Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF POSSIBILITIES OF GRAPHIC ORGANIZERS APPLICATION IN FOREIGN LANGUAGE TEACHING

The article highlights the methodological potential of using graphic organizers as a modern visualization technology for the presentation and structuring of information in the educational process of a foreign language in higher education institutions. The approaches of domestic and foreign educators to the use of such organizers in the process of teaching disciplines in general and on certain aspects of teaching foreign languages in particular are analyzed. The purpose of the article is to summarize the experience of using graphic organizers and analyze the possibilities of their use in foreign language teaching in higher education institutions. Possibilities of using graphic organizers in the educational process in a foreign language are identified and generalized. These include: ease of creation, editing and verification; reusability; for individual, pair and group work; use in any learning format; use in various aspects of teaching; for students of any level of language proficiency; removing the language barrier; application of research approach in teaching and development of thinking; opportunity to consider the whole topic of the lesson; the ability to turn a large amount of information into a structured, easy-to-understand graphic image. The most common forms for use in foreign language teaching (hierarchical, cyclical, consistent, conceptual, comparisons, data organizers) and their examples are given. The stages of introduction of graphic organizers in the educational process in a foreign language are considered. The forms of presentation of graphic organizers and resources for their creation are indicated. The great potential for the use of graphic organizers in the educational process of a foreign language and the need for further research on the application of certain types in various aspects of teaching a foreign language in higher education.
SKIN CANCER IMAGE DETECTION SYSTEM USING THE CONVOLUTIONAL NEURAL NETWORK MODEL

The development of science and technology (IPTEK) in the current era is growing very rapidly in various fields such as industry, education, especially the health sector. Many technologies can be used, one of which is artificial intelligence technology. This study aims to detect skin cancer images using CNN so that they can be efficient and precise. This research method uses the convolutional neural network (CNN) method, namely image processing, the development of a multilayer perceptron (MLP), in which the neurons of the data are propagated in two dimensions. Because this method has very high accuracy compared to the fuzzy k-nearest neighbors. The results of this study are that there are 7 classes of skin cancer images including actinic keratosis, basal cell carcinoma, dermatofibroma, benign keratosis, melanocytic nevi, vascular lesions and melanoma. From the results of testing the 7 classes using the convolutional neural network (CNN) method with a very high accuracy rate of 99%, 96%, 98%, 99%, 100%, 99% and 96%, respectively. With the conclusion that using the convolutional neural network (CNN) method produces an average accuracy of 98% compared to the Mobilnetv2, Resnet50 and VGG16 models, which means that the CNN model is proven to be more accurate. So it is hoped that this detection system can be applied as a skin cancer detection system for the world of health.
Library Management Strategy to Strengthen Literacy Culture in Elementary Schools

This study seeks to determine the strategy of library management as a means of literacy culture in elementary schools. This study aimed to determine the involvement of school libraries and librarians in enhancing literacy culture in elementary schools. This study uses a qualitative method with a case study approach. Data collection techniques using in-depth interviews, observation, and documentation. Informants in this study were school librarians at MIN 1 Tulungagung. Data analysis techniques include data collection, reduction, presentation, and conclusion. The study results show that the role of the school library in implementing literacy culture includes preparing facilities and infrastructure, facilitating school literacy movement activities, as a facilitator and preparing all the needs for the continuation of the school literacy movement.
The Jurisprudence of Patentable and Non-patentable Inventions: Nigerian in Perspective

Patent is an industrial property right granted by the government of a state to a patentee. It is an intangible, incorporeal and exclusive right granted under the Law to an invention. While an invention is something that has never been made or existed before. Patents are granted to inventions, but not every invention qualify for grant of patent, hence there are patentable and non-patentable inventions. An invention is patentable if it is new, results from an inventive activity and is capable of industrial application (s.1(1) of the Patent and Designs Act 2004). Several textbooks, case laws, statutes(local and International), internet sources reveal that Nigerian legislation inadequately granted patent to inventions, wherein it did not grant patent to some inventions which International intellectual property regime stated patent should be granted. The Laws of developed countries like USA have wider protection of inventions than Laws of developing countries like Nigeria. Hence the Nigerian Law is narrow in the inventions that should be patentable as against the International Intellectual Property Regime.&#x0D;
Development of and research on machine learning algorithms for solving the classification problem in Twitter publications

Posts on social networks can both predict the movement of the financial market, and in some cases even determine its direction.The analysis of posts on Twitter contributes to the prediction of cryptocurrency prices.The specificity of the community is represented in a special vocabulary.Thus, slang expressions and abbreviations are used in posts, the presence of which makes it difficult to vectorize text data, as a result of which preprocessing methods such as Stanza lemmatization and the use of regular expressions are considered.This paper describes created simplest machine learning models, which may work despite such problems as lack of data and short prediction timeframe.A word is considered as an element of a binary vector of a data unit in the course of the problem of binary classification solving.Basic words are determined according to the frequency analysis of mentions of a word.The markup is based on Binance candlesticks with variable parameters for a more accurate description of the trend of price changes.The paper introduces metrics that reflect the distribution of words depending on their belonging to a positive or negative classes.To solve the classification problem, we used a dense model with parameters selected by Keras Tuner, logistic regression, a random forest classifier, a naive Bayesian classifier capable of working with a small sample, which is very important for our task, and the k-nearest neighbors method.The constructed models were compared based on the accuracy metric of the predicted labels.During the investigation we recognized that the best approach is to use models which predict price movements of a single coin.Our model deals with posts that mention LUNA project, which no longer exist.This approach to solving binary classification of text data is widely used to predict the price of an asset, the trend of its movement, which is often used in automated trading.
Analysis of performance difference when using knowledge distillation of efficient CNN-based super-resolution algorithm

In this paper, we used RLFN, the NTIRE 2022 Efficient Super-Resolution Challenge winning model, to implement a lightweight and efficient super-resolution algorithm. We also apply knowledge distillation in a partially modified form of the PISR framework and analyze the qualitative and quantitative results to improve the performance while maintaining the cost of RLFN.
Spatially Adaptive Image Deblurring Module Based on Dilated Deformable Convolutions

In this paper, we propose an image deblurring module based on modified deformable convolutions (MDCs) to deal with spatially varying blur. The proposed method first reduces the number of channels of the input feature map. Then, we extract spatially adapted features by applying a MDC that uses two 1D convolution kernels for the deformable offsets. An additional MDC with dilation enlarges the receptive field and further strengthens features. We fully preserve previous features and propagate them using shortcut connection. Experimental results show that the model incorporating our modules outperforms the baseline model.
Learning global spatial information for multi-view object-centric models

Recently, several studies have been working on multi-view object-centric models, which predict unobserved views of a scene and infer object-centric representations from several observation views. In general, multi-object scenes can be uniquely determined if both the properties of individual objects and the spatial arrangement of objects are specified; however, existing multi-view object-centric models only infer object-level representations and lack spatial information. This insufficient modeling can degrade novel-view synthesis quality and make it difficult to generate novel scenes. We can model both spatial information and object representations by introducing a hierarchical probabilistic model, which contains a global latent variable on top of object-level latent variables. However, how to execute inference and training with that hierarchical multi-view object-centric model is unclear. Therefore, we introduce several crucial components which help inference and training with the proposed model. We show that the proposed method achieves good inference quality and can also generate novel scenes properly.
Credit Card Fraud Detection using Machine Learning

Evolving technologies make human life easier with increasing challenges. Online payments have become an integral part of our lives in the era of digitalization. The credit card payment system has made transactions hassle-free. This led to E-Commerce appraisal. Digitalization of transactions has given rise to new forms of fraud and cyberattacks that can affect individuals and organizations. This had set hackers at a great deal to steal the cardholder details using different schemes. Credit card companies must recognize these fraudulent transactions at the earliest to retain credibility among the stakeholders. Traditional methods of fraud detection have proven ineffective in identifying and preventing these fraudulent activities and cyberattacks in real time. This paper discusses various Machine Learning algorithms that predict fraudulent transactions in real-time. Fraudulent activities are solved using data science and machine learning techniques with substantial processing power and the capacity to manage massive datasets. The model is trained on large volumes of the dataset. This paper emphasizes comparison of various machine learning algorithms' performance over the input. The accuracy and efficiency of several machine learning algorithms are measured and analyzed through tabulation and comparison. The trained model is integrated with a website to categorize financial transactions as either legitimate or fraudulent. On utilizing advanced machine learning algorithms, credit card fraud detection systems have become more refined and accurate in recent years. As a result, financial organizations and customers are protected against such fraudulent activities, leading to increased trust and confidence in utilization credit card payments.
Educational Resource Management System (ERMS)

The ERMS aims to curate a management system for universities that provides us with a web application that eases the academic activities for students and faculty. Learning is fun when accessing the resource is easy. A user-friendly UI. The project environment and usage are completely secure at both ends. This is a system for the collection, integration, processing, maintenance, and dissemination of data and information to support decision-making, policy analysis, and formulation, planning, monitoring, and management at all levels of an education system. There are 3 main Users namely · Faculty - Who upload class materials, update marks and attendance. · Students - Who use this for accessing study materials, checkmarks, attendance. Admin/Management - Who keeps up with the changes like marks and attendance.
Assessment of Game Elements for 3D VR Serious Games for Production

The subject of 3D VR learning environments is a fascinating enhancement to the learning strategies of educational institutions.As large improvements in device technology and computer power have boosted the immersive experience in the last decade enormously, 3D VR learning kits have received wide-spread recognition and acceptance.3D VR Serious Games can save many resources, and thus contributes directly to sustainability, for example, simulations, investigations of new concepts and processes in manufacturing environments or avoid oft-repeated expensive training courses for production engineers.The logistics IT group of the University of Applied Science Cologne (TH Köln) has recently initiated a 3D VR environment together with a Serious Game for manufacturing and logistics.Key to this is an emphasis on the 3D immersive experience together with a serious game, or the requirement to integrate the right game elements to increase the fun factor and motivation for trainees.We find in the literature and case studies of gaming projects plenty of options for game elements to be integrated.In this paper, we present a strategy for the selection of the right game elements and give a preview of the first prototypes of these game elements.
Peer Review Statement

Abstract All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 108 • Number of submissions sent for review: 86 • Number of submissions accepted: 10 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 9 • Average number of reviews per paper: 3 • Total number of reviewers involved: 69 • Contact person for queries: Name: Miguel Botto-Tobar Email: m.a.botto.tobar@tue.nl Affiliation: Eindhoven University of Technology - Mathematics and Computer Science
Comparative Analysis of Temperature and Solar Radiation Data Available in Mubi-Northeastern Nigeria with Solar Photovoltaic Manufacturer's Standard Test Condition

In extension to the accelerated growth of the solar photovoltaic industry, the type of solar PV and reliability of solar radiation, temperature and air mass data to adopt at a particularly place has recently caught considerable attention by researchers, manufacturers, investors and consumers. Most solar panels are specified with Standard Test Condition (STC), but not all the locations are in agreement with the standard. These conditions are somehow misleading, as the values of solar radiation, temperature and air mass do not align across the full range of Earth surface. This observation encouraged the author to carryout comparative analysis to figure out whether the temperature and solar radiation data available in Mubi during 2000-2020 years align with that of manufacturer's STC specification or not. A time series analysis was adopted to compare the result obtained with STC values. It was found that the available average temperature was 28.83°C and the direct solar radiation was 3278.93W/m2 in Mubi, which was higher than the STC values. At the same time, the values are capable for harvesting efficient energy, when compared to the respective 25°C and 1000W/m2 of STC specification. The result also recommends poly-crystalline, thin-film or amorphous silicon as most suitable PV modules types to be adopted in Mubi region, because of their less tolerance to high solar radiation and temperature.Energy, Mubi, Radiation, Solar, Temperature
National Education Policy-2020 and Ancient Indian Education System: A Case Study on the Students’ Homes &amp; Hostels of Ramakrishna Math and Ramakrishna Mission

Swami Vivekananda, a visionary saint opined that “The old institution of ‘living with the guru’ and similar systems of imparting education are needed. What we want are western science coupled with Vedanta, brahmacharya as the guidance motto, and shraddha and faith in one’s own self.” In this context India is adapting National Education Policy-2020 which is being guided by the rich heritage of ancient and eternal Indian knowledge and thought. This policy document clearly states that, the aim of education in ancient India was not just the acquisition of knowledge as preparation for life in this world, or life beyond schooling, but for the complete realization and liberation of the self. Ramakrishna Math and Mission since its inception, along with spiritual and other service activities have taken up many initiatives in imparting quality education based on ancient Indian values and uplifting the society. This study is focused on throwing light on the contributions of Ramakrishna Math and Ramakrishna Mission in imparting education based on ancient Indian education system through its Students’ Homes &amp; Hostels and also orphanages in India which may considered as Gurukuls of new India. This study is based entirely on secondary data.
ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR FACIAL EXPRESSION RECOGNITION

Many deep learning-based methods have been suggested in recent years to improve facial expression recognition performance, but they are unable to extract the small facial changes in skin textures, such as wrinkles and furrows, which indicate changes in expression. We put forward an attention mechanism-based Convolutional Neural Network (CNN) for face expression recognition to get around this issue. The attention module, classification module feature extraction module and the reconstruction module make up the architectures four components. The feature extraction module, which consists of two distinct CNN processing streams-one for raw images and the other for Local Binary Pattern (LBP) feature maps. The LBP features extract image texture data before capturing the minute facial movements, which can enhance network efficiency. The neural network can pay more attention to useful characteristics by using an attention mechanism. To improve the attention model and produce improved results, we combine LBP features and convolutional features. We use the CK+ and Oulu-CASIA datasets to test the proposed methodology. The experimental findings show that the suggested method is workable and efficient. KEYWORDS: Convolution neural network, Local binary pattern, Facial expression recognition.
Accessible Publishing at the AMS

In this article, we define what is meant when discussing "accessibility" in publishing, discuss the challenges, and provide an overview of the work being done in the AMS Publications Division to address these challenges.The mission of the Publications Division of the AMS is twofold: to disseminate mathematical research and provide services and information of interest to the AMS membership and the worldwide mathematical community.These are broad goals, aiming to provide access to valuable mathematics content to as wide an audience of the global mathematical community as possible, without limitations due to gender, sexual orientation, race, ethnicity, economic status, geographical location, institutional affiliation, physical disabilities, or learning differences.Barriers to access, for both readers and authors, can be economic or technical.In order to address economic barriers, the AMS publishes a large amount of content online that is publicly accessible, introduced tiered pricing based on factors such as institution size, created the MathSciNet for Developing Countries (https:// www.ams.org/news?news_id=6672)program, and participates in Research4Life (https://www.research4life.org) to bring our journal content to developing countries.
BOREx: Bayesian-Optimization-Based Refinement of Saliency Map for Image- and Video-Classification Models

Explaining a classification result produced by an image- and video-classification model is one of the important but challenging issues in computer vision. Many methods have been proposed for producing heat-map–based explanations for this purpose, including ones based on the white-box approach that uses the internal information of a model (e.g., LRP, Grad-CAM, and Grad-CAM++) and ones based on the black-box approach that does not use any internal information (e.g., LIME, SHAP, and RISE). We propose a new black-box method BOREx (Bayesian Optimization for Refinement of visual model Explanation) to refine a heat map produced by any method. Our observation is that a heat-map–based explanation can be seen as a prior for an explanation method based on Bayesian optimization. Based on this observation, BOREx conducts Gaussian process regression (GPR) to estimate the saliency of each pixel in a given image starting from the one produced by another explanation method. Our experiments statistically demonstrate that the refinement by BOREx improves low-quality heat maps for image- and video-classification results.
The Effect of Hello English Application on Speaking Ability

Technology has received a lot of attention because of its potential to solve learning problems in this modern era. Disinterest and low participation of students in the learning process, encourage teachers to use technology as a medium to increase students' enthusiasm for learning English, especially speaking. This research aims to determine whether Hello English application had a significant impact on speaking ability. Pre-experimental research was employed to conduct this research. The sample of this research consisted of 35 students from class XI MIA 1 at SMAN 3 Padang Sidimpuan. The pretest is used to determine the students' initial speaking ability. The posttest was conducted after the treatment was applied in four meetings utilizing Hello English application as a supporting medium in learning English. The t-test formula was used to analyze the collected data. Based on the findings of this research, the t-count value was 26.727, and the t-table value was 2.032. It denotes the alternative hypothesis (Ha) regarding there is a significant effect of Hello English application on the speaking ability of eleventh-grade students at SMAN 3 Padang Sidimpuan is accepted.
A Need Analysis of EFL Learning at Elementary School : A Case Study

This study aims to identify obstacles and analyze needs during the English learning process at SD Negeri 1 Kaba-Kaba, Tabanan, Bali. Learning outcomes for the previous two academic years showed unsatisfactory results. Therefore, this study was conducted to determine the constraints and needs of the learning process in this elementary school. This research is a field research with a qualitative descriptive approach. The data population in this study were 107 students in grades 3-5 and an English teacher. Data were obtained through observation and interviews with 31 students and English teachers. The results of data analysis showed that there were several obstacles faced during the English learning process, namely (i) students who had difficulty concentrating during the learning process, (ii) students' physical condition during the learning process, (iii) active students were found to interfere with the learning process, (iv) lack of supporting facilities and infrastructure to support classroom learning and (v) monotonous teaching methods. In relation to these constraints, several needs in the learning process are (i) good classroom management, (ii) interesting teaching methods, (iii) use of teaching aids in the learning process and (iv) determination of learning outcomes based on the curriculum.