Reviewerly/exHarmony huggingface dataset:
Creation and Expansion of a Virtual COVID-19 Information Exchange Platform Using the Brazilian University Telemedicine Network (RUTE)

Introduction: Due to Brazil's alarming situation, Brazilian professionals decided to create a Special Interest Group about coronavirus disease-2019 (SIG COVID-19 Brazil RUTE-the Brazilian University Telemedicine Network) with the objective of promoting training sessions by web conference for professional and medical students.Aim and objective: To describe the experience of rapidly organizing this professional network based on telemedicine activity and share its initial results.Materials and methods: The structure adopted was MConf, a web conference platform that provides a virtual conference channel for 75 active participants and online video streaming for an additional unlimited number of attendees.The agenda started with meetings occurring 3 times a week, which were changed to weekly encounters in July, and are scheduled to be monthly in August.The interactive presentations lasted 30-40 minutes, with approximately 20 minutes for written or spoken debate.After each presentation, all participants were invited to register their presence and answer brief evaluation questions about the quality of the presentation, with an evaluation score ranging from 1 (poor) to 10 (excellent).Results: From March 23 to June 30, 2020, forty-one web conferences were presented in the areas of epidemiology, medicine (different specialties), nursing, physiotherapy, dentistry, and new technologies, with the participation of 52 speakers from 12 Brazilian states and 12 international guest speakers from 9 countries: Israel, Italy, United States of America, Spain, Switzerland, Portugal, Canada, Ecuador, and Qatar.The program had in its coverage 2,170 registrations by 857 participants from 135 distinct institutions.The conference's attendance average reached up to 100 participants.Around 60% of the attendees answered the program's quality survey; the average evaluation score for quality of transmission, professional contribution; and topic relevance were 9.0, 9.5, and 10, respectively. Conclusion:The experience of working with telemedicine was determinant to create the innovative SIG COVID-19 Brazil RUTE, providing an important learning experience on diagnosis, treatments, and outcomes for the COVID-19 crisis management.
Machine Learning for Text, by Charu C. Aggarwal, New York, Springer, 2018. ISBN 9783319735306. XXIII + 493 pages.

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PENYELENGGARAAN PELATIHAN BLENDED LEARNING PADA PEMBELAJARAN DI RUMAH PINTAR NURUL FALAH KOTA BANDUNG

The very rapid changes in society as a result of the revolution in communication technology have made all areas of life more competitive. Likewise with the determination of learning innovations must be adjusted to the consideration of approaches in learning in order to produce quality Human Resources (HR). Therefore, especially in the conditions of Covid 19 which require the holding of distance learning, it is necessary to increase the competence of managers and students by applying information technology-based learning such as the blended learning model. This service aims to describe the implementation of a blended learning training program for learning at the Nurul Falah Smart House, Bandung. To reveal the data and information, the community service team conducted a blended learning training program on learning at the Nurul Falah Smart House, Bandung city by applying the lecture, question and answer, discussion, demonstration and brainstorming methods. The blended learning training system in its cycle is divided into three stages, namely, the planning, implementation and evaluation stages. In practice, it always begins with a needs analysis, then compiles a training design then develops blended learning training materials, conducts training and ends with an evaluation. The results of this training evaluation indicate an increase in the cognitive aspects of the training participants, an increase from before and after training.
A Discriminative Model for Multiple People Detection

Group activity recognition is becoming more important day by day for video surveillance, sports analytics, etc. Monitoring various cameras manually through human resources is a complex job; due to this, computer vision algorithms are being developed to perform lower and higher level tasks. This paper presents multiple people detection using the histograms of oriented gradients (HOG) feature descriptor algorithm through a support vector machine (SVM) based on the different group action class of persons. Multiple people detection for group action identification is a complex problem as accurate detection of individual persons requires extensive computation. To achieve multiple people detection for group activity, HOG feature extraction is proposed. HOG is precise and accurate person detection algorithm in the recent computer vision application. In addition, thresholding algorithm is implemented to collect the HOG feature vectors of definitely detected windows and firms the pathway followed by a person in the video frames. The proposed algorithm is evaluated through different aspects like group action categories and existence of occlusion over Haar and HOG features.
Keypoint-based static object removal from photographs

When taking photos of historical buildings and landmarks, one can often encounter more modern and less attractive objects blocking the view (poles, electricity cables, road signs, and others). Photographers strive to take the unobstructed shot, without random objects popping up in the image. However, they avoid deleting unwanted objects with inpainting or exemplar-based methods, because it can introduce notable artefacts. We propose a new algorithm for removing large static objects such as road signs, sitting people, and parked cars from a photograph keeping its originality and unique details. We do not use artificial patterns to fill covered regions, only two shots taken from different angles. Usually, removing long thin objects such as road sign poles cause image deformations, but our approach avoids this. For comparison, we created a dataset from “Caltech Buildings” and “Kaggle Architecture” Datasets and added road signs, cars, and other objects to photos. We compared our approach with state-of-the-art methods such as Deep Image Prior, Gated Convolution, and the Region Filling by block sampling. The real photographs of historical building demonstrate the effectiveness of our algorithm. Code and example images are available on GitHub<sup>1</sup>.
The Design of Chinese Learning Product Based on Mobile Internet

In the wake of the rapid development of mobile Internet technology, education embarks on an “AI+ education” age. As COVID-19 sweeps the world, the trending becomes overwhelming at a faster pace this year. In the context, it is imperative to develop a Chinese learning product for second language (L2) speakers based on the mobile Internet. Targeting 25 types of online learning products that prevail in China, this study is concerned with each product's design characteristics regarding form, content and promotion. Based on relevant research findings, an attempt is made to design a Chinese learning product based on mobile Internet for reference for online Chinese teaching in Korea.
DIGITAL ENVIRONMENT – INNOVATIV TEACHERS

This paper presents the activities of teachers team from Kindergarten „Edinstvo Tvorchestvo Krasota“, Vratsa on the Erasmus + Programme, KA – Key Action 1: „Transnational mobility of learners“, sector „School Education“. Reveals the role of teamwork, motivation and qualification of teachers to achieve its main goal – increasing the skills to work with ICT with based innovative educational technologies and didactic models. The possibilities for comprehensive and quality education of the children in a unified information and communication environment are shown. We share with the highly respected audience the unlimited opportunities of the teacher to self-educate and improve their skills to meet the natural need of children to play by integrating information and communication technologies in the educational process.
INC-MPPT Algorithm for Maximizing Energy Conversion Efficiency of Thermoelectric Generating System

Thermoelectric Generator (TEG) is one of the renewable energy sources that can generate electrical power from waste heat energy from geothermal, solar or industrial plants. To maximizing the output power of a TEG system, the internal resistance of the TEG module must be matched with the load resistance. In this paper, TEG system has been simulated and implemented with a DC-to-DC boost converter including the maximum power point tracker (MPPT) control algorithms to maximize the energy conversion efficiency. Incremental Conductance (INC-MPPT) has been simulated with the MATLAB-Simulink and implemented with Arduino microcontroller and tested at different operating conditions. Simulation and experimental results showed that the experimental TEG system and simulation model have maximized the electrical output power of the TEG module with good accuracy and faster response to track the MPP at rapid changes of temperature on both sides of the module. The experimental results show that the harvested power duplicated by more than six times using the INC-MPPT algorithm applied in comparing with the connection without MPPT at the same operating condition.
Deep Learning and Autoregressive Approach for Prediction of Time Series Data

&lt;div&gt;A deep neural network based approach for prediction of non-stationary data has been proposed in this work. A new&lt;/div&gt;&lt;div&gt;regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of&lt;/div&gt;&lt;div&gt;the proposed model, however in this work stock data has been used due to the fact that stock data has a property&lt;/div&gt;&lt;div&gt;of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some&lt;/div&gt;&lt;div&gt;statistical models and artificial neural networks. Traditional statistical models did not yield any expected results;&lt;/div&gt;&lt;div&gt;artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best&lt;/div&gt;&lt;div&gt;method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.&lt;/div&gt;
The Exploration of Blockchain Technology in Supply Chain Finance

Today's world has officially entered the information age, bringing great convenience to people's work and life, especially financial institutions based on the Internet. Through the construction of information platforms, information resources can be effectively integrated and financial institutionsRealizing the reasonable allocation of resources and applying blockchain technology to supply chain finance are currently research hotspots.Based on this, the article summarizes the significance of blockchain technology, and studies the application and practical exploration of blockchain technology in supply chain finance, hoping to provide useful suggestions.
BubbleUp: Supporting DevOps With Data Visualization

BubbleUp is a tool that lets DevOps teams—data analysts who specialize in building and maintaining online systems—rapidly figure out why anomalous data have gone wrong. We developed BubbleUp with an iterative, human-centered design approach. Through multiple rounds of feedback, we were able to build a tool that presents a paired-histogram view to help make high-dimensional data make sense.
IoT Based Smart Health Monitoring with CNN Using Edge Computing

In the last years, healthcare monitoring has followed a great upwards grown compared to the past decade with the intrusion of the Internet of Things (IoT). The IoT based health paradigm has a vibrant role in health care services to enhance data processing and data prediction. Fall accidents are common in the elderly persons. IoT with Artificial Intelligence (AI) provides a major paradigm to predict the human control, to analyze the cause of human tendency. Fall detection is a major prevailing need with the elderly person, and in order to mitigate this problem an AI based deep Convolutional neural network is proposed to analyze the cause of falling. The deep convolution neural network has been proposed together with the fog and edge computing to analyze the health monitoring tasks. This work analyses the architecture and motions of the persons for fall detection with the sensor nodes. An experimental study is carried out with a benchmark dataset and the higher accuracy in the classification is obtained with this proposal in the simulation results.
Automated Ontology-Based Security Requirements Identification for the Vehicular Domain

Many electronic and electrical systems are now incorporated with modern vehicles to control functional safety. Lack of security protection mechanisms in vehicular design may lead to different ways of executing malicious attacks against the vehicular network. These attacks may have various types of negative consequences, such as safe vehicle operation. This work presents an ontology-based framework as a new automated approach to verify and validate security requirements against security issues in the vehicular domain. The system also applies a set of logical rules to identify a set of security requirements as a category of necessary security requirements that could be proposed to be integrated within the vehicle design to address a specific security issue.
Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.
An applied ontology-Oriented Case Study to Distinguish Public and Private Institutions Through Their Documents

The institutions we create shape many of the activities we engage insofar as they are pervasive entities in our society. In an era full of new technologies, including the semantic web, there is a movement toward sound conceptual modeling for socio-technical solutions applied to government institutions. To develop these complex solutions, one needs to deepen the ontological status of entities in the institutional domain, because literature is full of ambiguous and ad-hoc hypotheses about distinctions between public and private corporations. We believe we can find better explanations for such distinctions in the interdisciplinary field of library a information science. Within an ongoing semantic web project, we focus on a study case of official documents. First, we analyze theories about public and private corporations, seeking a reliable on‘tological distinction between them; then, by focusing on documents produced by each type of corporation, we hope to provide a well-founded analysis. Second, we adopt the aforementioned theories and the new analysis as recommendations for the improvement for the access and understanding of public documents, through appropriate classification of them within government information systems. This project, ultimately, aims to maximize the transparency of public government documents by favoring retrieval and comprehension by a society with plenty of automated information systems.
Relation-oriented faceted search method for knowledge bases

We propose a relation-oriented faceted search method for knowledge bases (KBs) that allows users to explore relations between entities. KBs store a wide range of knowledge about real-world entities in a structured form as (subject, predicate, object). Although it is possible to query entities and relations among entities by specifying appropriate query expressions of SPARQL or keyword queries, the structure and the vocabulary are complicated and it is hard for non-expert users to get the desired information. For this reason, many researchers have proposed faceted search interfaces for KBs. Nevertheless, existing ones are designed for finding entities and are insufficient for finding relations. To this problem, we propose a novel "relation facet" to find relations between entities. To generate it, we apply clustering over predicates based on the Jaccard similarity. We experimentally show the proposed scheme performs better than existing ones in the task of searching relations.
Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images

In recent years, many improvements have been made in image processing techniques which aid pathologists to identify cancer cells. Nowadays, convolutional neural networks (CNNs), also known as deep learning algorithms have become popular for the applications of image processing and examination in histopathology image (tissue and cell images). This study aims to present the detection of histopathology images associated to detection of invasive ductal carcinoma (IDC) and non-IDC in breast. However, detection of IDC is a challenging task in histopathology image which needs deep examination as cancer comprises of minor entities with a diversity of forms which can be easily mixed up with different objects or facts contained in image. Hence, the proposed study suggests three types of CNN architectures which is called 8-layer CNNs, 9-layer CNNs and 19-layer CNNs, respectively, in the detecting IDC using histopathology images. The purpose of the study is to identify IDC from histopathology images by taking proper layer in deep layer CNNs with the maximum accuracy, highest sensitivity, precision and least classification error. The result shows better performance for deep layer-convolutional neural networks architecture by using 19-layer CNNs.
Why elliptic curve Diffie-Hellman (ECDH) is replacing Diffie-Hellman (DH)

The Elliptic Curve Diffie-Hellman is an anonymous key agreement protocol that allows two parties, each having an elliptic-curve public-private key pair, to establish a shared secret over an insecur...
Japanese numeral quantifiers that count events

An event classifier is a unit for counting events rather than objects. This chapter discusses the Japanese event classifier that appears in the verbal domain. Since this topic has not been extensively discussed in the literature, the chapter provides a survey of Japanese event classifiers, sorting them into several types. Along the way, it examines two event classifiers, do ‘times’ and kai ‘times’, which are both frequently used due to their generality, both applying to virtually any type of event. The focus then shifts to one of the more productive constructions with Japanese event classifiers, namely those involving the light verb suru ‘do’. The chapter concludes with some general comments on the linguistic phenomena of counting events and counting objects, with special attention to the flexible nature of both modes of counting.
The Safety Culture As the Basis of the Digital Society

This article discusses the contradictions that emerge in global digital communication in a democracy and cultural diversity. The purpose of the study is to determine the dependence of a person’s goals on his \ her values in the dialogue of cultures and ways to solve the contradictions of intercultural communication. The social system does not only respond to the rapidly occurring social changes, but also respond to making political decisions. Russian scientist S.P. Kurdyumov expressed the idea that there are rules of the ban, the laws that limit the development. ’Safety Culture’ can be considered as the preservation and development of the ideals, values, norms and traditions of the individual, the family and society, social institutions and network. This study considers ’Safety Culture’ as value foundation for the digital society. This should be at the heart of every management decision referring to the digital future of our planet. A safety culture based on knowledge and observance of the ’prohibition rules’, the rules of behavior of people in global network communications will ensure the integrity of the world.&#x0D; Keywords: ‘Safety Culture’, digital society, values, dialogue of cultures
Study of Deep Learning Techniques on Image Denoising

Abstract With the advancement in the area of Artificial Intelligence, the technology of deep learning is widely used in the field of Digital Image Processing and it is showing good results in the domain of Image denoising. To understand the area and progress of the field of deep learning in the domain of denoising, the research work is to be reviewed on various techniques, so that research scholars, academicians and industry professionals can take benefits out of this. The three models are introduced in this paper, such as wavelet neural network, pulse coupled neural network and convolutional neural network which are typically used in the field of noise reduction i.e. image denoising. The method of reduction in noise which is nonlocal in nature is considered as the heart of this technique. The aim of this paper is to better understand the recent developments in the field of machine learning and deep learning with the domain of reduction in noise of digital images.
Evaluation of the global solar irradiation from the artificial neural network technique

In this study, many experiments were carried out to assess the influence of the some input parameters on performance of the multilayer perceptron techniques, which is architectures one of the artificial neural network (ANN). To estimate the daily global solar irradiation (GSI) on horizontal surface, we have developed eight models by using four weather input parameters collected from a radiometric station installed at Ghardaia site (southern of Algeria), such as sunshine duration, daily mean temperature, daily relative humidity, and Solar declination. In order to select the best configuration among the chooses combinations which provides a good accuracy, three statistical formulas (or statistical indicators) have been evaluated, such as the normalized root mean square errors, (nRMSE), normalized mean bias error (nMBE, and coefficient of determination R²). We noted that the ANN model provides best performance compared to the developed empirical models. results of the nMBE, nRMSE, and R² are about 0.048%, 4.422%, and 98.00%, for ANN and 0.0772%, 5.5381%, and 97.22% for the quadratic model. Moreover, it was proved that the sunshine duration is important parameter, for predicting the GSI.