Reviewerly/exHarmony huggingface dataset:
GENERAL SECONDARY TEACHERS’ VIEWS ON EDUCATIONAL PROCESS AMID THE COVID-19 PANDEMIC: TWO-YEAR EXPERIENCE OF BLENDED LEARNING

The main purpose of the paper is to identify and generalize the tendencies of changes in the system of education influenced by the unexpected shift from face-to-face learning (traditional) to blended learning (as being regarded as a pattern of a certain mix of face-to-face and distance learning). The research is also targeted at investigating general secondary teachers’ readiness to respond to the educational challenges during the Covid-19 pandemic. The paper presents the information obtained at the very beginning of the sudden and unpredictable shift to total distance learning and then after the gradual transition to blended learning. The pros and cons of blended learning under conditions of the Covid-19 pandemic are revealed. The ideas that are closely connected with the accepting or not accepting absolutely new ways of teaching and learning from the points of view of general secondary school teachers, schoolchildren and their parents are given in the paper. The main problems of general secondary school teachers, schoolchildren and their parents face and practical ways to address these problems are enumerated and analyzed. The authors offer a variety of possible ways and means for overcoming the problems identified. Some certain innovative approaches and technologies, methods, techniques and means of blended learning aimed at delivering quality education to general secondary schoolchildren are mentioned. The research sample includes 3600 general secondary school teachers from different regions of Ukraine.
Non-dyadic human–robot interactions and online brand communities

Purpose This research investigates and conceptualizes non-dyadic human–robot interactions (HRI). Design/methodology/approach The authors conducted a netnographic study of the Facebook group called “iRobot – Roomba,” an online brand community dedicated to Roomba vacuums. Their data analysis employed an abductive approach, which extended the grounded theory method. Findings Dyadic portrayals of human–robot interactions can be expanded to consider other actants that are relevant to the consumption experiences of consumer robots. Not only humans but also nonhumans, such as an online brand community, have a meaningful role to play in shaping interactions between humans and robots. Research limitations/implications The present study moves theoretical discussions on HRI from the individual level grounded in a purely psychological approach to a more collective and sociocultural approach. Practical implications If managers do not have a proper assessment of human–robot interactions that considers different actants and their role in the socio-technical arrangement, they will find it more challenging to design and suggest new consumption experiences. Originality/value Unlike most previous marketing and consumer research on human–robot interactions, we show that different actants exert agency in different ways, at different times and with different socio-technical arrangements.
Weather Classification using Fusion Of Convolutional Neural Networks and Traditional Classification Methods

— The weather phenomenon is very important in routine lives. The weather prediction, road electronic monitoring, traffic communication, capping inversion (CAP), afforestation, and the adjustment of the environmental issues are important factors to many decisions. Weather images classification may help in decision support systems. There are traditional and intelligent ways that can sufficiently achieve weather image classification. Traditional methods enhance the classification accuracy and the usability of weather phenomena. Researchers approve that machine learning has achieved better accuracies based on deep learning neural networks. This paper compares three different intelligent models by using a weather image dataset. The first model uses a convolution neural network (CNN) to classify five categories of weather images. The second model uses a fusion of convolution neural network and Decision Tree (DT). The third one uses a fusion of CNN and Support Vector Machine (SVM). The three models are applied to the collected dataset from Github and Kaggle. The study has achieved 92%, 93%, and 94% for CNN, CNN+DT, and CNN+SVM respectively. The Proposed methods have achieved high recognition accuracies for weather forecasting.
Construction of Sports Training Performance Prediction Model Based on a Generative Adversarial Deep Neural Network Algorithm

The generative adversarial neural network algorithm is used for in-depth research and analysis of sports training performance prediction, and the corresponding model is built and used for practical applications. To address the problems of gradient disappearance, training instability, lack of local consistency of repair results, and long training time in the image restoration algorithm based on generative adversarial networks, this paper proposes a multigenerative adversarial image restoration algorithm based on multigranularity reconstruction sampling. The algorithm changes the distribution initialization of the generative network and uses reconstruction sampling to ensure that the Lebesgue measure of the overlapping part of the generative sample space and the real sample space is not 0 to further stabilize the gradient, and it is demonstrated that reconstruction sampling can stabilize the training and gradient. In addition, segmentation invariance is used to shorten the training time while ensuring the quality of the restored images, and an algorithm adaptability metric is proposed to comprehensively evaluate the image restoration algorithm. Based on the results of the fusion model analysis, an attention-based mechanism for the student performance prediction model is proposed. First, deep student behavioral features are extracted using a generative adversarial deep neural network, and the salient features in the student behavioral features are selected using a maximum pooling method; then, the extracted features are used as the input of the generative adversarial deep neural network for student performance prediction. Finally, a temporal attention mechanism is introduced at the output of the generative adversarial deep neural network to assign attention weights to different weekly student behavioral features.
Metode Bernyanyi dalam Meningkatkan Penguasaan Kosa Kata Anak Usia Dini

This study aims to analyze and examine more deeply the implementation or application of the Learning Program through the singing method as an implementation method applied by RA Khairiyatul Amien, Ajung sub-district, Jember district, East Java. The singing method as an effort to succeed in children's learning has many benefits, including as a method of acquiring vocabulary and vocabulary for early childhood which tend to be very difficult to implicate. This study uses a descriptive qualitative approach with the type of case study research at RA Khairiyatul Amien. The data collection instrument in this study used observation and in-depth interviews as well as documentation to support the completeness of the data. The data analysis technique in this study was carried out with the stages of data collection, data reduction, data presentation, and conclusion of research results. In its implementation, the children voluntarily and cheerfully recorded the lyrics to the children's song under the direct guidance of the teacher. From this study, it was found that the singing method is very efficient and effective in continuous learning as well as mastering vocabulary in children because children have very high sensitivity to their hearing and sight so they tend to memorize and imitate easily. Of course, this success is coupled with consistency and continuous effort
An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.
Research and Analysis on the Integration of Artificial Intelligence in College English Teaching

With the rapid development of modern information technology, students’ education and computer technology begin to blend, and the modern teaching mode is quite different from the traditional education mode known in the past. In view of the current college English teaching in the information age, this study puts forward the way of integrating computer information technology with college English teaching, improves MLP algorithm, puts forward a new artificial intelligence algorithm, improves its calculation efficiency, and uses the optimized GA-MLP-NN (Genetic Neural Network Algorithm for the Multilayer Perceptron) algorithm in college students’ oral correction program. Firstly, GA-MLP-NN algorithm is used to optimize college English teaching so that more complex structures can be learned and dealt with. Incremental hidden layer unit neural network is added, which makes the operation more accurate based on S-type recursive function. Then, the oral English system is established, using the GA-MLP-NN neural network model. Finally, we evaluate the parameters of the model, design a comparative experiment and a questionnaire survey to verify the rationality and feasibility of the guess, which proves that this method can deal with more complex programs, and make students learn English more handy and close to students’ needs by using computer technology.
Improving Students’ Reading Skills in Understanding English Texts Using Culture-Based Text Teaching Materials

One of the factors causing the lack of success in reading comprehension in English in high school is the text used in learning that is set in a foreign culture. This study aims to describe the improvement of students' skills in understanding the contents of reading texts by using a model of text-based teaching materials based on local culture. This research uses classroom action research with implementation steps; (1) initial observations to determine research problems and data collection; (2) formulate research action plans, formulate models of teaching materials (3) development of teaching materials; (4) carry out the action; (5) collaborate with English teachers; (6) draw up a teaching plan; (7) reflection and revision of actions; (8) classroom action; (9) improvement of teaching materials. The subjects of this study were two English teachers and 28 high school students in class XI who were studying English with the topic of Reading Comprehension. The results showed an increase in student involvement in discussions, higher student initiative to ask questions, higher student activity in digging information. Learning outcomes indicated by the level of students' ability to answer questions are also getting higher. Learning becomes more effective and efficient in achieving learning objectives.
Pengelolaan Media Pembelajaran di Sekolah Dasar Negeri 62/III Mukai Mudik

The purpose of this study was to describe the planning, utilization, and maintenance of instructional media in SDN 62/III Mukai Mudik. The research is a qualitative research design with phenomenology. Sources in the research is the principal and teachers at SDN 62/III Mukai Mudik. Data collection technique were interview, observation and documentation. Analysis of data using an interactive model. This study has three outcomes: (1) Planning the learning media is an inventory of types of instructional media, relevancing teaching media to teaching materials, confirming media with other teachers or friends of the group, preparing a book support package from the government, and pour the planned use of the media in the learning device; (2) The use of instructional media is able to show a medium of learning makes the students more easily understand the teaching materials, instructional media were able to show increasing curiosity about the new sciences, capable of showing the learning media help teachers explain the learning material, and be able to demonstrate learning media reduces verbal learning theory; (3) Maintenance of instructional media is keeping the medium of learning material or objects in special cabinets, learning media in the form of a file stored on the computer, non-academic learning media such as marching band and tambourine are stored in a special room, and the media is damaged beyond repair maintenance direct teachers or experts.
User Story Extraction from Online News with FeatureBased and Maximum Entropy Method for Software Requirements Elicitation

Software requirements query is the frst stage in software requirements engineering. Elicitation is the process of identifying software requirements from various sources such as interviews with resource persons, questionnaires, document analysis, etc. The user story is easy to adapt according to changing system requirements. The user story is a semi-structured language because the compilation of user stories must follow the syntax as a standard for writing features in agile software development methods. In addition, user story also easily understood by end-users who do not have an information technology background because they contain descriptions of system requirements in natural language. In making user stories, there are three aspects, namely the who aspect (actor), what aspect (activity), and the why aspect (reason). This study proposes the extraction of user stories consisting of who and what aspects of online news sites using feature extraction and maximum entropy as a classifcation method. The systems analyst can use the actual information related to the lessons obtained in the online news to get the required software requirements. The expected result of the extraction method in this research is to produce user stories relevant to the software requirements to assist systems analysts in generating requirements. This proposed method shows that the average precision and recall are 98.21% and 95.16% for the who aspect; 87,14% and 87,50% for what aspects; 81.21% and 78.60% for user stories. Thus, this result suggests that the proposed method generates user stories relevant to functional software.
FORMATION OF INTERETHNIC TOLERANCE THROUGH EDUCATIONAL SITUATION AT THE STAGE OF ADAPTATION OF STUDENTS TO THE EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY

Background. Political and socio-economic transformations taking place in the modern world, an orientation of Russian education to humanization and democratization impose a number of requirements on a specialist professional training quality at a university. Particularly in the modern labor community there are requirements regarding the readiness of a future specialist to carry out interpersonal interaction in professional activity in a multicultural, multinational and multi-confessional society. For the higher professional education of the Russian Federation the task of training a specialist with the ability to interpersonal interaction in professional activities (including empathy and interethnic tolerance) is of prime importance.&#x0D; The purpose of the work is to describe the educational situation aimed at the formation of interethnic tolerance among students at the stage of adaptation to the educational environment of the university.&#x0D; Methods. In the course of the study, a theoretical analysis of psychological and pedagogical literature of the problem was carried out. It helped us to clarify the degree of relevance of the problem being studied. The method of observation and pedagogical experience analysis allowed us to describe the educational situation aimed at the interethnic tolerance formation.&#x0D; The results of the study. Interethnic tolerance is considered to be a manifestation of the future specialist’s ability to interpersonal interaction in professional activities. The levels of interethnic tolerance formation among university students are given: amorphous-fragmentary, interiorization-reflexive and activity-holistic. In the article the educational situation “Adaptation” is described which is aimed at the formation of interethnic tolerance among students of multicultural educational groups the period of adaptation to the educational environment of the university..&#x0D; Practical implications. The article will be useful for lecturers, curators of study groups, specialists in educational work with students, including tutors.
Machine Learning Models for Content Classification in Film Censorship and Rating

Automated Film Censorship and Rating (AFCR) has recently turned out to be a major research area of Machine Learning (ML). The production and streaming services of films including movies, tv-series, animations and other audio-visual contents have been widely expanded leading to their manual censorship and rating to be a more exhausting task. Development of ML based methods has thus been emerging to designing an AFCR system. However, the initial ad-hoc efforts of developing the AFCR system demand a “complete” conceptual model of the system with its potential classes and their criteria. This paper primarily attempts to determine both the general and contextual classes of the content, and their criteria for an AFCR system. Besides, the state-of-the-art AFCR systems have been systematically reviewed to identify their underlying ML models, advantages and limitations. With a comparative analysis of the exiting ML models, we have demonstrated the effectiveness of sequential and multimodal analysis in the development of an efficient AFCR system.
Malaria Cell Detection Using Machine Learning

Abstract: Malaria is a major public health issue that affects people all around the world. The diagnosis of red blood cells contaminated with insects under a microscope by a skilled specialist is a typical way of diagnosing malaria. This method does not function effectively, and the diagnosis is made based on the test taker's experience and expertise. Malaria blood tests have been using automated imaging technologies based on machine learning for early detection. However, thus far, practical performance is insufficient. The convolutional neural network CNN is used in this paper to present an innovative and resilient machine learning model. for automatically distinguishing single cells in tiny blood smears from normal microscope slides, such as infected or non-infected with this virus. Our new CNN model's average accuracy for 16 layers is 97.37 percent based on ten-fold confirmation using 27,578 single-image pictures. In the same photos, the learning transfer model only gets 91.99 percent. All performance indicators, such as sensitivity (96.99 percent vs 89.00 percent), clarity (97.75 percent vs 94.98 percent), accuracy (97.73 percent vs 95.12 percent), F1 school (97.36 percent vs 90.24) percent, and Matthew's correlation coefficient, demonstrate that the CNN model outperforms the transfer learning model (94.75 percent vs 85.25 percent). Index terms: Deep Learning; Convolutional Neural Networks; Malaria; Computer-Aided Diagnostics; Machine Learning
Multi-Key Homomorphic MACs with Efficient Verification for Quadratic Arithmetic Circuits

Multi-key homomorphic MACs (MKHomMACs) allow multiple clients to authenticate data with their own secret keys and outsource their data together with the tags to an untrusted server. Upon receiving any user 's request of computing a function on the data, the server is able to generate both the computation result and a short tag that vouches for the correctness of the result. MKHomMACs provide a solution with minimal communication and interaction to the problem of delegating computations over outsourced data. Fiore, Mitrokotsa, Nizzardo, and Pagnin (Asiacrypt 2016) constructed a PRF-based MKHomMAC where the result verification could be as costly as the delegated computation. In this paper, we show a PRF-based MKHomMAC for quadratic arithmetic circuits such that the verification can be substantially faster than the delegated computation in an amortized setting. The efficiency improvement is achieved by using PRFs with multi-key amortized closed-form efficiency.
Generation and Evaluation of Synthetic Data in a University Hospital Setting

In this study, we propose a unified evaluation framework for systematically assessing the utility-privacy trade-off of synthetic data generation (SDG) models. These SDG models are adapted to deal with longitudinal or tabular data stemming from electronic health records (EHR) containing both discrete and numeric features. Our evaluation framework considers different data sharing scenarios and attacker models.
A Weighted Evidence Combination Method for Multisensor Data Fusion

&lt;p&gt;Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster’s combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;