as respondents. The research method was descriptive statistical analysis. The results of the study by sampling 49 respondents as lecturers in online learning during Covid-19 Pandemic at STMIK Sinar Nusantara can be concluded that Synchronous learning (Conference by Google Meet, Skype, Zoom, Youtube Live, Umeetme) reached 57% and those who did not apply Synchronous learning (no conference) reached 43%. Meanwhile, the results of analysis in Asynchronous learning (handouts, tutorials, exercises / discussions, and videos) reached 88% and those who did not apply Asynchronous learning reached 12%.
Reflection Function and Periodic Solution of Time-varying Луръе Control System

The study of periodic differential systems is of great significance in many fields. Poincare mapping plays an important role in the study of periodic systems, but it is difficult to apply it in practice. The reflection function method created by Belarusian differential equation expert Mironenko provided a new way to establish Poincare mapping of periodic system. It provides a new method for studying the behavior of solutions of differential systems. Since then, more and more experts and scholars have begun to use reflection functions to study the behavior of solutions of periodic systems. So far, some important achievements have been made in the application of reflection function theory to study the geometric behavior of solutions of differential systems. This paper applies Mironenko’s reflection function theory for the first time to discuss reflection function structure of the time-varying Луръе control system and the behavior of its periodic solution. As the method is novel, many new results will be obtained. It also has a certain reference significance for the study of some multivalued differential equation control systems.
Comparative analysis of clustering techniques in the internet of things

One of the most important topics in the last decade is the Big Data (BD) and how to link it and benefit from its consumption in different fields, included as the introduction in this research analysis of the BD belonging to devices of the Internet of Things. The concept of managing objects and exploring devices is connected to the Internet and sensors deployed in the world, all these devices are pumping a lot of data through the Internet of Things (IoT) into the world. In order to make the right decisions for people and things, BD using data mining techniques and machine language algorithms help make decisions. The Internet of Things that insert large amounts of data need to be studied, analysed and disseminated in order to access valuable, useful and bug-free information for the purpose of making the right decision and avoiding problems. In this paper, two clustering algorithms simple K-means and self-organising map (SOM) in IoT are presented. Next, comparing the clustering models’ output in the IoT data set that improved the SOM is better than K-means, but it is slower in creating the model. Keywords: Internet of things (IoT), big data, machine learning, filtered cluster, K-means, SOM.
Leticia Isabel Cabezas Bermejo: IES Clavero Fernández de Córdoba, Almagro, Ciudad Real

espanolA pesar de doctorarse en Ingenieria Quimica, sabia desde el primer momento que su vida estaria dedicada a la docencia. Muy activa en las redes sociales, encuentra en Twitter el espacio ideal para ver como se desenvuelven sus companeros de profesion en ese «gran campo de batalla» que es la Educacion. Cada manana, canta mientras conduce de camino al colegio movida por la felicidad irrefrenable que le produce volver a encontrarse con sus alumnos. EnglishDespite a PhD in Chemical Engineering, he knew from the first moment that his life would be dedicated to teaching. Very active on social networks, she finds on Twitter the ideal space to see how her colleagues work in that «great battlefield» that is Education. Every morning, she sings while driving to school driven by the unstoppable happiness that comes from meeting her students again.
Development of a Two Link Robotic Manipulator

Development of a two link robotic manipulator - Robots play important roles in day to day activities of human endeavour and
The Closed Sequence Patterns for DNA Data without Candidate Generation

Sequential pattern mining is a technique which efficiently determines the frequent patterns from small datasets. The traditional sequential pattern mining algorithms can mine short-term sequences efficiently, but mining long sequence patterns are in efficient for these algorithms. The traditional mining algorithms use candidate generation method which leads to more search space and greater running time. The biological DNA sequences have long sequences with small alphabets. These biological data can be mined for finding the co-occurring biological sequence. These co-occurring sequences are important for biological data analysis and data mining. Closed sequential pattern mining is used for mining long sequences. The mined patterns have less number of closed sequences. This paper proposes an efficient Closed Sequential Pattern Mining (CSPAM) algorithm for efficiently mining closed sequential patterns. The CSPAM algorithm mines closed patterns without candidate generation. This algorithm uses two pruning methods namely, BackScan pruning, and frequent occurrence check methods. The former method prunes the search space and latter detects the closed sequential pattern in early run time. The proposed algorithm is compared with PrefixSpan algorithm, better scalability and interpretability is achieved for proposed algorithm. The experimental results are based on sample DNA datasets which outperform the other algorithms in efficiency, memory and running time.
Perancangan Aplikasi Pencarian Guru Les Privat Komputer Berbasis Android

The purpose of this research is to help students get learning outside the school environment, this is a better education because in addition to school children also get education from private lessons. In addition, this application also functions as a forum for private tutors to boost the economy. Gathering the information students need to do computer learning which is currently all done is easier. So it is hoped that with the design of this private computer tutoring application, it can help students get learning outside the school environment and around the student's residence. The research method used is grounded research, which is a research method based on facts and uses comparative analysis with the aim of making empirical generalizations, establishing concepts, proving theories, developing theories, collecting and analyzing data at the same time. After the author analyzes and collects data, finally the writer can draw the conclusion that with this application system, it can help students get the learning they need easily and efficiently.