Reviewerly/exHarmony huggingface dataset:
CREATING MOODBOARDS WITH DIGITAL TOOLS: A NEW EDUCATIONAL APPROACH

The paper presents methodologies and first results of an educational experimentation (Brevi, Celi, & Gaetani, 2018) held at the Bachelor of Science in Product Design programme of Politecnico di Milano.In order to build a body of knowledge for Design freshmen, the experience had the aim of matching basic skills developed at the first year with the need of an autonomous approach required at the second year from Metadesign Studio throughout the tools for creating moodboards: a collection of images, colors and texture with the aim of representing emotions, feelings or 'moods', suggested by the design research.Moodboards have an important function in developing students' ability to articulate their thinking.The enacted experimentation had the aim to present and test some practical tool for moodboards development.During the Methods and Instruments for Design course these skills have been developed through targeted exercises of awareness and techniques of photographic processing.To enable students to act in a more conscious way the educational approach has provided lessons with practical visual examples, a selection of on-line materials and step by step guide with suggestions.
Educational Personnel and Information Technology in Higher Education Governance

This study aims to measure the perception of educational personnel in the management of Higher Education (HE) based on information technology, and to formulate a work culture for education personnel in implementing information technology.This study is an evaluative study, which measures research variables based on respondents' perceptions and their experience in running information systems in the research object.The respondents of this study consisted of 160 education personnel who working at Universitas Islam Negeri (UIN)/ Islamic State University of Sunan Gunung Djati Bandung.The study tested the Technology Acceptance Model (TAM) paradigm, with the variables observed were perceived ease of use, usefulness, behavior intention to use, and actual system use.TAM test results are used as a basis for discussing the role of education personnel in the governance of HE.Descriptive research results indicate that education personnel have good adaptability in implementing information systems owned by HE.The results of the structural diagram show that simultaneous perceived ease of use, usefulness, behavior intention to use, have a positive and significant effect on actual system use.This study recommends strengthening the work culture as a moral and ethical foundation for education staff in implementing information technology in governance at HE.
A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features

The elastic net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its attractive properties, originated from a combination of ℓ1 and ℓ2 norms, endow this method with the ability to select variables, taking into account the correlations between them. In the last few years, semi-supervised approaches that use both labeled and unlabeled data have become an important component in statistical research. Despite this interest, few researchers have investigated semi-supervised elastic net extensions. This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic net (s2net), which extends the supervised elastic net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems. In addition, a flexible and fast implementation for s2net is provided. Its advantages are illustrated in different experiments using real and synthetic data sets. They show how s2net improves the performance of other techniques that have been proposed for both supervised and semi-supervised learning.
Implementation of a low cost hands free wheelchair controller

The aim of this project is to ease mobility for people with upper and lower disability in order to live independently. This paper presents the design steps and specification to a low cost hands free eye-blink controller to control and electric wheelchair. Nowadays, people are using joystick to control motorized wheelchair. The eye-blink controller technology gives an alternative solution to mobility problem; especially for the people who are quadriplegics. By interfacing eye-blink controller, the directions of the wheelchair are controlled. This report will provide the designing step, related solutions, and component details and specifications.
Learning Technology Review: Vocabulary.com

Vocaburary.com (https://www.vocabulary.com/) is an application (hereafter app) designed to promote English vocabulary learning. The primary learning activities users can engage in with the app include: 1) taking short personalised online quizzes; and 2) learning or consolidating vocabulary by accessing immediate feedback. The website describes the strengths of learning vocabularies using the app by stating it as “the quickest, most intelligent way to improve vocabulary”. To help users to learn vocabulary efficiently, Vocabulary.com adopts “sophisticated algorithms” to choose the essential words for each user from over 14,000 words. Based on the results, the app then creates personalised quizzes for each user.&#x0D; In addition to the quiz, Vocabulary.com provides multiple functions to promote user vocabulary learning including: 1) online dictionaries; 2) vocabulary lists; 3) vocabulary games; 4) spelling practice while listening to the pronunciation; and 5) progress reports. The app provides both computer- and smartphone-based platforms (both iPhone and Android). The computer-based platform is free; whereas, users are required to purchase the smartphone-based platform. However, accessing the app via a smartphone allows users to use their downtime efficiently to learn English vocabulary anytime and anywhere. “Educator Edition” provides educators with useful functions to promote online and blended learning courses, including the capability to monitor the progress of each learner on the platform.
AI CLASSROOM ANALYSIS SYSTEM FOR LANGUAGE TEACHING DEVELOPMENT

One of the highly validated language classroom observation methods is Communicative Orientation of Language Teaching initiated by N. Spada and M. Fröhlich.This method, however, is not widely used by language teachers because of its time-consuming coding procedures.To overcome this shortcoming, the authors developed a new portable language classroom analysis tool named Mobile COLT and have started to plan its automatic version.Mobile COLT facilitates real-time class analysis using a Windows tablet.Since it can quantitatively display the features of the class, Mobile COLT can be an innovative tool for language teacher development.Its manual version has been already constructed, and the research to examine its effectiveness has been conducted in primary and secondary schools.This paper expounds the functions of Mobile COLT, the application in classroom practices, and the development of the automatic version of Mobile COLT.
Text-to-Speech Application for Foreign Language Learner’ Listening Comprehension in Indonesia

Listening takes role to provide the main aural input which is really important.The use of difference speech origins for the listening material will bring different effect for students' listening comprehension.This study identifies whether the students who are taught by synthetic speech have significant difference on their listening comprehension than those who are taught by natural non-native speaker.It used quasi experimental research design.The experimental group consisted of 34 students who treated by using synthetic speech audio material which produced by Text-To-Speech application.The control group consist of 32 students treated by non-native speaker.The instrument used in this research was test.The result of independent sample t-test from the post-test showed significant value of the data was 0.016.From the result above, it can be concluded that teaching listening using different speech origins has effect for students' listening comprehension.It was proved by the result of the test.It is suggested that English teacher can use synthetic speech audio material which produced by Text-To-Speech application for teaching listening to make the teaching and learning process more enjoyable.
Software-driven big data analytics

Data analytics is the crucial step to reveal essential values of datasets and complete the value chain of big data. In practice, both the hardware infrastructure and the software stack play a fundamental role in big data analytics (BDA). Unfortunately, it is evident that a disproportionately larger amount of effort is being invested in the hardware infrastructure development over the software stack development. Given our concern about a software crisis brewing in the big data ecosystem, we argue that it is time to further strengthen and expand the role of software in BDA implementations. This special issue is then aimed to create a common ground and a reference point for both researchers and practitioners from multiple disciplines to discuss the rigor, relevance, experience and challenges of software-driven BDA as an emerging domain. We also expect to use this special issue to attract more attention and efforts to tighten communication and collaboration between the software engineering community and the data science community.
Sparse generalized canonical correlation analysis via linearized Bregman method

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting mutual information between two sets of multi-dimensional random variables. Unlike CCA, Generalized CCA (GCCA), a natural extension of CCA, could detect the relations of multiple datasets (more than two). To interpret canonical variates more efficiently, this paper addresses a novel sparse GCCA algorithm via linearized Bregman method, which is a generalization of traditional sparse CCA methods. Experimental results on both synthetic dataset and real datasets demonstrate the effectiveness and efficiency of the proposed algorithm when compared with several state-of-the-art sparse CCA and deep CCA algorithms.
Integration of Image and Video Signature in Graphical Password Authentication System

In this study we are providing the security and authentication for the user.This paper includes two parts, Image processing using cued click point and video processing using clicked intervals, where the combination of both will generate a password for the user to login.To login its necessary that both the combination need to match.The user is allowed to select their choice of images and video for the process and it is stored in a private database so that they are not available to other users.The password generated by both image and video is hidden from both users and developers.This method is obtained for prevent unauthorized access to important and confidential data and to protect them.
Social Media Analytics: Impact &amp; Implications in Healthcare

Sharing of ideas, opinions or any information with the help of the medium of virtual networks, online communities and computer-based innovations is known as social media. Blogs, social networking sites like Facebook, WhatsApp, YouTube etc, Microblogging mediums, Photos etc. that run with the help of internet, provides the end-users with swift communication and aids in providing awareness, by influencing large audiences contributes to social media. Healthcare sector is rapidly adapting social media by providing a medium to the patients, physicans and HCP’s to discuss and communicate medical information and knowledge outside the vicinity of a hospital or clinic. These days, patients, their relatives, friends or family use the social media to provide knowledge to the world and educate them incase they have similar ailments. There are several blogs and forums related to medical knowledge where users tend to ask their problems to the physicians and get a reply back to their problems.Medical specialists join such online medical forums that helps them to research in a better manner, refer colleagues, increase their network and read health related articles and updates. With every good thing, comes an attached risk which is related to the use of social mediums, where the image of a Healthcare professional could be at stake due to the unprofessional content against them. There are various limitations to the HCP’s in case they seek to share patient stories online as it could be a case of breach &amp; privacy. Nevertheless, this research study highlights the role played by social media in digitally redefining and empowering healthcare sector from the perspective of clinical and patient centric health literacy and health guides.
Multi-Labelled Emotion with Intensity Based Sentiment Classification Model in Tweets using Convolution Neural Networks

In present days, people utilize social networking sites like Twitter for sharing their emotions and opinions.Detection and examination of the emotions present in the social media becomes advantageous in several application areas like e-commerce, government, public health, entertainment and so on.Several earlier studies have on sentiments and emotions classification has concentrated only on single label classification and does not considered the co-occurrence of many emotion classes in one tweet or post.This study presents a new multi-labeled emotion with intensity based sentiment classification in twitter data using convolutional neural networks called CNN-EISC model.The proposed CNN-EISC method has been validated using SEMEVAL2018 Task-1Emotion Intensity Ordinal Classification dataset.The CNN-EISC model has classified a set of four intensities namely Anger, Sad, Fear and Joy under diverse classes (0, 1, 2 and 3).The obtained experimental outcome stated that the CNN-EISC model has exhibited maximum results with the average precision of 77.08%, recall of 62.52%, F-measure of 65.76% and accuracy of 82.72%.
A COMPARISON OF CLASSIFIERS APPLIED TO THE PROBLEM OF BIOPSY IMAGES ANALYSIS

The purpose of the research is to compare classification algorithms for the histopathological images analyzing issue and to optimize the parameters for obtaining better classification accuracy. The following tasks are solved in the article: preprocessing of BreCaHAD dataset images, implementation and training of CNN, applying K-nearest neighbours, SVM, Random Forest, XGBoost, and perceptron algorithms for classifying features that were extracted by CNN, and results comparison. The object of the research is the process of classifying tumor cells in the microscopic biopsy images. The subject of the research is the process of using ML algorithms for classification of the features extracted by CNN from input biopsy image. The scientific novelty of the research is a comparative analysis of classifiers on the task of “tumor” and “healthy” cells images classification from processed BreCaHAD dataset. As a result it was obtained that from chosen classifiers SVM reached the highest accuracy on test data – 0.972. This is the only algorithm that shows better accuracy than perceptron. Perceptron gets 0.966 classification accuracy. K-nearest neighbours, Random Forest, and XGBoost algorithms reached lower results. The algorithms' hyperparameters optimization was carried out. The results have been compared with related works. The following research methods are used: the theory of deep learning, mathematical statistics, parameters optimization.
Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron

In the process of student assessment in SD Negeri 1 Kota Ternate, they still use MS Excel software and it takes approximately 2 to 4 days, so it is assumed to be less effective. This study aims to apply the Perceptron Artificial Neural Network to facilitate the work of teacher assessment in SD Negeri 1 Kota Ternate. Artificial neural network is an information processing system that is designed to mimic the workings of the human brain in solving problems quickly and accurately. In processing the assessment using the perceptron algorithm, training data and testing are needed to classify student learning outcomes data. The system development method used is prototype. System testing is done by the white box method’s. The data used are data classification of student learning outcomes in the realm of knowledge, namely Daily Assessment Results (HPH), Mid Semester Assessment Results (HPTS), and Final Semester Assessment Results (HAS). The results showed that the highest level of accuracy occurred in the initial treatment with an accuracy of 96%, so it can be concluded that the Artificial Neural Network system with the Perceptron algorithm can be implemented for student assessment in the 2013 curriculum.
Perbandingan Metode Classification and Regression Trees (CART) dengan Naïve Bayes Classification (NBC) dalam Klasifikasi Status Gizi Balita di Kelurahan Pagesangan Barat

This study aims to compare the Classification and Regression Trees (CART) and Naïve Bayes Classification (NBC) methods in classifying the nutritional status of toddlers in West Pagesangan by looking at their accuracy and also knowing the variables that influence the classification of toddler nutritional status. The data used in this study were toddlers who come to the posyandu in May 2019, with predictor variables used namely gender, ages, weight, mother’s employment status, mother’s education level, number of children and parents income. The result showed that Naïve Bayes Classification (NBC) is better in classifying the nutritional status of toddlers in West Pagesangan than Classification and Regression Trees (CART). This can be seen from the accuracy values obtained with three comparisons of training data and testing data. In the comparison of 90% of training data: 10% of testing data, obtained an accuracy value of 90% for NBC and 85% for CART, in the comparison of 80% of training data: 20% of testing data, obtained an accuracy value 0f 82.5% for NBC and 80% for CART, while in comparison 70% traing data : 30% testing data, obtained an accuracy value 72% for NBC and 70%for CART. This study also showed that significant variables the classification of nutritional status of toddlers in West Pagesangan village are age, gender, weight and parents income.
Principles for designing a system of assessment tools for modular architecture educational programmes in higher education

Е. И. Сахарчук, Е
The Musical Text of Western European Baroque Instrumental Music in an Analytical Reevaluation by the Student-Performer of Institutions of Higher Education

No matter how much we approach the urtext of Western European Baroque music reflecting the world picture contemporary to it and preserving the diversity of meanings, its semantic meanings shall never be exhausted. Having been generated by the “turning point” epoch which lasted for over a century as a peculiar document of the time, the urtext, undoubtedly, presents a complex task for the researching musicologist. However, an even more complex problem is presented by the adaptation of scholarly perceptions about the urtext for specialist performers who study in institutions of higher education. Being open for new methodological approaches, this type of musical text is in need of evaluation of its didactic potential, as well as of search for special approaches and means of artistic interaction of the performer with it. Pedagogical approaches must be oriented on a certain ideal model of the professional performer – the soloist, member of a music ensemble or pedagogue. Without awareness by the musician of the specificity of the content of the musical text, it is not possible to elaborate a personal attitude towards the authorial conception, which provides correctness, brilliance and originality of interpretation of the performed composition. The article indicates certain methodological approaches to analytical achievement by student performers of the early urtext of Western European baroque instrumental compositions approbated by the author. At their core lies the adaptation of scholarly research and pedagogical elaborations of the Laboratory of Musical Semantics. Keywords: urtext, instrumental music, Western European Baroque, intonational lexis, semantic organization of music, Laboratory of Musical Semantics.
A High-Performance Computing of Internal Rate of Return using a Centroid-based Newton-Raphson Iterative Algorithm

A popular financial metric in estimating the profitability of a project or investment is the internal rate of return.However, the IRR variable cannot be easily isolated from the equation.This is effectively solved by using iterative root-finding algorithms, some of the most frequently used of which are secant, bisection, false position, and Newton-Raphson algorithm.Although the Newton-Raphson method is considered to be the fastest to converge and the most popular method, it still requires an initial guess value from the user, which could result in the algorithm to not converge to the root if the user input is far from the actual root.This issue is addressed by a midpoint-based Newton-Raphson technique, which sets the midpoint of cash flows as the initial guess input.However, the midpoint technique is static as it does not adjust with unequal cash flows.This study presents a centroid-based Newton-Raphson algorithm in estimating IRR, which dynamically takes into consideration the values of cash flows.The experimental results show that the proposed algorithm ensures convergence by producing an initial IRR with an accuracy of 91.41%.This indicates that it is 26.75% more accurate in approximating the initial IRR than the midpoint-based Newton-Raphson algorithm.It also reduced the required iterations of convergence by 35.33% over the midpoint-based Newton-Raphson algorithm.These findings show that the employment of the centroid-based Newton-Raphson algorithm in approximating IRR provides a significantly better approach in evaluating investments than the current method.
Cautious Label-Wise Ranking with Constraint Satisfaction

Ranking problems are difficult to solve due to their combinatorial nature. One way to solve this issue is to adopt a decomposition scheme, splitting the initial difficult problem in many simpler problems. The predictions obtained from these simplified settings must then be combined into one single output, possibly resolving inconsistencies between the outputs. In this paper, we consider such an approach for the label ranking problem, where in addition we allow the predictive model to produce cautious inferences in the form of sets of rankings when it lacks information to produce reliable, precise predictions. More specifically, we propose to combine a rank-wise decomposition, in which every sub-problem becomes an ordinal classification one, with a constraint satisfaction problem (CSP) approach to verify the consistency of the predictions. Our experimental results indicate that our approach produces predictions with appropriately balanced reliability and precision, while remaining competitive with classical, precise approaches.