Reviewerly/exHarmony huggingface dataset:
Effectiveness of Scramble to the Students’ Reading Comprehension

The purpose of the research are to know the influence of teaching reading by using scramble technique on reading comprehension skill of junior high school, to know what extent the influence of teaching reading by using scramble to the student’s reading comprehension skill of junior high school, and to know how the students’ response towards the using Scramble to students’ reading comprehension skill of junior high school. The sample for this research is the eighth grade students of SMP Al-Qon GKB Gresik. The research design is quasi-experimental designs. The writer used test and questionnaire sheet. The test consisted of the pre-test and post-test. The technique to analyze the test is resulted from the students’ scores and calculated by using ANCOVA (Analysis of Covariance). For, questionnaire, the researcher uses Likert scale to know the students’ responses. he result shows that F-value statistical calculation is 12, 95 and in F-table are 4, 02 for d.f 55. So, it shows that there is any influence of teaching reading by using scramble to the students’ reading comprehension skill at the eighth grade students’ of SMP Islamic Qon GKB. The result of the student’s responses is 76% which categorized as “good”. So it can be said that Scramble to the students reading comprehension at the eighth grade students of SMP Islamic Qon GKB is effective.
Stem technology in the study of educational robotics

STEM education is aimed at the formation of adaptation skills to those changes that occur in technology and science and will become key for further education and employment. One of the directions for the introduction of educational STEM technologies is educational robotics. In recent years, advances in robotics and automated systems have changed personal and business areas of life. Modern production and industry need specialists with knowledge of robotics. Therefore, the introduction in the educational program of the section related to robotics is gaining increasing importance and relevance now. The objectives of the study are to justify the relevance and importance of introducing educational robotics in schools, determine the state of teaching robotics and outline ways of introducing educational robotics and ways of preparing future robotics teachers. In the study, the current state and tasks of educational robotics in the secondary education system, as well as an approach to the study of robotics using STEM education, are discussed at the theoretical level. Based on an expert survey, the main characteristics of STEM education and the tasks of introducing educational robotics in STEM education are determined and a characteristic of the state of teaching robotics in schools is given.
Tracking and identification for football video analysis using deep learning

We describe the technique used to train and customize deep learning models to detect, track, and identify soccer players, who are recorded during soccer games using custom camera settings. The player detection model is customized to allow the detection of person class objects from video input. Two newly developed filters, spatial feature filters, and bounding box location filters have described that help in classifying players and audiences. A new tacking paradigm is illustrated to generate tracks of soccer players with fewer swaps, thereby reducing efforts of human annotators in later stages. A new method of identifying every player by detecting player t-shirt numbers has been developed and illustrated. This method provides tracks with high confidence and identity to most of the player corresponding to individual t-shirt number. Finally, we provide a unique result assessment technique to judge the performance of the complete model.
Former Employees' Negative Online Comments Are Unprotected Under the Act

Many employers have had the problem of disgruntled former employees posting negative online comments about the company. The same problem arises with current workers who have an axe to grind against their employer. Such complaints give rise to questions of whether they are protected by federal labor law. For example, employees banding together on Facebook or Instagram to gain support for their grievances or enlist public support for their opposition to employer practices are regularly granted the protection of the National Labor Relations Act. However, General Counsel of the National Labor Relation's Board recently opined that such protection is not unlimited.
Formation of Legal Competence of Future Bachelors of Psychological and Pedagogical Education

This article presents the author’s vision of the content and technology of forming the legal competence of future teachers and psychologists, which includes six synergistically interrelated competencies: legislative, regulatory, ethical, social, informational and psychological. The purpose of the study: to develop didactic games for students - future bachelors of psychological and pedagogical education and experimentally prove the effectiveness of their implementation in extracurricular activities to form their legal competence. Based on the analysis of various educational technologies for the formation of students' legal competencies, a special set of didactic games has been identified and systematized. The scientific novelty of the author’s research lies in the originality of the approach to the independent implementation by students of didactic games in extracurricular activities on the basis of the student scientific community. The research results are statistically proven (p&lt;0.05) through an increase in the level of formation of legal competence in students of psychological and pedagogical sphere.
Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines

This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweets that report adverse effects, and task 3: Automatic extraction and normalization of adverse effects in English tweets. Fine tuning RoBERTa large for classifying English tweets enables us to achieve a F1 score of 56%, which is an increase of +10% compared to the average F1 score for all the submissions. Using BERT based NER and question answering, we are able to achieve a F1 score of 57.6% for extracting adverse reaction mentions from tweets, which is an increase of +1.2% compared to the average F1 score for all the submissions.
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task

We present our systems for the WMT20 Very Low Resource MT Task for translation between German and Upper Sorbian. For training our systems, we generate synthetic data by both back- and forward-translation. Additionally, we enrich the training data with German-Czech translated from Czech to Upper Sorbian by an unsupervised statistical MT system incorporating orthographically similar word pairs and transliterations of OOV words. Our best translation system between German and Sorbian is based on transfer learning from a Czech-German system and scores 12 to 13 BLEU higher than a baseline system built using the available parallel data only.
Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.
Elhuyar submission to the Biomedical Translation Task 2020 on terminology and abstracts translation

This article describes the systems submitted by Elhuyar to the 2020 Biomedical Translation Shared Task, specifically the systems presented in the subtasks of terminology translation for English-Basque and abstract translation for English-Basque and English-Spanish. In all cases a Transformer architecture was chosen and we studied different strategies to combine open domain data with biomedical domain data for building the training corpora. For the English-Basque pair, given the scarcity of parallel corpora in the biomedical domain, we set out to create domain training data in a synthetic way. The systems presented in the terminology and abstract translation subtasks for the English-Basque language pair ranked first in their respective tasks among four participants, achieving 0.78 accuracy for terminology translation and a BLEU of 0.1279 for the translation of abstracts. In the abstract translation task for the English-Spanish pair our team ranked second (BLEU=0.4498) in the case of OK sentences.
Students' Online Fashion Studio Class Experience and Factors Affecting Their Class Satisfaction

This study explored students'' online fashion studio class experiences, and investigated the factors affecting their class satisfaction. An online survey of college students who were enrolled in online studio classes within apparel and fashion-related departments during the spring of 2020 was conducted in June 2020. Responses from a total of 213 participants were included in the final data. Respondents rated lecture clips as the most useful, followed by teacher demonstration and feedback, PowerPoint (PPT) supplements, and Q&As. Frequently mentioned areas of improvement were online platform stability and video quality. Many respondents also stated that more streamlined teacher-student communication channels, immediate and meticulous teacher feedback, the adoption of course contents developed specifically for an online environment, and provisions for equipment usage would be desirable. Student satisfaction of an online fashion design studio class was significantly affected by teaching presence, social presence, online learning system stability, perceived usefulness of teacher’s demonstration, and affective response toward COVID-19. Students satisfaction of an online garment construction studio class was significantly affected by teaching and social presence, online learning system stability, and perceived usefulness of teacher’s demonstration. Based on these findings, we recommend developing teaching contents and methods that allow students to feel included in class and establish an online system with various functions to enhance the sense of social connection that can enable two-way communication.
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese

Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. The first strategy is about dynamical learning word similarity of tokens in the shared space among source languages while another one attempts to augment the translation ability of rare words through updating their embeddings during the training. Besides, we leverage monolingual data for multilingual MT systems to increase the amount of synthetic parallel corpora while dealing with the data sparsity problem. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs and released our datasets for the research community.
PENERAPAN LOCATION BASED SERVICED (LBS) DALAM PROTOTIPE PENGENALAN RUANGAN DENGAN METODE EXTREME PROGRAMMING

Location Based Service (LBS) is a service to provide information that has been created, compiled, selected or filtered by considering the location of users or other people or current mobile devices [1]. It is possible to implement LBS using GPS because mobile devices have GPS services [6]. Based on these services, making an application for room recognition can be done by utilizing the Google Maps API by implementing GIS, LBS, and GPS. Implementation of application with extreme programming methods is carried out in accordance with the stages. In the planning part, an analysis is carried out on the business needs and user stories, in the design, use case diagrams and activity diagrams are made, coding is done by implementing the design using the mobile programming language, and testing is done using black box testing. The application of LBS is carried out when calling the room information page which functions to find the location of the room and displays notifications and room information when the user enters the radius that has been determined in the room data. Determining the radius is very important to do so that the information about the room that is displayed does not overlap with other rooms. This is done to keep the room information displayed to the user only to display information about one room only. Therefore, when determining the radius of the room, it must be determined carefully so that the radius can create a circle that does not intersect with other radius circles. The room information displayed is obtained from data management by the admin through the building management page, rooms, and items. In managing, admins can add, edit, and delete data.
Tencent submission for WMT20 Quality Estimation Shared Task.

This paper presents Tencent’s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transformer-based Predictor-Estimator models. For the XLM-based Predictor-Estimator architecture, the predictor produces two types of contextualized token representations, i.e., masked XLM and non-masked XLM; the LSTM-estimator and Transformer-estimator employ two effective strategies, top-K and multi-head attention, to enhance the sentence feature representation. For Transformer-based Predictor-Estimator architecture, we improve a top-performing model by conducting three modifications: using multi-decoding in machine translation module, creating a new model by replacing the transformer-based predictor with XLM-based predictor, and finally integrating two models by a weighted average. Our submission achieves a Pearson correlation of 0.664, ranking first (tied) on English-Chinese.
An integrated framework of generating quizzes based on linked data and its application on medical education field

E-learning has greatly developed in recent years because of the popularity of smartphones and tablets. Because of the improvement of the device performances and the data transmission capability of the internet, more and more complex e-learning materials are provided to learners. Quiz games are a kind of e-learning format which can both test and train learners. How to automatically generate 'good' quizzes is discussed by many researchers. In this paper, we proposed a new framework which can support users to create quiz games based on Linked Data. Linked Data stores both the values and the linkages among values. The quizzes generated by our framework are easy to improve and extend, have more changes, and support the learning analytics of users' activities. For obtaining better educational effects, we further extend our framework for supporting the generation of quizzes in 3D environments. Especially, we discuss how to apply our framework in medical education.
Learning Transversal Competences in the Digital Society

We are moving towards an increasingly digital society, where artificial intelligence and the virtual dimension are central to all societal and individual dimensions (social, economic and political).This is a new reality in which the learning of transversal skills will be central to successful social integration as well as to a reduction of existing inequalities.Based on a selection of the most relevant bibliography, we develop a perspective paper.This communication aims to discuss, mobilizing a reasoned argument, the importance and the way in which the learning of these transversal competences can be fostered, focusing on the school (more formal) and educational (broader) dimensions.It was concluded that the teaching-learning process needs to undergo a transformation towards learning based on a participatory research-action logic in which all stakeholders, while respecting the role of each one, assume an active centrality in their own learning.As implications, we highlight that this teaching-learning logic calls into question the traditional school culture that is widely shared at all levels of education, based on a large asymmetry between those who teach and those who learn, which is an extremely challenging factor for the school culture transformation that we believe is essential to be implemented.
PENGEMBANGAN MODUL PEMROGRAMAN WEBSITE CONTENT MANAGEMENT SYSTEM UNTUK MENINGKATKAN KEMAMPUAN GURU DALAM PENGELOLAAN WEBSITE SEKOLAH SMA NEGERI 1 CEPOGO BOYOLALI

The development and advancement of science and technology can provide effectiveness and interactivity in the world of education. The school website is a technology product that must be used by schools. This study aims to develop and determine the feasibility of a website programming module to improve teachers' abilities in managing school websites. This research is a Research and Development (RnD) development research using the ADDIE development method. Data collection techniques using interviews and questionnaires then analysed using qualitative and quantitative data. The subjects of this study were media experts, teachers and operators at SMA Negeri 1 Cepogo. The results of this study indicate that research and development have carried out using the ADDIE model steps, namely (Analysis, Design, Development, Implementation, Evaluation). The average assessment by the score expert validation test obtained was 87.1 included in the "Very Good" category, the response of the phase 1 field test teacher and stage 2 field test obtained an average value of 81.8 in the "Very Good" category. The quantitative data analysis took the average results of the assessment of expert validation tests and user trials. The score is 84.45 with the category "Very Good" and shows that the website programming module is suitable for use by teachers in managing school websites.
Annotating MWEs in the Irish UD Treebank

This paper reports on the analysis and annotation of Multiword Expressions in the Irish Universal Dependency Treebank. We provide a linguistic discussion around decisions on how to appropri- ately label Irish MWEs using the compound, flat and fixed dependency relation labels within the framework of the Universal Dependencies annotation guidelines. We discuss some nuances of the Irish language that pose challenges for assigning these UD labels and provide this report in support of the Irish UD annotation guidelines. With this we hope to ensure consistency in annotation across the dataset and provide a basis for future MWE annotation for Irish.
Overcoming Resistance: The Normalization of an Amazonian Tribal Language

Languages can be considered endangered for many reasons. One of the principal reasons for endangerment is the disappearance of its speakers. Another, more identifiable reason, is the lack of written resources. We present an automated sub-segmentation system called AshMorph that deals with the morphology of an Amazonian tribal language called Ashaninka which is at risk of being endangered due to the lack of availability (or resistance) of native speakers and the absence of written resources. We show that by the use of a cross-lingual lexicon and finite state transducers we can increase accuracy by more than 30% when compared to other modern sub-segmentation tools. Our results, made freely available on-line, are verified by an Ashaninka speaker and perform well in two distinct domains, everyday literary articles and the bible. This research serves as a first step in helping to preserve Ashaninka by offering a sub-segmentation process that can be used to normalize any Ashaninka text which will serve as input to a machine translation system for translation into other high-resource languages spoken by higher populated locations like Spanish and Portuguese in the case of Peru and Brazil where Ashaninka is mostly spoken.
İnsani Yardım Depolarının Değerlendirilmesi için Bulanık Mantığın Entegre Edildiği Çok Kriterli Karar Verme Yaklaşımı: Suriye’de Bir Uygulama Çalışması

A new disaster to humanity, called Coronavirus Disease , arose in and spread to worldwide at late December 2019.The most developed countries are affected from this pandemic more.However, the situation is more complex in some countries that are witnessed/witnessing a conflict, as in Syria.In Syria, the conflict continues more than 9 years and within the country there are more than 6 million internally displaced people (IDPs).This situation signifies millions of people living in hard conditions and seeking healthcare service, sheltering, food, safety and other related vital needs.In this context, since during a pandemic supplies and aid kits need to be stockpiled in a humanitarian relief warehouse to be protected and then distributed effectively to the most pandemic-affected people, we focused on the location research of relief warehouses in this study.We evaluated the locations of the relief warehouses to determine the most appropriate location based on a scientific humanitarian aid-based hybrid methodology.This novel methodology is implemented to a real case study in north of Aleppo/Syria.For this aim, firstly, data is collected directly from the target area; then humanitarian and economic criteria are selected by three experts to be included in the study.Criteria weights are computed by the Fuzzy Analytic Hierarchy Process (F-AHP).Finally, MULTIMOORA technique as a Multi Criteria Decision Making (MCDM) method is applied to assess the candidate warehouses and rank them.The proposed methodology showed its efficiency and effectiveness in evaluating relief warehouses and it can be utilized to facilitate the decision-making process.As a result, the suffering of the disaster-affected people can be reduced and high efficiency from donations in the target area can be achieved.
PELATIHAN JURNAL TERAKREDITAS NASIONAL DAN INTERNASIONAL BEREPUTASI DI FAKULTAS TEKNIK INDUSTRI UNTAG SURABAYA GUNA MENUMBUHKAN KESADARAN MAHASISWA AKAN PENTINGNYA PENULISAN ARTIKEL ILMIAH

Journal writing has become an important part that must be understood and also studied by students in tertiary institutions. The definition of the journal itself is a special article that contains articles in a particular field of science where in the journal writing it contains at least three people from a related institution related to a particular scientific discipline, research and also community service which will later become a reference for others who need renewable scientific resources. The refore it is very important for someone to learn and also improve their skills in writing scientific articles in the form of journals so that later someone's penchant for writing new discoveries can be useful for everyone. Therefore, in this service, accredited and internationally acclaimed journal training is held at the Untag Surabaya Industrial Engineering faculty in order to foster student awareness of the importance of writing scientific articles in the form of journals.