Reviewerly/exHarmony huggingface dataset:
The significance of calligraphy training for the future teacher in the context of art and graphic competence

The article examines the problems of calligraphy training in the context of artistic and graphic competence during the training of future teachers. The main problems related to the insufficient preparation of the teacher for the formation of artistic and graphic skills are identified, namely the absence or a small number of hours, devoted to the study of such educational components as «Calligraphy», «Drawing», «Art graphics», lack of explanatory work from the scientific and pedagogical staff of higher education institutions about the importance of artistic and graphic skills. A characterization of the components of the artistic and graphic training of future teachers was carried out, in which the mastering of calligraphy skills by students was identified as a key aspect. The components of calligraphy training are characterized, including mastering the basics of fonts, developing the muscles of the arm and hand, learning the correct posture of the arm and body, using various materials for writing, training hand motility, learning the elements of calligraphy, creating and analyzing calligraphy samples.&#x0D; The article examines the font as the basic unit of calligraphy and font composition as the art of creating harmonious and aesthetically balanced layouts using different fonts. The main methodical components of the study of fonts are outlined and some methodical techniques are proposed for teaching future teachers the basics of creating a font composition. As a result, the value of calligraphy training of future teachers for their professional activities was determined, in particular, improving the quality of teaching writing for 1st grade students, ensuring the writing culture of students, developing and supporting the speaking skills of education seekers, ensuring a creative approach, increasing the general aesthetic level of the personality of the future teacher, diversification of methods of working with students in classroom and extracurricular activities
Reconfigurações teóricas do eixo produção textual: (des)continuidades entre a segunda versão e a versão homologada da BNCC

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TEMCA-Net: A Texture-Enhanced Deep Learning Network for Automatic Solar Panel Extraction in High Groundwater Table Mining Areas

Long-term coal mining has led to a series of ecological problems, constraining society's sustainable development. Ecological restoration is a crucial component of achieving sustainability, and with the continuous advancement of photovoltaic technology, the comprehensive utilization of photovoltaics has become one of the important restoration methods in mining areas. The area and location of solar panels, as key indicators for assessing the ecological restoration approach, require precise extraction and positioning. This paper proposes a Texture-Enhanced Multi-Context Attention Network (TEMCA-Net). In the encoding part, the network utilizes residual connections (RN) in conjunction with the Convolutional Block Attention Module (CBAM) to preliminarily extract contextual information. Then, low-level features were input into the Statistical Texture Learning (STL) texture enhancement module and high-level features into the Horizontal Atrous Spatial Pyramid Pooling (H-ASPP) module. In the decoding part, the high-level features processed by the H-ASPP were combined module with the texture-enhanced features from the STL module. Experiments were conducted in the Peibei Mining Region located in Xuzhou City, Jiangsu Province. We established the SPPMR (Solar Panels of Peibei Mining Region) dataset. Experimental results on the SPPMR dataset demonstrate TEMCA-Net's outstanding performance in solar panel extraction, with precision at 90.24%, recall at 93.07%, an F1-Score of 91.63%, and a mean Intersection over Union (mIoU) of 92.21%. It significantly outperforms three classic deep learning networks: Deeplabv3+, U-net, and PSPnet. In summary, this study provides an efficient and feasible solution for the extraction of solar panels in mining areas with high water tables.
Collusive Model Poisoning Attack in Decentralized Federated Learning

As a privacy-preserving machine learning paradigm, federated learning (FL) has attracted widespread attention from both academia and industry. Decentralized FL (DFL) overcomes the problems of untrusted aggregation server, single point of failure and poor scalability in traditional FL, making it suitable for industrial Internet of Things (IIoT). However, DFL provides more convenient conditions for malicious participants to launch attacks. This article focuses on the model poisoning attack in DFL for the first time, and proposes a novel attack method called collusive model poisoning attack (CMPA). To implement CMPA, we propose the dynamic adaptive construction mechanism, in which malicious participants can dynamically and adaptively construct malicious local models that meet distance constraints, reducing the convergence speed and accuracy of consensus models. Furthermore, we design the collusion-based attack enhancement strategies, where multiple participants can collude in the process of constructing malicious local models to improve the strength of attack. Empirical experiments conducted on MNIST and CIFAR-10 datasets reveal that CMPA significantly impacts the training process and results of DFL. Attack tests against representative defense methods show that CMPA not only invalidates statistical-based defenses but also skillfully overcomes performance-based methods, further proving its effectiveness and stealthiness. In addition, experiments based on practical IIoT scenario have also shown that CMPA can effectively disrupt system functionality.
POLITENESS STRATEGIES USED BY MAJOR CHARACTERS IN JANE AUSTEN’S NOVEL, PRIDE AND PREJUDICE

The purpose of this study was to describe the language politeness employed by the major character in The Pride and Prejudice novel. The method used was descriptive qualitative. The researchers found: first, the data of positive politeness strategies and the data of negative politeness strategies were in line with Brown and Levinson's theory, 10 positive types of politeness strategies. The mostly used were: paying attention to the speaker’s interests, wants, requirements and commodities. When the speaker truly means “you” or “me”, he can employ the strategy of inclusiveness form to invoke the cooperative presumptions and rectify FTAs, giving or asking for explanations, insisting on reciprocity, goodness, sympathy, understanding, and cooperation. Out of 8 negative types of politeness strategies, the mostly used were: adopting a customarily indirect stance, not taking compliance for granted, expressing pessimism about one’s capacity or willingness, to reduce the burden, as cited from the FTAs as an illustration of a general principle. Then, three factors influenced the use of polite language, namely: social distance, power, and degree of impositions.
A new computer‐aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics

Abstract Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision‐support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic‐based parameter optimization is applied to improve the class‐based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.
A Study on Automatic Grading of Chinese Text Complexity Combining Deep Machine Learning and Language Complexity Features

This paper explores effective methods for the automatic grading of Chinese text complexity and conducts three types of experiments to this end: (1) employing traditional machine learning models for automatic text complexity grading experiments, syntactic dependency construction complexity features and semantic dependency construction complexity features, the latter of which would be the innovative features; (2) using the deep machine learning model BERT for automatic grading of Chinese text complexity; (3) researching Chinese text complexity automatic grading using a fusion model of "language complexity features + BERT". The study found that the "language complexity features + BERT" fusion model achieved the best results in text complexity grading for Chinese text and is therefore is an ideal method for the grading of Chinese text complexity.
Multi-image Multiplexing Pixel Synchronization Encryption based on Optical Scanning Holography

In the network environment, the efficiency and security of image transmission are mutually constrained. A multi-image pixel multiplexing synchronous encryption method based on optical holographic scanning technology is proposed, which uses diffraction distance as the multiplexing parameter to synchronously encrypt the multi-image pixel information on the same hologram. After the transmission is completed, the synchronization separation of multiple images can be achieved by using reverse problem modeling and regularization to solve the reconstructed pixels, While ensuring the secure transmission of images, it also takes into account the improvement of transmission. The feasibility and robustness experiments have confirmed that the proposed method can achieve synchronous encryption and decryption of multiple images. After applying different proportions of JPEG format lossy compression, varying degrees of superimposed Gaussian white noise, and different orders of random pixel cropping, the difference between the extracted image and the original image is not significant in terms of visual quality and effect. The pixel grayscale distribution is uniform and has strong pseudo randomness, Solved the contradiction between the efficiency and security of image information transmission in the internet environment.
Utilising Tiktok Features for Speech Communication

The art of teaching speech communication should be varied according to current trends. Teaching speaking should focus on the practice of the language, which should be done in pairs or groups. However, the COVID-19 pandemic restricted students from attending schools, making speaking in English impossible. Social distancing was enforced and still is now, when they returned to school, ruling out group activities or much speaking in classrooms. This was an issue among English language teachers in Malaysia as the teaching of English uses a modular system that comprises Listening and Speaking, Reading, and Writing. Listening and speaking could not be conducted at will. Pupils were deprived of opportunities to practice the English language in classrooms. Therefore, the suggested course of action was to improve speech communication by utilising the technologically useful application TikTok and its essential functionalities. This study aims to investigate how TikTok features can improve speech communication and examine learners' perceptions after applying the TikTok features to improve their speaking skills. The instruments used for this study are speaking rubrics for speaking tests and a questionnaire. A purposive sampling of 100 students was used. The results show that the respondents scored higher in their post-test than their pre-test after using TikTok to improve their speech communication. All respondents perceived the use of TikTok as positive and encouraging. The implication of the findings includes introducing innovative speaking practice measures such as the development of mobile learning, and a renewed teaching methodology to teach speech communication.
Effective motivation, effective personnel management in rehabilitation centers of Kazakhstan

This article discusses effective personnel management in rehabilitation centers in Kazakhstan. In modern business, with its inherent volatile competitive environment, the implementation of effective activities is impossible without effective personnel management. As a result of sociological research, the degree of satisfaction with the incentive system for medical personnel of the Rehabilitation Center Kokshe was revealed. From these data it can be seen that for the employees of the rehabilitation center, the most significant motivational factors are economic methods, and among the preferred motivational methods are socio-psychological ones. To substantiate the importance of applying socio-psychological methods in the system of medical personnel management and the formation of a personnel management model, methodological approaches of classical theories of motivation were considered: a questionnaire according to A. Maslow's theory and F. Herzberg 's tests. The effectiveness of the organization's activities is determined by the ratio of the results achieved and the resources expended on this and is made up of the efficiency of using various factors, one of which is motivated work. The success of healthcare institutions, including rehabilitation centers, depends on the extent to which medical workers realize their professional potential. We have proposed a more advanced and effective management approach – the KPI method for motivating medical personnel in Kazakhstan, which allows us to organically combine material and non-material incentives with the motivational impact of all healthcare workers. The presented calculations showed that the introduction of personnel assessment according to the KPI system for all medical services will provide a multiplier effect and improve the competitiveness of rehabilitation centers and healthcare in general.
Aspect-based Sentiment Analysis Model for Evaluating Teachers' Performance from Students' Feedback

Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86\% F1 score for classifying sentiments into three classes.
Reliability Analysis on the Effect of Subsea Buoy to the Tension of Spread Mooring System

A lot of research has been carried out regarding variations in mooring systems, one of which is the addition of buoys to the mooring system. In analyzing variations in mooring system designs, what needs to be considered is the reliability of the mooring lines. Therefore, in this research will analyze reliability on the effect of subsea buoy to the tension of mooring line with a variation position of subsea buoy. The variations on the position of one subsea buoy is arranged at the distance of the anchor 605 m, 577.5 m, 550 m, 522.5 and two subsea buoys at the distance from the anchor 605 m and 467.5 m. The analysis was performed for stand alone and offloading conditions with wave directions of 0 °, 45 °, 90 °, 135 °, 180 °. In this study to find the reliability of mooring lines, the author uses the Mean Value First Order Second Moment (MVFOSM) method. The results from this study, the probability of failure in the offloading condition without subsea buoy is 4.897E-17 and with subsea buoy (522.5 m) is 4.018E-17. Probability of failure in the stand alone condition without subsea buoy is 2.763E-16 and with subsea buoy (522.5 m) is 1.881E-16. From the probability of failure, the reliability of mooring lines in the offloading condition and stand alone condition without subsea buoy and with subsea buoy (522.5 m) is 1.00. The reliability calculation determined by DNV-OS E301, the results obtained meet the specified reliability criteria.
Dynamic Hybrid Reversible Data Hiding Based on Pixel-value-ordering

&lt;p&gt;Reversible data hiding (RDH) using pixel-value-ordering (PVO) is a well-established technique for embedding data in a cover image by modifying the maximum and minimum in each block. This paper proposes a dynamic hybrid RDH method based on PVO. Specifically, a 3&amp;times;3 block according to its complexity and two thresholds T1 and T2 is classified as three levels: extremely smooth, smooth, and rough. Different processing algorithms are used for different levels. For rough blocks, they are ignored to avoid reducing the peak signal-to-noise ratio. For smooth blocks, the proposed method employs a block subdivision algorithm that can embed up to 6 bits of data. For extremely smooth blocks, no subdivision is done and a median pixel prediction algorithm is used to predict the remaining eight pixels, which can embed up to 8 bits of data. Moreover, this paper presents a new method that computes complexity by dynamically selecting relevant pixels to enhance block classification accuracy. Extensive experiments demonstrate that the proposed method outperforms existing PVO-based methods, offering larger embedding capacity while maintaining low distortion.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Analog Optical Differentiator and Integrator Based on Cascaded Metalenses

Analog optical computing has been a research interest due to the low power loss and tremendous data processing in the last few years. In this study, we demonstrate an on-chip cascaded metalenses assisted by the phase change materials, which facilitates the multifunctional analog optical computing. The first metalens, which was designed to realize fast Fourier transform, has a low insert loss of 0.5 dB. Regarding the proposed cascaded metalenses, the switching of analog differentiators and integrators could be realized efficiently. The developed device is anticipated to play an integral role in complex optical flow control and versatile optical computing.
Development of Alphabetic Cards as A Learning Medium for the Community Learning Activity Center Community

The development of the Out-of-School Education sector has a strategic role in improving the quality of human resources. Learning media in alphabet cards is needed to increase students' motivation and learning achievement in reading learning, which are used as a learning resource to develop learning models while playing for students. This research aims to determine and test the effectiveness of implementing the development of the learning process for reading while playing through alphabet card learning media for residents learning literacy in PKBM North Sulawesi Province. This research uses the "Research and Development" method, following the research stages of developing the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate) in 4 districts: 1). Bolaang Mongon-dow South, 2). East Bolaang Mongondow, 3). Talaud Islands, and 4). North Minahasa. This research was designed for two years, and in the first year, 2022, it was carried out in 3 stages: 1). Analysis Stage, 2). Design stage, and 3). Development Stage. Meanwhile, two stages were carried out for the second year, namely the Implementation and Evaluation stages. It can be concluded that the learning process is effective because students who were previously literate but forgot the letters have learned them again. There is an increase in students' cognitive abilities, better ability to arrange letters to form meaningful words, and students' learning becoming more involved.
Object Tracking with Restrain Learning Anomaly and Adaptive Feature Fusion

The object tracking algorithm is prone to tracking drift in complex scenes, which lead to abnormal learning of the algorithm. Aiming at this problem, Adaptive feature fusion object tracking to suppress learning anomaly was proposed. Firstly, based on the Staple algorithm, a temporal regularization term was added to control the intensity of the time penalty by using the change of the response value to suppress the learning anomaly of the algorithm. Secondly, the Bhattacharyya coefficient was used to calculate the similarity between the foreground and background color histograms of each frame in real-time. And then the similarity and the fusion coefficient were combined into a fusion factor, and the optimal fusion factor could be obtained by using the log loss function to achieve better feature fusion for each frame. Finally, the method of alternating direction multipliers was used to optimize and solve the correlation filter model for improving the speed of the algorithm. The experimental results on the datasets OTB 100, Temple Color 128, and LaSOT demonstrated the effectiveness and robustness of the object tracking algorithm with suppress learning anomaly and adaptive feature fusion.
The Influence of Learning Arabic Vocabulary on Students' Reading Skills at Islamic Junior High School

This study aims to analyze the effect of learning Arabic vocabulary on reading skills in class VII students of MTs Madinatussalam Deli Serdang. The research method used is the quantitative method with a survey method approach by distributing questionnaires to 25 students. Then in the interferential analysis, a correlation coefficient test will be carried out using the product moment formula, the coefficient of determination, and the significance test of the correlation coefficient on learning Arabic vocabulary as variable X and reading skills as variable Y. The results of this study can be seen that there is an effect of learning Arabic vocabulary on reading skills in Class VII Students of MTs Madinatussalam. Where the lowest score of (Variable X) learning vocabulary is the lowest 60, and the highest score is 90, with a mean= of 77.8, median= 80, standard savings= 8.43, and mode= 77 and 85. Meanwhile, (variable Y) reading skill shows the value the lowest is 60 and the highest is 90, mean= 76.5, mode 80, median 79.5, standard deviation 7.67. So Efforts to improve learning Arabic vocabulary, a teacher must master several learning methods, as well as the ability to converse in Arabic and emphasize students memorizing vocabulary, increase Arabic vocabulary, provide motivation in Arabic both in learning and outside the classroom, and adequate facilities such as language labs.