Reviewerly/exHarmony huggingface dataset:
SAGAD: Synthetic Data Generator for Tabular Datasets

The accuracy of machine learning models implementing classification tasks is strongly dependent on the quality of the training dataset. This is a challenge for domains where data is not abundant, such as personalized medicine,or unbalance, as in the case of images of plant species, where some species have very few samples while others offer large number of samples. In both scenarios,the resulting models tend to offer poor performance. In this paper we present two techniques to face this challenge. Firstly, we present a data augmentation method called SAGAD, based on conditional entropy. SAGAD can balance minority classes in conjunction with the increase of the overall size of the trainingset. In our experiments, the application of SAGAD in small data problems with different machine learning algorithms yielded significant improvement in performance. We additionally present an extension of SAGAD for iterative learning algorithms, called DABEL, which generates new samples for each epoch usingan optimization approach that continuously improves the model’s performance. The adoption of SAGAD and DABEL consistently extends the training dataset towards improved target classification performance.
Performance Comparison of Support Vector Machines, Random Forest and Artificial Neural Networks in Binary Classification: Descriptive Comparison Study

Objective: In this study, it was aimed to find the method with high classification success among the methods used in the study by comparing the supervised machine learning methods according to the classification performance. Material and Methods: In our study, both the real data set obtained from 302 patients with invasive ductal carcinoma and 24 different data sets obtained by simulation were used to compare the classification performance of support vector machines, random forest and artificial neural networks. The success of classifications of the methods used was compared according to the general accuracy, F-measure, Matthews correlation coefficient, area under the curve (AUC) and discriminant power in breast cancer data. In addition, the difference in training-test accuracy in the simulation data and the significance of this difference were also evaluated. Results: The highest survival classification accuracy (80%) for the test set of stage III patients with invasive ductal carcinoma was obtained from support vector machines (SVM) with the radial-based kernel. The highest values in other performance metrics (F-measure=0.87, Matthews correlation coefficient=0.22, AUC=0.89 and discriminant power=0.52), and the most successful results in simulation data were generally obtained from SVM. Conclusion: SVM had higher accuracy in both the real data set and simulation data than random forest and artificial neural networks.
2020 Impact Factor of <i>The Photogrammetric Record</i> Increases

The Photogrammetric RecordVolume 36, Issue 176 p. 444-444 Notes 2020 Impact Factor of The Photogrammetric Record Increases First published: 18 December 2021 https://doi.org/10.1111/phor.2_12391Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume36, Issue176December 2021Pages 444-444 RelatedInformation
Explainability for Linear Models

This chapter explores the use of the SHAP, LIME, SKATER, and ELI5 libraries to explain the decisions made by linear models for supervised learning tasks for structured data. In this chapter, you are going to learn various ways of explaining the linear models and their decisions. In supervised machine learning tasks, there is a target variable, which is also known as a dependent variable, and a set of independent variables. The objective is to predict the dependent variable as a weighted sum of the input variables or independent variables.
Visual Surveillance Transformer

In a visual surveillance system, even the same object should exhibit different detection results depending on the surrounding environment configuration. To this end, the model for visual surveillance needs to detect an object by understanding the state of the object according to the environment on the image. In this study, for such visual surveillance, an object segmentation model applied with a transformer structure suitable for image processing was used to divide objects inside the image into foreground and background. A modified attention structure was presented for the corresponding transformer structure, and the results of object segmentation models according to the type of input data were compared.
Lifetime Status Evaluation of On-Board ATC Equipment Based on Cloud Model

As the key part of metro trains, on-board automatic train control (ATC) systems play the functions of train operation control preventing speeding. Aiming at the problem of lifetime status evaluation in the life cycle management of on-board ATC equipment, an indicator system of influencing factors and an evaluation model were established, and a cloud model-based lifetime status evaluation method for on-board ATC equipment was proposed. Combined with a practical case of a metro line in Shanghai, the applicability of the method was verified, and targeted maintenance suggestions were given. It is expected to provide a reference for the ideas and methods in the life cycle management of rail transit signal system.
Acceleration Anomaly Detection Method for Sensor Axle Box of Unmanned Vehicle

Detecting the anomaly acceleration of the sensor’s axle box of unmanned vehicles is very important for judging the wear condition of vehicle track and evaluating the state of the track. A capacitive accelerometer is connected with acquisition equipment to collect the information of train axle box’s acceleration change when the vehicle is running; instrument amplifier AD8250 with a digitally programmable gain is selected as system signal conversion chip to realize acceleration signal conversion; sliding variance of axle box’s acceleration of the unmanned vehicle is calculated based on sliding variance statistical analysis method, which is confirmed by time window and distance window. Fixing the width of a sliding window according to the response statistics caused by the line excitation link, the acceleration sliding variance is compared with the standard one to determine whether the acceleration is in an anomaly state. The test results show that the anomaly acceleration of the sensor axle box of the unmanned vehicle detected by the proposed method is consistent with the actual results, which provides a reliable basis for vehicle track condition assessment.
Using Governance Mechanisms to Raise the Efficiency of Internal Control Performance to Confront Government Corruption in Iraq: An Empirical Study

The study aimed to reduce the phenomenon of government corruption by developing internal control systems using governance mechanisms, and the factors influencing their support to improve their professional quality, as one of the most important mechanisms of an effective internal control structure that affects the discovery and prevention of fraud and innovative practices, increasing the credibility and transparency of financial and control reports, and contributing to activation Practicing the system of governance, to reduce administrative deception and methods, to control and minimize corruption, and to raise the productive efficiency of government performance, by controlling public spending, preserving public money, and promoting economic rationalization in the use and exploitation of property General resources, and a questionnaire list was designed to achieve these goals, which was distributed to those interested in government internal control divided into three categories: financial monitors in the ministry, inspectors of the central apparatus, and government accountants. The study concluded the importance of applying governance mechanisms in raising the level of efficiency of government internal control systems, in order to reduce the manifestations of government corruption.
References

The research described in this book searches for the answers on how learners learn in todays open and networked learning environments and how learners, educators, institutions, and researchers can best support this process. There is sufficient data available on virtual learning environments, provided by learning analytics, on student and teacher behaviour and performance, but there is no common practice among teachers in higher education for using this data to improve the learning and teaching process. Learning analytics and data may inform and improve open and online learning from the point of view of teacher and learner awareness about their behaviour and their learning and teaching methods. The idea of describing learning analytics as a metacognitive tool, suggesting a development of metacognitive decision-making skills in teacher education, and focusing on learning design in higher education by using data from learning analytics served as the main focus of this research. The aim of the research was to create the model of application of learning analytics method as a metacognitive tool to enhance student success. The aim of the research was reached through theoretical and empirical objectives, namely: describing the learning analytics method as a metacognitive tool; revealing teacher metacognitive practices
Teaching Communicatively in a Classroom with Mobile-sourced Materials

Mobile Assisted Language Learning (MALL) is now common in extra-curricular language learning, but, more recently, teachers have increasingly sought ways to utilise MALL as a communicative classroom tool. Research into the extent that MALL can transform a whole communicative language course, and learners’ impressions of such courses, is scarce. This study, therefore, sought the opinions of five undergraduate learners on a short communicative English language course based on communicative principles, with materials entirely sourced from learner’s own devices. Learner reflections elicited in interviews suggested that MALL had aided the facilitation of an environment that was interactive, motivating, differentiated, authentic and autonomous, at times potentially more so than on a course using traditional material sources. The novel aspect of allowing freedom in choosing materials caused some complications, though none were considered insurmountable with minor adjustments to the course plan. In summary, student reactions implied that a communicative course could be taught exclusively through mobile-sourced materials, but further research is required to identify exactly how this would best be achieved. Nevertheless, findings here give reasons for practitioners to explore methods of classroom teaching inclusive of MALL that encourage self-directed learning, the creation of a platform for interaction, personalization, differentiation, a shared experience for learners and elements of game-play.
Implementation of Encoder/Decoder to Support SNN Model in an IoT Integrated Development Environment based on Neuromorphic Architecture

Neuromorphic technology is proposed to complement the shortcomings of existing artificial intelligence technology by mimicking the human brain structure and computational process with hardware.NA-IDE has also been proposed for developing neuromorphic hardware-based IoT applications.To implement an SNN model in NA-IDE, commonly used input data must be transformed for use in the SNN model.In this paper, we implemented a neural coding method encoder component that converts image data into a spike train signal and uses it as an SNN input.The decoder component is implemented to convert the output back to image data when the SNN model generates a spike train signal.If the decoder component uses the same parameters as the encoding process, it can generate static data similar to the original data.It can be used in fields such as image-to-image and speech-to-speech to transform and regenerate input data using the proposed encoder and decoder.
Mean-Based Breakpoint Selection on Circular Histogram

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.
E-DONATION BY CLOUD BASED ETHEREUM PUBLIC BLOCKCHAIN

E-Donation cloud funding system is an indispensable part of the society. It is a feasible method to assist any donors in any part of the world. Donors require fully secured system where there all information will not be hacked by hackers. As Web 3.0 is the third generation of internet services that can make the system fully secured which is distributed decentralized and semantic. It means with the Artificial Intelligence (AI); it does not have a centralized control node. Public Blockchain- a decentralized, distributed ledger technology - is the implementation of Web 3.0 that technology will be used to develop this system which would be an open source. This paper will look into Ethereum Blockchain technology and recognize the fundamental support it provides for Web 3.0 framework.
Deploying Your Blazor Application

At a certain point in time, your Blazor application will be ready for the big public. Yeah! But the work is not yet done. We need to take our application and copy it to a server connected to the network so other people can use their browser to admire your work! Let us look at how we can deploy our Blazor application.
An experimental study on the production of the double accusative ditransitives in Korean

The purpose of this study is to revisit the production of the Korean ditransitive constructions reported in Park & Yi (2021). The phenomenon is puzzling particularly because the double accusative ditransitive construction tends to be judged highly unacceptable and rarely observed in natural language corpora, while speakers produced a non-trivial number of them in an experimental setting. This study attempts to replicate the results of their production experiment on a different pool of participants and with a much larger sample size (N=96). Based on this new dataset, we conduct quantitative and qualitative analysis of speakers’ free continuations after two types of prompts, i.e., including either dative- or accusative-marked animate argument as a potential recipient. We examine the validity of the previous results and also discuss a variety of continuations provided by participants and their implications for Korean sentence production.
Evaluasi Penggunaan Learning Management System Sebagai Alat Bantu Pembelajaran Matematika Sekolah Dasar

Teaching and learning activities need to be interactive to increase children’s interest in learning. With the utilization of technology, there are so many learning tools had been made. One of them is Moodle. Moodle is a learning management system (LMS) application to support learning activity on electronic based (e-learning). The purpose of this research is to implement Moodle as LMS for mathematics learning on primary school children. By using Moodle, the lessons are delivered interactively for children to learn. This research will find out the influence of using this application towards the test score before and after using the application. T-Test analysis will be applied to analyze the differences. Besides analyzing the test score, questionnaires for the children who test the application will be given to know the effect of using the application for them. Based on the evaluation result using T-Test analysis, there were not any significant differences, but there were enhancements in average, highest, and lowest scores, along with the decrease in standard deviation. The result of correlation coefficient calculation between exercise frequency and quiz result was 0.2162, which meant that the correlation was weak or almost no correlation. Based on the questionnaire result, this application is considered helping children in understanding the subject.
Proxy

When we looked at the Decorator design pattern, we saw the different ways of enhancing the functionality of an object. The Proxy design pattern is similar, but its goal is to preserve exactly (or as closely as possible) the API that is being used while offering certain internal enhancements.
Computer Programming Optimization Based on Numerical Algorithms of Fractional Differential Equations

In computer programming, the reasonable use of the advantages of the numerical algorithm of the fractional differential equation can not only be a good summary and classification of the problems that have. This research mainly analyses the actual situation of the application of fractional differential equations in computer programming optimization, and discusses the application of fractional differential equation modelling ideas in computer programming algorithms, and the numerical algorithms of fractional differential equations in different computer programming languages. On the application. Case analysis proves that the effective application of fractional differential equations not only improves the overall effect of computer programming optimization, but also promotes the overall improvement of related program functions and efficiency to a large extent.
Using RMarkdown to Present Your Findings

This chapter covers how to utilize <italic>RMarkdown</italic> to present <italic>SSD for R</italic> findings in a well-ordered and reproducible manner. <italic>RMarkdown</italic> is a plain text formatting syntax that makes writing research reports simple. The language provides a simple syntax that formats text such as headers, lists, boldface, and so on. This language is popular, and you will find many apps that are compatible with it. For example, combined with other packages, like <italic>SSD for R</italic>, users can easily create tables and graphics to present their research findings. Another important feature of this markdown language is that it will make your findings reproducible in that all of your files are connected. Thus, if there are changes to your data, rerunning the analysis is simple.
21 Encouraging activity and exercise among children and young people using computer vision and machine learning during gaming

<h3>Introduction</h3> Population health and wellbeing is a priority in the UK, with new initiatives that empower children to live healthier lives. Excess weight has also been associated to worse outcomes during the COVID-19 pandemic period, complicated by reduced activity within the confinements of a home environment and coupled by increased screen time with remote classroom practices. As a result, children and young people now interact with computer interfaces in their home environment for education, gaming and healthcare purposes for prolonged periods and in new ways. <h3>Method</h3> There is a growing interest in Natural User Interfaces (NUIs) that use natural hand and body gestures to interact with computers. Advances to these technologies mean that they are now more accurate, easier to use and instead of requiring expensive depth cameras, can be operated using simple webcams. In this study, OpenCV library is used to track user movement by calculating the pixel difference between two frames and create a catalogue of exercises. We use PyTorch exercise recognition model to check the status of the user every 8 frames. These are recognised by using Convolutional Neural Networks (CNNs) with static training from datasets and offer users the option to create personalised exercises. <h3>Result</h3> We present University College London’s (UCL) Motion- Input supporting DirectX: Gestures for at-home exercises. This exercise module can recognise six repetitious static exercises, such as running on the spot, squatting, cycling on an exercise bike, and rowing on a rowing machine using a webcam. This is intended for integrated exercise triggers during gaming in place of a handheld control panel (i.e., jumping to trigger commands), remote coaching for fitness and bespoke treatment plans for physical rehabilitation. <h3>Conclusion</h3> Webcam-based computer vision exercise catalogues using everyday devices like webcams, hold the potential to encourage healthier and more active behaviours during screen-based activities.
SPIRITUAL DEVELOPMENT OF LEADER - IS NECESSARY CONDITION OF HIS EVOLUTION

the content of imposed on a person requirement of the modern era and the need for a new worldview are revealed. The content of the concepts “evolution” and “spirituality” is considered. The role of spirituality in human evolution and the formation of leaders’ worldview is shown. It was emphasized that leader’s worldview should be based on his broad humanitarian training, a new philosophy. The essence, approaches, methods of spiritual, moral and mental improvement and the role of the system of training, education and self-upbringing in the process of training future leaders are revealed.
An application of an AI chatbot automatic pronunciation scoring system to elementary school students

The present study aims to explore the effectiveness of the automatic pronunciation scoring system of a newly released AI chatbot, ‘AI Pengtalk’. This English educational application, designed by EBSe and ETRI, was distributed to public elementary schools nationwide free of charge in April, 2021. This study investigates 6th graders’ recognition of AI Pengtalk and the effects of four subscales of the pronunciation score given by AI Pengtalk: intonation; segment accuracy; utterance speed; stress and rhythm. It also examines the correlation between holistic pronunciation scores and students’ English achievement test scores. The results show that 6th graders have quite positive attitudes to AI Pengtalk, and suggest that the instant feedback of AI Pengtalk’s automatic scoring system appears to have little effect. It is also shown that the subscale stress and rhythm significantly influences the corresponding holistic pronunciation score and that the correlation between pronunciation and English achievement seems to be different according to the consisting phonemes of the specific sentences. The present study suggests that elementary students’ pronunciation can be improved by the automatic scoring system of AI chatbots. Further research is recommended with the comprehensive data collection to draw more pedagogical implications.