Reviewerly/exHarmony huggingface dataset:
Monitoring and assessment of pre-service foreign language and culture teachers testing competence

When testing pre-service foreign language and culture teachers methodological competence formation it is important to monitor as well the process and outcomes of its constituents formation. One of the prerequisites of efficient professional pre-service foreign language and culture teachers training is a scientifically and methodically appropriate selection of forms, methods, techniques and means of monitoring testing competence (as a constituent of methodological competence), which is developed in pre-service teachers within the framework of the course “Modern approaches to testing foreign languages and cultures teaching”. The purpose of monitoring testing competence is to evaluate students’ academic achievements and define the correlation between the foreseen and objective results of mastering specific methodological knowledge and skills of developing linguodidactic tests by preservice foreign language and culture teachers. The assessment is actualized through current, midterm and final control. The methods of testing competence formation assessment under implementing its various types are oral interviews, written testing and a module test, which is realized in the form of individual project tasks presentation. The assessment objectives are methodological knowledge and skills as the constituents of testing competence. The article also highlights the criteria for assessing methodological knowledge and fulfilling professional tasks (current control), the criteria and parameters for evaluating individual project tasks (mid-term control), the criteria for evaluating students’ answers during the interview and the results of the practical tasks completion (final control). In terms of its arrangement, testing competence assessment is individual and performs such functions as feedback, training and evaluation.
Comparison between XY Spin Chains with Spin 1/2 or 1 Interacting with Quantized Electromagnetic Field by One and Two Photon Jaynes-Cummings Model

This paper describes two cases of interaction between a quantized electromagnetic field and two different XY spin molecules; one with spins ½, and the other with spins 1. Both interact with a quantized electromagnetic field, with one of the spins in the chain interacting with the electromagnetic field. The interaction between the field mode and the spin chain with spins 1 is described by the one- and two-photon Jaynes-Cummings model (JC model). On the other hand, the interaction between the spins ½ and the electromagnetic field is described only by the one-photon Jaynes-Cummings model. Analytical and numerical calculations were made for the case of a different number of photons in the field mode, a different number of spins, and a different position of spin, interacting with the electromagnetic field. The invariant and block structures of such a chain are shown with a comparison made between the evolution of the magnetic moment and the number of photons in both cases.
SME Performance: The Effects of Social Media Marketing Adoption and Competitive Intelligence

Organizational performance is a crucial variable in studies on management the success of all businesses whether small or medium enterprises or large organizations can be seen through their performance, by their capability to lead which include creating employment and wealth by business start-ups, survival and sustainability. Previous studies on the adoption of internet among Malaysian SMEs have discovered the limitations and acceptance barriers, as manifested through only 32.3 percent of local business owners who have extensively adopted social media as their marketing tool. This study aimed to explore the relationship between the adoption of social media marketing towards SME performance in Malaysia, focusing on those SMEs within the east coast region of Malaysian peninsula. It also explored the potential relationship between competitive intelligence towards SME performance. A quantitative approach was used to test the hypothesis in order to get reliable results where 400 respondents who were mainly SMEs business owners were selected from Kelantan, Terengganu and Pahang. The study found that there was positive relationship between social media marketing adoption with SME performance where a moderate strength of association with SME performance existed. It was also found that competitive intelligence has positive relationship and a moderate strength of association between competitive intelligence and the performance of SME thus supported these hypotheses. As a sum, SMEs business owners should seriously consider social media as their important weapon tool in their marketing strategies.
Having a cluttered year from the coronavirus

It can be said that 2020 was the year of the coronavirus Coronavirus began to pandemic in China’s Yuhan from the beginning of the year and hit the world, and the aftereffects are still ongoing, and the world has not escaped from the shock It was the year 2020 that started with the coronavirus and ended with the coronavirus © 2020 Korean Society of Exercise Rehabilitation All Rights Reserved
A Comparison of Using L1 (Indonesian) and English to Answer English Listening Comprehension Questions

English test results show that a lot of Indonesian students obtained low scores in listening. Students say they understand what speakers say in an English listening test but they have difficulties in answering the questions in English. This shows that students’ English receptive skills are higher than students’ productive skills. This research intends to find out what the difference in results is when students answer English listening comprehension questions in L1 (Indonesian) and English, and what the students’ perception is on the use of Indonesian language in English listening test. This research uses the mixed method along with a listening test, questionnaires, and interview as data collection tools. The findings show that these learners have difficulties to answer the test in English. The test answered in Indonesian received better scores than in English. Moreover, the questionnaires and the interview result show positive feedback on the use of Indonesian language. This shows that the learners actually have good listening skills and that their low-test scores are mostly due to their difficulty in expressing their thoughts in English.
DoLFIn: Distributions over Latent Features for Interpretability

Interpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a tradeoff: either the models are too constrained to be very useful, or the solutions found by the models are too complex to interpret.We propose a novel strategy for achieving interpretability that -in our experiments -avoids this trade-off.Our approach builds on the success of using probability as the central quantity, such as for instance within the attention mechanism.In our architecture, DoLFIn (Distributions over Latent Features for Interpretability), we do no determine beforehand what each feature represents, and features go altogether into an unordered set.Each feature has an associated probability ranging from 0 to 1, weighing its importance for further processing.We show that, unlike attention and saliency map approaches, this set-up makes it straight-forward to compute the probability with which an input component supports the decision the neural model makes.To demonstrate the usefulness of the approach, we apply DoLFIn to text classification, and show that DoLFIn not only provides interpretable solutions, but even slightly outperforms the classical CNN and BiLSTM text classifiers on the SST2 and AG-news datasets.
Special Issue on Probabilistic Logic Programming (PLP 2018)

This paper introduces a variational analysis approach to the learning of a deep model formed via a nested composition of mappings. The fuzzy sets, being characterized by Gaussian type of membership functions, are used to represent unknown functions associated to the layers of the model. The learning of the deep model would require a quantification of the uncertainties on the signals across the layers of the deep model. We derive analytically the mathematical expressions for membership functions using variational optimization to quantify the uncertainties on variables. The most significant feature of the learning approach is that all of the unobserved variables and parameters, associated to the deep model, are averaged out where the averages are computed taking into account the uncertainties (on variables and parameters). The uncertainties are quantified by means of fuzzy sets with membership functions optimally learned from the observed data. A rigorous mathematical treatment of the learning problem results in the development of a competitive classification algorithm. The study is a theoretical contribution to the field of fuzzy machine learning, nevertheless, offering practical machine learning algorithms.
Assessment of Augmented Reality Mobile Application for Educational Purposes

Augmented Reality (AR) is among the advanced technology accessible worldwide, which constantly noticeable in the entertainment industry. However, there is no universal nor accessible augmented reality to be found offering in Malaysia higher education. This research intends to apply augmented reality application within an educational perspective and evaluate its effectiveness based on four (4) criteria that comprises: performance, usefulness, learnability, and accuracy. An augmented reality application, Floristic was developed to cooperative in teaching plant material at Tropical Park of Universiti Teknologi Malaysia (UTM), Skudai. A pilot study has conducted within a limited period where the participants had responded to collect and evaluate several flora species using the median score system. Based on the results, the Floristic ‘performance’ scored four (4) out of five (5), the ‘usefulness’ scored five (5), the ‘learning skill’ 4.8, and the ‘accuracy’ scored four (4). Meanwhile, the questionnaires survey was analysed as a recommendation for further improvement. In conclusion, the implementation of augmented reality for teaching and learning purposes will make the learning process more engaging and exciting. AR technology is also very fast-growing, and there seems to be a great deal of potential in the use of augmented reality rather than in the choice of floral identification alone.Keywords: Augmented reality, education, Floristic, mobile application
Nonlinear filtering of image contours defined in a complex-valued code

The article describes special aspects of the organization of nonlinear filtering of image contours defined as a sequence of complex numbers. An effect of scale and rotation of the contour on the filtering result is studied. Changes in the contour spectra caused by different nonlinear filters are studied.
Ultrabright Photons for Single-Chip Quantum Devices

A microcavity-based source of photon pairs achieves one hundred times the efficiency of state-of-the-art devices.
IMPLEMENTASI ALGORITMA K-MEANS UNTUK PEMILIHAN KERAMIK DAN PELANGGAN POTENSIAL PADA CV. JAYA TUNGGAL KERAMIK

CV. Jaya Tunggal Keramik is a company that sale of ceramics. CV. Jaya Tunggal Keramik experienced some problems regarding ceramics and customers such as difficulties in sale ceramics to customers so that some ceramic products accumulate in the warehouse, such as being damaged and ceramic display becomes less good because it is stored too long and difficulty retaining customers because some customers do not want to order ceramic products. Lack of precise decision taken by the management CV. Jaya Tunggal Keramik in determining the strategy to supply ceramic and how to make it CV. Jaya Tunggal Keramik is difficult to estimate the stock of ceramic products to be provided and it is difficult to determine which potential customers can be maintained as a regular customer. This research uses K-Means algorithm. K-Means algorithm is a partitioning clustering method that separates data into different groups with iterative partitioning. By using this application, users can find out the estimated stock and price of ceramics as well as information about potential customers. Testing in this research using data of November 2017 that compared with data of December 2017. Based on ceramic data test results, there are some ceramics that are not in accordance with the predicted results so it can be concluded that the K-Means algorithm on the test inventory data inventory in this study is not fully can provide accurate estimates, this is because the use of the K-Means algorithm is strongly influenced by the cluster center results and the attributes used.
Металлогения Камчатского срединного массива

The article describes the ore bearance of three stages of reflected activation of the Kamchatka middle massif: Late Cretaceous, Eocene, and Miocene. In the first stage, gold mineralization of the gold-quartz and gold-sulfide-quartz formations was formed; in the second - sulfide platinoid-copper-nickel; in the third - gold-copper-molybdenum-porphyry. The relationship of gold, sulfide platinoid-copper-nickel and gold-copper-molybdenum-porphyry mineralization with certain igneous rock complexes - (Kola, Dukuk and Lavkin) is shown. The mining of nickel, copper from the Shanuch deposit and gold from placers has been noted.
Research on the Enrollment Model of Music Majors in American Comprehensive Universities

The enrollment model of music majors in American comprehensive universities is formed in the overall American social environment. In practice, it integrates the four elements of maintaining social fairness, abiding by admissions justice, following discipline rules, and emphasizing the ability of candidates. The optimal structure is formed. Thus, in the process of enrollment, it can not only maintain the fairness of society and the fairness of enrollment, but also follow the law of education to better predict the ability of students to succeed. This paper discusses that some good practices in the enrollment of music majors in the United States can be used for reference, such as the algorithm of GPA (Grade Point Average) scores, the defense link of the enrollment examination, the interview and so on.
On the influence of different reading on the course of associations

Not more than 20 years have passed since then, as the first attempts were made to apply the experimental method to the study of the association of ideas. The first researchers in this area (Galton, Travtscholdt, Catell) were mainly engaged in the speed of associations, working with the help of the well-known Ніррs chronoscope. Then we moved on to the study of the self-content of the associations.
SISTEM DETEKSI EMOSI MENGGUNAKAN SINYAL EEG “EMOCLASS”

An emotional detection system has been developed using EEG signals with the help of a computer program. The results of this development are an important step in progress in learning the classification of emotional detection because it can be obtained more quickly. This study uses a support vector machine approach with a statistical analysis model that can be used to classify emotions into the Russell Emotion Model. Emotions included are Amused, Fear, Calm, Sad, and Neutral. With some assumptions, this system can provide benefits to the multimedia sector by producing applications that automatically detect human emotional experiences.
Analysis of the Errors in the Chinese Students’ Composition

Writing is a complicated process and being able to write well is usually considered to be the last skill in language learning. Teachers firstly need to find out the errors the Chinese students made in their English writing process in order to improve students’ writing ability. Errors are common characteristic of language acquisition and learning. Via studying the types of errors on the basis of error analysis theory, the author hopes to seek an effective approach to the teaching of English writing.
Covid-19, Contact Tracing and GDPR

Provisions recently issued relating to the Covid-19 pandemic spread emergency showed a considerable impact (also) about personal data processing and, consequently, on the right to their protection.Among the measures adopted (in several countries, including Italy) to cope the pandemic emergency, there are those based on collection and tracking of personal health data in order to control and contain the virus and in particular the so-called "Immuni" app, involving, prima facie, an invasive control in people's lives: hence, once the terms and characteristics of this tracking system have been reconstructed, the need to identify its legal basis in light of the General Data Protection Regulation (GDPR) and, in the event of a positive outcome of this assessment, the guarantees capable to ensure the necessary balance between the protection of public health and the protection of the private sphere from excessive compression or intrusiveness.SUMMARY: 1. Introduction.-2.The so-called app "Immuni" as emergency measure.-3.The problem of "Immuni" app lawfulness: legal basis.-4.
Learning Graph Topology Representation with Attention Networks

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better word understanding by capturing contextual structure on sentence level. However, to understand a document better, it is necessary to involve contextual structure from document level. Moreover, some words contributes more information to delivering the meaning of a document. Motivated by this, in this paper, we take the advantages of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAN) to model global word-relation structure of a document with attention mechanism to improve context-aware document ranking. We propose to build a graph for a document to model the global contextual structure. The nodes and edges of the graph are constructed from contextual embeddings. We first apply graph convolution on the graph and then use attention networks to explore the influence of more informative words to obtain a new representation. This representation covers both local contextual and global structure information. The experimental results show that our method outperforms the state-of-the-art contextual language models, which demonstrate that incorporating contextual structure is useful for improving document ranking.
DrCaptcha: An interactive machine learning application

The creation of a Machine Learning system is a typical process that is mostly automated. However, we may address some problems in the during development, such as the over-training on the training set. A technique for eliminating this phenomenon is the assembling of ensembles of models that cooperate to make predictions. Another problem that almost always occurs is the necessity of the human factor in the data preparation process. In this paper, we present DrCaptcha [15], an interactive machine learning system that provides third-party applications with a CAPTCHA service and, at the same time, uses the user's input to train artificial neural networks that can be combined to create a powerful OCR system. A different way to tackle this problem is to use transfer learning, as we did in one of our experiments [33], to retrain models trained on massive datasets and retrain them in a smaller dataset.
A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.
Application of Random Forest Method Classification to Predict BPJS Kesehatan Card Users Who Receive Contribution Assistance in Karangasem District, Bali Province 2017

BPJS Kesehatan is a social security facility provided by the government to all people who are registered as members. BPJS Kesehatan membership is divided into two, namely BPJS for Contribution Assistance Recipients (BPJS PBI) and BPJS Non-Contribution Assistance Recipients (BPJS Non-PBI). In 2019, Bali Province is targeted to achieve Universal Health Coverage of 95 percent so that the Bali Provincial Government has budgeted funds worth IDR 945 billion to finance JKN - KBS services which are integrated with JKN - KIS. Karangasem is one of the four districts in Bali Province that received the most percentage of financing, which is 51 percent of the total budget needed when compared to other areas. This study aims to classify the BPJS-PBI recipient community based on education variables, employment indicators, age, and per capita expenditure in Karangasem Regency in 2017. The classification method used in this study is the random forest method. The results showed that the per capita expenditure variable had the largest contribution in classifying the status of PBI participants. The model that is formed produces an accuracy of 0.8017. This means that the model can predict 80.17 percent testing data correctly.