Reviewerly/exHarmony huggingface dataset:
State and prospects of distance learning during the COVID-19 pandemic

Problem and goal. In Russian society, the simultaneous and mass transition to distance learning of educational organizations in March 2020 in connection with a new coronavirus infection caused a sharp discussion about the effectiveness and consequences of this format of training, as well as about the prospects for its use after the COVID-19 pandemic. In the current conditions, the education system is faced with the task of training specialists in the face of the uncertainty of the future and the accelerated digitalization of the economy. The purpose of the research is to analyze the main factors influencing the development and spread of distance learning in the coming years, as well as the possible short and long-term consequences of switching to distance learning in higher education institutions. Methodology. The analysis of the scientific literature and practical activities, as well as experienced teaching, was used in the study. Results. The content and form of education will significantly transform in the long term. Today the necessary digital competencies cannot be acquired within the framework of the traditional educational process (lectures, seminars, exams in oral form), without the systematic use of information technologies in the educational process. Information technologies will be in high demand by graduates and will allow them to be prepared for the practical use of IT in the workplace. In addition, it can significantly improve the effectiveness of training because it allows to implement an individual learning trajectory, personalize training, remove restrictions on the time and place of training, and increase the visibility and interactivity of the educational process. Conclusion. Online education, e-learning using distance learning technologies in the long term will meet the demand of students and society for training in an uncertain future. Thanks to information technologies, conditions are created for active cognitive and educational activities of students, individualization of training is provided, turning the student into an active and equal participant in the educational process, including by refusing to provide ready-made differentiated knowledge, which leads to the formation of the reproductive nature of thinking in students.
Structural imbalances of regional higher education systems and the labor market

Introduction: the article analyzes the higher education efficiency and discusses problems and imbalances in its development. Objectives: to determine the relevance of the areas of training offered by the higher education institutions to the economy structure and the needs for specialists in the labor market, to assess the balance of regional higher education systems. Methods: methods of comparative, statistical, graphical and cartographic analysis of the data available from Unified Interdepartmental Information and Statistical System (EMISS), Federal State Statistic Service, Monitoring of higher education organizations and Monitoring of graduates employment of the Ministry of Science and Higher Education of the Russian Federation for the period of 2016−2018. Results: the structure of specialists graduation in regional higher education systems, due to the influence of many factors, is often determined not by the needs of regional economies, but by the demand of the population in the field of education as well as the preferences and ideas of the applicants. This leads to an imbalance on the labour markets and inefficiency in spending public and private funds on higher education. Conclusions: the development of coordinated management decisions in the personnel training system aimed at its congruent balanced development and achieving a balance of economic interests of all actors should be focused on creating effective foresight tools for forecasting the promising directions for the education system development and taking into account the needs and competitive advantages of the region.
Negative language transfer in learner English: A new dataset

Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language.This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources.This dataset contains manually annotated error causes for learner writing errors.These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge.This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners' first language.The dataset can also be applied in personalizing grammatical error correction systems according to the learners' first language and in providing feedback that is informed by the cause of an error.
Self-attention based sentiment analysis with effective embedding techniques

The problem of sparse vector representation exists in handling the large-scale data and also semantics of words are not considered in many existing works of sentiment analysis. Effective word embed...
Challenges and Opportunities for Teaching Practical Skills at Higher Education Institutions under the conditions of COVID-19 Pandemic

The lack of practical skills of graduates of higher education institutions is one of the main challenges of the modern education system of Georgia, which has become even more urgent in the face of the COVID -19 pandemic. Within the framework of the research, we studied the latest scientific literature, researches and reports of various state and international organizations on the experience and current state of practical skills' teaching in Georgia; on the new requirements for the workforce under the pandemic and post-pandemic; and on the future directions of providing students with practical knowledge. The paper also presents an analysis of the results of our research on the attitude of students towards the internship and the approaches of higher education institutions in the evaluation of practical knowledge and skills acquired by the students within the framework of the educational program. Based on the best practice and challenges identified as a result of the research, we have developed appropriate recommendations that we think will facilitate the production of professional graduates equipped with practical skills.
Uji Validitas Dan Relibilitas Instrumen Pengukuran Implementasi E-Learning Terhadap Kepuasan Pengguna Pada Masa Pandemi COVID 19 Di Lingkungan Politeknik Negeri Banjarmasin

Implementation of online learning requires adequate preparation and support of resources, first is the software or tools used in online lectures, where Poliban has e-learning that can be used or can use other tools. Both hardware or computer equipment with the required specifications, one of which is a camera used for video conferencing, and what is very important is a stable internet network connection. Therefore, if at least these three things can be fulfilled, then online lectures can be carried out well. However, in reality, there are several problems faced by both lecturers and students, especially related to the unstable internet network signal, so that the implementation of online lectures cannot run optimally. To find out the problems faced by users, a measurement instrument for the implementation of E-learning on user satisfaction is needed. In making a good instrument model, a test is needed to ensure that the indicators used in the instrument used are valid and reliable and have a good level of reliability.&#x0D; Based on the problems mentioned above, this research will conduct instrument testing using validity tests and reliability tests where in the analysis process a Partial Least Square (PLS) application framework is used in SEM which is a statistical model that provides an approximate calculation of strength. hypothetical relationships among variables in a theoretical model&#x0D; The results of the research that have been carried out show that the results of the validity test of the instrument for measuring the implementation of e-learning on user satisfaction at the Banjarmasin state polytechnic get a loading factor value above 0.5 on the intended construct, these results indicate that the indicators used in this study are valid. or has met convergent validity, then based on the reliability test it shows that the composite reliability value for all constructs is above 0.7 which indicates that all constructs in the estimated model meet the criteria for discriminant validity. The lowest composite reliability value is 0.837 in the System Quality construct, this shows that the test instrument shows a good level of reliability, or in other words, the questionnaire results can be trusted.
Blockchain Technology in Education System

The aim of this paper is to review the blockchain technology and its benefits in relations to education system.Blockchain technology is widely researched and highly evaluated and appraised for its unique infrastructure.In general, blockchain researched for its association with Bitcoin and cryptocurrency advantages.In this survey the plan is to conduct a full review of previous literatures focused on blockchain in education systems.Provide overall reviews of blockchain concepts and architecture behind the technology and to examine verification software that used by the technology to improve security and immutability.Brief discussion on the consensus algorithms and hashing function and how these operate and difference type of blockchain will be discussed.In this survey, the existing technology used in Saudi Arabia will be reviewed.Indepth research conducted for over 70 papers, of which 35 noted in this survey.Blockchain emerging promise a real time democracy and justice to all users over the world.Educational Industries said to revolutionize its communication system and accessibility and extend their market globally by widening their admissions and providing secure cost-effective, transparent and immutable communications across their educational platforms.
Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get?

Because of globalization, it is becoming more and more common to use multiple languages in a single utterance, also called codeswitching.This results in special linguistic structures and, therefore, poses many challenges for Natural Language Processing.Existing models for language identification in code-switched data are all supervised, requiring annotated training data which is only available for a limited number of language pairs.In this paper, we explore semi-supervised approaches, that exploit out-of-domain monolingual training data.We experiment with word uni-grams, word n-grams, character ngrams, Viterbi Decoding, Latent Dirichlet Allocation, Support Vector Machine and Logistic Regression.The Viterbi model was the best semi-supervised model, scoring a weighted F1 score of 92.23%, whereas a fully supervised state-of-the-art BERT-based model scored 98.43%.
PanGEA: The Panoramic Graph Environment Annotation Toolkit

PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments.PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen.It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators.Out of the box, PanGEA supports two tasks -collecting navigation instructions and navigation instruction followingand it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks.We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room dataset.We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support.
MultiOpEd: A Corpus of Multi-Perspective News Editorials

We propose MULTIOPED 1 , an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery.News editorial is a genre of persuasive text, where the argumentation structure is usually implicit.However, the arguments presented in an editorial typically center around a concise, focused thesis, which we refer to as their perspective.MULTIOPED aims at supporting the study of multiple tasks relevant to automatic perspective discovery, where a system is expected to produce a singlesentence thesis statement summarizing the arguments presented.We argue that identifying and abstracting such natural language perspectives from editorials is a crucial step toward studying the implicit argumentation structure in news editorials.We first discuss the challenges and define a few conceptual tasks towards our goal.To demonstrate the utility of MULTIOPED and the induced tasks, we study the problem of perspective summarization in a multi-task learning setting, as a case study.We show that, with the induced tasks as auxiliary tasks, we can improve the quality of the perspective summary generated.We hope that MULTIOPED will be a useful resource for future studies on argumentation in the news editorial domain.
The Interplay of Information Literacy, Learning Facility, Learning Achievement &amp; Motivation Toward Online Learning Experience During COVID-19 Crisis: PLS-SEM Approach

The COVID-19 crisis has forced the learning system in Indonesia from face-to-face learning to online learning. This rapid transformation presents various challenges, especially for universities in remote areas such as Papua. This study aims to explore the factors that can influence the students' learning experience. The research approach used is quantitative with an analysis model of PLS-SEM. The data were obtained using a simple random sampling technique from four universities in Merauke, with 207 respondents. Four hypotheses were accepted, and one hypothesis was rejected in this research. Online information literacy experience and online learning facility perspective were affecting online learning experience significantly. Meanwhile, the online learning facility perspective significantly affecting online information literacy experience and online learning achievement motivation. However, online learning achievement motivation has a positive correlation but does not contribute significantly to an online learning experience. This research implies that higher education institutions can focus on factors that significantly contribute to the student's learning experience to provide a positive experience and increase online learning effectiveness, especially during the COVID-19 crisis.Keywords: learning experience, online learning, PLS-SEM, COVID-19
Service Learning to Increase Utilization of X Video Editing Software Features within Limited Facilities for the Youth of Gereja Kristen Pasundan Cideres

The service learning in multimedia skills for teenagers and young people as training program for the community was conducted through the expertise of Bachelor Program in Computer Engineering, Faculty of Engineering, Universitas Kristen Maranatha, for Gereja Kristen Pasundan (GKP) Cideres. The purpose of this training was to equip people with skills or knowledge that can be useful in daily activities. As this training was given during the Covid-19 pandemic, by requirement of health and safety protocols, the service learning was implemented online via the internet in a form of webinar. Online digital activities are increasingly important as conventional physical interactions and communication in work or study environment become unsafe and to be avoided or to be done less often than usual. Unfortunately for most people, technical skills for working or studying online are not yet adequate, especially in multimedia using some recent tools and softwares. So in this program, basic multimedia producing skills using Microsoft Photos (video editing feature) and Microsoft Power Point were introduced. The end result of the training was satisfactory, as those who participate are being able to produce simple digital multimedia presentation using the aforementioned tools and softwares. The training program was held online on November 28th, 2020 via Zoom Meeting Application, and was made possible thanks to the collaboration of Bachelor Program in Computer Engineering with the Directorate of Student and Alumni Affairs, Universitas Kristen Maranatha.&#x0D; Keywords: Covid-19 pandemic; multimedia; service learning
Discovering Topics in Long-tailed Corpora with Causal Intervention

Topic models are effective in capturing the latent semantics of large-scale textual data while existing methods are normally designed and evaluated on balanced corpora.However, it contradicts the fact that general corpora in our world are naturally long-tailed, and the longtailed bias can highly impair the topic modeling performance.Therefore, in this paper, we propose a causal inference framework to explain and overcome the issues of topic modeling on long-tailed corpora.In a neat and elegant way, causal intervention is applied in training to take out the influence brought by the long-tailed bias.Extensive experiments on manually constructed and naturally collected datasets demonstrate that our model can mitigate the bias effect, greatly improve topic quality and better discover the hidden semantics on the tail.
DEVELOPMENT OF TEACHING MATERIALS IN WRITING DESCRIPTIVE TEXTS OF VOCATIONAL SCHOOL STUDENTS

The objective of the study is to develop the teaching materials in writing descriptive texts in Grade X students of SMK Binaan UMN Al-Washliyah 3, Medan. The study is conducted by the Development Research method, and the evaluators are the instructors of the Indonesian Subject (specific instructors), and the subjects are Grade X students. Pre-test and post-test are done to find out the effectiveness of the result. The result of the pre-test shows that the average score is 67% with the category of satisfactory; the result of the post-test shows that the average score is 85% with the category of distinction. The process of developing teaching materials includes three aspects of assessment: 1) teaching materials, 2) presentation, and 3) language structure. The validation of material experts includes content with an average score of 4.9% and a percentage of 82.2% with the category of distinction. The feasibility of presenting teaching materials has an average score of 113% with the category of distinction, and the language element has an average score of 11.4% with the category of distinction. The conclusion indicates that the design for developing teaching materials of descriptive texts by using a scientific approach in the subject of Indonesian is very effective.
AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation

In end-to-end speech translation, acoustic representations learned by the encoder are usually fixed and static, from the perspective of the decoder, which is not desirable for dealing with the cross-modal and cross-lingual challenge in speech translation.In this paper, we show the benefits of varying acoustic states according to decoder hidden states and propose an adaptive speech-to-text translation model that is able to dynamically adapt acoustic states in the decoder.We concatenate the acoustic state and target word embedding sequence and feed the concatenated sequence into subsequent blocks in the decoder.In order to model the deep interaction between acoustic states and target hidden states, a speech-text mixed attention sublayer is introduced to replace the conventional cross-attention network.Experiment results on two widely-used datasets show that the proposed method significantly outperforms stateof-the-art neural speech translation models.
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension

Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth.We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA).VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model.We also demonstrate that our model can also be effectively adapted to a completely different domain -TechQA -with large improvement over a model fine-tuned on a previously published large PLM.
Implementation multiple linear regresion in neural network predict gold price

&lt;p&gt;The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.&lt;/p&gt;
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals.To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards.Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing.Qualitative analysis also shows that when combined with diversitydriven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.