Reviewerly/exHarmony huggingface dataset:
Dilematis Antara Kesulitan Siswa Dengan Guru Dalam Pembelajaran Daring Pada Masa Pandemic Covid-19: Sebuah Upaya Aksidental MTs. Muhammadiyah Wuring

The purpose of this study was to determine students' difficulties in online learning and to determine the efforts of teachers in overcoming student difficulties in online learning during the Covid-19 pandemic. The method used in this research is a descriptive qualitative approach by describing the teacher's efforts in overcoming student difficulties in online learning during the Covid-19 period at MTs. Muhammadiyah Wuring. This study uses data collection techniques, namely observation, interviews, and documentation. The results of this study can be seen from several student difficulties in online learning such as a) absence of 4G networks, b) reduced income c), assignment methods and d) absence of Android phones. The teacher's efforts in overcoming student difficulties in online learning such as a) assignment methods, b) providing motivation, c) providing advice, d) providing online explanations, e) installing Wifi. By knowing the various difficulties of students and the efforts of teachers in overcoming student difficulties in online learning, researchers hope that all of us, especially schools, teachers, parents, and the government to make input in the future.
Mining for Causal Regularities

This paper reports on an algorithmic exploration of the theory of causal regularity based on Mackie’s theory of causes as MINUS conditions, i.e., minimal insufficient but necessary member of a set of conditions that, though unnecessary, are sufficient for the effect. We describe the algorithm to extract causal hypotheses according to this model and the results of its application to a number of real world data sets. Results suggest further promising applications, modifications and extensions that might derive further insights of a dataset.
Knowledge sharing in the covid-19 era of remote teaching: What can academia learn from business?

Purpose: The COVID-19 pandemic outbreak resulted at the university level in a massive transition from traditional ways of teaching to remote forms. This sudden conversion has led to changes in the patterns of knowledge sharing (from teachers to students and among students). The aim of this paper is to investigate how universities may benefit from experiences of businesses that were also forced to use remote forms of business operations. Large companies, operating in the international context may be a valuable source of information on how to improve knowledge sharing processes among university students and between students and teachers. Methodology: The paper uses a qualitative research method (in-depth interviews) to explore the possibilities of knowledge sharing improvements at the university-level teaching, based on the experiences elicited in the business sector. The theoretical sampling was used to find informants familiar with both transition to remote teaching and shift to online business operations forced by the COVID-19 pandemic. Findings: Interviews were conducted with experienced managers participating in an MBA programme. During the outburst of the COVID-19 pandemic the programme has been switched to a remote mode. At the same time, the interviewed managers had to cope with the transition to remote mode at work. Their work-related ordeals were contrasted and compared with their university-related experiences regarding knowledge sharing. Conclusions and recommendations have been formulated on the basis of evidence gathered during the interviews. Research limitations: This paper is of an exploratory character. The triangulation of findings presented here is limited. The sample consists of participants of one MBA programme only, hence the conclusions cannot be generalised. At this stage the paper may serve as a basis for further development. Practical implications: The paper presents possible improvements to be introduced by academia in the remote mode teaching. Business sector and especially large businesses may offer experience-based solutions that allow to boost knowledge sharing processes among students and increase the efficiency of knowledge sharing between teachers and students. Originality/value: The topic of the paper is very up-to-date and of considerable significance. Knowledge sharing processes at the university-level teaching have been significantly distorted by the COVID-19 pandemic outburst. Using experiences from the business sector can be very helpful in this regard. © The Authors, 2021. All Rights Reserved.
Using Card Sorting to Design Faceted Navigation Structures

This paper describes how a variation of card sorting-'repeated single-criterion sorting', can be applied to the information architecture design of digital music services.52 respondents were asked to sort, using their own choice of criteria, 12 popular songs using an online card sorting tool.Once respondents had chosen a construct for a particular sort, e.g."Genre", they placed each card into a named category, e.g."Rock", "Pop", and were encouraged to repeat this process until they could think of no more constructs.High levels of agreement were found for a small number of constructs such as "genre", "gender", and "speed of song" but the remaining constructs were individual to each respondent, e.g."songs that make me cry".The results highlighted differences with current approaches to music categorisation, as well as the potential for repeated single-criterion sorting to be used to design faceted navigation structures.
Ambientes de aprendizaje en el aula: hacia la diversificación de las opciones pedagógicas

Overview of classroom learning environments today is a concern on the part of Directors, rectors and other members of the educational institutions of basic education in Colombia. Is why the author made is article with the purpose of its advances of his doctoral thesis on this subject as part of the compendium of existing in the formerly named academic document significant learning content. Methodologically, the subject was treated documents, using the exhaustive search for bibliographical and electronic material on various aspects of the environments of learning, building with authors like Burguet (1999), Monereo (2001), Silva (2011) and Silva (2011), among others. The results pointed to recognize learning environments as diverse, predominantly online type in combination with the person under the management of different techniques and different resources audiovisual, electronic and Digital. The conclusions provided by the documentary methodological work allowed the analysis of the transformation of the learning environment in the classroom, the design of learning environments in the classroom and the aesthetic nature of classroom learning environments.
An Analysis of Clipping Words on Whatsapp Group Chat

Clipping word occurs whenever a word is shortened by deleting parts of the base word. Furthermore, clipping is the word formation process wherein words are reduced or shortened without changing the meaning. This research was about an analysis of clipping words on WhatsApp group chat. The aims of this study were to find out the types of clipping word that frequently used by WhatsApp users. The clipping words used was in Indonesian language. The primary sources of the data in this research were the screen shoot of WhatsApp group chats of KBM ESA USK and HMI Komsat FKIP USK. The purposive sampling technique is used by the researcher in order to select the sample of this research as the research thought it was more appropriate to be carried out when research requires special criteria in accordance with the research objectives in order to solve the research problems and can provide more representative value. Five males and 5 females were selected as the research samples. Type of this research was descriptive qualitative research which is purely data-derived in that codes are generated from the data in the course of the study. Researcher analysed four types of clipping words adapted from Hosseinzadeh (2014, p. 19) which are being the object of this research, there are Back Clipping, Fore Clipping, Middle Clipping and Complex Clipping used on WhatsApp group chat. The result showed that the most dominant type of clipping word used was complex clipping word (61.7%) then followed by back clipping word (28%), fore clipping word (8.7 %) and middle clipping word 1.6%. it was concluded that the most dominant clipping word used was complex clipping word.
TIME SCAVENGERS: A DIGITAL LEARNING PLATFORM'S ADAPTATIONS TO INCREASED NEED FOR ONLINE LEARNING RESOURCES

Time Scavengers is an educational website that explains climate change and evolutionary theory. Since the website’s launch in summer 2017, we have amassed >40 static pages, hundreds of blogs from contributing authors, and cultivate three social media communities. In the early stages of the Covid-19 crisis, Time Scavengers adapted to the needs of geoscience educators and students by curating a list of online education resources, initiating an online digital internship program, and launching the Tilly Edinger Travel Grant. Time Scavengers added a page collating online teaching resources on March 12, 2020, which includes remote teaching resources specific to geosciences. The new 'Online Teaching' has had 3,071 pageviews (0.86% of total page views since Aug. 1, 2017; 1.51% of total page views since Jan. 1, 2020). Summer student internships were recently added as a program sponsored by Time Scavengers in summer 2020 for students who were required to have an internship for graduation or whose internships were cancelled due to Covid-19. Four University of South Florida students in the internship were required to write blog posts and create the social media graphics for both. Students wrote a total of 40 articles, gained valuable experience in tailoring language and graphics for public consumption, and learned from science and communication experts. This program will be expanded with newly acquired funding in 2022. The Tilly Edinger Travel Grant was established to offset the financial burden of conferences for geoscience students and avocational researchers. Time Scavengers as an online community is inherently international and therefore is able to accept applications from students globally. Although many conferences were held virtually this year, so far this grant paid for the registration and abstract submission fees of 7 students and graduates, located in 6 countries, for a conference of their choice. In many ways, the Covid-19 crisis mobilized Time Scavengers to launch programs to meet the needs of our online community and early career professionals. The Tilly Edinger Travel Grant is open to biannual applications and the internship program will continue with future iterations. Online learning underwent a critical but forced evolution in the last year and will continue to improve into the future.
Aggregating Quantum Networks

The implementation of quantum error-correction codes needs high efficiency transmission channels and significant physical resources. Here we show that aggregating quantum networks alleviate those issues when applied to the quantum Reed-Salomon code.
Automatic Taxonomy Classification by Pretrained Language Model

In recent years, automatic ontology generation has received significant attention in information science as a means of systemizing vast amounts of online data. As our initial attempt of ontology generation with a neural network, we proposed a recurrent neural network-based method. However, updating the architecture is possible because of the development in natural language processing (NLP). By contrast, the transfer learning of language models trained by a large, unlabeled corpus has yielded a breakthrough in NLP. Inspired by these achievements, we propose a novel workflow for ontology generation comprising two-stage learning. Our results showed that our best method improved accuracy by over 12.5%. As an application example, we applied our model to the Stanford Question Answering Dataset to show ontology generation in a real field. The results showed that our model can generate a good ontology, with some exceptions in the real field, indicating future research directions to improve the quality.
Null Complement Anaphora: A Corpus-Based Perspective

Null complement anaphora (NCA) is a phenomenon where a predicate’s complement is elided. Traditionally, NCA has been taken to be deep anaphora whose interpretation make reference to objects in a discourse or other semantic model, not to syntactic structures as surface anaphora does. It has also been noted that there is no uniform semantic class of verbs that license NCA. This paper investigates the real-life uses of NCA with the corpus COCA (Corpus of Contemporary American English), focusing on the authentic uses of a total of 98 NCA-licensing predicates discussed in the previous literature. We then discuss theoretical implications of the corpus data.
Research Guides: Book Displays: Reading Together

Browse curated collections of library resources on a variety of topics. New displays added monthly.
The Relevance of the Problem of Developing Mathematical Models for Solving the Problems of Education Export in Russian Federation

The article is devoted to the current situation in the implementation of domestic and international projects with the participation of Russian universities in the field of education export. Based on the rich personal experience of the authors, a description of typical problems arising in this area is carried out. A combination of organizational and mathematical approaches is proposed as the way that it is advisable to move to achieve practically significant results in the area under consideration. The article does not consider the practical aspects of projects that are currently underway with the participation of Russian and foreign universities and industrial partners. The results of these projects and the obtained practical results will be reported separately. In this article, only the statement of the problem is carried out.
Multi-domain learning target tracking algorithm based on objective regression optimization

Convolutional Neural Network (CNN) is widely used in target tracking for the computer vision, where Intersection of union (IOU) is the most popular evaluation metric in the target detection criteria, but IOU cannot be optimized for tracking algorithms in the case of non-overlapping bounding boxes. GIOU can be optimized for tracking in the case of non-overlapping bounding boxes, but the slow convergence speed of GIOU leads to inaccurate detection, which results in low tracking accuracy. To solve the above problems, a DIOU-based MDNet tracking method is proposed in this paper. In order to solve DIOU loss does not have a penalty term for the aspect ratio of the target box, we propose CIOU-based MDNet and experiments show that the accuracy of this method is improved by 3% compared with MDNet trained with traditional IOU, GIOU or DIOU.
Educación virtual bajo condiciones mínimas de conectividad durante la emergencia sanitaria de la COVID-19

Objective: This paper is intended to answer the following question: how to develop multimedia content for asynchronous virtual teaching, as light as possible and that guarantees, at the same time, the quality of the transmitted content and the completeness of the taught syllabus under the connectivity restrictions of the public health emergency COVID-19? Methodology: Follows a quantitative approach, both analytical and experimental, mainly based on the comparison between the resulting sizes of the multimedia material generated with the proposed procedure and the minimum connectivity requirements of the students during the COVID-19 health emergency. Finding: A solution to the question posed in the objective is suggested, which, although it requires a certain degree of processing by the teacher, gives him full control over the quality, size and format of the resulting teaching material. Conclusion: The suggested solution allows to build audiovisual material to support the courses —small enough for storage and asynchronous transmission under minimal connectivity conditions— without losing the expected quality regarding both perception and content.
Identifying routes from path similarities

83 trajectories of 27 bumblebees in a cluttered tunnel containing 49 objects. The trajectories were obtained from 5 synchronised cameras.Implementation of our method to identify routes from bumblebees' paths can be found in the associated git.Intermediate results of the analyses can be found in the archive results.tar.xz.
Mineral chemistry studies of dunites in the Abgarm region (south of Kerman Province): An approach to tectonomagmatic position.

Mineral chemistry studies of dunites in the Abgarm region (south of Kerman Province): An approach to tectonomagmatic position.
Design and Implementation of Compressed Medical Data over an Authenticated Secured Channel

Ideally, secure transmission of medical image data is one of the major challenges in health sector. The National Health Information Network has to protect the data in confidential manner. Storage is also one of the basic concern along with secure transmission. In this paper we propose an algorithm that supports confidentiality, authentication and integrity implementation of the scrambled data before transmitting on the communication medium. Before communication the data is compressed while keeping data encrypted. The research work demonstrate with simulation results. The results shows that the proposed work effectively maintains confidentiality, authentication and integrity. The experimental results evaluated medical image quality like PSNR, MSE, SC, and NAEetc.
일본 간사이(関西) 지역의 한국학 현황

In Japan, under the special situation that Korea was under Japanese colonial rule, modern Korean studies education began and an educational institution of Korean studies was established. Korean studies concerns general knowledge of Korea including various fields such as language, culture, society, history, geography, politics, and economy. For Koreans who were living in Japan during the period of Japanese imperial occupation in Korea, there were evening schools and temple schools, which were educational institutions that teach children their heritage language, history, and culture. In the Kansai region, Osaka Foreign Language School and Tenri Foreign Language School were established by the Japanese, which were later reconfigured into universities. With the liberation of Korea from Japanese occupation in 1945, Koreans residing in Japan started to establish their own educational institutions to educate ethnically Korean children on Korean language, history, and culture, which were related to the identity of Korean people, training institutes, seminars, Korean language institutes, established various schools. However, when the Korean school closure order to shut down educational institutions established by Koreans in Japan was issued in 1948, the Hanshin education struggle occurred in the Kansai region around Osaka. As a result, ethnic classes were given after school at public elementary and junior high schools in the Kansai region, and various other educational institutions conducted ethnic education on Korea. In the 1960's, when the number of students attending ethnic classes fell, the Korean government began teaching Korean language and Korean culture throughout Japan, starting with the Korean Education Institute in Osaka in 1963, with the intent of complementing the existing ethnic education. From the beginning of the 1980s, Korean academic education was conducted mainly at Japanese universities, and it was decided that Korean ethnic classes in Japan and educational institutions of Korean will be conducted with Korean studies Since entering the global era, the Korean boom in Japan has caused many Japanese universities to adopt Korean language for education and to have Korean majors established. Furthermore, colleges, vocational schools, high schools, supplemental schools, and even companies also began to offer Korean studies. In particularly, the ethnic classes of Koreans in Japan and the educational institutions of Japanese Korean studies coexist in the Kansai region of Japan.
Pengaruh Perubahan Jumlah Layer Pada Prediksi Konsumsi Daya Listrik

Electrical load forecasting is very important to meet consumer needs according to load patterns. The electricity generated if it exceeds the amount needed will cause waste, on the other hand if the generator is smaller than the demand it can result in rotating power outages which result in losses to consumers. The purpose of this study was to determine the performance metric of the mean absolute percentage error (MAPE) in forecasting the load one day ahead by using an artificial neural network (ANN) algorithm. Forecasting with this ANN uses variations in the number of layers, namely 1 layer, 2 layers, 3 layers and other constant parameters. Forecasting uses time series data in period one from 1-28 December 2017. Forecasting output finds that ANN 1 layer provides the best value with a MAPE of 5,0053%, and is able to identify existing load patterns.
Analisis Faktor Penyebab dan Dampak Kualitas Belajar Mahasiswa Berbasis Daring di Masa Pandemi Covid-19

The e-learning system can create individuation, enrichment, acceleration, expansion, productivity and effectiveness in learning so that the quality of education can increase during the Covid-19 pandemic. This research was conducted to find the right formulation of the online lecture system in the future. In this study, the research population was all students of the communication science study program, totaling 259 people. For the needs of analysis and hypothesis testing, the entire population is used as the research sample. So the research is included in population research. The analytical technique used in this study uses a structural equation model (SEM) with the AMOS 21.0 program which consists of several stages. The results showed that of the nine paths and hypotheses built, seven hypotheses had a positive and significant effect, while two hypotheses had an insignificant effect.
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations to characterize tissue organization, while allowing the incorporation of prior pathological knowledge to further support model interpretability and explainability. However, entity-graph analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art machine learning algorithms applied to graph-structured data, which can potentially hinder their adoption. In this work, we aim to alleviate these issues by developing HistoCartography, a standardized python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology. Further, we have benchmarked the computational time and performance on multiple datasets across different imaging types and histopathology tasks to highlight the applicability of the API for building computational pathology workflows.