Reviewerly/exHarmony huggingface dataset:
ORGANIZATION OF METHODOLOGICAL ACTIVITIES AS A FACTOR OF PRESCHOOL TEACHERS` PROFESSIONAL DEVELOPMENT

The article considers methodical preparation of the teacher as a process and the result of mastering the system of methodical knowledge, skills and abilities and readiness for their implementation into professional activity.Organization of methodological activity is determined as an important factor of improvement the professional level of teachers of preschool educational institutions and one of the ways of reforming of education.It is noted that the main aim of methodological activity is scientific achievements and pedagogical experience as a complex system of interrelated measures, aimed at improving of qualification, professional skills and abilities of the teacher of the preschool educational institution and the entire pedagogical team.The content of methodological work in the preschool educational institutions is determined in accordance with the specific goals and objectives.Also, the results of the educational process, qualifications of teachers are taken into account.Creation of effective conditions for the professional development of teachers of the preschool education and comprehensive continuous development of children, the quality of interaction with the family, determines the main tasks of methodological work: training and development of teaching staff; management of their qualification improvement; identification, studying, generalization and dissemination of advanced pedagogic experience of teachers; preparation of methodical support for the educational process; coordination of the activities of the preschool educational institution and the family in ensuring the comprehensive continuous development of pupils; coordination of activity of the preschool educational institution with other institutions for the realization of the tasks of development of children and the institution as a whole; analysis of the quality of work in order to create conditions for ensuring positive changes in the development of the personality of pupils through increased professional competence of teachers.Implementation of methodological work allows developing the initiative and creativity of the teaching staff by involving each member in active professional activity.
Parallel multi-layer selector S-Box based on lorenz chaotic system with FPGA implementation

&lt;p&gt;&lt;span&gt;The substitution box (S-Box) is the main block in the encryption system, which replaces the non-encrypted data by dynamic secure and hidden data. S-Box can be designed based on complex nonlinear chaotic systems that presented in recent papers as a chaotic S-Box. The hardware implementation of these chaotic systems suffers from long processing time (low speed), and high-power consumption since it requires a large number of non-linear computational models. In this paper, we present a high-speed FPGA implementation of Parallel Multi-Layer Selector Substitution Boxes based on the Lorenz Chaotic System (PMLS S-Box). The proposed PMLS chaotic S-Box is modeled using Xilinx System Generator (XSG) in 32 bits fixed-point format, and the architecture implemented into Xilinx Spartan-6 X6SLX45 board. The maximum frequency of the proposed PMLS chaotic S-Box is 381.764 MHz, with dissipates of 77 mwatt. Compared to other S-Box chaotic systems, the proposed one achieves a higher frequency and lower power consumption. In addition, the proposed PMLS chaotic S-Box is analyzed based on S-Box standard tests such as; Bijectivity property, nonlinearity, strict avalanche criterion, differential probability, and bits independent criterion. The five different standard results for the proposed S-Box indicate that PMLSC can effectively resist crypto-analysis attacks, and is suitable for secure communications.&lt;/span&gt;&lt;/p&gt;
Fall Detection with Neural Networks

Falls have been one of the main threats to people's health, especially for the elderly. Detecting falls in time can minimize the severity of injury and save lives. On the other hand, neural networks have shown superior performance in human activity recognition field, but their performance in detecting falls has barely been evaluated. In this paper, benefits of using neural networks, including feed-forward, recurrent and convolutional neural networks, in fall detection based on wearable sensors were explored. Experiment results showed that neural networks performed much better than conventional machine learning methods. The best result was obtained from the convolutional neural network with a sensitivity of 99.05% and a specificity of 99.68%.
Predicting Popularity of Movie Using Support Vector Machines

There are many movies performed, from low until high rating, which is the movie maybe popular or not popular. If many people watched that movie maybe it is popular, in other hand if a movie is watched by a little person so that movie can called as not popular movie. Popularity of movie can determined by several factors, such as likes, ratings, comments, etc. To determine popular or not popular of movie based on features, will use two classification methods that is logistic regression and Support Vector Machine (SVM). In this research, the data are Conventional and Social Media Movies Dataset 2014 and 2015. To get the best model and without ignoring the principle of parsimony, will do feature selection. The selected features are genre, sentiment, likes, and comments. That features will be used to classify the popularity of movies. This research used two classification methods namely logistic regression and Support Vector Machine (SVM). When used logistic regression, the accuracy is 77.29%, while used SVM the accuracy is 83.78%. Based on the accuracy of both methods, it is found that SVM gives the highest accuracy for CSM dataset. The highest accuracy is obtained from the SVM method with non-stratified holdout training-testing strategy.
Conceptual and Rating Model of the V-SMARTH Smart University

The conceptual model of smart university has been generalized as a digital transformation-oriented higher educational institution using digital infrastructure (digital legal, digital human resources, digital data, digital technologies and digital applications) to provide personalized learning services to learners of all generations in the country and around the world, meeting the lifelong learning requirements and sustainable development of individuals as well as nations. It is described through the V-SMARTH model, including 6 basic components of digital resources, open access learning materials, virtual learning environment, individualized education, interactive learning and digital platform. These elements come together in three pillars of digitization, digital learning model innovation and comprehensive digital transformation process. The study also approached the notion of the smartness and the readiness level of the smart university. In particular, the issue of performance metrics of smart universities has been developed and implemented with the UPM rating for the VNU University of Engineering and Technology.
Dynamic Expert System-Based Geographically Adapted Malware Risk Evaluation Method

Fast development of information systems and technologies while providing new opportunities for people and organizations also make them more vulnerable at the same time. Information security risk assessment helps to identify weak points and preparing mitigation actions. The analysis of expert systems has shown that rule-based expert systems are universal, and because of that can be considered as a proper solution for the task of risk assessment automation. But to assess information security risks quickly and accurately, it is necessary to process a large amount of data about newly discovered vulnerabilities or threats, to reflect regional and industry specific information, making the traditional approach of knowledge base formation for expert system problematic. This work presents a novel method for an automated expert systems knowledge base formation based on the integration of data on regional malware distribution from Cyberthreat real-time map providing current information on newly discovered threats. In our work we collect the necessary information from the web sites in an automated way, that can be later used in a relevant risk calculation. This paper presents method implementation, which includes not only knowledge base formation but also the development of the prototype of an expert system. It was created using the JESS expert system shell. Information security risk evaluation was performed according to OWASP risk assessment methodology, taking into account the location of the organization and prevalent malware in that area.
The effectiveness of test-driven development approach on software projects: A multi-case study

Over recent years, software teams and companies have made attempts to achieve higher productivity and efficiency and get more success in the competitive market by employing proper software methods and practices. Test-driven development (TDD) is one of these practices. The literature review shows that this practice can lead to the improvement of the software development process. Existing empirical studies on TDD report different conclusions about its effects on quality and productivity. The present study tried to briefly report the results from a comparative multiple-case study of two software development projects where the effect of TDD within an industrial environment. Method: We conducted an experiment in an industrial case with 18 professionals. We measured TDD effectiveness in terms of team productivity and code quality. We also measured mood metric and cyclomatic complexity to compare our results with the literature. We have found that the test cases written for a TDD task have higher defect detection ability than test cases written for an incremental NON-TDD development task. Additionally, discovering bugs and fixing them became easier. The results obtained showed the TDD developers develop software code with a higher quality rate, and it results in increasing team productivity than NON_TDD developers.
Quantum statistical mechanics, quantum probability and quantum characteristic function

The theory of quantum statistical mechanics is reviewed in the light of the quantum probability theory developed in recent years by mathematicians. We show that—which is our main motivation here—the former is a special model in the framework of the latter, by extending the characteristic function in the classical probability theory to the quantum probability theory based on the requirements of quantum statistical mechanics. We find that due to the fact that fundamental dynamical variables of a quantum system must respect certain commutation relations, one has to reform the classical characteristic function defined on a Euclidean space to a normalized, nonnegative definite function defined on a group which is generated by a Lie algebra formed from these commutation relations. Our key finding is that the group-theoretical characteristic function could be adopted to replace the density matrix commonly used in quantum statistical mechanics for representing a state of the quantum ensemble, and more than that, the new representation has some significant advantages in applications. To show its advantages compared with the conventional density matrix scenario, we illustrate its applications in: solving the master equation that describes the irreversible evolution of a quantum open system; determining the pointer states of a quantum open system; and expanding the density matrix with the pointer states. In particular, thanks to the quantum characteristic function, we are able to write down the solutions of the quantum-optical master equation explicitly, obtain its steady solutions and verify that the pointer states are exactly coherent states. Besides, the Caldeira-Leggett master equation for the quantum Brownian motion and its extension to the Lindblad type are also studied.
Shield: An Intelligent and Affordable Solution for Home Security

Reliable home security is vital for progression of our societies. Traditional methods to protect house premises against intruders include security guards and CCTV cameras which is labor intensive, expensive and unreliable. In this paper, we propose a wireless, intelligent, affordable and portable IoT based home security system. The input unit consisting of multiple sensors is placed at the entry points of the house. The SONAR, motion and sound sensor capture the situation at entry points. The sensor data is collected and wirelessly transmitted by NodeMCU to the Raspberry Pi server via an online database. The server uses logistic regression classification algorithm to detect human presence at the gate. Once intruder is detected, alerts are generated to the user and camera mounted on the input unit is turned on to take pictures. Facial recognition unit is also incorporated to detect known versus unknown faces. The information of intrusion including image and timestamp are send to the Shield's mobile application.
Comparison of Profile Matching Method with SMART Method for Laboratory Assistant Selection

Laboratory assistant in university are the main factors in determining the course of practical in the laboratory. So it needs to be selected to get an assistant with good competence. Assistant selection is done by assessing four aspects namely administration, competence, microteaching, and interview. So far the assessment is still done manually, the criteria value still has the same importance. The calculation method which is also not optimal has an impact on the results and the long time of decision making. So we need a method to overcome these problems. In this study the calculation methods used are Profile Matching and SMART (Simple Multi Attribute Rating Technique). Based on research conducted both methods work by grouping criteria according to their level of importance. There are 12 criteria divided into four aspects, and alternative data of 7 participants were taken from 2019 participant data. The results of the two methods are ranking sequences compared with ranking results in 2019 selection. The results of this study show better profile matching because it has an accuracy value 100% is exactly the same as the results of the previous selection, while SMART is only 42.8%.&#x0D;
A New Metric for Class Cohesion for Object Oriented Software

Various class cohesion metrics exist in literature both at design level and source code level to assess the quality of Object Oriented (OO) software. However, the idea of cohesive interactions (or relationships) between instance variables (i.e., attributes) and methods of a class for measuring cohesion varies from one metric to another. Some authors have used instance variable usage by methods of the class to measure class cohesion while some focus on similarity of methods based on sharing of instance variables. However, researchers believe that such metrics still do not properly capture cohesiveness of classes. Therefore, measures based on different perspective on the idea of cohesive interactions should be developed. Consequently, in this paper, we propose a source code level class cohesion metric based on instance variable usage by methods. We first formalize three types of cohesive interactions and then categorize these cohesive interactions by providing them ranking and weights in order to compute our proposed measure. To determine the usefulness of the proposed measure, theoretical validation using a property based axiomatic framework has been done. For empirical validation, we have used Pearson correlation analysis and logistic regression in an experimental study conducted on 28 Java classes to determine the relationship between the proposed measure and maintenance-effort of classes. The results indicate that the proposed cohesion measure is strongly correlated with maintenance-effort and can serve as a good predictor of the same.
Indonesian language email spam detection using N-gram and Naïve Bayes algorithm

Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.
FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud

Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability.
PERANAN TURNAMEN BERBASIS ICT DENGAN APLIKASI QUIZIZZ TERHADAP PEMBELAJARAN KIMIA

ICT bases tournament with quizizz application has role play to chemistry leaning study. This role can be seen from motivation and student assessment aspect. This research is design experimental quasi “The randomized pre test-post test control group design” and the data analysis is using t-test for comparing experiment and control class. Learning with this bases can improve the motivation and result of student learning. Students believe that they can be the best in study with this quizizz application. It becomes the wonderful time to study with excited, happy, and the material is easy to understand. Beside that, with ICT function, students can apply this tournament is out of the schedule time to study, so it can be doing at anywhere and anytime
Recomendaciones para describir de forma adecuada una curva epidémica de COVID-19

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Privacy in the time of Covid-19

EVEN THOUGH I understand how it works, I consistently find Google's ability to know how long it will take me to drive somewhere nothing less than magical. GPS signals stream location and speed data from the legion of smartphones in vehicles on the roads between my origin and my destination; it takes only a bit of math to come up with an estimate accurate to within a few minutes.
Customer gender prediction system on hierarchical E-commerce data

Abstract Background E-commerce services provide online shopping sites and mobile applications for small and medium sellers. To provide more efficient buying and selling experiences, a machine learning system can be applied to predict the optimal organization and display of products that maximize the chance of bringing useful information to user that facilitate the online purchases. Therefore, it is important to understand the relevant products for a gender to facilitate the online purchases. In this work, we present a statistical machine learning (ML)-based gender prediction system to predict the gender “male” or “female” from transactional E-commerce data. We introduce different sets of learning algorithms including unique IDs decomposition, context window-based history generation, and extract identical hierarchy from training set to address the gender prediction classification system from online transnational data. Results The experiment result shows that different feature augmentation approaches as well as different term or feature weighting approaches can significantly enhance the performance of statistical machine learning-based gender prediction system. Conclusions This work presents a ML-based implementational approach to address E-commerce-based gender prediction system. Different session augmentation approaches with support vector machines (SVMs) classifier can significantly improve the performance of gender prediction system.
Case Based Reasoning Method untuk Sistem Pakar Diagnosa Penyakit Sapi

This study discusses about the Application of Case Based Reasoning (CBR) Method for Expert Systems in Diagnosing Cattle Disease. Beginning with data collection by consulting experts in the Department of Agriculture in Animal Health, Pohuwato Regency. The data obtained in the form of data names of disease and symptom data. The data is obtained based on the steps of the CBR method calculation in order to obtain the results of the diagnosis and the solution provided for handling the disease. Researcher have analyzed and create program listings to build a system that will be used by farmers. Based on CBR calculations Scours case which has the lowest weight is 0.09 while the highest weight is owned by the Pink Eye case 1. In this process provides a solution to the similarity of the case weight from the old case to the new higher case. In the case of Pink Eye having a higher weight and positive exposure to pink eye disease, the solution given is the provision of anti-allergic, anti-biotic and vitamin. Based on the results obtained, it can be concluded that the application of the CBR method is good for using cattle disease and is very helpful for farmers in dealing with cattle disease.