Reviewerly/exHarmony huggingface dataset:
STUDENTS’ PERCEPTION ON THE DESIGN OF ASYNCHRONOUS ONLINE DISCUSSION USING SCHOOLOGY IN ENGLISH LANGUAGE EDUCATION GANESHA UNIVERSITY OF EDUCATION

This study was aimed at investigating students’ perception on the design of asynchronous online discussion using Schoology. This study was a case study research. The data in this study were collected quantitatively and qualitatively by using observation, questionnaire, and interview guide. The observation was conducted toward the design of asynchronous online discussion implemented by 2 lecturers in ICT and Poetry courses. The questionnaire was administered to one hundred students of English Language Education, Ganesha University of Education who were taught using Schoology in academic year 2017/2018. The interviews were done toward six students. The result of this study showed that 1) the design of the asynchronous online discussion in ICT and Poetry courses was different in terms of flexibility of time, flexibility of place, instruction or guidance, and topic, 2) the students had positive perception on the design of asynchronous online discussion.
Clustering Visualization and Class Prediction using Flask of Benchmark Dataset for Unsupervised Techniques in Machine learning

Cutting edge improved techniques gave greater values to Artificial Intelligence (AI) and Machine Learning (ML) which are becoming a part of interest rapidly for numerous types of researches presently. Clustering and Dimensionality Reduction Techniques are one of the trending methods utilized in Machine Learning these days. Fundamentally clustering techniques such as K-means and Hierarchical is utilized to predict the data and put it into the required group in a cluster format. Clustering can be utilized in recommendation frameworks, examination of clients related to social media platforms, patients related to particular diseases of specific age groups can be categorized, etc. While most aspects of the dimensionality lessening method such as Principal Component Analysis and Linear Discriminant Analysis are a bit like the clustering method but it decreases the data size and plots the cluster. In this paper, a comparative and predictive analysis is done utilizing three different datasets namely IRIS, Wine, and Seed from the UCI benchmark in Machine learning on four distinctive techniques. The class prediction analysis of the dataset is done employing a flask-app. The main aim is to form a good clustering pattern for each dataset for given techniques. The experimental analysis calculates the accuracy of the shaped clusters used different machine learning classifiers namely Logistic Regression, K-nearest neighbors, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. Cohen Kappa is another accuracy indicator used to compare the obtained classification result. It is observed that Kmeans and Hierarchical clustering analysis provide a good clustering pattern of the input dataset than the dimensionality reduction techniques. Clustering Design is well-formed in all the techniques. The KNN classifier provides an improved accuracy in all the techniques of the dataset.
Governance of IoT in order to Move ahead of the calendar &amp; live the future today

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to address, especially by developers. In this handwriting, learn how to meet the security, analytics, and testing requirements for IoT applications. A thing, in the Internet of Things, can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low -- or any other natural or man-made object that can be assigned an IP address and provided with the ability to transfer data over a network. IoT has evolved from the convergence of wireless technologies, micro-electromechanical systems (MEMS), micro services and the internet. The convergence has helped tear down the silo walls between operational technologies (OT) and information technology (IT), allowing unstructured machine-generated data to analyze for insights that will drive improvements. The Internet of things (IoT) is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. If one thing can prevent the Internet of things from transforming the way we live and work, it will be a breakdown in security. Therefore, there are some important challenges in privacy and maintaining security of the Internet of things. In this paper, we are going to list the typical items which can be major challenges facing IoT.
Video Steganography using IWT, DWT, LBP Methods and its Research

Steganography is a method used for inserting the secret information into an another medium such as text, images, audio signals or video signals, without revealing its existence in the medium. In video steganography, a video file will be used as a cover medium and secret message embedded inside the cover medium. Video are picture perfect for embedding the secret message because of its large embedding capacity. We propose three secure steganography algorithms that embed a bit stream of the secret message into the approximation coefficients of the integer wavelet transform(IWT), DWT and using LBP method to form stego video. The geometric alteration between the cover video and the stego video is measured by using the Mean Square Error (MSE) and the PSNR. The new results show that, the proposed algorithms can hide the secret message with a great payload capacity with a high level of security and a greater invisibility.
Monitoring of Mercury and Arsenic in Water Samples from Proximity to Abandoned Cinnabar Mine

Concentrations of mercury and arsenic were monitored in water samples from the Záskalská water reservoir (Czech Republic) and its tributaries.The total mercury concentration in the reservoir ranged between 0.06-0.11µg L -1 and in tributaries between 0.14-0.20 µg L -1 .The effect of water erosion from abandoned cinnabar mine on mercury concentration in tributaries and reservoir was observed.The arsenic concentrations in water samples were between 0.21-0.54µg L -1 .The 98±10% of arsenic concentration in the water was determined as potentially bioavailable, whereas only 7-20% of the total mercury concentration in the water was determined as bioavailable using diffusive gradients in thin films technique.
Capital market based on blockchain technology and the efficient market hypothesis: theoretical and conceptual analysis

The purpose of this paper is to describe the theory of market efficiency and verify if their characteristics and principals are applied to the digital capital market based on blockchain technology, such as initial coin offerings (ICOs). Indeed, this work presents a comparative approach between the efficient capital market principals and the fundamentals of ICOs based on a documentary and qualitative analysis of the scientific literature and ICOs professional reports concerning the most relevant keywords related to the conceptual framework of market efficiency. As a result, we conclude according to our comparative conceptual model that ICOs market based on smart contract has many similarities with the efficient capital market where the self-regulation is the rule and each investor has free access to relevant information, and then all transactions are executed in such trust and transparency. Moreover, many factors such as regulation and institutional disclosure, liquidity and systemic risk have a significant impact on the market efficiency according to the scientific literature and professional experiences.
Теоретическая модель системы мобилизационной подготовки в Российской Федерации

The article is devoted to the theoretical vision of the modern system of mobilization training in the Russian Federation, taking into account new trends in the world development. The author considers mobilization training in the form of a social system. It allows revealing the goals and objectives of the system, and through the structural and functional components of the system, due to the current state of development of the economy, technology and law, to simulate the processes of ensuring the country's defense capability and to predict possible states of the system. It in turn, will make it possible to give recommendations to state and municipal authorities.
LexRank Algorithm: Application in Emails and Comparative Analysis

Text summarization can be described as the process that helps to shorten long pieces of text, with the goal of producing succinct and factual content that places specific focus on the basics present in the document.This is a known issue in machine learning and natural language processing, and the amount of attention given to it has only increased over many years, keeping in mind that there are copious quantities of data online.It also has the ability to collect useful information that can be managed fairly easily by humans and could be used for a wide range of purposes, such as text assessment.In this paper, we are attempting to present an automated text summary method that relies on LexRank Algorithm to find the most significant and appropriate statements in the long input text and make them a part of the short summary.In this project, given a set of data for a particular topic, the appropriate summary is produced using the LexRank algorithm.It is also capable of summarizing a single data as well.We are using college circulars as the data for testing the relevance of the produced summaries.We are also testing its relevance by testing the already available data by generating the ROUGE scores where the automatically generated summaries are compared with the manually written summaries of the same.
Application for 3D Interface using Augmented Reality

Augmented reality (AR) has been used in the last years as a weapon for enhancing co-operation between the real world and virtual environments. The goal of this work is to develop an android application which will raise the value of tourist on-site experience in an innovative way via AR technology. This survey will especially focuses on museum tourism. Museum consisting of distinct sections faces difficulties of tourist management. If guides are not accessible this application can take place of human guide. In historical section of museum, one can scan the 2D image of object and can gain its 3D view along with its information. In music section, tourist can scan the image of singer and application will play famous melodies related to that singer. This data will be stored on the Cloud. This application is done by using well known technologies such as Unity 3D and Vuforia. Usage of such tools and technologies gives great opportunity to convert imagination into reality.
Towards Protecting Privacy in Retrieval of Image

Protection assurance in Content Based Image Retrieval (CBIR) is another exploration point in digital security and security. The state of-workmanship CBIR structures generally get wise instrument, specifically criticalness analysis, to improve the recuperation precision. Directions to guarantee the customer's assurance in such Relevance Feedback based CBIR (RF-CBIR) is a test issue. In this paper, we investigate this issue and propose another Private Relevance Feedback CBIR (PRFCBIR) scheme. PRF-CBIR can utilize the execution increment of congruity info and spare the customer's request point meanwhile. The new PRF-CBIR involves three stages: 1) private inquiry; 2) private information; 3) neighbourhood recuperation. Private request plays out the fundamental inquiry with a security controllable component vector; private input develops the criticism picture set by presenting confounding classes following the K-secrecy standard; nearby recovery at long last re-positions the pictures in the client side. Security investigation demonstrates that PRF-CBIR satisfies the protection necessities. The analyses completed on this present reality picture gathering affirm the viability of the proposed PRF-CBIR plot.
THE IMPLEMENTATION OF SCAFFOLDING READING EXPERIENCE STRATEGY IN INCREASING STUDENTS’ COMPREHENSION

The aim of this research is to find out whether there is a significant difference in achievement between students who were thought reading comprehension by using SRE strategy and those who are not, and to examine the students’ responses toward the SRE strategy used in teaching reading comprehension. The method used in this study was experimental design in which classified into experimental and control groups. The population of the study was the first-grade students of State Vocational School 1 Nisam, North Aceh. The data were collected through a reading comprehension test and a set of questionnaires. The finding showed significant differences between the pre-test and post-test score. The result showed that the means of pre-test for both class of experimental was M=46, 79, SD = 14, 15) to post test (M = 75, 36, SD = 12, 01), t (27) = -11, 92, p &lt; .001 (two-tailed). The data squared statistics (.84) indicated a large effect size. The mean increase from the test illustrated significant difference between pre-test and post-test, hence the hypothesis was accepted. Furthermore, the result of data analysis from the questionnaire showed that most of the students responded positively.
Identification of Non-linear Systems through Convolutional Neural Network

The theory of control systems deals with the analysis and design of interacting components of a system in a configuration that provides the desired behavior. This paper deals with the problem of the identification of non-linear systems through Convolutional Neural Network (CNN). We propose a structure of a CNN and perform simulations with test data using unsupervised learning for the identification of nonlinear systems. Also, MLP is used to compare the results when there is noise in the training data, which allows us to see that the proposed CNN has better performance and can be used for cases where the noise is present. The proposed CNN is validated with test data. Tests are carried out with Gas oven data, comparing the proposed structure of CNN with a MLP. When there is noise in the data, CNN has better performance than MLP.
Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture

Recognition of handwritten digit is one of the popular problem associated with computer vision applications. The goal of our research work is to develop scalable Neural Network(NN) and Convolutional Neural Network (CNN) model that would be able to recognize and determine the handwritten digits from its image. Capability of developing the new algorithms and improve the existing algorithms is determined by the accuracy and speed factor for training and testing the models. In this context, performance of the GPUs and CPUs for handwritten digit system and effects of accelerating the training models have been analyzed. The training and testing has been conducted from publicly available MNIST handwritten database. Web based, offline and online handwritten digit recognition system is developed by using Convolutional Neural Network
The Effect of using Picture Strip Story on Students’ Speaking Skill

This research was conducted at SMP YayasanPangeranAntasari, which located in Jl. Veteran No. 1060/19 Helvetia, Medan, North Sumatera. The data were collected by using a speaking test using pieces of pictures in narrative texts as the media. The results show the picture strip story has a positive effect on students' speaking skills through improving the students' speaking scores. This subject of this research were the students at the second grade A. This research was conducted by researchers starting with giving the pre-test to know the students' speaking ability before conducted the post-test in the last meeting, researchers treat the students using the picture strip story. This study shows the significant effect of using PST has a positive effect on students' speaking skill through the improvement of students' speaking scores. It was proven by the means in Post-test was higher than in Pre-test (73,55- 48,00). The t.obs&gt;t.table (P = 0,05), df(19). It means that Ha was accepted. Through the scores between Pre-test and Post-test shows a significant difference after being taught using the PST. Therefore, PST is one of some media which can be used to improve the students' speaking skill. &#x0D; Keyword: Picture Strip Story, Speaking Skill, Effect.
Online Data Security for Secure Cloud Storage

The capacity benefit given by cloud server is not completely trusted by clients. In existing system, data can be modified and corrupted by the unauthorized user with the assistance of the employees. Typically the information are safely taken care of by the organization yet a few employees offered the specifiers to programmers for cash. Because of this issue, the information are not protected. Hence the advanced safe technology is used. The information are uploaded by the encryption design with video mode and the information are downloaded by the client with the assistance of face detection video mode, at that point the administrator acknowledge the solicitation by the face recognition video mode and information are shared starting from one place to another. Utilizing the AES algorithm the information is shared safely. It is implemented in both hardware and software. It utilizes higher length key sizes, for instance, 128, 192 and 256 bits for encryption. It is most basic security convention utilized for wide range of uses, for example, wireless communication, money related exchanges, e-business, encoded information storage and so forth.
Social Media Addiction, Culture Code and Mediation Effect of Mindfulness: A Structural Equation Modelling Access

This study has been initiated with a view to understand the social media addiction and its influence on culture codes a mediation effect of mindfulness. The social media addiction would be a very common problem in the social and the organizational set up. The employees are getting distracted easily because of social media addiction. The organisations are taking much action to rectify this problem but end up with the talent lose because the employees are ready to quit the job when they are not allowed to use social media in the organisation. It has been an inevitable problem for the organisations so many techniques have been initiated in order to solve this problem. Many studies are concentrating on mindfulness which is a tool to remove the distractions and to be a focused employee in the organisation. The social media addiction many a times create a toxic culture among the employees. It needs a high attention to be cured. The employee relationship are highly under stake when they are social media addict. This study would attempt to understand the empirical evidence on this relationship by examining the association among addiction in social media and its effect on culture code and mediating role of mindfulness. This study also concentrates on the moderating role of gender on the proposed model.
Impact of knowledge leadership on the challenges and innovative performance of virtual teams: an empirical examination in oil sector companies

This study aims at exploring the relations between the knowledge leadership, overcoming virtual teams (VTs) challenges and VTs' innovative performance in organisations involved in the Iraqi northern oil sector. For this purpose, the descriptive analytical approach was used, where an online survey (including 34 items in the context of the three main variables) was designed to collect data and information from more than 10 oil companies. Using the structural equation modelling on SPSS v.23 and AMOS v.24, the study data has been analysed. The study found that there is a direct, indirect, positive and significant relationship between the main variables. The study suggests that all skills and behaviours of knowledge leadership must be adopted to overcome the challenges faced by VTs. Additionally, these skills and behaviours help leaders to invest all subordinates' efforts for improving the VTs' innovative performance.
Factors Influencing Academic Achievement in Preclinical Students

Academic achievement is a major concern of preclinical students. This study aimed to determine factors influencing academic achievement in preclinical years. Questionnaires were sent to all students of the 2019 class at the 1st (2019/1) and 2nd preclinical years (2019/2) and the 2018 class at the 2nd preclinical year (2018/2) without sampling, with 85.11% (280/328), 86.32% (284/328), and 83.54% (274/328) being returned, respectively. Students were divided into Q1 (lowest)-Q2-Q3-Q4 (highest) according to quartiles of their GPA. Q4 students had higher examination expectation and achievement of study targets in all classes and more time spent on non-recorded-e-lecture study in the 2018/2 and 2019/2 classes; but lower time spent on recorded-e-lecture study in the 2019/1 class and non-academic-internet use in the 2019/2 and 2018/2 classes than other groups. In contrast, Q1 students had higher instances of lateness and absence/year in the 2019/2 and 2018/2 classes but lower happiness scores in the 2018/2 class compared to other groups. Obese and overweight students had significantly lower GPA than normal weight and underweight students of the 2019/2 class (p&lt;0.05 all). The current year GPA had significant positive correlations with the previous year GPA, examination expectation, achievement of study targets, and time spent on non-recorded-e-lecture study; but had significant negative correlations with time spent on recorded-e-lecture study and non-academic internet use, instances of lateness and absence/year, and BMI (p&lt;0.05 all). In conclusion, students with more self-regulation strategies in many aspects of self-regulated learning, including time management, learning strategies, emotion control, motivation, and self-efficacy, had better academic outcomes.
Restructuring with Moora and Measuring Code Smells

The paper presents measuring various code smells by finding critical code smells and thereby concentration is increased in those parts through Structural Modeling for arranging those code smells. Arranging the code smells in the way that they will not produce a new smell on their detection and removal is very necessary. Structural modeling helps in clarifying Interrelationship among these code smells. The code smells that contains high driving effects are ordered as optimized code which resulted in the increase in the overall code maintenance of the software code which will be used afterwards for achieving the concept of re-usability. In addition to this we have added a technique for restructuring technology for the purpose to achieve high accuracy. It involves more objectives related to the performance and the code smells are implemented with the concept called as pairwise analysis based on the priority method. Pairwise analysis based on the weights attained by the bad smells provides a better optimized results since more problematic areas are neglected here. This work gives optimized results for the process of overall code maintainability by applying restructuring before the refactoring process with Fuzzy technique and it is followed by finding the code smells which results in high ripple effects and then removing them. Still more research ideologies are needed for removing the bad smells in the code.