Reviewerly/exHarmony huggingface dataset:
DIGITAL COMPETENCE DEVELOPMENT OF THE ARTS EDUCATION INSTITUTIONS TEACHERS

The COVID-19 pandemic caused the education to go online and showed digital skills gaps of all participants in the educational process.This forced transition to online mode and lack of time for preparation has become a challenge for all parts of the arts education system, for all participants in the educational process: administration, teachers, parents, and students and highlighted the need to ensure a sufficient level of digital competence of the teachers of the arts education institutions.Undoubtedly, the introduction of digital technologies in the educational process should be accompanied by the training of teachers who should be ready to interact and cooperate in the latest digital environment.The article presents the experience of professional development of teachers of arts education institutions in the direction of digital competence development.The training program "Digital tools for distance learning" was developed in cooperation with the public organization "Ukrainian Association of Information Technology Professionals". It aims to increase the level of digital competence of teachers of arts education institutions and promote the using digital technologies in the educational process.The learning materials developed considering the modern European standards of digital competence (DigComp, DigCompEdu, UNESCO ICT Competency Framework for Teachers), the experience of international projects, and the needs of the target audience. The developed model of online learning has received positive feedback from participants.The paper presented the survey results of participants of the refresher program on the novelty and relevance of the studied digital tools, the use of digital technologies in the educational process of arts education, changes in approaches to teaching. Comments and suggestions on the format of conducting and educational materials content are analyzed. Prospects for expanding the list of in-service training programs in the field of digital competence for teachers of arts education are described.
Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept

Lifting the 2D human pose to the 3D pose is an important yet challenging task. Existing 3D pose estimation suffers from 1) the inherent ambiguity between the 2D and 3D data, and 2) the lack of well labeled 2D-3D pose pairs in the wild. Human beings are able to imagine the human 3D pose from a 2D image or a set of 2D body key-points with the least ambiguity, which should be attributed to the prior knowledge of the human body that we have acquired in our mind. Inspired by this, we propose a new framework that leverages the labeled 3D human poses to learn a 3D concept of the human body to reduce the ambiguity. To have consensus on the body concept from 2D pose, our key insight is to treat the 2D human pose and the 3D human pose as two different domains. By adapting the two domains, the body knowledge learned from 3D poses is applied to 2D poses and guides the 2D pose encoder to generate informative 3D "imagination" as embedding in pose lifting. Benefiting from the domain adaptation perspective, the proposed framework unifies the supervised and semi-supervised 3D pose estimation in a principled framework. Extensive experiments demonstrate that the proposed approach can achieve state-of-the-art performance on standard benchmarks. More importantly, it is validated that the explicitly learned 3D body concept effectively alleviates the 2D-3D ambiguity in 2D pose lifting, improves the generalization, and enables the network to exploit the abundant unlabeled 2D data.
Recommending Knowledge Concepts on MOOC Platforms with Meta-path-based Representation Learning

Massive Open Online Courses (MOOCs) which enable large-scale open online learning for massive users have been playing an important role in modern education for both students as well as professionals. To keep users' interest in MOOCs, recommender systems have been studied and deployed to recommend courses or videos that a user might be interested in. However, recommending courses and videos which usually cover a wide range of knowledge concepts does not consider user interests or learning needs regarding some specic concepts. This paper focuses on the task of recommending knowledge concepts of interest to users, which is challenging due to the sparsity of user-concept interactions given a large number of concepts. In this paper, we propose an approach by modeling information on MOOC platforms (e.g., teacher, video, course, and school) as a Heterogeneous Information Network (HIN) to learn user and concept representations using Graph Convolutional Networks based on user-user and concept-concept relationships via meta-paths in the HIN. We incorporate those learned user and concept representations into an extended matrix factorization frame- work to predict the preference of concepts for each user. Our experiments on a real-world MOOC dataset show that the proposed approach outperforms several baselines and state- of-the-art methods for predicting and recommending con- cepts of interest to users.
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly designed for image classification that generate single-scale low-resolution representations, which makes dense prediction tasks such as semantic segmentation challenging for ViTs. Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs. We balance the model performance and efficiency of HRViT by various branch-block co-optimization techniques. Specifically, we explore heterogeneous branch designs, reduce the redundancy in linear layers, and augment the attention block with enhanced expressiveness. Those approaches enabled HRViT to push the Pareto frontier of performance and efficiency on semantic segmentation to a new level, as our evaluation results on ADE20K and Cityscapes show. HRViT achieves 50.20% mIoU on ADE20K and 83.16% mIoU on Cityscapes, surpassing state-of-the-art MiT and CSWin backbones with an average of +1.78 mIoU improvement, 28% parameter saving, and 21% FLOPs reduction, demonstrating the potential of HRViT as a strong vision backbone for semantic segmentation.
Peran Teknologi Informasi dan Sistem Operasional Bank Syariah pada SMK Hang Nadim Batam

Community service is one form of the Tri Dharma of Higher Education that must be carried out by lecturers as a form of implementing knowledge possessed by schools, institutions or the surrounding community. The purpose of this community service is to provide an understanding of the information technology education process and provide an understanding of the operating system of Islamic banks. The method used in the service is the method of presentation and discussion of students at SMK Hang Nadim Batam. There are several majors at Hang Nadim Vocational School in Batam, such as the Department of Accounting, Light Vehicle Engineering, Electronics and Computers. The results of the service carried out are Increasing students' understanding of basic information technology and being able to use the technology; Students can distinguish the roles and functions of Islamic banks and conventional banks so that they can choose to use bank products and services in saving and investing or invest.
SERVICE DESIGN EXPERIMENTAL LEARNING IN ADULT EDUCATION

O. Bogdanova1, K. Brusila-Meltovaara1, J. Iso-Aho2, T. Vuorela3 1LAB University of Applied Sciences (FINLAND) 2HUMAK University of Applied Science (FINLAND) 3Laurea University of Applied Sciences (FINLAND)
A Secured Storage and Communication System for Cloud Using ECC, Polynomial Congruence and DSA

Abstract Security is necessary today’s fast computer world during the data communications in internet and cloud environment. The cloud security is becoming very challenging task today due to the presence of huge volume of cloud users. Even though, various cloud secured storage mechanisms are available and tried to fulfill the current requirement. However, not yet fulfill the cloud user’s requirements in terms of security while storing and sharing the data in cloud. For this purpose, this work proposes a new secured storage and communication system for providing data security while storing the data in cloud database and sharing the data between the cloud users in cloud. This system consists of three different algorithms for performing prime number generation, digital signature creation for generating keys, encryption, decryption and authorization. In this paper, we propose a new technique to find a co-prime number which is used to generate keys in key generation process and also useful for performing encryption and decryption process. Moreover, a new key generation technique is also introduced for ECC and DSA using polynomial congruence for performing key generation process securely. In addition, a new Elliptic Curve and Polynomial Congruence based Encryption / Decryption algorithms for performing data encryption and decryption in the process of data storage and communication. Finally, the user authenticity is verified by the proposed digital signature algorithm in this work. The experiments have been conducted for evaluating the proposed secured storage and communication system and proved as better than others in terms of efficiency and security level.
Emotional Intelligence And Self-Assessment Of School Teachers

In recent years, plenty of attention has been paid to emotional intelligence as the ability to understand the meaning of emotions and use this knowledge to find out the causes of problems and solve these problems.The main components of emotional intelligence are found in the classification of professionally important qualities of teachers: emotional restraint, emotional balance, empathy, communication, and managerial abilities, including the ability to manage their own emotions in the process of communication.The inability to understand one's own emotions and the emotions of other people, to correctly assess the reactions of others, as well as the inability to regulate one's own emotions in moment of making decisions, lead to many life failures.Thus, teachers, since they are constantly in interaction with their students, need to develop this skill as one of the most important in preparing for leadership.This study is aimed at studying the emotional intelligence of modern teachers, we also looked at the effect of the level of emotional intelligence on their self-esteem.The study involved 100 teachers aged 24 to 50 years.The following methods were used: the questionnaire of emotional intelligence by D. V. Lyusin, the method for self-assessment Dembo-Rubinstein.The results of this study confirmed the hypothesis that there is a link between emotional intelligence and the self-esteem of schoolteachers.
From the history of private libraries of local people in Turkestan

The article presents historical information about the libraries opened in Turkestan (1870–1917) by Mahmudhoja Behbudi, Munnavar Kori Abdurashidkhonov and Abdullah Avloni. Based on historical documents, the article describes the «Library of Behbudiya», opened by Mahmudhoja Behbudi in 1907 in Samarkand.
QUALITY MANAGEMENT IN THE HARMONISATION OF EDUCATIONAL STRUCTURES AND LEARNING OUTCOMES AT ENHANCING COMPETENCE REQUIREMENTS IN HIGHER EDUCATION

Information about the paper titled "QUALITY MANAGEMENT IN THE HARMONISATION OF EDUCATIONAL STRUCTURES AND LEARNING OUTCOMES AT ENHANCING COMPETENCE REQUIREMENTS IN HIGHER EDUCATION" at IATED Digital Library
USER EXPERIENCE DESIGN EDUCATION - THE DEVELOPMENT AND EFFICACY OF A COLLABORATIVE, MULTIDISCIPLINARY, PROBLEM BASED LEARNING MINOR PROGRAM

Information about the paper titled "USER EXPERIENCE DESIGN EDUCATION - THE DEVELOPMENT AND EFFICACY OF A COLLABORATIVE, MULTIDISCIPLINARY, PROBLEM BASED LEARNING MINOR PROGRAM" at IATED Digital Library
INNOVATION AND ENTREPRENEURSHIP PERFORMANCE IN ABA, ABIA STATE, NIGERIA

This study examined the influence of innovation on the performance of entrepreneurship in Aba, Abia State, Nigeria. Three hypotheses were formulated to ascertain whether or not there is significant relationship existing between the two intervening variables i.e. innovation and entrepreneurship performance in Aba, Abia State. Survey research design was adopted, while data were obtained through structured questionnaire and analyzed using Pearson’s product-moment coefficient of correlation. From the analysis, the result showed that there is an existing relationship between innovation and performance of entrepreneurship. Based on the findings it was concluded that for entrepreneurship to stand the test of time in the face of escalating competition in the emerging economies like Nigeria, premium must be placed on formulating and developing innovative culture aimed at encouraging employees to bring on board innovative ideas and strategies capable of providing innovative product and services to the customers with valued and satisfying benefits better than competitors to enhance performance and productivity. The study therefore, recommended among others that entrepreneurship policies and program should be designed in ways that addresses horizontal concerns and which generates better and viable inducement for innovation activities.&#x0D; Keywords: Innovation, Inducement, Entrepreneurship, Emerging Economy, Innovative Culture
Hidden-Fold Networks: Random Recurrent Residuals Using Sparse Supermasks

Deep neural networks (DNNs) are so over-parametrized that recent research has found them to already contain a subnetwork with high accuracy at their randomly initialized state. Finding these subnetworks is a viable alternative training method to weight learning. In parallel, another line of work has hypothesized that deep residual networks (ResNets) are trying to approximate the behaviour of shallow recurrent neural networks (RNNs) and has proposed a way for compressing them into recurrent models. This paper proposes blending these lines of research into a highly compressed yet accurate model: Hidden-Fold Networks (HFNs). By first folding ResNet into a recurrent structure and then searching for an accurate subnetwork hidden within the randomly initialized model, a high-performing yet tiny HFN is obtained without ever updating the weights. As a result, HFN achieves equivalent performance to ResNet50 on CIFAR100 while occupying 38.5x less memory, and similar performance to ResNet34 on ImageNet with a memory size 26.8x smaller. The HFN will become even more attractive by minimizing data transfers while staying accurate when it runs on highly-quantized and randomly-weighted DNN inference accelerators. Code available at https://github.com/Lopez-Angel/hidden-fold-networks
Implementation of Genetic Algorithm and Adaptive Neuro Fuzzy Inference System in Predicting Survival of Patients with Heart Failure

Purpose: Heart failure is a disease that is still a global threat and plays a major role as the number one cause of death worldwide. Therefore, accurate predictions are needed to determine the survival of heart failure patients. One technique that can be used to predict a decision is classification. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an algorithm that can be used in the classification process in making predictions. Genetic Algorithms can help improve the performance of classification algorithms through the feature selection process. Methods/Study design/approach: In this study, predictions or diagnoses were made on the survival of heart failure patients based on the heart failure clinical record dataset obtained from the UCI Machine Learning Repository. The data used is 299 data with 12 attributes and 1 class. The result of this research is the comparison of the accuracy of the ANFIS algorithm before and after using the Genetic Algorithm. Result/Findings: The ANFIS algorithm produces the highest accuracy of 94.444%. While the ANFIS algorithm after attribute selection using the Genetic Algorithm produces the highest accuracy of 96.667%. This shows that the Genetic Algorithm is able to improve the performance of the ANFIS classification algorithm through the attribute selection process.
Implementing clustering technique in writing descriptive texts

This experimental research was conducted to find out the effect of using clustering technique on the students’ writing skills at Senior High School No. 1 Delima, Pidie District. It also has several sub-focuses, namely figuring out the students’ writing content, organization, and language use. The subjects of the study were the 10th grade students that were divided in random sampling into the experimental and the control groups. This procedure was carried out through homogeneity of the selected samples. To expose the focuses of answers, the pretest and post-test instruments were applied to both groups. The result showed that the students in experimental group had an improved quality of writing a descriptive text than that of the control-group students. At a level of significance 0.05, the ttestscore was higher than ttablescore (8,441,684). In addition, the mean score of the experimental group (78,79) was higher than the mean score of the control group (53,75). This figure has corresponded with the results of the three writing aspects of the students’ work, which also brought a relatively positive achievement. This means that there were significant improvements regarding the students’ writing aspects of content, organization, and language use in descriptive texts respectively.
THE ETHNOGRAPHIC RESEARCH ON TEACHERS’ DIGITAL COMPETENCE AND LIFELONG E-LEARNING

The digital revolution has changed our world to great extent including the educational sphere. The rapid transition from the technical training aids to the digital ones has led to many crucial changes and big challenges. The questions: what, why, when, where and how to apply the digital technology, resources, tools, applications – have emerged and appeared to be difficult for finding the clear answers. The only way for the teachers to go with the times is to raise their professional competency, promote their expert knowledge, adjust the teaching strategies to the digital era requirements.To elicit the teachers’ interest to the educational digital technology use and their digital competence, the questionnaire was elaborated in the electronic format, providing the rating scale. The anonymous pool has revealed the high level of interest, however, some respondents’ competence rate has been lower than expected. The one-to-one interviews with the teachers have disclosed the various ways they have been passing through to raise their digital competence. The author has analysed her experience and reflected her best practices and unavoidable failures. Considering e-learning as learning via information communication technology, this research also provides the overview on the open educational e-resources and the available professional development online courses for the English language teachers. The drawn conclusions have allowed to outline the guidelines for the teachers on mastering their digital skills and competence via the electronic communication means and tools as cornerstone of successful professional life.
Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images

Abstract Malignancy is one of the leading causes of death globally. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this article, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant / non-malignant). The CNN model uses an encoder-decoder architecture as well as a parallel feed-forward network. The proposed model provides higher cumulative training accuracy and statistical scoring after five-fold cross-validation. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels.
Learning Media of Pakakala Boardgame Can Increase Student Learning Motivation

This study aims to develop a learning media in the form of boardgame Learning Media (Optional Board and Career Cards) to improve students’ career knowledge in a fun, interactive and active learning under. This research uses Research and Development Method, with The phases in this research consist of; (1) product analysis, (2) developing products, (3) validation and revision, (4) play-trials and final products. Data collection techniques using interviews and questionnaires. Feasibility check and assessment by theory expert, learning media expert and users using Likert scale with the value used are 5 to 1. The results of the validation by theory expert get score 5 with a very worthy category, while the validation by media expert gets score 4.89 with the very worthy category. Whereas in the usage test by users or students and teacher response as an education practitioner showed very worthy category with scores 4.53 and 5 respectively. Learning media supposed to be more interactive and attracted for students in case to gain their learning experience. The researchers succeeded in developing a game based learning in boardgame type to extend the way teacher in primary school teaching. As a result, the developed pakakala boardgame for year 4 students at SDN 84 Parepare&nbsp; is suitable to use in teaching theme 4 concerning various occupations.
Development of Happy Reading Interactive Media to Stimulate the Pre-Ability to Read Children Aged 5-6 Years

Abstract: The purpose of this media research and development is to produce interesting, effective, and efficient learning media. The method used in developing interactive media is happy reading, that is, research and development that refers to the development model of Borg & Gall. In this development, using three stages, namely the validation of learning experts and media experts, small group testing, and testing of large groups of group B. The instruments of data collection used are qualitative and quantitative. The results of the products developed are CDs. Keywords: interactive media, happy reading , pre-reading
Scaling Up Vision-Language Pre-training for Image Captioning

In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art VinVL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million image-text pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail visual concepts when used in a zero-shot manner.