Reviewerly/exHarmony huggingface dataset:
The use of digital resources in the education system of foreign countries

The article examines digital learning technologies, shows the effectiveness of information technologies, introduces the aspects of digitalization of the educational space, analyzes the current state of the use of information technologies in the conditions of digitalization of education. The main goal of digital education is the high-quality and modern preparation of students for their effective participation in professional and social life in the conditions of the information society. The article examines the importance of the concept of digitalization of educational resources is substantiated based on the study of the experience of different 256countries. Leading features of digital education are proposed: anticipatory nature, quality, flexibility, accessibility, creativity. The leading principles are highlighted and the features of digital education are clarified. The possibilities of digitalization of educational resources in the education system of foreign countries characterized by openness are analyzed. Digitization of educational resources in the education system in foreign countries provides cultural and linguistic education for those seeking education, promotes the development of informational and communicative competence, provides greater opportunities for quality education and employment of citizens. The leading principles of digitization of educational resources in the education system are defined.
Innovations in the field of education in the context of improving the personnel incentive system

The article summarizes the trends of change in education as a context for the development of social innovations and posing the problem of their open management in the interaction of educational organizations. The concept of "social innovation" is concretized, revealing their essential features and characteristics, including management features them. The possibility of integrating process, project, system approaches to the management of social innovations on the basis of their comparative analysis is substantiated. A pedagogical strategy for managing social innovations in intra-organizational and inter-organizational network interaction of educational organizations has been developed and its effectiveness has been tested.
Tinkering with social touch technology

Social touch technology, haptic technology to mediate social touch interactions, potentially contributes to reducing negative effects of skin hunger and social isolation. This field is developing and while there are a number of prototypes, few became products and less persisted in the market today. Viable social touch technology is essential for research on social touch and it has an unexplored market potential. Making prototypes and evaluating them is the approach of generating knowledge in Research through Design (RtD). In RtD, researchers investigate the speculative future, probing on what the world could and should be, leaving the exact method of designing prototypes open. One possible method is tinkering, characterized by a playful and creative exploration. Tinkering environments, however, need a careful design of toolkits and setting. In this study, we report on the toolkit and setup we used for a tinkering-based teaching unit on social touch technology, held within an introductory course of an Interaction Technology master program, and describe the resulting prototypes. With a qualitative analysis of the results, we consider the teaching unit as a success, w.r.t. the diversity of the concepts developed. Tinkering is well-known as a playful method for education in Science, Technology, Engineering, and Maths, aiming at school children and high school students. It is not yet established as a design method in itself, and not considered as element of an academic skill set. Here, we argue that tinkering is a valuable design method in the context of social touch technology, and that it has a place in the design approaches within an academic setting. In a further step, we also want to include experts from other domains in the design process, such as psychologists or fashion designers. For that end, we suggest expanding a current toolkit for wearable technology with concepts from the teaching unit, more scaffolding tools, a variety of tactile actuators, and a software tool that allow for (re)configuring designs rather than programing them.
Redrawing historical weather data and participatory archives for the future

The DRAW (Data Rescue: Archives and Weather) project promotes the creation of weather data records in database form to be used by scientists to support climate change research. Archives all over the world may have buried somewhere in their holdings weather data in handwritten or printed form. Scientists rely on weather data created and input into databases over the last fifty years for their predictive models and older weather data remains to be discovered. Archives working in conjunction with multi-disciplinary teams can be a key resource to make this data available. This outreach tool for archives and archivists is also in this online digital environment rife with educational opportunities to showcase the social context of records creation and to develop products to share with other archives such as lesson plans. The crowdsourcing component of DRAW is now supported by a participatory archives element where users collect their own data and can use it to create narratives and engage in the research process to support the heritage value of archival holdings. The future of DRAW data in digital humanities applications is an area to be explored and the possibilities of AI projects to collect the buried data is being investigated by DRAW. As amply demonstrated during the DRAW project archives and archivists are the key links to the past, present and future use of archival holdings.
Building research competence of students of technical majors in the context of distance education

The aim of the study was to establish ways of developing research competence in students of technical majors in the context of distance learning, and to evaluate their effectiveness. The research methods were: comparison, Forester’s method, Cohen’s coefficient, Pearson’s correlation coefficient. It was established that the use of the research method of teaching in the context of distance education had a high effect on the level research competence of students of technical majors. A slightly lower effect was also achieved when introducing a course on research work. The maximum effect is achieved when combining both methods of building research competence. An increase in the research competence level was found. At the same time, students’ confidence in discussing research ideas with colleagues, conducting literature search, editing and shortening the text, and presenting research results also increased. Prospects for further research consist in the diagnostics of innovative methods of building research competence that can be applied in technical majors and increase the success rate of students in the context of distance education.
Trading excess consumption certificates on the blockchain using the cooperative game theory

Purpose The purpose of this study is to propose a decentralized multi-party cross-trading scheme based on a certificate transaction mechanism for the transaction of excess consumption certificates (ECCs) of renewable energy. The aim is to address the problems associated with the existing centralized transaction mode and to promote the development of the green electricity industry. Design/methodology/approach The proposed scheme involves calculating the quotation difference for the same type of certificate transaction based on the quotations of all users of both buyers and sellers. The transaction volume is then determined based on the order of quotation difference from large to small, and the total interests of cooperation are calculated. The nucleolus method is adopted to allocate the total interests to each member of the alliance and calculate the final transaction price. The blockchain technology is used for the transaction to achieve accurate traceability and efficient supervision, and a corresponding smart contract is designed and simulated in the Ethereum consortium chain. Findings The results of the simulation show the rationality and effectiveness of the proposed scheme. The decentralized multi-party cross-trading scheme can overcome the problems associated with the existing centralized transaction mode, such as low transaction efficiency, difficulty in obtaining the optimal transaction strategy and efficient supervision. The proposed scheme can promote the development of the green electricity industry by stimulating users' demand potential for green electricity. Originality/value The proposed scheme is original in its use of a certificate transaction mechanism to facilitate the trading of ECCs of renewable energy. The scheme adopts a decentralized multi-party cross-trading approach that overcomes the problems associated with the existing centralized transaction mode. The use of the nucleolus method for the allocation of total interests to each member of the alliance is also original. Finally, the use of blockchain technology for accurate traceability and efficient supervision of the transaction is an original contribution to the field.
Technological Tools to Develop Competences for the 21st Century: A Project to Empower Students in the English Teaching Major at UNED

Being a «native digital», that is to say an individual who has grown up using Internet, computers and mobile devices, and being an individual able to communicate ideas effectively in a foreign language are definitely traits of a profile for the 21st century. Society is requiring future English teachers to graduate with these linguistic and technological competences to eventually equip them to provide their pupils with meaningful learning experiences. The following article intends to describe all the actions, stages and activities carried out in a project about technological tools offered to students from the English Teaching Major at Universidad Estatal a Distancia (UNED) during the year 2021. The article presents a description of the process followed and the information obtained through a questionnaire and a group discussion applied to the participants. One important conclusion was that projects with a hands-on perspective where students not only get information from professors but need to practice, construct, and propose their own ideas will always contribute to the participants’ development.
Algorithm for minimum bounding rectangle based on key point set

This paper proposes a ship detection method based on contour extraction networks and minimum bounding box generation algorithms. The proposed method utilizes contour extraction networks to extract the contour of the ship target and generate a set of key points. Then, the minimum bounding box generation algorithm is used to transform these key points into the rotational detection box of the target, which can more accurately describe the shape and orientation of ships, thus improving the accuracy and reliability of detection. The experimental results show that the proposed method can effectively detect ships of various orientations and postures and has better performance than traditional rectangular box detection methods. The paper compares rotated bounding box detection methods based on traditional machine learning and deep learning and concludes that rotated bounding box detection methods based on deep learning can better solve the problem of ship detection in complex situations, but require a large amount of data for training and may require a higher computational cost. Finally, the paper introduces the Minimum Bounding Rectangle Algorithm used in the instance segmentation process.
Adversarial resilience of self-normalizing convolutional neural networks for deep learning-based intrusion detection systems

Deep learning-based intrusion detection systems (IDSs) can easily be fooled by the presence of adversarial examples, thus, limiting their usefulness in security-critical applications such as in 5G and 6G wireless networks. The cause for the adversarial vulnerability of the neural network requires an elaborate investigation. Still, some researchers have proposed that regularization and normalization techniques applied to the neural network models play a significant role. In this paper, we evaluate the role of self-normalization in the adversarial vulnerability of neural network models within the context of IDSs for application in the envisioned 6G wireless networks. We propose the design and implementation of a deep learning-based IDS for botnet traffic and subject it to various forms of adversarial attacks. We then investigate the impact of self-normalization on the adversarial resilience of our deep learning-based IDS and compare our findings with that of image classification neural network models. Our study proposes a customized convolutional neural network (CNN) model that utilizes self-normalizing activation in the fully connected layers. Our results show that self-normalization of the deep learning-based IDS using scaled exponential linear unit (SELU) results in greater resilience to various adversarial samples. The projected adversarial resilience of self-normalizing CNNs for deep learning-based IDSs would be useful in future wireless communication systems such as 6G networks.
An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches

Sentiment Analysis (SA) is an active research field in modern times by studying people’s sentiments posted on different web platforms over the internet. To study these sentiments, different machine and deep learning techniques are in use. Deep learning is an efficient approach for SA. It gives more accurate results by learning text features from sentiments using a deep neural network. In this paper, we proposed a framework for sentiment classification;. The model was, trained, and tested on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Deep Learning (DL) algorithms for SA. The performance of proposed framework was evaluated using accuracy, precision, recall, and F-1 score values. To calculate the performance, the data set was split in the ratio of 70:30, 80:20, and 90:10 for training and testing the model and observed that LSTM performs well with 88% accuracy and precision value, 87% recall value, and 87% F-1 measures.
Research on Full Coverage Path Planning Based on Reinforcement Learning in Nuclear Environment

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of [Formula: see text]-learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, [Formula: see text]-learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the [Formula: see text]-learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain.
PENGARUH MODEL INDEX CARD MATCH TERHADAP HASIL BELAJAR SISWA KELAS V SD NEGERI 21 PALEMBANG

Abstract :The Effect Of The Index Card Match Model On The Students Learning Outcomes In Class V Sd Negeri 21 Palembang. This study aims to determine the effect of the index card match modl on the learning outcomes of fifth grade students at SD Negeri 21 Palembang. The research method in this study was using an experimental method with the design of The Nonequivalent Control Group Design, the population in this study amounted to 90 students from all fifth grade students of SD Negeri 21 Palembang. The data collection technique in this study used multiple choice test questions. Contect validaty was calculated from experience judgment and construct validity was calculated using product moment correlation by Pearson and reliability using Cronbach's Alpha. The data analysis technique used in this study is the normality test with the kolmogorof-smirnov test, the homogeneity test with the F test and hypothesis testing with the Independent Sample T test. The results showed that there was an effect of the index card match model on the learning outcomes of fifth grade students at SD Negeri 21 Palembang. It is shown from the results of the Independet Sample T test of Pretest and Posttest data on learning outcomes, the value of 𝑡𝑐𝑜𝑢𝑛𝑡= 3,98 &gt; 𝑡𝑡𝑎𝑏𝑒𝑙= 1,672, then Ho is rejected amd Ha is accepted.
An Optimized Deep Learning Approach for Predicting the Electric Motor Temperature Using IOT Sensors

Based on that, the Internet of Things (IoT) is used in industrial applications for monitoring and controlling various sensor operations. In existing work, IoT-based monitoring and controlling operations for industries are proposed. But, in this, real-time monitoring of the data is performed to take necessary actions. This may fail at a fraction of a second when the device crossed its breakpoint or threshold value. Hence, in this, an optimized deep learning approach (Convolutional Neural Network) is proposed for monitoring and controlling the temperature in electrical motors. Here, the controlling is performed by predicting the temperature using a deep learning approach. This helps to improve the controlling operations in the IoT environment and protect the device from Malfunctioning. The proposed approach is tested on the Kaggle Sensor dataset for electrical motors. The optimal hyperparameters for the CNN are determined through the hybrid particle swarm and genetic algorithm by minimizing the cost function. The cost function is to reduce the RMSE rate. This method’s presentation is evaluated and the fidelity of root mean square merits. The whole process is implemented using MATLAB R2020a version under Windows 10 environment.
The digital competence of the rural teacher of primary education in the mentoring process: a study by teaching speciality and gender

Purpose To know the digital competence of rural teachers to carry out the tutoring process with members of the educational community through digital resources (teacher-student, teacher-families and teacher-teaching team). As specific objectives, gender, teaching specialties, interaction between gender*teaching speciality, and significant predictors were analysed. Design/methodology/approach The research was quantitative, with a non-experimental, cross-sectional, descriptive and inferential design. Findings The results showed an explorer-expert teacher, where the generalist teachers had a superior competence compared to the rest of the specialties. Gender and teaching speciality were significant predictors in the communication that the teacher has with all the agents involved, while the interaction of both predictors was only significant between the teacher-teaching team and teacher-families. Research limitations/implications Another issue worth considering relates to the development of the classification tree for the use of digital resources in tutorial action. Due to lack of space, the proposal has focused on gender and particular subjects, but it would be interesting to focus on the dimensions of the instrument with regard to tutorial action with the different agents (students, teaching staff and families). Originality/value After reviewing the literature, the authors can conclude that very little quantitative research is focused on the level of self-perception of digital competence of teachers in rural schools. Furthermore, the teaching speciality of teachers has up until now hardly been taken into account as a variable that can determine the levels of digital competence. Not many studies have analysed the use of digital resources to communicate with the different members of the educational community.
Improving Node Classification Accuracy of GNN through Input and Output Intervention

Graph Neural Networks (GNNs) are a popular machine learning framework for solving various graph processing applications. This framework exploits both the graph topology and the feature vectors of the nodes. One of the important applications of GNN is in the semi-supervised node classification task. The accuracy of the node classification using GNN depends on (i) the number and (ii) the choice of the training nodes. In this article, we demonstrate that increasing the training nodes by selecting nodes from the same class that are spread out across non-contiguous subgraphs, can significantly improve the accuracy. We accomplish this by presenting a novel input intervention technique that can be used in conjunction with different GNN classification methods to increase the non-contiguous training nodes and, thereby, improve the accuracy. We also present an output intervention technique to identify misclassified nodes and relabel them with their potentially correct labels. We demonstrate on real-world networks that our proposed methods, both individually and collectively, significantly improve the accuracy in comparison to the baseline GNN algorithms. Both our methods are agnostic. Apart from the initial set of training nodes generated by the baseline GNN methods, our techniques do not need any other extra knowledge about the classes of the nodes. Thus, our methods are modular and can be used as pre-and post-processing steps with many of the currently available GNN methods to improve their accuracy.
PERAN GURU DALAM MENINGKATKAN MINAT BACA SISWA KELAS IV DI MI NU JATIREJOYOSO

One of the tasks of an educator is to coordinate students in learning activities, within the scope of education, reading is an important skill for students to learn. This research focuses on the urgency of educators who are required to have a contribution in increasing interest in reading in individual students. The fact that interest in reading is low among students then becomes the reason for this research being carried out. A fact, where growing, caring for and accommodating students' interest in reading is the role of a teacher. Reflecting on field conditions, this research then uses a qualitative model that is collaborated with a descriptive approach. For the understanding of the descriptive approach, it can be interpreted as research based on a specified group, one situation, one form of idea, or one class of events. While the expected goals of qualitative research with a descriptive approach are oriented towards accurate writing which includes the preparation of sketches in a structured, valid, and on target manner related to events that are of temporary concern. The research results show that the teacher's role is as a teacher, inspirer, initiator, motivator, facilitator, class manager, mediator, and evaluator. Then for supporting conditions in the implementation of increasing students' interest in reading, between academic aspects, environment, infrastructure and learning materials. The inhibiting factors are academic, environmental, and infrastructure that are not used as well as possible, such as the internet network. Characteristics of grade 4 students, who like picture books.