Reviewerly/exHarmony huggingface dataset:
A Semantic Network Analysis of Student Assessment Methods in the Revised National Curriculum 2022 of Vocational High Schools

This study aimed to identify key features of student assessment in the revised national curriculum 2022 of vocational high schools in Korea. Semantic networks were analyzed with 1,170 paragraphs retrieved from the national curriculum text of 216 vocational high school core subjects, which delineated the methods of student assessment matched with the national standards. Among various assessment methods, presentation, paper/report, experiment/hands-on activities, portfolio, and discussion were frequently identified in the curriculum. Assessment methods were mostly aligned with action verbs of the corresponding standards; however, some were not associated with the standards, mainly when the standards contained higher-order thinking skills such as problem-solving or critical analysis. While digitalized student assessments have been increasingly highlighted in Korea, their utility has barely been confined to supporting student learning activities, such as office tools or information searches. However, the application of digitalized assessments has not yet fully extended to personalized or customized learning, as envisioned in the revised curriculum objectives.
A basic study on the development of data literacy subjects based on student demand survey

The purpose of this study is to present a course development process and model that reflects the needs of students as learners along with a review of various literature in developing a course to cultivate data literacy capabilities required in the era of the 4th industrial big data. To this end, we looked at the case of J University's development of a data literacy liberal arts subject. In order to develop a data literacy subject, elements for deriving a teaching model were derived through an academic review of prior research, and based on this, a survey was conducted on the needs of students as learners for the elements. Through this, weekly class contents, teaching methods, teaching and learning methods, and class use media were derived to customize learners that can be applied to actual classes and to strengthen learner data literacy capabilities.
Implementation of Artificial Intelligence in Healthcare

&#x0D; Health is one of the pillars in determining human performance in their daily activities. Someone with good health can work optimally because there are no health problems they have. On the other hand, artificial intelligence is a form of technology that is developing rapidly. This technology has various benefits that can be provided, especially in the health sector to help health workers. The technologies that are often used are expert systems and artificial neural networks because of their ease of operation and accuracy in carrying out the work of health workers. Various other technologies are being developed to facilitate the performance of health workers to lighten their workload, such as robots to help paralyzed patients, automatic operating robots, and other technologies that can help ease the burden on health workers' performance.&#x0D; &#x0D; Keywords—health, artificial intelligence, neural network, expert system
A Study on the Establishment of a Short-term Forecasting Model for the Composite Index of Business Indicators

After the declaration of the ‘Endemic,’ it is crucial to understand the cyclical fluctuations and turning points in the overall economic status of the nation. To promptly and accurately grasp the magnitude and speed of these economic changes, a short-term forecasting analysis was conducted using the composite index of business indicators data from January 2003 to April 2023. In this study, a two-step analysis was performed to propose a standardized forecasting process.&#x0D; Firstly, for short-term forecasting of the composite index of business indicators, the results of time series analyses considering only endogenous variables were compared with those incorporating exogenous variables. Subsequently, the study explored machine learning methods, which have been actively discussed recently, and compared and analyzed their prediction performance with traditional time series analysis methods. The analysis revealed that the addition of exogenous variables in time series analysis resulted in higher prediction accuracy compared to analyses considering only endogenous variables. Furthermore, machine learning models exhibited superior prediction performance compared to traditional time series models. As a result of the analysis with the Time-GAN model, which has the best predictive performance, it was found that there would be a rise for 1-2 months due to the growth of private consumption caused by the endemic, but it would decrease again in the 3rd month.
A study on expert perception of the current status of early childhood creative character education in the 2019 revised Nuri curriculum: Focusing on FGI

This study conducted an FGI study of early childhood education experts to examine experts' perceptions of the current status of early childhood creative character education in the 2019 revised Nuri curriculum. The study participants were 5 experts, and FGI was conducted over 3 sessions from April to August 2023. As a result of this study, the research participants said that although the content of the 2019 revised Nuri Curriculum contains the importance of creativity and character, they are presented separately, and as a result, they are also implemented separately in educational settings. However, experts recognized that creativity education and character education are being carried out in the same direction within early childhood education.&#x0D; In particular, given that creative character education is often intertwined and implemented in play, experts recognized that the 2019 revised Nuri Curriculum provides a more suitable environment for implementing early childhood creative character education. In addition, experts responded that although early childhood creative character education can be realized based on the curriculum, it tends to be influenced by the teacher's capabilities and the educational philosophy and atmosphere of the educational institution and the head of the institution. This study is meaningful in that it sought an approach that could integrate and operate creative character education, which can be said to be a competency required for the future society, within the Nuri curriculum.
Arabic Opinion Mining System for E-Services Based On Ontology and Machine Learning

With the growing use of E-Services in the Arab world, there is a need for efficient methods to analyze customer feedback and opinions. This research is proposing an Arabic opinion mining system for E-Services based on ontology and machine learning techniques. The system utilizes an Arabic ontology to capture domain-specific vocabulary and relationships between features and a machine learning model to classify customer feedback into positive, negative, or neutral sentiments. By evaluating the performance of the system using several domains datasets for Arabic customer reviews of various e-services, and the results show that the system achieves a high accuracy level. Comparing the performance of the proposed system to traditional polarity and subjectivity approaches.
Enhanced Global Image Segmentation: Addressing Pixel Inhomogeneity and Noise with Average Convolution and Entropy-Based Local Factor

In the field of computer vision and digital image processing, the division of images into meaningful segments is a pivotal task. This paper introduces an innovative global image segmentation model, distinguished for its ability to segment pixels with intensity inhomogeneity and robustly handle noise. The proposed model leverages a combination of randomness measurement and spatial techniques to accurately segment regions within and outside contours in challenging conditions. Its efficacy is demonstrated through rigorous testing with images from the Berkeley image database. The results significantly surpass existing methods, particularly in the context of noisy and intensity inhomogeneous images. The model's proficiency lies in its unique ability to differentiate between minute, yet crucial, details and outliers, thus enhancing the precision of global segmentation in complex scenarios. This advancement is particularly relevant for images plagued by unknown noise distributions, overcoming limitations such as the inadequate handling of convex images at local minima and the segmentation of images corrupted by additive and multiplicative noise. The model's design integrates a region-based active contour method, refined through the incorporation of a local similarity factor, level set method, partial differential equations, and entropy considerations. This approach not only addresses the technical challenges posed by image segmentation but also sets a new benchmark for accuracy and reliability in the field.
Transfer Learning Method for Malaria Identification

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase.
The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use

During the current pandemic, smartphones have become a means of learning for all students in Indonesia, including high school students. Students use smartphones to send assignments, learn via video calls, and conduct online exams. The prolonged use of smartphones, from the beginning of learning hours in the morning to study hours in the evening, has a terrible impact on the ear health of high school students in Padang. Excessive smartphone use caused a decrease in the student's hearing function. Therefore, this study aims to group the audiometry results of high school students in Padang who have a hearing loss function. The audiogram result is only performed as the result of a frequency test of the subject's hearing in both the left and right ear. Conventionally, an otolaryngologist concluded the final decision of hearing loss ability. This research proposed an automatic classification of audiometry results using machine learning methods. The K-Medoids clustering was selected to classify the audiometry data in this research. Of 210 audiometry data, 91 data is confirmed by an otolaryngologist as valid data. By using the K-Medoids clustering, 93 data is classified into Normal hearing, Mild Hearing loss, and Moderate Hearing loss. The proposed model successfully grouped the audiometry data into three categories. The confusion matrix is applied to measure the model performance, which has 28,3% accuracy, 64,3% precision, and 21,4% recall.
Platforms for the application of blockchain technology in order to achieve the sustainable development of urban tourism in Iran

Platforms for the application of blockchain technology in order to achieve the sustainable development of urban tourism in Iran
Allow Knowledge to Prevail: Complements in Information Modelling and Prehistorical Archaeology Cases – Fact-based Contextualisation of Cultural Heritage and Historico-cultural Contexts

Knowledge complements are a core foundation, a central fundament, when targeting theoretical and practical knowledge. Knowledge complements are inherent with knowledge and all of its contexts. Information modelling based on knowledge complements is not a mono-dimensional venture. Knowledge complements enable fact-based, sustainable contextualisation and analysis. Information modelling in multi-disciplinary contexts, e.g., integrating prehistorical archaeology, natural sciences, and humanities, requires solid methodological approaches and overall systematic courses of action. However, knowledge complements are often successfully neglected, in theory and daily practice. The hiatus in methodological practice between disciplines may mostly result from hermeticism in artificially limited dimensionalism. The more, contextualisation of cultural heritage and historico-cultural contexts, e.g., in prehistorical archaeology, enable fact-based methods and reveal the multi-disciplinary character. Understanding knowledge and structure are essential information science assets for realistic, efficient, and sustainable practice in many scientific disciplines. Starting point for structured data comprehension in theory and practice are the scientists and researchers. Prehistorical archaeology does provide numerous relevant and exemplary cases for employment and integration of multi-disciplinary knowledge, structure, and scientific methods, for gaining new insight and cognition. This paper presents methodological and epistemological fundaments of an advanced multi-disciplinary group of cases of fact-based contextualisation of cultural heritage and historico-cultural contexts in prehistorical archaeology.
IndoBerea: Evolving Semantic Search in Theological Context

This paper presents IndoBerea, a semantic search model pre-trained on an Indonesian Bible dataset and based on SentenceTransformers and IndoBERT. It aims to enhance theological research by providing contextually relevant verses in Bible study. The model’s output is evaluated through similarity scores and compared with human relevance judgments, revealing a moderate correlation. The analysis highlights the model’s varying performance across different queries and identifies potential improvement in expanding the model’s context recognition ability. Currently, IndoBerea is designed to output a single verse per query. However, incorporating a broader context, like a group of verses or an entire chapter, could align model judgments more closely with human interpretations. Future work should focus on this aspect, necessitating a different dataset structure and model architecture. The results demonstrate the potential of semantic search models in theological contexts and suggest promising future research directions in Indonesian NLP community.
Penerapan Algoritma C4.5 Dalam Penilaian Kepuasan Pelanggan Terhadap Layanan (Studi Kasus: Nilla Wedding Gallery)

Nilla Wedding Gallery is a service provider engaged in cosmetology and wedding management. During the run, existing customer data has never been managed optimally only limited to piles of data and has never used the data mining process to find patterns or information from service sales data to customers. Data Mining is a technique that processes or manages data into information. In this study the author uses the C4.5 algorithm method, the C4.5 method is used as an approach to generate a prediction model based on attributes relevant to customer satisfaction. Data obtained from a case study at Nilla Wedding Gallery was used to train and test the C4.5 model. As a form of this effectiveness, the author built a web mining system using the PHP programming language. The results showed that the C4.5 algorithm is effective in identifying factors that affect customer satisfaction, and is able to produce decisions that are beneficial for policy making by Nilla Wedding Gallery in improving service quality and achieving higher customer satisfaction.&#x0D;
Perancangan Platform User Generated Content untuk Publikasi Sekolah inklusi

The Muhammadiyah Gamping Special School (SLB) in Sleman is an inclusive school founded in 2012. The Muhammadiyah Gamping SLB was built to facilitate a place of learning for children who have deficiencies or disabilities. The problems faced by SLB Muhammadiyah Gamping are not yet known to the public and have never been reported by the media. In fact, inclusive schools are a necessity for some members of society. The solution to the problems faced by SLB Muhammadiyah Gamping is to develop publications about the school's work through mass media. The science and technology used in this service are user-generated content technology and digital journalism. Activities were carried out with digital journalism workshops and operational assistance for user-generated content. Sustainability means that the school can continue to write news after the service is completed. The implementation results exceeded the target with 28 news stories, from the initial target of 12 news stories for the SLB Muhammadiyah Gamping journalism brand.
The Linguistic Consequences of Digital Technology on the Learning of English in the Senegalese University Environment: The case of the SMS

The present work is devoted to the analysis of the linguistic consequences of digital technology in the learning of the English language in a university environment. In other words, this article talks about the linguistic consequences of SMS on the learning of English in a context dominated by the implementation, by the State of Senegal, of the "Digital Senegal" strategy and the concretization of certain programs in building an “inclusive digital society”, in order to promote a learning society. SMS (Short Message Service) is a method of communication that allows short messages to be sent by telephone, computer or other mobile devices. Thus, this work aims to analyze the grammatical, morphological and orthographic consequences of this method of communication on the learning of English at university. However, to collect data in order to develop a corpus, process it and analyze it to end up with results, research, interviews, exchanges by SMS, etc. have been done. And these approaches have allowed us to point out that SMS have a negative impact on learning English and on the writing of educational and administrative documents at the grammatical, morphological, orthographic, etc. levels. Nevertheless, we have also noticed that for some, the great majority of the "errors" or "mistakes" present in the written discourse are not the result of a lack of knowledge of the norm or a lack of interest on the part of the speakers, but rather the mark of an underlying structure.
Geodynamics and Metallogeny of Copper in the Middle Zone of Central Kazakhstan and its Prospects

In the middle part of Central Kazakhstan there are linear copper-bearing riftogenic-orogenic zones of sublatitudinal strike (Spassk and Uspensk zones) with numerous sulfide and porphyritic deeply eroded deposits and ore occurrences of copper not currently presenting commercial interest due to insignificant reserves and Zhezkazgan basin conjugated with them in which first-rate deposits of cupriferous sandstones are located. Formation of sulfide deposits occurred in the process of riftogenesis, and porphyritic deposits were formed in orogeny. Copper was carried away by river waters together with broken rocks, minerals and organic matters from the mentioned zones with humid climate into arid littoral-deltoid areas of the Zhezkazgan sea basin in the Carboniferous-Permian, where water soluble copper compounds sedimented under the influence of hydrogen sulphide of biogenic sulfate-reduction. Gradual upthrusting of the north side of the basin shifted the riverbed in the undercurrent many times. This resulted in forming of deposits of different ages: from the Middle (The North group) and Middle-Upper Carboniferous (Zhezkazgan) to the Upper Permian (Taskora). It is possible that between the latter ones there are some large deposits localized in lenses of alluvial deposits controlled by paleofaults situated in multiple distances. To discover them a complex of geological and geophysical prospecting methods is proposed.
Theoretical and Methodological Foundations for the Professional Orientation of Mathematics

The problems of low level of mathematical knowledge among students of technical universities are considered. An analysis of scientific and pedagogical literature is conducted to identify ways and means of achieving a higher level of knowledge in mathematics among technical university students. The level of pre-university mathematical knowledge of different specialties for 2019-2021 and the final level of knowledge in mathematics are compared. One of the ways of solving the existing problem is proposed – the way of professional orientation of the entire course of mathematics. This should arouse interest in learning, which will lead to an increase in academic performance
Formation of inclusive competencies of future specialists in the social sphere

This article discusses the necessity of enhancing the inclusive competence of future specialists in the social sector during their university education. The experience of implementing a new academic discipline within the framework of the university’s policy on developing inclusive skills is described. The study of student opinions confirms the effectiveness of addressing the development of inclusive competence through the introduction of a specialized discipline into the curriculum.
Kondisi Sanitasi Mesjid di Wilayah Desa Kejapanan Kabupaten Pasuruan

ABSTRACT&#x0D; Background: The mosque in Kejapanan Village, Pasuruan Regency has sanitation problems in the form of inadequate physical buildings. Based on a preliminary survey, there was 1 mosque using well water which had an E.Coli content of 650 compared to the required level of 0/100 ml. The aim of this research is to describe the sanitary conditions of mosques in the Kejapanan Village area, Pasuruan Regency.&#x0D; Method: The design of this research is descriptive observational research, with a cross-sectional approach. The variables studied included the sanitary conditions of mosques and E.Coli parameters of well water in the Kejapanan Village area, Pasuruan Regency. Data collection techniques use observation. The data collected was then analyzed descriptively through table analysis.&#x0D; Results: Mosque sanitation assessments have been carried out, in the 4 mosques in Kejapanan Village, Pasuruan Regency, there are still mosques that do not meet the requirements. The mosque studied did not have facilities such as prayer carpets and trash cans with lids. Mosque well water samples tested by the UPTD Regional Health Laboratory, resulted in an Escherichia coli (E.Coli) content of 25 - 650 from a maximum level of 0/100 ml so it did not meet clean water quality standards.&#x0D; Conclusion: Mosque sanitation in Kejapanan Village, Pasuruan Regency, there are still mosques that do not comply with sanitation requirements. For mosque managers, efforts need to be made to improve mosque sanitation and for future researchers, they can carry out research using input variables that are thought to cause contamination of mosque sanitation and sources of clean water for ablution in mosques.&#x0D; Keywords: Mosque Sanitation, Well Water Microbiology&#x0D;