Reviewerly/exHarmony huggingface dataset:
Improving Speech Writing Skills Through Mind Mapping Of Class Vi Students Of SDN 004 Kabun

This research is motivated by students who are not yet skilled in writing speech texts with systematic and effective language. This is caused by several factors such as the lack of knowledge of students about linguistic elements in writing so that the writings produced by students are not in accordance with the applicable grammatical rules. In addition, teachers have not been able to develop an active, innovative, creative, effective, and fun learning atmosphere in learning. This causes students to lose their motivation and passion to learn to write. The results showed that mind mapping which is used as a learning tool can improve students' activities and learning outcomes in writing speech texts. The learning process with the help of mind mapping helps students generate main ideas and explanatory ideas, and connects these ideas in coherent and coherent sentences. Thus, learning to write speech texts through mind mapping makes students think critically, logically, and systematically
Application Of Snowball Throwing Model Learning To Improve Social Studies Learning Outcomes For Class Vi Students At SD Negeri 0807 Tanjung Baru

This research is motivated by the low student learning outcomes in social studies subjects. This happens because the teacher does not use a different learning model and does not use the potential of students to provide material, and students are less cooperative in their learning. This research uses qualitative and quantitative methods. The results of the study increased the assessment of lesson plans from 81% to 97%, teacher ratings from 82% to 97%, and student ratings from 75% to 95%. The average academic achievement of students ranged from 72 to 86. It was concluded that the snowball throwing model improved the social studies learning achievement of 6th graders at SD Negeri 0807 Tanjung Baru
Analysis Of Online Learning Difficulties In Fifth Grade Students Of SD Negeri 0701 Rotan Sogo

The purpose of this study was to identify the factors that affect the learning difficulties of fifth grade students with access to topical materials through online learning activities and the factors that affect the fifth grade students' learning difficulties with access to topical materials through online learning activities. This type of research is descriptive qualitative research. In this study, researchers analyzed the learning difficulties experienced by students when participating in online learning activities. Therefore, researchers used descriptive methods and used qualitative research types. Because the research produces data in written or oral form from the observed students as well as resource staff (ie classroom teachers). The results of this study indicate that the factors that influence the difficulty of learning the subject matter of class V students through online learning activities are internal factors and external factors that exist in students. Such as student interest in learning factors, student learning motivation factors, parental support factors, family economic factors, playmates factors, intelligence factors, etc. The most important learning difficulty factor occurred in five classes of students who studied topic material through online learning activities, namely student interest in learning with an average of 13.24
Correlates of Presence in a Virtual Reality Gamification Environment for Rehabilitation after Musculoskeletal Injury

Abstract Virtual reality (VR) has emerged as a powerful option for rehabilitation by providing real-time performance feedback and a safe and customized training environment. This study aimed: (1) to investigate the association between presence in the virtual environment, usability of the system, intrinsic motivation, and immersion in VR gaming designed for rehabilitation after musculoskeletal injury; and (2) to examine the users’ emotional response in terms of pleasure, arousal, and dominance after participating in VR gaming. Thirty-seven individuals aged 23.69 ± 6.98 years participated in five customized VR games designed to provide a complete rehabilitation session after a musculoskeletal injury. Pearson product-moment correlation coefficients and multiple regression analyses were used to investigate the relationship between presence in virtual environments and immersive tendencies, the usability of the system, and intrinsic motivation. The Wilcoxon Signed Rank Test was conducted to evaluate the impact of participation in VR gaming on participants’ presence in virtual environments. Significant correlations were found between presence and immersive tendencies (r = −.40, p = .017), intrinsic motivation (r = .42, p = .013), and usability of the system (r = .64, p &amp;lt; .001). The linear regression model explained 59% of the total variance in the presence of virtual environments. There was a statistically significant increase in the pleasure scores from the beginning to the end of the session. Our results indicate that VR may be useful in increasing adherence to treatment to recover from musculoskeletal injuries.
ROBOTICS EDUCATION IN ECE: TEACHERS’ OPINION AND WAYS OF INTEGRATION

Educational robotics in Early Childhood Education is proven to empower children to understand the basic functions of technology and become digitally literate. The article investigates the current opinion of teachers about educational robotics at preschool level. The study takes them through several stages of questioning: from what they understand robotics to be at preschool level, if they consider the integration of robotics elements useful, how easy they find the integration of these elements and how quickly they can integrate practical robotics elements into games didactic. With a well-done PhD process Educational Robotics can have maximum effectiveness throughout its life. The natural curiosity and way of thinking of the new generation are advantages that must be exploited. Numerous studies have demonstrated the effectiveness of robotic education in the early education of children, although all we have to do is guide the specialists to integrate it into the teaching-learning process. The results of the quantitative analysis carried out with the help of teaching staff are presented by category and the significant differences that resulted are highlighted as well as recommendations for the integration of robotics elements in ECE activities.
Disease Detection on Rice Leaves through Deep Learning with InceptionV3 Method

The rate of growth in the agricultural sector in Indonesia puts pressure on people who work as farmers to maintain and improve the quality of agriculture. Rice, which is one of the basic needs of the community, is currently in high demand. Therefore, the need for rice continues to increase year by year with the increase in the population of Indonesia. To maintain the quality and quantity of rice, it is necessary to continuously monitor which for developing countries, there are limited tools and costs to develop technology to deal with problems of maintaining rice quality, especially diseases in rice. Rice disease is influenced by various factors, some of which are season, weather, temperature, media, availability of water sources, etc. The purpose of this research is to prevent diseases from spreading and spreading in rice by making disease detectors in rice using a deep learning approach using the InceptionV3 method. There are four classes of rice diseases diagnosed, namely bacterial blight, blast, brown spot, and tungro. The total loaded data set is 5932 images used in this study. The InceptionV3 model used can learn hidden patterns in the image thanks to CNN transfer learning method technology with an accuracy of 97.47%. The results show that InceptionV3 can be one of the choices of various existing CNN methods due to its accuracy.
PENGGUNAAN MEDIA BERBASIS TEKNOLOGI DALAM MEMBANGUN LITERASI DIGITAL ANAK USIA DINI

The utilization of technology-based learning media is something that can no longer be avoided in the learning process by teachers. In the era of increasingly massive technological developments, teachers should be able to provide experience to children so that they can use technology wisely. In line with that, in the context of early childhood education, the introduction to technology is an important thing to do considering that at this time technology is no longer only used by adults, but early childhood has also been faced with a life that is closely related to technology. Thus, it is important for teachers to instill technological and digital literacy skills to early childhood, so that they can then become wise people in using technology.
A Federated Learning Based Connected Vehicular Framework for Smart Health Care

Data privacy and data security are the main concerns in the digital era. The 3.5% centralized increase in annual digital data and the use of machine learning and deep learning approaches in the centralized computing environment endanger data privacy and security. The evolution of various body sensors also increases the digital health parameter data, which also demands privacy and security, which are difficult to achieve in a centralized computing environment. In the article, a two-level VANET-based federated learning framework is proposed for the classification of health parameters, in which Road Side Unit (RSU) acts as the local server in the first level and cloud networks act as the remote server in the second level. Health parameters considered for the proposed work are body temperature, heart rate, and systolic and diastolic blood pressure. The proposed work classifies health parameters into four categories, such as normal, low-risk, medium-risk, and high-risk data. Accuracy, precision, recall, and loss are used to evaluate the proposed work. In addition, in terms of average false classification rate and multi-class classification accuracy, the proposed federated learning computing is compared to centrally managed computing.
COACHING AS AN EFFECTIVE EMPLOYEE DEVELOPMENT METHOD IN AN OR-GANIZATION

The purpose of the article is to consider the system of employee professional development, to determine its role in ensuring the effectiveness of the activities of economic entities and to reveal the main and prospective employee development methods in an organization. The article deals with several methods of coaching in the system of employee professional development in an organization. The research methodology is based on both the works of domestic and foreign scientists in the field of employee professional development and the results of empirical studies of international companies, as well as systematic, descriptive and comparative research methods. As a result of the research, the authors draw conclusions that the management sys-tem of employee professional development is a complex continuous systematic process aimed to upgrade the skills and to improve the psychological characteristics of the employees of an organization. In addition, the authors substantiate the importance and prospectivity of coaching as one of several different employee development methods.
Practical Accuracy Evaluation for Deep Learning Systems via Latent Representation Discrepancy

As deep learning systems have been widely deployed in many safety-critical scenarios, their quality and reliability have raised growing concerns. Assuring the quality and evaluating the accuracy of deep learning models could be challenging because, unlike traditional software, DL systems rely on large amounts of labeled data for training and evaluation. The DL models have variability in their behavioral features on datasets with different distributions. In practical application, the potential distribution shift between training and usage scenarios may have an impact on the performance of the model and bring extra vulnerability to DL systems. Although some neuron coverage testing criteria have been proposed to assist in testing the DL systems, they are still limited by the amount of labeled data. Meanwhile, manual labeling test data collected from real-world application scenarios is very time-consuming and costly.
Development of an image processing software for quantification of histological calcification staining images

Quantification of the histological staining images gives important insights in biomedical research. In wet lab, it is common to have some stains off the target to become unwanted noisy stains during the generation of histological staining images. The current tools designed for quantification of histological staining images do not consider such situations; instead, the stained region is identified based on assumptions that the background is pure and clean. The goal of this study is to develop a light software named Staining Quantification (SQ) tool which could handle the image quantification job with features for removing a large amount of unwanted stains blended or overlaid with Region of Interest (ROI) in complex scenarios. The core algorithm was based on the method of higher order statistics transformation, and local density filtering. Compared with two state-of-art thresholding methods ( i . e . Otsu’s method and Triclass thresholding method), the SQ tool outperformed in situations such as (1) images with weak positive signals and experimental caused dirty stains; (2) images with experimental counterstaining by multiple colors; (3) complicated histological structure of target tissues. The algorithm was developed in R4.0.2 with over a thousand in-house histological images containing Alizarin Red (AR) and Von Kossa (VK) staining, and was validated using external images. For the measurements of area and intensity in total and stained region, the average mean of difference in percentage between SQ and ImageJ were all less than 0.05. Using this as a criterion of successful image recognition, the success rate for all measurements in AR, VK and external validation batch were above 0.8. The test of Pearson’s coefficient, difference between SQ and ImageJ, and difference of proportions between SQ and ImageJ were all significant at level of 0.05. Our results indicated that the SQ tool is well established for automatic histological staining image quantification.
Inducement factor of talent agglomeration in the manufacturing industrial sector: A survey on the readiness of Industry 4.0 adoption

China’s economy has progressed from a rapid growth phase to one of high-quality development and innovation. Industry 4.0 manufacturing technology and processes include cyber-physical systems (CPS), Industrial Internet of Things (IIOT), Cognitive Computing and Artificial Intelligence (CCAI) as advancements in computerization and information exchange the relevant variables data, and a survey questionnaire are used to accumulate three-year data from 2017 to 2019. The Structured Equation Modeling (SEM), analytic hierarchy process (AHP), and mediating variable in a SOBEL test are applied. The results show that Industry 4.0 is the primary practical corridor to official and familiar in sequence substitute policy and collaboration for talent agglomeration on research projects. It lowers the fixed price of human capital and significant factors active long-term innovation and profit at the end of the inferential test results. Hypotheses findings show that the associations between dependent and independent variables are essential, and latent variables GFI, CFI, TLI, and IFI have acceptable values. CMINDF and RMR fulfill the fit criteria and results will assist managers and policymakers in spotting talent agglomeration activities implemented to increase manufacturing businesses’ readiness to reap the most benefits from Industry 4.0 adoption.
Art project as a way of creative communication

Interview of the artist, member of the International Creative Association “Palette of the World”, teacher of the Samara Art School named after K.S. Petrov-Vodkin Maria Peshkova with the artist, designer, author and curator of numerous art projects, member of the Creative Union of Artists of Russia, associate professor of the SSIC Alexey Davydov. The issues of creative interaction of artists, musicians, writers, theater workers, scientists in creating a creative environment in modern exhibition activities are discussed.
Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

Histopathological grading of the tumors provides insights about the patient’s disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei.
Enhancing Machine Translation Quality Estimation via Fine-Grained Error Analysis and Large Language Model

Fine-grained error span detection is a sub-task within quality estimation that aims to identify and assess the spans and severity of errors present in translated sentences. In prior quality estimation, the focus has predominantly been on evaluating translations at the sentence and word levels. However, such an approach fails to recognize the severity of specific segments within translated sentences. To the best of our knowledge, this is the first study that concentrates on enhancing models for this fine-grained error span detection task in machine translation. This study introduces a framework that sequentially performs sentence-level error detection, word-level error span extraction, and severity assessment. We present a detailed analysis for each of the methodologies we propose, substantiating the effectiveness of our system, focusing on two language pairs: English-to-German and Chinese-to-English. Our results suggest that task granularity enhances performance and that a prompt-based fine-tuning approach can offer optimal performance in the classification tasks. Furthermore, we demonstrate that employing a large language model to edit the fine-tuned model’s output constitutes a top strategy for achieving robust quality estimation performance.
A Systematic Literature Review and Meta-Analysis of AIML Experience in Banks

The prime concern of this research article is to engender the existing knowledge, trends and gaps in AIML in bank domains. Additionally, the article focuses on the applications of PSALSAR framework for conducting systematic literature review and meta- analysis of artificial intelligence (machine- learning) adoption in banks. Under the PSALSAR (Protocol, Screening, Appraisal, Synthesizing, Analysis and Reporting) framework PICOC (Population, Intervention, Comparison, Outcome and Context) method is also used to define the research scope. Although meta- analysis is useful for both the qualitative and quantitative content analysis, only the qualitative aspects are considered in this research article.
Achievements and Challenges of Smart Education Development in China

AbstractBased on the China Smart Education Development Index calculated by China National Academy of Educational Sciences (CNAES) in 2022, this chapter analyzes the overall development of smart education in four dimensions: governance, teaching and learning, environment and talent literacy,as well as in the subsectors, that is, basic education, vocational education, and higher education. In 2022, the SEDI in China is 0.74.KeywordsSmart education in ChinaSmart Education Development Index (SEDI)Intelligent education governanceTeaching and learningDigital education environmentTalent literacy
TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles’ content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro , which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.