Reviewerly/exHarmony huggingface dataset:
TMU NMT System with Japanese BART for the Patent task of WAT 2021

In this paper, we introduce our TMU Neural Machine Translation (NMT) system submitted for the Patent task (Korean Japanese and English Japanese) of 8th Workshop on Asian Translation (Nakazawa et al., 2021).Recently, several studies proposed pre-trained encoder-decoder models using monolingual data.One of the pre-trained models, BART (Lewis et al., 2020), was shown to improve translation accuracy via fine-tuning with bilingual data.However, they experimented only Romanian→English translation using English BART.In this paper, we examine the effectiveness of Japanese BART using Japan Patent Office Corpus 2.0.Our experiments indicate that Japanese BART can also improve translation accuracy in both Korean Japanese and English Japanese translations.
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation

This paper introduces our neural machine translation systems' participation in the WAT 2021 shared translation tasks (team ID: sakura).We participated in the (i) NICT-SAP, (ii) Japanese-English multimodal translation, (iii) Multilingual Indic, and (iv) Myanmar-English translation tasks.Multilingual approaches such as mBART (Liu et al., 2020) are capable of pre-training a complete, multilingual sequence-to-sequence model through denoising objectives, making it a great starting point for building multilingual translation systems.Our main focus in this work is to investigate the effectiveness of multilingual finetuning on such a multilingual language model on various translation tasks, including low-resource, multimodal, and mixed-domain translation.We further explore a multimodal approach based on universal visual representation (Zhang et al., 2019) and compare its performance against a unimodal approach based on mBART alone.
Feasibility of Android-Based iSpring Suite Learning Media in Fiqh Subjects

This research aims to know the properness of Ispringsuite based android system in Fiqh Material for The first grade in MTSN 14 Megaluh, Jombang. This research using Design-Based Research in Brog and Gall Approach. The research Instrument is formed as questionnaire validation of Material expert, linguistic expert, IT expert. On the other hand, there is also questionnaire validation for students respons. The assessment is conducted by classroom mean Score, Likert Scale. The result from a material expert is 81, 91% which claim as extremely proper, from the linguistic expert is 80,25% which named by very good. Besides, the score from IT experts is 70,61% which state as an appropriate category. And the score from the students is around 90%, Which state is highly proper. In conclusion, Ispring suite Learning Media based Android System in Fiqh Material is Proper to use.&#x0D;
UAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via Translations

We describe the University of Alberta systems for the SemEval-2021 Multilingual and Crosslingual Word-in-Context (MCL-WiC) disambiguation task.We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the word.Our focus is on developing principled theoretical approaches which are grounded in linguistic phenomena, leading to more explainable models.We show that translations from multiple languages can be leveraged to improve the accuracy on the WiC task.
Hypotheses testing and posterior concentration rates for semi-Markov processes

In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of the posterior distribution in continuous time and general state space semi-Markov processes. In particular, we obtain posterior concentration rates for semi-Markov kernels. For the purposes of this study, we construct robust statistical tests between Hellinger balls around semi-Markov kernels and present some specifications to particular cases, including discrete-time semi-Markov processes and finite state space Markov processes. The objective of this paper is to provide sufficient conditions on priors and semi-Markov kernels that enable us to establish posterior concentration rates.
A linear-circular regression estimate for data fusion: Application to GNSS carrier-phase signal processing

This article is dedicated to the estimation of the parameters of a linear-circular regression model. For this model, the response is circular and defined between −π and π, the predictor is linear and several sensors provide noisy observations of the response. In our approach, the noise is assumed to be distributed according to a von Mises distribution with a concentration parameter that models the accuracy of the sensors. We propose a maximum likelihood circular fusion operator for the estimation of the intercept, the slope of the regression line and the concentration parameter associated with each sensor. The proposed estimate is not direct as in the linear case and requires an iterative algorithm to maximize a periodic contrast function. In order to characterize the accuracy of our fusion operator, the theoretical expression of the variance of the proposed estimator slope is first derived. For this derivation, we approximate the von Mises distribution by a Wrapped normal distribution and we consider unwrapped observations. Then, we derive an iterative procedure to maximize the contrast function. We show, using synthetic data, that the variance of the slope of the regression line derived using the proposed estimate is in good agreement with that obtained using the theoretical expression of the variance. The proposed estimator is also used to process the carrier-phase difference between GNSS signals provided by two antennas. The objective in terms of signal processing is to estimate the linear parameters of this difference in order to derive the height between the two antennas. We show that fusing the observations provided by several satellite signals improves the accuracy of the estimated height. We also show, using real data, that the theoretical study of the proposed estimator can be used to predict the length of integration of the signal necessary for obtaining an estimate of the height with a given accuracy.
Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.
On Formation Of Civic Education And Upbringing In Russia

The article examines the genesis of the formation of knowledge about civic education and upbringing in domestic pedagogical science with an emphasis on the post-war period of the Soviet era and the end of the 20th-beginning of the 21st century.It is shown that this process was of a concrete historical nature, reflecting the state of society, the state and the dominant ideology at one or another evolutionary stage.The views of individual teachers of the twentieth century, who managed to generalize and systematize the theoretical and practical experience of educating a citizen accumulated in the country, filling it with their own thoughts and practical experience, are presented.Considerable attention is paid to the research of the post-Soviet period.The characteristic of modern conceptual approaches and models of education and upbringing is given.With the help of comparative historical, pedagogical and theoretical analysis, three stages in the development of knowledge about the formation of civic upbringing and education in Russian pedagogical science have been identified: the Soviet period (from the mid-1950s to 1985), the period of perestroika (restructuring) (1985)(1986)(1987)(1988)(1989)(1990)(1991) and the democratic period (from 1991 to the present).It is noted that today the priorities of humanistic pedagogy lie at the heart of domestic civic education and upbringing, as well as the focus on fulfilling the state and social order for building a civil society and the rule of law with the recognition of the human right to individual expression, personal opinion and protection of personal dignity.
Digital Technologies Assisting Migrant Population Overcome Language Barriers: The Case of the EasyRights Research Project

The issue of migrant integration in host communities is of major importance, because its effectiveness can influence employment opportunities, social cohesion, and economic welfare, creating equal opportunities among citizens. One of the greatest challenges towards this goal is the limited knowledge of the language of the host community, which can create miscommunication and additional difficulties to migrants. Basic language training is important, but it is of little help when migrants are confronted with the language used in complex procedures that are required for their inclusion in the new country of residence. This paper, which is descriptive and explorative in nature, focuses on the targeted digital solutions offered by the EU-funded easyRights research project that can help migrants effectively communicate and receive guidance, in order to handle the demands of various inclusion-related procedures that may differ from one country to the other. One of the digital tools presented in the paper aims at facilitating familiarization with the required domain-specific vocabulary, while the second one intends to offer pronunciation training, including training for the domain-specific words, in order to equip migrants with the knowledge and skills they need to communicate effectively. The two digital tools, which could act synergistically, employ advanced technology and are part of a technological pathway, whose aim is to assist migrants exercise their rights in the process of their integration in a new country. Implications are also discussed.
Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models

Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.
Diagnosis and Severity of Depression Disease in Individuals with Artificial Neural Networks Method

Depression is a disease that causes physiological and psychological problems. Depression in individuals; sleep disturbance, constant fatigue, anorexia, inability to do daily activities, and feeling constantly tired and tired. Among the causes of depression; sociological, biological and psychological conditions are counted. The first step in treating depression is to make the correct diagnosis. Beck Depression Inventory (BDI) is a self-report scale developed by Beck et al. (1961) that evaluates the severity of depressive symptoms and the risk of depression, and consists of 21 questions in total. The purpose of BDI is not to define a diagnosis of depression, but to objectively quantify the degree of depression. The aim of this study is to determine the most successful algorithm from artificial neural network algorithms by using a data set of BDI scale. In this study, Random Forest, Decision Tree, Naive Bayes and Neural Network methods were used within the scope of algorithms and the best results; The training rate was 99.9%, the test rate was 98.5%, and the loss rate was 0.1% for training and 1.5% for testing. The lowest rates were obtained with the "Decision Tree" technique, with 90.8% training and 87.1% test rates. In addition, different results were obtained with Adam, SGD and L-BFGS-B optimizations used in ANN algorithms and the best success percentage was obtained as a test result in Adam technique.
The Study on Students' Vocabulary Size

This study intends to know the vocabulary size of English Department Students in eight semesters. The population of this research is all students in English Department, specifically in eight semesters (class of 2013). This research used purposive sampling in taking the samples, and there are 21 students as the sample obtained from 122 students as the population. The method of this research is quantitative method. The instruments which the researcher used in collecting the data are fix test to measure how large students’ vocabulary size, that divided into two test; Receptive Vocabulary Size Test (RVST) and Productive Vocabulary Size Test (PVST). In analyzing the data, it is used numerical descriptive statistic to describe students’ vocabulary mastery first, and then calculate the vocabulary size. The result of this research shows that students’ receptive and productive vocabulary size were 3,110 words and 1,841 words respectively or in general around 4,905 words,with the receptive vocabulary knowledge was higher than productive vocabulary knowledge. However, it has shown a low result for the students who learned English for about eight semesters, it should be more than that.
Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.
A Human Of Information And Digitalcivilization

The article discusses human changes during the transition to an information and digital civilization.The transformation of the industrial and social spheres, economy, and education affect the style and ways of human life.A civilization created on the basis of digital technologies produces new values, norms, behavioral practices, and ultimately changes human nature.A new view of a human place in the world is being formed.It is emphasized that a human of the future is an intellectual with high professional qualities, the ability to conceptually think and make non-standard decisions.This human is distinguished by mobility in the sphere of intellectual work, by the ability to retrain and adapt to new professions.Obtaining fundamental knowledge for generating new knowledge, developing the intellectual and creative abilities of a person is impossible without a radical change in the existing education system.The article shows how the intellectualization of labor and the expansion of high-tech technologies form a new motivation system for a human: intellectual abilities and the quality of education determine both the level of income and social status.The intellectual elite is becoming the wealthiest stratum of society.The authors compile a list of strategic tasks that ensure the information and intellectual culture, scientific worldview, new ethical norms, as well as the change of the value system.If solved comprehensively, these tasks provide for the harmonious development of man and sustainable development of civilization.
IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity Prediction

This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words.Our model leverages standard morphosyntactic and frequencybased features that proved helpful for Complex Word Identification (a related task), and combines them with predictions made by Transformer-based pre-trained models that were fine-tuned on the Shared Task data.Our submission system stacks all previous models with a LightGBM at the top.One novelty of our approach is the use of multi-task learning for fine-tuning a pre-trained model for both Lexical Complexity Prediction and Word Sense Disambiguation.Our analysis shows that all independent models achieve a good performance in the task, but that stacking them obtains a Pearson correlation of 0.7704, merely 0.018 points behind the winning submission.
Export Of Educational Services Under Conditions Of Changing Reality

In the present article, the authors demonstrate that the development of the world economy, globalization, internationalization have contributed to the development of the export of educational services.The role of this sector is undoubtedly important, since, on the one hand, the development of the modern economy is based on knowledge and the training of highly qualified personnel is required, on the other hand, the export of educational services is a profitable part of the budgets of many states.However, the new conditions, and above all, the global pandemic, along with global competition, the development of Industry 4.0 and a number of others, have a significant impact on the educational sphere.Therefore, consideration of the impact of the current situation on the export of educational services is of scientific interest.The authors analyzed the problems faced by both countries, providing services and receiving them: how to provide education services, how to complete the learning process under conditions of total isolation of people.The authors have concluded that the global network, a generation of young people who have quickly adapted to distance learning, helps in solving these problems.People have gotten the opportunity to receive education services while being isolated in their own country or in another country, as well as to receive other education remotely.According to the authors, it is important to study promising directions of the export of educational services offered by international organizations, large corporations, and leading universities.
Features of Using Health-Saving and IC-Technologies in the Formation of a Socially Mobile Teacher in a Pandemic Condition

The new reality caused by the coronavirus pandemic opens up the possibility of updating the content of professional training of socially mobile educators by means of health-saving and information-communication technologies. The purpose of the study is to analyse the features of the use of health and IC-technologies in the process of forming a socially mobile teacher in a pandemic. The research methodology includes theoretical analysis and generalisation of psychological and pedagogical sources, elaboration of legal documents in the field of education, study and generalisation of experience of scientific and pedagogical workers, disclosure of basic theoretical provisions, identification and substantiation of key concepts. The authors present a detailed analysis of the concepts of “health”, “health-saving technologies”, “information and communication technologies”, “social mobility”. The purpose and tasks of health-saving and IC-technologies are clarified. Ways to implement and use health-saving technologies in educational institutions during the pandemic COVID-19 have been identified. Methods of application of information and communication technologies in the conditions of distance learning are offered. A number of advantages and disadvantages of ICT in education are identified. The guidelines of successful realisation of a socially mobile teacher in the modern educational reality are determined. It has been found that social mobility as an integrative ability of an individual to adapt to the changing environment and changes dictated by society is perhaps the most important and relevant feature in our time. It is determined that the readiness to implement, combine and use in professional pedagogical activities of health-saving and IC-technologies indicates the formation of social mobility of the teacher. The experience of using health-saving and information-communication technologies by scientific and pedagogical workers of Ivan Franko National University of Lviv in the process of becoming and formation of socially mobile educators in the conditions of the COVID-19 pandemic is described. The practical value of scientific work lies in the substantiation and generalisation of pedagogical conditions for updating the content of professional training of future teachers through the prism of their ability to social mobility
Procedure Generation for Algorithm Learning System using Comment Synthesis and LSTM

We have constructed a learning system by organizing procedures for learning program creation. However, manually creating procedures for learning systems requires a significant amount of time. In this study, in addition to automatically generating program procedures using natural language processing, we generate new program content and procedures by learning program code and comments through deep learning long short-term memory.
Answering the Cybersecurity Issues: Confidentiality, Integrity, and Availability

Protecting the flow of sensitive information processed on computers is a challenge facing cybersecurity professionals. This proof of concept uses universal encryption of all data in transit with no hands-on management. This revolutionary process secures data in a virtually unbreakable system protecting the flow of data unlike any system available.