Reviewerly/exHarmony huggingface dataset:
El aprendizaje de competencias en los MOOC: una Revisión Sistemática de Literatura

Massive Open Online Courses (MOOC) are an essential element to understand the digital, ubiquitous and segmented transformation of learning processes in the last decade. This article aims to analyse this issue through a Systematic Literature Review (SLR) that delves into scientific research whose main focus is the teaching-learning processes and the development of educational competences in MOOCs. To develop this work, we started from the criteria and recommendations of the PRISMA protocol (2020) and the search using keywords in the SCOPUS and WOS databases in the period from 2010 to 2021. The search combinations have been carried out using Boolean operators and screening using the following selection criteria: 1) Educational Stage (Primary, Secondary and Higher Education), 2) Target audience (Teachers, Students, Managers), 3) Type of Studies (Quantitative, Qualitative and Mixed Studies) and 4) Types of Competences Developed (Digital, Didactic, Pedagogical and Curricular). All these criteria lead us to an initial sample of 1,766 scientific articles that, finally, after the filtering process was reduced to 43 scientific publications. The results of the research point to the importance of personalising and making learning environments more flexible in MOOCs oriented towards the development of educational competences in order to mitigate problems such as dropout or lack of motivation.
Critically Evaluation the Potential Growth of the Nigerian Economy

This report is mainly based on the recent economic development in recently, divided into two section. The Nigeria how to development in recently, As Africa's oil production, is the richest countries, China and more countries need oil and oil cooperation in Nigeria, on the other hand, the Chinese government provides the loan conditions to improve transport infrastructure in Nigeria. The other part is focused on the analysis of foreign direct investment and globalization in Nigeria potential aspects of economic development, oil prices falling, Nigeria must constantly development of other industries, to support a sustainable development, to manufacturing for potential development, will attract more foreign investment, also can have more resources to develop economic foundation, improve the employment rate and increase the economic income, on the development of these opinions are given. Under the social ethics according to polygamy don't respect for women's rights, and government corruption leads to poor people without basic social medical education condition, this is the need to improve in Nigeria, Suggestions are given.
Boosting Speech-to-Text software potential

The article focuses on finding ways of boosting efficiency and accuracy of Speech-to-Text (STT)-powered input. The effort is triggered by the growing popularity of the software among professional translators, which is in line with the general trend of abandoning typing in favor of speech-to-text applications. Insisting that better effectiveness of such programs is contingent on their accuracy, the researchers analyze major factors, both linguistic and technical in nature, affecting the computer-assisted speech transcribing quality. This leads to an experiment, putting the hypothesis to a test. Based on numerical and performance data, errors and their breakdown into categories in an attempt to figure out their origins, it dwells on various approaches to dictation in a combination with several hardware options and configurations. These pave the way for recommendations on the improvement of STT performance based on the Dragon software. The authors arrive at a conclusion that it is possible to boost the STT accuracy up to 99 percent by adjusting the program profile to accommodate phonetic features of the speaker with due consideration of his accent, adding to the dictionary the most complex and rare vocabulary beforehand, and fine-tuning input hardware. Other noteworthy results include ways to overcome the most complex transcribing challenges, i.e. proper names, placenames, abbreviations, etc.
Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.
Cryptographic Tokens as an Incentive Mechanism for Patient-Reported Outcome Measures Surveys

(1) Background: As Patient-reported outcome measures face challenges with low response rate on surveys, different incentive mechanism have been proposed to achieve a higher response rate. However, it seems that monetary incentives are the only mechanism with proven effect. Nevertheless, it is less likely to be used than other mechanisms due to the monetary cost. (2) Methods: In this research work, a cryptographic scheme for rewarding patients with cryptographic tokens is developed and implemented on the Ethereum test network. (3) Results: The model is able to distribute decentralised tokens to patients who complete the PROMs in a fair, private, decentralised approach. The token can be further used by patients to exchange more healthcare services, encouraging more patients to participate in PROMs. At the same time, an IER detection method is built to improve the quality of PROMs, avoiding the healthcare provider paying for meaningless PROMs from patients who barely participate for incentives. (4) Conclusions: This work provides an privacy-preserving incentive model to increase the response rate for PROMs surveys. Our model prevents patients providing invalid responses to gain rewards.
Efficient Reconfigurable Architecture to Extract Image Features using Local Binary Pattern for Face recognition

Abstract Recognition of the face is a widely held method to detect human features. In various claims, like detecting criminals over video surveillance, face recognition is the most practical and computable recognition technique for humans. In this paper, we propose Efficient Reconfigurable architecture to extract image features using Local Binary pattern (LBP) for Face Recognition. The face image is preprocessed using Gaussian filter to remove high frequency components. The preprocessed image is then applied to optimized LBP block to obtain the LBP features followed by Histogram. The counter and comparators are used along with moving window architecture which leads to less complex LBP architecture. The Histogram LBP features obtained for both database sample and test sample are further compared to make the decision for recognition. The simulation is performed for Olivetti Research Laboratory (ORL) dataset using MATLAB by showing FAR, FRR and TSR values. Further, the proposed architecture is synthesized on Spartan 6-xc651x4c-3csg432 Digilent FPGA board. It is observed that the recognition time of our architecture is 1.05 µS which is better compared to existing methods.
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physics-based model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models.
Identifikacija komercijalne blockchain tehnologije te izazovi i opasnosti primjene kroz konkretne primjere

Blockchain is a revolutionary technology with great potential for application in many branches of industry and integration into people’s daily lives as a technology of reliable data exchange through the Internet of Everything (IoE). The development of blockchain technology and cryptocurrency are closely related and have a common source in the Genesis block of the first fully functional blockchain, best known globally through the digital cryptocurrency Bitcoin. But even though blockchain technology has been commercially available for more than ten years, the average public still superficially and insufficiently distinguishes the terms blockchain from the name of the most famous cryptocurrency. The true value and benefit of blockchain technology is visible through observing the process of digital trust. In the way in which classic business transactions or contracts are initiated, there is no universal trust of all parties involved in the transaction. During classic transactions, that’s why for decades we put institutions accredited by law at the center of such transactions and agreements. Until the advent of blockchain technology (which was also created due to the impact of the great economic crisis of 2008, caused by the dishonesty and illegal acts of the world’s major banks, then ruling politicians and real estate dealers as the direct causes of the crisis), real technological solutions to the problem of mistrust did not actually exist. This article analyzes the commercial application and use cases of blockchain technology, and identifies the impact, challenges and dangers of blockchain technology. The article describes the specifics of blockchain technology, digital cryptocurrencies, the current commercial application of blockchain technology and recognizes existing and possible future problems of blockchain development through technological transformation and implementation in IoT and IoE.
An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

Students’ willingness to participate in bilingual communication is greatly influenced by their positive emotions in the bilingual class. The automatic recognition of students’ emotional state in bilingual class can assist teachers to correctly master the laws of students’ emotional changes during the bilingual learning process as fast as possible. However, the speech emotion features extracted by existing speech emotion recognition models are not universal and not suitable for bilingual speech emotion recognition, and the accuracy needs to be improved. To cope with these issues, this paper aims to study the emotional responses of students in bilingual class based on emotional analysis and provide adjustment strategies for it. At first, the signals of students’ speech records in bilingual class were pre-processed, the one-dimensional features of the signals were converted into 2-dimensional speech spectrum features so as to attain more useful information to facilitate the emotional recognition of students’ speech records in the bilingual class. Then this paper combined the bi-linear Convolution Neural Network (CNN) with capsule network to construct a bilingual class student emotion recognition model, and experimental results verified the effectiveness of the constructed model.
The Semantic Shifts of the Topical Structure in the Corpus of Lentach News Posts

Nowadays the interests of linguists are aimed at analyzing text collections with the help of automatic procedures. They pay special attention to the texts on social networks as their language features differ from the features of fiction texts or scientific articles. The paper is dedicated to the creation of dynamic topic models of Lentach news posts on VK social network. We use a mixture of NLP libraries to identify the semantic shifts of main topical sets since the end of 2018. The corpus contains more than 26,000 posts on various topics. In contemporary Russian linguistic papers, the collection of Lentach posts is rarely analyzed in terms of NLP. The results show that the main topics that are widely discussed in this community cover sports events, health issues, and protests.
Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner

Backpropagation is a method of Artificial Neural Networks that is quite reliable in solving prediction problems (forecasting). However, in its application, this algorithm still has weaknesses such as optimizing the artificial neural network weights to avoid local minimums, the problem of long training times to achieve convergence and the process of determining the right parameters (learning rate and momentum) in the training process. The purpose of this research is to solve this problem by using Particle Swarm Optimization (PSO) which is a simple and reliable optimization algorithm to solve optimization problems. The data source is obtained from the site sumut.bps.go.id. There are 5 network architecture models used in this study, including 2-5-1, 2-7-1, 2-9-1, 2-11-1 and 2-13-1. The results of trials conducted with Rapid Miner software, the best architectural model is the 2-9-1 model with a total RMSE of 0.056 +/- 0.000 in the implementation of Backpropagation, while in the implementation of Backpropagation + particle swarm optimization the amount of RMSE is 0.055 +/- 0.000. The smaller the RMSE (Root Mean Squared Error), the better the model
Perancangan UI/UX Webinar Booking Terhadap Kepuasan Pengguna Menggunakan Metode Design Thinking

In order to meet user needs, designing an exemplary user interface requires in-depth knowledge of design principles and guidelines and an understanding of the multi-component design space. Multi-components are the image parts that can be used, their layout options, and visual effects options. This research was conducted to meet user needs. With the Design Thinking method, we can find out the user's needs and adjust to the user's interests. With the Webinar Booking application, it is hoped to be a solution for today's life
Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN

As the times move forward, technologies are following to develop with the times, especially internet technology. Nowadays, people can find out information about shopping center via the Internet. One of the best-known shopping centers in Bandung is Paris van Java resort lifestyle place. The person's consideration of going to the shopping center is based on the location and opinions of the person who once visited that shop. With a huge amount of opinion and an underlying amount of information that is of great value causes the information to become dangerous if the person misunderstood it. With all these problems, sentiment analysis one of the solution that can prevent the problems of these misunderstanding, in which sentiment analysis works to analyze each text to determine the level of positive or negative sentiment values. In this study, the sentiment analysis dataset goes first to the preprocessing stage, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature then classified using the K-Nearest Neighbor method, The K-Nearest Neighbor method was chosen because this method is one method that can classify text and data, besides K-Nearest neighbor method has a good classification accuracy where the classification process is easy and quite simple in its implementation. With a system that built using TF-IDF Unigram and Euclidean Distance, the best accuracy value is 88.29%.
Segmentasi Tingkat Kematangan Buah Pisang Cavendish Sangat Matang Berdasarkan Warna Menggunakan Watershed

The Cavendish banana is considered to be one of the most sought-after export-oriented fruit in Indonesia. The large market size of export fruits, especially Cavendish bananas, opens opportunities for Indonesia to raise both its product quantity and quality in order to increase its competitiveness. Various methods have been conducted to increase the quantity and quality of Indonesia's cavendish bananas, one of which is the adoption of the image processing technology. This method aims to simplify and resolve cultivation and processing issues regarding Cavendish bananas, among other things by minimizing human error in determining fruit ripeness (which is traditionally conducted by manual labor). This research uses HSV segmentation and a Watershed algorithm in segmenting images of 50 ripe Cavendish bananas with 40 training data and 10 testing data. Based on our research, we founda out that ripe Cavendish bananas have 42% red, 37% green, and 21% blue in average, with an accuracy rate of 65%
Sistem Informasi Manajemen Keluhan Pelanggan Hotel Menggunakan Metode Waterfall

Hotel Grand Istana I Syariah Padangsidimpuan is a business entity engaged in services and services. The customer service proces starts from registration and then provides services to customers according to their needs. All information relating to hotel customers who have stayed is recorded in the book. From the results of interviews, obtained the problem that often occur to hotel customers are the lack of place to submit criticism, input and suggestions to the hotel management and also the absence of feedback from hotel management to customers regarding the criticism, input and suggestion submitted. For this reason, in overcoming the problems that accur in the hotel, an information system development is carried out related to te problems that occur. Where this information system uses the waterfall methode and analysisi system used is data collection adan interviews. The implementation of this system will use PHP As programming language and MySQL as the database. The purpose of making this information system later is that customers will have a place to convey criticism, input and suggestions to hotel management and will alaso make it easier for hotel to provide information to customers so that hotel customers can increase their loyalty to the Grand Istana I Syariah Padangsidjmpuan Hotel
Penerapan Metode CRM Pada Perancangan Sistem Informasi Penjualan Pakaian dan Aksesoris

VJ Collection is one of the stores engaged in trading that has sold various clothing and accessories products specifically for men, originating from various well-known brands. Sales are still made manually, and there is a risk of errors when calculating the total amount of the purchase price. To overcome this problem, a sales promotion information system will be proposed that can facilitate customers in getting to know the products on offer and make online purchase transactions. . The use of the CRM method is to record customers, give product ratings, and E-mail marketing. This research aims to design a website that suits the needs of VJ Collection and customers in carrying out the necessary transactions. The conclusions that can be obtained in this study are the system is considered to be able to assist the store in managing all transaction data and can view and create the desired report instantly. As for the application of CRM to the system, it is assessed that it can increase customer loyalty by using E-mail marketing which offers in the form of discount bonuses.
Preprocessing: A method For Reducing Time Complexity

Data can be processed quickly if it is in some order, whereas unsequenced data can take more time to obtain results. Sorting is used for data arrangement. It is also one of the essential requirement for most applications and this step helps to boost performance. Sorting is also a prerequisite in several computer applications like databases. Over time computer scientists have not only introduced new sorting techniques considering various factors to be improved but they have also presented enhanced variants of existing sorting methods. The main objective has always been to reduce the execution time and space of the sorting algorithms. With every passing day, digital content is growing rapidly, which is a significant cause that encourages researchers to design new time-space efficient sorting algorithms. This paper presents some preprocessing strategies for quicksort and insertion sort to improve their performances. Tha main idea of using these preprocessings is to make input data more suitable for sorting algorithm, as most sorting function performs extraordinary for a specific type of input, such as insertion sort works better on nearyly sorted data. To authenticate the efficiency of existing sorting algorithms, these have been compared with proposed preprocessing strategies. The results with proposed techniqes outperforms the results of original sorting methods. It also helps to convert worst case into average case. By using this approch complexity of many algorithms can be reduced, therfore this is very important.
Hypothesis Testing for Error Mitigation: How to Evaluate Error Mitigation

In the noisy intermediate-scale quantum (NISQ) era, quantum error mitigation will be a necessary tool to extract useful performance out of quantum devices. However, there is a big gap between the noise models often assumed by error mitigation techniques and the actual noise on quantum devices. As a consequence, there arises a gap between the theoretical expectations of the techniques and their everyday performance. Cloud users of quantum devices in particular, who often take the devices as they are, feel this gap the most. How should they parametrize their uncertainty in the usefulness of these techniques and be able to make judgement calls between resources required to implement error mitigation and the accuracy required at the algorithmic level? To answer the first question, we introduce hypothesis testing within the framework of quantum error mitigation and for the second question, we propose an inclusive figure of merit that accounts for both resource requirement and mitigation efficiency of an error mitigation implementation. The figure of merit is useful to weigh the trade-offs between the scalability and accuracy of various error mitigation methods. Finally, using the hypothesis testing and the figure of merit, we experimentally evaluate $16$ error mitigation pipelines composed of singular methods such as zero noise extrapolation, randomized compilation, measurement error mitigation, dynamical decoupling, and mitigation with estimation circuits. In total our data involved running $275,640$ circuits on two IBM quantum computers.