Reviewerly/exHarmony huggingface dataset:
Effectiveness of online teaching &amp; learning during COVID19 pandemic: A case study at college of science &amp; technology, Royal University of Bhutan

A study was carried out to assess the effectiveness of online teaching and learning at the College of Science and Technology, Royal University of Bhutan. As a case study, two modules under the electrical engineering program taught at the sixth and eighth semesters respectively were chosen. A non-statistical purposive sampling method with a mixed-mode research approach was used to assess the effectiveness of online teaching and learning during the COVID 19 pandemic with 73 respondents. Analysis of the data revealed that students were coping up well with the sudden shift of lesson delivery from traditional offline mode to online mode and learning outcomes were achieved with 43% of the respondents agreeing to it. The offline mode of teaching and learning is still the choice of mode of learning followed by mixed-mode learning. Online learning is also able to engage students meaningfully with 44% of the respondents saying they were able to communicate in the online class effectively. Generally, students were not satisfied with the online lesson mainly citing weak internet connectivity, increased distractions at home, and lack of motivation. The case study also revealed that online learning gave more flexibility, it is more convenient, and learning at its own pace is one of the greatest benefits of online learning which shows that CST has a good foundational platform for online teaching and learning. The study also highlighted the importance of changing the instructional strategies to suit the online lesson delivery for learning to be effective.
Sosialisasi Aplikasi Pembelajaran Interaktif Berbasis Multimedia

The socialization of multimedia-based interactive learning applications is a community service for STMIK Bina Adinata lecturers at Inpres Elementary School Number 207 Linrungloe which aims to increase knowledge and understanding of educators, especially classroom teachers about multimedia-based learning applications. This activity is in the form of counseling about an interactive multimedia-based learning application using a computer. The method used in this service is socialization and training, starting with an explanation of the material about applications, interactive learning, and multimedia, after which an introduction to the use of applications to educators and finally explaining the use of applications to students. The results of this socialization increase the understanding and ability of educators in the application of multimedia-based learning and increase the learning interest of SDI 207 Linrungloe students in the learning process in the classroom using multimedia-based learning applications.
PROSPECÇÃO GEOQUÍMICA NA MINA DO PAQUEIRO, ESTADO DO PARANÁ

This paper presents the results of a geochemical exploration at the Paqueiro area, wich is a small lead mine of the rio Ribeira de Iguape district, near Adrianopolis, State of Parana. Brazil. The geochemical exploration was made in soils and some limestones to detect tile eventual extension of the veins bellow the residual soil. Some 1.200 soil samples were collected to E and W of the central area, where two parallel veins oriented N60oE are known. The wall rock of the lead ore deposits are limestones of the Acungui Group. Practically all the area is covered by a residual soil blanket with an average 2 meter thickness. The soil samples were taken at 0,80 meters depth with a 1 inch auger, at an interval of 10 meters in N30oW transverses, normal to the strike of the limestones and veins. Limestones samples were analized to prove the feasibility of the geochemical prospecting in rocks of the region. The determination of lead content in soils and rocks was made by colorimetric and X-ray sp ectrometry methods. In this survey, soil sample content more than 200 ppm Pb, in two or more adjoining sampling points was considered interesting. The exploration work detected several anomalies and two of them had the mineralization confirmed by trenching.
Least Mean Fourth Based Constrained Adaptive Order Statistic Filters For Image Restoration

This paper proposes the adaptive constrained order statistic L and combination C (Ll) filter using the least mean fourth algorithm (LMF) with linear and non-linear structures at the output. Though, the LMF problem involves many stability problems due to noise variance and increase of input power. This can be avoided in Normalized Least Mean Fourth (NLMF) algorithm by normalising the weight update terms by the fourth power norm of the regressor. Here the LMF based adaptive L filter is derived with linear and non linear output of a fixed window. Whereas the adaptive C filter uses the rank order and temporal order information from the input sequence of fixed window. These filters use ordered data to remove non-Gaussian noise components, preserves the edges and details of an image. In this paper, the performance of C filter overrides the performance of the LMF-L and other LMF based filters.
Lessons learned: Data viz and communicating research [slides]

Slides from an invited panel presentation given at the 2020 Triangle CommSciCon on January 30, 2021. A discussion about the role that effective data viz can play in communicating your research, and why you should seek out your institution’s data viz librarian if you have one.
Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks

Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.
Development of College Common English Teachers in Higher Vocational Colleges from the Perspective of ESP

The reforms of vocational education in China are making steady progresses. college common English teaching in higher vocational colleges is constantly reforming and innovating in order to meet the needs of social development. ESP teaching theory provides a direction for the reform of college common English teaching in higher vocational colleges. This article discusses the development of higher vocational college common English teachers and the realization of their own professional development under the ESP horizon and put forward the path for the development of the team construction and teaching material writing in order to improve the quality of college common English teaching and train more complex technical talents to meet the requirements of social development.
PELATIHAN PEMBUATAN BAHAN AJAR DAN MEDIA PEMBELAJARAN

Distance learning was the main option during the time when the corona virus was still spreading in Indonesia. In implementing PJJ, teachers are required to be creative and innovative in order to spur students' enthusiasm for learning. Even though the Ministry of Education and Culture's website has a PJJ guide feature, the less massive socialization resulted in this information not being fully conveyed to teachers. As happened in SDN Pamulang Timur 2. There are many obstacles faced by teachers in mastering this technology, starting from operating computers, using applications used in learning and difficulties in developing learning models used in distance learning. The purpose of this community service is to improve the skills of SDN Pamulang Timur 2 teachers in developing learning media and making interesting teaching materials. The method is done by providing training directly to the teacher council. The result of this community service is that there is an increase in teachers' understanding of the various learning media from only 5 teachers who are able to develop learning media to 10 people who are able to develop these technology-based learning media. In other words, with the holding of instructional media training and the manufacture of teaching materials, it provides an increase in teacher understanding in developing learning media in distance learning. Key words: training, teaching materials, learning media
Penerapan Algoritma Backpropagation Untuk Memprediksi Jumlah Pasien Pra-Kanker Serviks (Studi Kasus Di Puskesmas Padang Pasir)

According to the Global Cancer Observatory report in 2018, Indonesia ranks second in the largest cervical cancer cases in the world. 80% of cervical cancer sufferers come in an advanced stage, and 94% of patients of these cases die within 2 years. Preventive action from the government is expected to reduce the number of sufferers. By knowing the increase in the number of patients, the government can take action on what must be done to reduce the number of pre-cervical cancer patients. Data on the number of patients with cervical pre-cancer is continuous, so the method needed to make predictions is a complex method and study the uncertainties in each period that can be accommodated with the Artificial Neural Network (ANN) Backpropagation algorithm. Backpropagation architecture used is 5 input layers, 5 hidden layers, and 1 output layer, with a learning rate (lr) of 0.5; constant momentum (mc) 0.3; the Mean Square Error (MSE) value of network training is 0.001 with the logsig activation function for hidden layers and purelin for the output layer. Resulting in a 5-5-1 network architecture pattern with the epoch = 322 processes and the achievement of MSE when testing with MSE = 0.001 with an accuracy of 99.999%.
Italian Dictionaries of Abbreviations and the Preparation of Entries of the Italian–Slovene Dictionary of Abbreviations

The present article addresses the two contemporary Italian dictionaries of abbreviations Malossini (1999) and Righini (2001) and outlines the position of the Italian abbreviations in some other Italian dictionaries, namely the monolingual Zingarelli (2000) and Garzanti (2021) and bilingual Slovene–Italian (Šlenc 2006) and Italian–Slovene (Šlenc 1997). The aim of the paper is to give an insight into the compilation of the abbreviation dictionary entries in monolingual Italian, bilingual in tandem with the Slovene language and specialised abbreviation dictionaries; highlight the elements present within the dictionary entries; present the deficiencies in compiling and examples of good practice; with the aim of introducing the outcomes in the compilation of the bilingual Italian–Slovene dictionary entries of the Slovene Contemporary Dictionary of Abbreviations. Within the paper we present the composition of the bilingual dictionary entries giving the reader an insight into the elements of the dictionary entry, namely the usage of language and field qualifiers, articulated prepositions, i.e. preposizioni articolate (composed of prepositions and definite articles), official translations and additional descriptions.
SECURE ONLINE TRADING BY STANDARDIZATION OF THE LOGO

Now a days most of the people giving Priority to online shopping. In online trading there is a possibility that the customers are get cheated by the e-commerce websites, that are run by the fake logos. Logos have high importance in today’s marketing world. Online product frauds in the booming e-commerce market have become a major concern for the market surveillants and commercial companies, the logo detection plays a crucial role in preventing the increasing online counterfeit trading attempts. There are many techniques for the detection of fake logos. The Least significant bit (LSB) approach is an efficient method to provide security from online counterfeit attempts. The LSB based approach is a popular type of steganographic algorithms. Steganography plays a major role in data communication security. So, the main aim of this project is to secure online trading.
Intelligent Face Recognition Based on Regularized Robust Coding with Deep Learning Process

Face recognition has become a widely researched field due to its extensive applications, especially in the fields of security and surveillance. In recent years, various techniques have been propounded, one of which is the sparse representation-based classification (SRC). It is based on a sparse representation calculated by l1-minimization and provides classification for recognition. It assumes that the coding residual follows Gaussian or Laplacian distribution, which is, generally, not the case for practical systems. Thus, recognition is inaccurate when there is illumination variation, expression change, or occlusion involved. The proposed method is a face coding model called regularized robust coding (RRC), which can robustly regress a signal using regularized regression coefficients. An iteratively reweighted regularized robust coding (IR3C) algorithm is employed to handle the RRC model efficiently. The weight function is developed via MAP (Maximum A Posteriori) estimation. Specifically, RRC with l1-norm regularization can accomplish high recognition rates, as it yields a sparse solution and automatically performs feature selection. Also, it is robust to outliers, as it assigns insignificant features with zero weight and useful features with nonzero weight. Experiments performed on the Extended Yale B database indicate that superior performance is obtained with RRC-l1 compared to other sparse representation-based approaches in treating face occlusion, corruption, lighting, and expression variations. The important results are the 99.8% recognition rate for faces without occlusion and 96.7% for facial images with 40% block occlusion. Thus, the advantage of the proposed system is its ability to recognize faces even in the presence of poor lighting conditions, expression variations, and occlusion (sunglasses, scarves, etc.). Facial biometrics can be integrated with physical devices, like cameras, alarms, and other suitable peripherals, to identify people entering a building and issue an alarm if an unauthorized person attempts to enter. Face recognition-based security is especially useful for organizations and in smart cities that need to maintain tight control on those entering their facilities.
Efficient Modelling and Analysis on the Propagation Dynamics of Email Malware Filtering for Sustainable IT Development

Email system is one of the most effective and commonly used sources of Communication. Unfortunately, the Email system is getting threatened by spam emails. Email spam, also known as junk”email or unsolicited bulk email (UBE),”is a category of electronic spam which includes almost identical messages sent by email to multiple recipients. In existing system highly parallel encoding technique was used to detect the spam campaigns. Privacy preserving collaborative spam detection is used at the Receiver side. In the proposed system the modern spam filtering techniques are deployed at Sender Side. To increase the internet bandwidth and storage of the mail server to deploy WorldNet and Spambot. In this paper to use bro Intrusion detection to analyze the network traffic. It counts the number and regularity of the same”email”sent from a known IP address to various routes, and identifies disruptions on the network. Because of the large number of emails reported in the SMTP sessions, we efficiently monitor and process them in the Bloom filters. The process of spam filtering classified the email data into spam and harm mails.
Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]

Yoruba language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yoruba language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yoruba documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yoruba handwritten word recognition system (OYHWR) that recognizes Yoruba uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features,
Multilevel Image Thresholding Based on Exchange Market Algorithm

With a rapid expansion of image segmentation throughout the decades, the development of scientific optimization as image segmentation is enormous in the segmentation. A need to organize the image thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. Image segmentation dependent on computational intelligence approaches is utilized online to cluster the clinical imaging into a positive or negative diagnosis. The proposed market exchange algorithm (EMA) is relied upon to quickly get the top-notch optimal thresholds are controlled by maximizing the Kanpur entropy of various classes. Different from previous optimization techniques, EMA has been utilized as a prime optimization method as it has been ended up being a successful optimization when applied to different down to earth optimization issues and its execution is straightforward including less computational exertion. The technique has been tried on standard benchmark test images and the steady for all images even with the increase of the threshold. Numerical outcomes judgment shows that this algorithm is a promising choice for the multilevel image thresholding issue.
Regression Tree Based Correlation Technique in Spatial Data Classification

Data mining is the process of discovering useful patterns from large geo-spatial datasets with the help of machine learning methods. . The machine learning methods plays an important role for data analytics modeling and visualization. Geo-spatial data is a significant task in many application domains, such as environmental science, geographic information science, and social networks. However, the existing spatial pattern discovery and prediction techniques failed to predict the event accurately with minimum error and time consumption. . In this paper, a novel Pearson Correlated Regression Tree-based Affine Projective spatial data Classification (PCRT-APSDC) technique is proposed to improve the spatial data classification and minimize error based on the Affine Projective classification technique. The proposed algorithm employs a fuzzy rule procedure that constructs the regression tree. The fuzzy rule is applied for linking the inputs (i.e. spatial data) with the outputs (i.e. classification results). Our goal is to classify the data into two subsets such as fired region and non-fired region. Experimental evaluation is carried out using a forest fire dataset with different factors such as classification accuracy, false-positive rate, and classification time. The results confirm that the proposed technique predicts the fired region with increased spatial data classification accuracy and minimized time as well as false-positive rate than the state-of-the-art methods.
Analysis on the Influence of Education Level on the Strategy of Rural Revitalization Based on the Stepwise Regression Analysis Method

The report of the 19th National Congress of the Communist Party firstly proposed the strategy of rural revitalization, and clearly pointed out that the important support for rural revitalization is education. This article mainly analyzes the impact of education on rural residents' income, and discusses how to develop rural economy through education and push through rural revitalization strategies. It collects data from gender, age, work and education level, working hours and work experience, with using stepwise regression analysis to sort and analyze and establishing a model. The result shows that education level has the most significant impact on farmers' income. The strategy of education to serve the rural revitalization not only is an inevitable choice for China to properly solve the “three rural” issues in the new era, but also is the due meaning of promoting the comprehensive construction of the country's socialist modernization. At the end, it emphasizes that the complementary relationship between education and rural revitalization strategy. At the same time, it gives several measures and suggestions for improving rural education as well.
Dispositivos móviles: ¿medios tecnológicos necesarios para transformar las actividades escolares?

The objective of this mixed research is to analyze the teachers' perception about the use of the mobile devices during the teaching-learning process. The sample is 58 teachers from the National Autonomous University of Mexico (UNAM) who completed the Classroom of the Future 2020 course. The results of the linear regression indicate that the creation of new educational spaces through the mobile devices positively influences the performance of the activities inside and outside the classroom. Finally, mobile devices are substantially changing the way of teaching and learning in the 21st century.