Reviewerly/exHarmony huggingface dataset:
THE ENGLISH TEACHERS’ STRATEGY TO STIMULATE STUDENTS’SPEAKING SKILL ON EIGHT GRADE AT MTS DARUL HIKMAHTAWANGSARI

ABSTRACTYuliani, Eli, Registered Number Student.17203163210. 2020 The English Teachers’Strategy To Stimulate Students’ Speaking Skill On Eight Grade At Mts DarulHikmah Tawangsari Tulungagung. English Education Program. State IslamicInstitute (IAIN) of Tulungagung. Advisor: Faizatul Istiqomah, M. Ed.Keywords: teaching speaking, teaching strategy, speaking skillEnglish is an international language, which must be mastered by the students. MasteryEnglish is important to be successful in the globalization era to achieve good ability inspeaking. Teaching speaking must covers some good aspects, those are materials, methods,media, strategy, and evaluation in teaching process. Thus starting point from those survey andstatements, it encouraged the researcher to conduct the research about a study on teachingspeaking at MTs Darul Hikmah Tawangsari Tulungagung. Since this, the students of thisschool has high achievement in speaking English.The formulation of the research problems were: 1) What are the strategies applied bythe English teachers in stimulating students’ speaking skill at Mts Darul Hikmah TawangsariTulungagung? 2) How do the teachers apply the strategies to stimulate students’ speakingability at Mts Darul Hikmah Tawangsari Tulungagung?.The purposes of this research were to: 1) To know some strategies are applied by theEnglish teacher in stimulating students’ speaking skill at Mts Darul Hikmah TawangsariTulungagung. 2) To describe how the English teacher applies the strategies to stimulatestudents’ speaking ability at Mts Darul Hikmah Tawangsari Tulungagung.Research Method: 1 ) Research design of this research was descriptive design withqualitative approach, 2) Subject of this research is English teachers and eighth graders ofMTs Darul Hikmah Tawangsari Tulungagung, 3) Data in form of information are gotten frominterview transcript, lesson plan and syllabus, and some photos, 4) Data collecting method isby using interview guide, observation guide, and documentation, 5) Data analysis by usinginductive analysis method.The result showed that The teacher materials used in teaching speaking at MTs DarulHikmah Tawangsari Tulungagung is based on the topic and Curriculum which is suggestedby the government and Gontor Islamic Boarding house. The methods used for teachingspeaking at MTs Darul Hikmah Tawangsari used some technique Communicative languageteaching, Total physical response, and Audi Lingual Method. The media are used in teaching English at MTs Darul Hikmah Tawangsari Tulungagung such as, internet, video, picture,magazine, handbook, LCD, laptop, and sound to show a video. The strategies on teaching atMTs Darul Hikmah Tawangsari Tulungagung are Oral Interview, Role-plays, StoryRetelling, Describe A Famous People. The evaluation that used in teaching speaking atSMAN 1 Boyolangu is written dan oral language assessment.
Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools

In the language domain, as in other domains, neural explainability takes an ever more important role, with feature attribution methods on the forefront. Many such methods require considerable computational resources and expert knowledge about implementation details and parameter choices. To facilitate research, we present Thermostat which consists of a large collection of model explanations and accompanying analysis tools. Thermostat allows easy access to over 200k explanations for the decisions of prominent state-of-the-art models spanning across different NLP tasks, generated with multiple explainers. The dataset took over 10k GPU hours (> one year) to compile; compute time that the community now saves. The accompanying software tools allow to analyse explanations instance-wise but also accumulatively on corpus level. Users can investigate and compare models, datasets and explainers without the need to orchestrate implementation details. Thermostat is fully open source, democratizes explainability research in the language domain, circumvents redundant computations and increases comparability and replicability.
El profesorado universitario de galicia y la enseñanza remota de emergencia

The study aims to analyses the university teachers starting conditions and the galician universities institutional intervention to face the crisis derived from CoVId-19 and a future blended- learning. It is a sequential mixed study in two phases, 1-quantitative: it collects the teachers' perception of teaching with a questionnaire; 2- qualitative: an institutional intervention documentary analysis was developed. The results show that 68.07% of the teaching staff have never taught online, in general they bet on a content transmission methodology and evaluation and there are conceptual inconsistencies in the didactic dimensions of their proposals. The institutional intervention is aimed at providing technical support to cover the instrumental deficiencies, with the absence of the pedagogical analysis; and articulate control measures. The conclusions show weakened teacher agency and their poor preparation to face a quality mixed education in the future.
INORGANIC CHEMISTRY PROBLEM-BASED LEARNING

The resolution of problem situations under the teacher’s guidance makes students compare, generalize, analyze phenomena, and not only memorize them mechanically. The processes of advancing and resolving problem situations are an unbroken chain, since when a problem is advanced, its solution begins simultaneously, which leads to the formulation of new problems. That is, a contradictory and continuous process of new scientific concepts active cognition is carried out. We see from the experience that using the methods of problem-based learning in the lessons that they promote development of cognitive activity, creative students’ independence, the formation of their worldview, intellectual development, and as a result, the improvement of the knowledge’s quality. Today, it is necessary when learning future specialists, in addition to the implementation of existing educational state standards in this specialty, to focus on the development of their creative qualities, creative thinking, which, ultimately, will promote to the formation of highly professional competent personnel.
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Specifically, we define the representation of a pair of nodes as the generalized sum of all path representations, with each path representation as the generalized product of the edge representations in the path. Motivated by the Bellman-Ford algorithm for solving the shortest path problem, we show that the proposed path formulation can be efficiently solved by the generalized Bellman-Ford algorithm. To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm. The NBFNet parameterizes the generalized Bellman-Ford algorithm with 3 neural components, namely INDICATOR, MESSAGE and AGGREGATE functions, which corresponds to the boundary condition, multiplication operator, and summation operator respectively. The NBFNet is very general, covers many traditional path-based methods, and can be applied to both homogeneous graphs and multi-relational graphs (e.g., knowledge graphs) in both transductive and inductive settings. Experiments on both homogeneous graphs and knowledge graphs show that the proposed NBFNet outperforms existing methods by a large margin in both transductive and inductive settings, achieving new state-of-the-art results.
Dynamic headspace analysis using online measurements: modeling of average and initial concentration

Abstract Dynamic headspace sampling is an important technique for the analysis of consumer products, the study of biological samples and environmental water analyses. This paper shows the influence of experimental conditions, such as the sampling time, sampling flow rate, headspace volume, liquid volume and Henry coefficient on the measured average concentration values. A corresponding closed expression as function of these variables is introduced in order to quantify the deviation of the initial headspace concentration. The proposed bi-exponential function embeds different current existing models for recovery calculation in dynamic sampling analyses in one single expression. A fully automated and user-friendly Excel® file to investigate or to model the dynamic headspace sampling results is added to everyone's easy use.
BIG DATA DAN PENYEBARLUASAN PENGETAHUAN: PENGALAMAN PERPUSTAKAAN UNSYIAH

Big data is a necessity for libraries. However, librarians still lack an understanding of the application pragmatically. The main obstacle is the level of mastery of librarians on information technology. Research and development must be directed at librarians' view of big data as an abstraction, the availability of tools to strengthen services and strategic operations, and easily re-implement existing systems and applications (reuse). This paper describes the system that is suitable for libraries to start the application of big data, the tools needed by libraries, how big data can do processing patterns, trends and associations related to library user behavior and interactions. This study produced a model of a big data system taken from the legacy application system that had been applied in the Unsyiah Library through reverse engineering. From verification of the model through the existing scenario, it can be seen that big data and cloud services can more easily acquire data on virtual user interaction through library web applications, the technical complexity of acquisition and managing data becomes abstracted. Furthermore, it is also seen that this system can dig deeper information about library performance. Trends and patterns for data can be identified more easily.
GRADUATE STUDENTS’ MOTIVATION REGULATION STRATEGIES IN FACING ACADEMIC WRITING AMID COVID-19 PANDEMIC

Abstract To be more proficient and self-regulated, L2 academic writers, the deployment of motivation regulation strategies should be incorporated at the commencement of academic writing learning enterprises, particularly during this pandemic situation. According to previous studies, the establishment of robust motivation regulation strategies will potentially help L2 academic writers to better manage the ongoing writing learning dynamics and cope with varied upcoming challenges. Specifically, this study was conducted qualitatively by lending support from qualitative content analysis in order to offer more overarching findings derived from actual events experienced by the participants. To comply with this objectivity, two research instruments were also utilized in this study namely the Likert-Scale questionnaire and open-ended interviews. Further, the Likert-Scale questionnaire was administered via Google Forms to be filled out by 36 English education master students from batch 2018 and 2019, and the open-ended interview inquiries were sent through WhatsApp application in order to obtain the responses from the three random selected interviewees. Results from this study revealed that the majority of English education master students had successfully become more competent and resilient academic writers through five major streams of motivation regulation strategies namely interest enhancement, performance self-talk, mastery self-talk, self-consequating, and environmental structuring.       Keywords: Motivation regulation strategies, academic writing, graduate students, Covid-19 pandemic
TOMAT BIKE (Automatic Bike) untuk Stimulasi pada Gangguan Sistem Gerak

ABSTRACT This article discuss about innovation of making a therapy to stimulation on the system disordes motion. The TOMAT BIKE or automatic bikes is a bicycle with pedaling automated in design in such a way, so it can be used by an individual who experience system disorders motion. Metodh used is Assembling Metodh that is process merging into some parts to from a construction. The process of assembly a starting from the choice of materials, making a prototipe, testing tools and evaluation. The results of making a tool is an individual who experience problems motion can use TOMAT BIKE and making of Intellectual Property Right (HKI) or copyright.
Exploiting Bat Algorithm to Optimize Association Rules for Web Personalization

From a user search perspective, ranking of Web pages has always been a prime concern of a search engine because it helps to find the most pertinent and highly rated pages satisfying the search and need of users. There have been various measures and metrics which are used in deciding the ranks of web-pages in line with the quality of page and interest-score of the user. With the help of web usage analysis based on the user's past usage history, one can effectively improve the ranking of the web pages according to the user’s requirement. The objective of the proposed technique is to provide an efficient service mechanism for personalized web page ranking of search engines based on web usage analysis.The web page personalization based on web mining technique has two major phases, offline and online. The objective of the offline phase is to model the user's navigation pattern using the history, web log data. The model is then used to predict a user navigation pattern and give recommendations to them based on prediction. Hybrid approach based on optimized association mining rule to get the ranking of webpages…To implement the proposed work in this paper the implicit information is collected from user on the basis of the past user activity data which is automatically stored in web server logs without their knowledge.In this paper a hybrid approach based on optimized association mining rule is used to get the ranking of webpages. The proposed technique is implemented through exploiting BAT algorithm and an extensive comparative analysis and as well as validation is done along with the existing state of art techniques for web page personalization. The results thus achieved have shown a phenomenal performance in the target domain of research.
Charon

Overload control is an important feature of modern cloud applications. As these applications grow increasingly complex, designing efficient overload control schemes at scale is tedious. In this paper we argue part of the challenge is a lack of first principles mechanisms one can use to design scalable and verifiable policies.
Libraries: Creating Pages on your Journal Website: Creating your Static Page

This guide will demonstrate how to create a static page on your journals website while making sure it is accessible to readers. This page will cover the rather straightforward process of setting up your static page. Making sure it is easy to find will be the focus of the next pages, and it is a little more complicated.
How Does Social Behavior Affect Your Password?

A password is an important method of identity authentication, so there are endless research attempts on passwords, but most of them are based on different languages and technologies. As far as we know, no one has studied the influence of one's social behavior (e.g., religious belief) on passwords. Based on this, we first study the influence of religion on the generation of user passwords. Taking Christianity as an example, we find that the passwords of Christians have very strong similarities by analyzing the popular passwords, password structure, letter distribution and length distribution. Furthermore, we find that they contain many words that stem from the Bible, such as Jesus, the Sun, and so on. In order to explore the influence of religious beliefs on password generation, we also select two ordinary social password datasets of non-Christians, and find that they have different characteristics. Finally, we use the classic PCFG attack method to test the anti-attack ability of the Christian password datasets and the ordinary password datasets. Interestingly, we come to the conclusion that the ability of Christian password datasets to resist attacks has a strong similarity, while ordinary password datasets do not have this feature. In particular, by adding Christian information to the rule generated by the ordinary password datasets, we show that it can effectively improve the cracking rate of password attacks.
Research on the Higher Education System Based on Principal Component Analysis

The higher education system is an important part of a country's education of citizens, and has important value to the country's economic development. Firstly, this paper conducted detailed research and analysis on the healthy education system and collected five indicators from eight representative countries (students from specific countries, scientific journal articles in higher education, and higher education staff, higher education enrollment rate, higher education sector's percentage of research and development expenditures) for ten years or more. In order to evaluate quantitatively, this paper establishes a principal component analysis model to evaluate the health status of the national higher education system, and uses SPSS to calculate the final comprehensive evaluation score.
LightTag: Text Annotation Platform

Text annotation tools assume that their user’s goal is to create a labeled corpus. However,users view annotation as a necessary evil on the way to deliver business value through NLP.Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.
Pengaruh Parameter Word2Vec terhadap Performa Deep Learning pada Klasifikasi Sentimen

The difficulty of sentiment classification on this big data can be overcome using deep learning. Before the deep learning training and testing process is carried out, a word features extraction process is needed. Word2Vec as a word features extraction is often used in sentiment classification pre-training because it can capture the semantic meaning of the text by representing a similar vector for each word that has a close meaning. Word2Vec has three parameters that affect the model learning process namely architecture, evaluation method, and dimensions. This study aims to determine the effect of each Word2Vec parameter on deep learning performance in sentiment classification. The accuracy results of the deep learning model were evaluated to determine the effect of the Word2Vec parameter. The results of this study indicate that the three Word2Vec parameters have an influence on the performance of the deep learning model in sentiment classification. The combination of Word2Vec parameters that produces the highest average accuracy include CBOW (Continuous Bag of Word) architecture, Hierarchical Softmax evaluation method, and a dimension of 100. CBOW produces better performance, because it has slightly better accuracy for words that often appear and in this research dataset there are many words that often appear. Hierarchical Softmax shows better results because it uses a binary tree model which makes words that occur rarely will inherit the vector representation above them. The dimension with a value of 100 produces better accuracy because it is in line with the number of datasets of 10,000 reviews.
Efficient Machine Learning in Hierarchical Distributed Systems to Protect Privacy

Data expansion in both quantities and scales has transformed distributed machine learning into a key instrument that completes big data tasks such as prediction, classification, etc. However, it is impossible to mix raw data from all data holders for learning purposes owing to actual physical restrictions and a potential data breach. In order to overcome this challenge, the distributed confidentiality learning techniques are used to know all distributed data without exposing any actual information. The present approaches, however, have limitations on the sophisticated distributed system. First, traditional techniques to privacy-saving learning are based on huge cryptographic basic training sets that significantly degrade the learning speed due to overhead processing. However, the complicated architecture of the system in the real system becomes a barrier. In this study, we present an effective machine-learning technology for privacy protection using hierarchical distributed networks.We adapt and enhance the algorithm for collaborative learning. In addition to reducing the overhead for the learning process, the suggested approach ensures total security at each level of the hierarchy. We also provide an asyncronous approach to enhance the learning efficiency on the basis of an evaluation of collaborative convergence in multiple research groups of the distributed hierarchical system. Finally, comprehensive testing of real data is carried out in order to evaluate the confidentiality, effectiveness and efficiency of our suggested solutions.
Об использовании визуализации при обучении учащихся основам алгоритмизации и программирования

In the context of the problem of improving the effectiveness of teaching students computer science, the article presents the didactic features of visual programming languages, describes the main components of the methodology of teaching students the basics of algorithmization and programming using visual programming languages and visual programming environments, and summarizes the results of the pedagogical experiment on introduction and evaluation of the effectiveness of the methodology using visualized environments and visual programming languages as a means of teaching students the basics of algorithmization and programming in educational institutions and educational centers of the Republic of Belarus
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks

The nature of no word delimiter or inflection indicating segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks.However, for unsupervised Chinese cross-domain segmenting and labeling tasks, the model trained on the source domain frequently suffers from the deficient word-level semantic knowledge of the target domain.To address this issue, we propose a novel paradigm based on attention augmentation to introduce crucial cross-domain knowledge via a translation system.The proposed paradigm enables the model attention to draw cross-domain knowledge indicated by the implicit word-level cross-lingual alignment between the input and its corresponding translation.Aside from the model requiring crosslingual input, we also establish an off-the-shelf model which eludes the dependency on crosslingual translations.Experiments demonstrate that our proposal significantly advances the state-of-the-art results of cross-domain Chinese segmenting and labeling tasks 1 .