Reviewerly/exHarmony huggingface dataset:
Mobile Assisted Language Learning across Different Educational Settings

When mobile technologies became widely available more than ten years ago, it was evident that they could potentially contribute to educational needs and particularly to language learning. However, understanding how to use them in education was not that obvious. The present collection looks at the use of mobile technologies and the opportunities they offer to students at different ages, in a variety of geographical locations and educational contexts. This introduction is divided into two parts. In the first part, I present the main features of MALL (Mobile Assisted Language Learning) and how they have been adjusted in the last ten years to fit with rapid changes in technology and education. I also describe the different theoretical approaches used to inform studies on MALL and the research methods applied in the studies. The second part is an introduction to each chapter, highlighting some connections between them.
The Influence of Family Environment, School Environment, and Motivation to Learn the Students’ Learning Behaviour

The research aims to determine if there is an influence between the family environment, school environment, and the motivation to learn about learning behaviors. This research was conducted for 2 months from May to June 2019. The method of research used is quantitative research method, the population in the study is high school, SMK and MA in Jabodetabek. The data collection techniques used by researchers in this research are by spreading polls or questionnaires. Of the 220 target questionnaire filled, there are 200 questionnaires received and processed. Data processing in this study uses SPSS (Statistical Package for Social Science) version 22 using validity tests, reliability tests, normality tests, classical assumption tests, multiple regression equations, hypothesis tests and coefficients Determination. The results showed that there was a positive influence between the variables of the family environment, the school environment and the motivation to learn about student learning behaviors.
The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.
Reform of Physical Education Teaching Evaluation in General Colleges

With the advent of the era of big data, it provides a brand-new vision and broad thinking for the evaluation of college physical education.Using big data related knowledge to study the evaluation system of physical education in colleges and universities can not only promote the implementation of physical education evaluation in colleges and universities, but also effectively improve the quality of college physical education.Based on this, this research uses methods such as literature data method, expert interview method, questionnaire survey method, Delphi method, mathematical statistics method, etc. to understand the existing problems of the college physical education evaluation system in Shanxi Province, so as to determine the college physical education evaluation indicators and Index weight coefficient, borrowing the background of big data application, preliminary exploration and construction of college physical education teaching evaluation system.
Deep over and Under Exposed Region Detection

The camera sensors often fail to capture all the brightness intensities present in the visible spectrum of light. This is due to the limited dynamic range of the sensor elements. When bright light falls on a camera sensor, it is not appropriately measured. The recorded brightness values that fall outside the sensor’s dynamic range are stored as the minimum or maximum value depending on the bit-depth of the sensor. This results in a loss of information and undesirable artifacts in the form of blown-out areas, referred to as over- and under-exposed regions. In this study, we propose to detect these areas in an image using deep learning tools. Our approach uses semantic segmentation to mark the under, over, and correctly exposed regions in the image. We have created a new dataset containing 4928 images to train and test the performance of the model using a pre-trained state-of-the-art model architecture and re-trained it on our custom dataset. To the best of our knowledge, this is the first attempt to use semantic segmentation and transfer learning methods to identify these regions in an end-to-end fashion. We obtain a Dice score and a Jaccard score of 0.93 and 0.86, respectively, which are better than the state-of-the-art methods. The quantitative and qualitative results show that the proposed method outperforms several existing methods for identifying the over and the under-exposed regions. We will make the dataset public for research work.
Automatic Essay Answer Rating Using the Text Similarity Method

Assesments are required in order to evaluate the performance of students. A typical method of learning assesments in class is by examination. An essay type exam is a form of assesment where there are no answer choices provided and generally applied to measure the students’ level of understanding of the knowledge. To asses the quality of the essay answers manually is a subjective task as well as time consuming. In this reasearch, we propose an automatic method of assessing essay answers by applying the cosine similarity method.&#x0D; In this research, the students’ answer document and the correct answer document are used as input. Both documents are then preprocessed and represented in vector form using word2vec. We then measure the similarity between the documents by calculating the cosine similarity of the two vectors. The cosine similarity values are converted back again and used as the final grades. The results of the final grade are then compared to the values given by the instructor to show accuracy of the proposed approach.
Cloud Computing with AWS

Cloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. Usually Cloud Computing services are delivered by a third party provider who owns the infrastructure. Cloud Computing holds the potential to the “CLOUD COMPUTING WITH AWS is designed to be the most flexible and secured cloud network. It has come of age since Amazon’s rollout of the first of its kind of cloud services in 2006.  It provides scalable and highly reliable platform that enables customers to deploy applications and data quickly and securely
Development Of Pathology Detection Algorithms Based On Convolutional Neural Networks (CNN)

Aims Development and research of algorithms for detecting pathologies in endoscopic images of stomach based on different architectures of CNN.
Dampak implementasi pembelajaran dalam jaringan (daring) mengenai kualitas pembelajaran ipa di sekolah dasar pada masa pandemi covid-19

&lt;em&gt;The Minister of Education and Culture of The Republic of Indonesia since March 24 2020 issued Circular No.4 of 2020 concerning the Implementation of Education Policies in an Emergency for the Spread of COVID-19, In this Circular Letter states that the learning process is carried out in their respective homes through online learning. The purpose of this study was to describe the impact of implementing online learning corncerning the quality of science learning during the COVID-19 pandemic. This research was conducted in class VI.I SDN Kleco 1 Surakarta. The research method used is descriptive qualitative with a case study approach. The data were collected by means of questionnaires and interviews from teachers and students as well as strengthened documentation. The results showed that through the implementation of online learning had an impact on not achieving on of the criteria in the indicator, namely the development of critical thinking in science learning. Teachers have not developed critical thinking in students, and students have not developed critical thinking. Based on this description it can be concluded that the quality of science learning in class VI.I SDN Kleco I Surakarta is quite good due to the impact of implementing online learning.&lt;/em&gt;
On Generating Extended Summaries of Long Documents.

Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in some cases to know more detailed information about its salient points that can't fit in a short summary. This is typically the case for longer documents such as a research paper, legal document, or a book. In this paper, we present a new method for generating extended summaries of long papers. Our method exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach. We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method outperforms or matches the performance of strong baselines. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for long-form summary generation task. Our analysis shows that our multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections. Our datasets, and codes are publicly available at this https URL
Domain Specific Fine Tuning of Pre-trained Language Model in NLP

Text analysis is essentially the computerization process of examination of the known contents to decide the sentiments passed on it. Data mining is the automated or semi-automated process of analyzing and modeling of the large data repository to extract interesting information. This paper presents a deep hashing method-based text analytics framework that deals with a large amount of textual data. The proposed method uses a deep belief network as a pre-trained model for reducing errors in the system. Deep learning techniques with the help of the hashing method provide fast response and accurate results in terms of accuracy. The study further provides a deep comparative investigation on convolution neural methodologies used in text analysis. In the experimental part, the Twitter database is used for sentiment analysis. The characterization of sentiment data is relying upon the various degrees of investigations. A deep hashing concept is used for improvement in the accuracy of the system. The experimental results show that the proposed technique outperforms other existing techniques.
ambrosia: An R package for calculating and analyzing food demand that is responsive to changing incomes and prices

The ambrosia R package was developed to calculate food demand for staples and nonstaple commodities that is responsive to changing levels of incomes and prices.ambrosia implements the framework to quantify food demand as established by Edmonds et al. (2017) and allows the user to explore and estimate different variables related to the food demand system.Currently ambrosia provides three main functions:(1) calculation of food demand for any given set of income levels and prices;(2) estimation of calibration parameters within a given a dataset.Note: ambrosia is used to calculate the calibration parameters for the food demand model implemented in the Global Change Analysis Model (GCAM; Calvin et al., 2019); (3) exploration and preparation of raw data before starting a calibration parameter estimation.
Segmentation of Satellite Images of Solar Panels Using Fast Deep Learning Model

Segmenting satellite images provides an easy and cost-effective solution to detect solar arrays installed on building tops and on ground over a region. Solar panel detection is the first step towards the estimation of energy generation from the distributed solar arrays connected to a conventional electric grid. Segmentation models for small devices require light weight procedures in terms of computational effort. Â State-of-the-art deep learning segmentation models have the disadvantage that these require long training times, large number of floating-point operations (FLOPS) and tens of millions of parameters which make these models less suitable for devices with limited computational power. This paper proposes a deep learning segmentation architecture that is suitable for small devices. The proposed architecture combines features of Mobilenet classification architecture and Unet architecture in such a way such that it is efficient in terms of computational effort and produces segmentation results with good accuracy. The results of the proposed model are compared with the results obtained by various state-of-the-art segmentation models. The results demonstrate that the proposed model is computationally efficient as it requires less number of model parameters, less training time, consumes less number of FLOPS and produces good segmentation results with competitive accuracy.
The Perceived Effects of Using Nonverbal Language to the Online Communication of the Junior High School Students

This study focuses on investigating the commonly used nonverbal language used by the Junior High School students in Philippine School Doha S.Y. 2020-2021 and its perceived effects during their online communication.Using the quantitative descriptive research design, a self-made survey questionnaire was made and was validated.287 Junior High School students were systematically selected as respondents of the study.It discusses the commonly used nonverbal language of the respondents during online communication, such as facial expressions, gestures, posture, eye contact, and tone of voice.The results of the study showed that the commonly used nonverbal language of the students is posture and using nonverbal language affects their online interactions for using nonverbal cues can be misinterpreted.
Parenting with Unintended Pregnancy in East Java, Indonesia: A Qualitative Study

Teenage pregnancy is known to significantly impact the health of mothers and babies. Lack of preparations and poor knowledge might lead teenage mothers to have difficulties in practicing good parenting. Therefore, it is essential to assess parenting styles in adolescents with unwanted pregnancy (UP). This study aimed to analyse how teenage mothers with unwanted pregnancy nurture their babies. This research used a qualitative method with Rapid Assessment Procedure (RAP) study design. In-depth interviews, triangulations and observations were employed to obtain the data. Six informants participated in this study who were chosen purposively based on the inclusion criteria. The results showed that all informants took care of their babies by themselves. However, the majority of them showed inadequate exclusive breastfeeding (EBF), immunization and posyandu (Indonesian-integrated health post system) visit. Lack of informants’ understanding was found to be the leading factor of the inadequacies.&#x0D; Keywords: parenting, unintended pregnancy, adolescent, sexual health
Full quantum one‐way function for quantum cryptography

Quantum EngineeringVolume 2, Issue 1 e33 RESEARCH HIGHLIGHT Full quantum one-way function for quantum cryptography Fei Gao, Corresponding Author gaofei_bupt@hotmail.com orcid.org/0000-0002-0020-368X State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China Correspondence Fei Gao, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. Email: gaofei_bupt@hotmail.comSearch for more papers by this author Fei Gao, Corresponding Author gaofei_bupt@hotmail.com orcid.org/0000-0002-0020-368X State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China Correspondence Fei Gao, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. Email: gaofei_bupt@hotmail.comSearch for more papers by this author First published: 20 February 2020 https://doi.org/10.1002/que2.33Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2, Issue1March 2020e33 RelatedInformation
MVC Implementation In Laravel Framework For Development Web-Based E-Commerce Applications

MVC is a concept that separates three main logical components namely model, view and controller. The model represents the database, the view controls the user interface and controller connecting the model and view. The MVC concept makes coding work more structured. Laravel is a PHP framework that uses the concept of MVC so that the use of Laravel to develop an application greatly facilitates the programmer. Besides other facilities offered by Laravel, Laravel provides lightweight layout templates, provides a lot of object oriented libraries and has artistic tools for carrying out commands at the command line that can help programmers build a website or web-based application. With the many conveniences offered by Laravel, the process of developing a large website becomes easier and faster. In this research the development of web-based e-commerce applications is made using the Laravel framework.
Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.
A Semantic-Based Information Retrieval System

Information retrieval (IR) is the process of retrieving relevant documents from a large collection of documents. The applications of these technologies are no longer restricted to only online search engines, instead these are widely being used by organizations to facilitate different organizational information searches. Efficient retrieval of documents from a large collection of organizational data is still a challenging task. In this paper, a semantic-based information retrieval system is presented for institutional student project report retrieval. The model uses a clustering-based technique for fast document retrieval and a semantic-based query processing technique for retrieving the most relevant documents with respect to the user query. A number of tests were conducted to evaluate the performance of the presented model on different random user queries, and the precision and recall measures were determined. The performance of the model is also compared with existing retrieval techniques, and the results obtained show the efficiency of the model in providing relevant documents quickly. The model achieves approximately 91% precision and 90% recall accuracy in the considered domains and data set.
Edge Based Robust and Secure Perceptual Hashing Framework

In this paper, a novel hashing framework is proposed using edge map and image normalization. In the proposed method, the input image is normalized using geometric moments and an edge map is then employed using the canny edge detector. The estimated binary image is divided into non-overlapping blocks and a chaotic map is used for random block selection. The singular value decomposition is carried out on the selected blocks for extracting the significant features followed by generation of hash value. The simulation results support the contention that the proposed technique is secure and considerately robust against a variety of image manipulations.
Influence of the dissipation on the N-level atom interacting with a two two-level atoms in presence of qubit–qubit interaction

Abstract The interacting of two qubits and an N-level atom based on su(2) Lie algebra in the presence of both qubit–qubit interaction and dissipation term is considered. The effects of the qubit–qubit interaction and the dissipation term on the dynamics of the proposed system are discussed in detail for certain values of the number of levels. The dynamical expressions of the observable operators are obtained using the Heisenberg equation of motion. The population inversion and linear entropy, as well as the concurrence formula as a measure of entanglement between the two qubits are calculated and discussed. The roles of the number of levels, the qubit–qubit coupling parameter and the dissipation rate on these quantities are also discussed. We explore the sudden birth and sudden death of the entanglement phenomena with and without the dissipation term.