Reviewerly/exHarmony huggingface dataset:
A Comparative Study on International Baccalaureate Diploma Programme(IBDP) Textbooks and Korean Textbooks by the 2015 Revised Curriculum: Focus on function from a mathematical modeling perspective

This study aimed to compare and analyze the number and characteristics of modeling problems in chapters related to function contents in International Baccalaureate Diploma Program (IBDP) mathematics textbooks and Korean high school mathematics textbooks. This study implies how the textbooks contributed to the improvement of students’ modeling competency. In this study, three textbooks from IBDP and all nine textbooks from the Korean 2015 revised curriculum were selected. All the problems in textbooks were classified into real-world problems and non-real-world problems. Problems classified as real-world problems were once again divided into word problems and modeling problems according to the need to set up mathematical models. Modeling problems were further categorized into standard applications and good modeling problems depending on whether all the necessary information was included in the problem-solving process. Among the 12 textbooks, the textbook with the most modeling problems was the IBDP textbook, 'Math: Applications and Interpretation', which accounted for 50.41% of modeling problems to the total number of problems. This textbook provided learners with significantly higher modeling opportunities than other IBDP and Korean textbooks, which had 2% and 9% modeling problem ratios. In all 12 textbooks, all problems classified as modeling problems appeared as standard applications, and there were no proper modeling problems. Among the six sub-competencies of mathematical modeling, 'mathematical analysis' and 'interpretation and evaluation of results' sub-competencies appeared the most with very similar number of modeling problems, followed by the ‘mathematization’. It is expected that the results of this study will help compare the number and ratio of modeling problems in each textbook and provide a better understanding of which modeling sub-competencies appear to what extent in the modeling problems.
Development of TPACK and mathematical communication of pre-service teachers in math classes using apps for group creativity

In this study, pre-service mathematics teachers cultivated technology content teaching knowledge (TPACK) in the regular curriculum of the College of Education. The course was designed to enhance pre-service teachers' mathematical communication skills by using an application, which is a mobile mathematics learning content for the development of group creativity of high school students. The educational program to improve mathematics teaching expertise using the application for group creativity expression consists of pre-education, goal setting, planning, teaching at school, and evaluation. In this process, pre-service teachers evaluated technology tools. They also wrote a task dialogue, lesson play, reflective journal, and lesson plan to guide high school students to develop group creativity in both app activities. As a result of the educational program, pre-service mathematics teachers cultivated TPACK and enhanced their mathematical communication skills with high school students to develop group creativity.
Development and Application of Assessment Items for the Diagnosis of Difficulties in Learning Elementary Mathematics

The purpose of this study is to develop an assessment to diagnose difficulties in learning mathematics and misconstructions that elementary students have. With thorough theoretical background and analysis of mathematics curriculum documents, we established learning trajectories for the following content areas in grades 3 to 6: number and operation, regularity, data and probability, geometry, and measurement. Then, the research team created the assessment items targeting a specific stage in the learning trajectories and including item options to identify possible misconceptions. Based on the unified validity theory, we report the detailed procedure of the assessment development and the evidence for the content, substance, and structural validity of the assessment. We collected the data of 675 elementary students. Rasch measurement modeling was applied, and Cronbach’s alpha was estimated. We considered how to report students’ assessment results to teachers appropriately and immediately, which suggested important implications for supporting teaching and learning mathematics in elementary schools. We also suggest how to use the assessment developed in this study in online and distance learning environments due to the COVID-19 pandemic.
IoT platforms assessment methodology for COVID-19 vaccine logistics and transportation: a multi-methods decision making model

Abstract The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&amp;T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.
Design and Implementation of Thai Vocabulary Memory Platform Based on WeChat Applet

This article provides a detailed introduction to a WeChat mini program called “Thai Echoes”, which aims to provide a convenient platform for learners to memorize Thai vocabulary more effectively. We have developed this platform based on technologies such as Spring Boot, MyBatis, and Uni-app, and designed rich functional modules such as word memory, challenge games, and voice imitation to meet the needs of Thai language learners. In addition, by analyzing user learning data, it is possible to track learners’ progress and continuously optimize teaching effectiveness. This study demonstrates the potential application of WeChat mini programs in the field of foreign language learning, providing ideas for further developing educational technologies with practical value.
A study on the abacus education using an after-school programs in elementary schools

In this study, in order to examine the importance of abacus education using the abacus, the history and flow of the abacus and the Chinese knowledge and use of abacus were recognized as intangible cultural heritage of humanity by UNESCO (United Nations Educational, Scientific and Cultural Organization) through a literature review. It was intended to provide an opportunity to re-aware of the current meaning of abacus learning, which is receiving attention again through after-school class programs in Korea. Abacus education, which was evaluated as ‘oriental wisdom’, gradually fell out of favor with the development of computers, but its value and usefulness are still recognized in the ‘After-School Programs in Elementary Schools’, which began to be operated separately from the regular curriculum. The continued participation of students is evaluated as a positive phenomenon. The main purpose of operating after-school is the educational system of‘complementing and expanding school educational functions’. It can be seen that it has unlimited development potential to resolve the ‘education gap’ by ‘supplementing and expanding the regular curriculum,’ such as the purpose of reducing parents ‘private education costs and meeting care needs, and to develop the concept, specialty, and aptitude of care classes. It is hoped that such after-school education programs will provide effective satisfaction so that elementary school students can develop basic computational skills such as numbers and calculations with familiarity with numbers.
Facial Expression and Description of Personality

This study aims to investigate the relationship between facial expressions and personality description and verify whether there is a difference between human-machine and human-human interactions. The data provided by Osaka University, including facial expressions and some questionnaires, are helpful. As a result, the relationships between facial expressions and personality descriptions are observed in cases of disgust and fear. Measurements related to the conversationalist's perception of interpersonal communication influence facial expressions such as disgust and fear. This result implies that participants' emotions when interacting with the machine are possibly expressed in such negative cases. Also, it was found that lots of personality traits widely affect happiness, sadness, and surprise.
Enhancing Interest in Reading Through Letter Puzzle Media in Early Childhood

The ability to read is essential for early childhood; seeing the current demands of elementary schools, students who will enter the elementary school level must have the ability to read, so this is urgent for early childhood educators to pay attention to oriented media to current needs. Therefore, this research seeks to enhance interest in reading through the media of letter puzzles in early childhood. Moreover, it aims to discover the inhibiting and supporting factors in increasing interest in reading in early childhood. Through a qualitative descriptive approach, data collection techniques used observation, interviews and documentation are complemented by data analysis techniques, namely data reduction, data presentation and conclusion. The results showed an increase in early childhood reading interest by using letter puzzle media in children's learning activities; this was indicated by the average child achieving learning mastery, meaning developing according to expectations. All of this cannot be separated from the supporting and inhibiting factors in terms of time, children's mood, and media design employed in the teacher's teaching and learning process. Thus, letter puzzle media is an alternative media teacher can use to enhance interest in reading in early childhood. It also helps teachers minimise time and creates a fun learning process for children.
ANALISIS SENTIMEN TERHADAP PILPRES 2024 BERDASARKAN OPINI DARI TWITTER MENGGUNAKAN NAÏVE BAYES DAN SVM

Ahead of the Presidential Election, although it will run for about two years in the future, the large number of public opinion tweets about the 2024 Presidential Election on Twitter has caused positive and negative, from the data collection can be used as material for analysis. Naïve Bayes algorithm and the Support Vector Machine aims to determine the accuracy, precision, and recall values of the classification of positive or negative tweets. The method used is a qualitative method, the data taken amounted to 1606 datasets during April and May 2022. Result of RapidMiner 9.10 Tools, SVM Algorithm gets higher results by having an accuracy value of 98.43%, precision 97.15%, and recall 99.71%, Naïve Bayes algorithm has an accuracy value of 96.63%, precision 94.30%, and recall 98.90%. Based on the results of tweets that have elements of rejection of the 2024 Presidential Election, it is hoped that the public will not be able to happen
Research on deep mining model of online learning data based on multiscale clustering

Online learning data mining model does not consider the nonlinearity of data, and has the problems of low recall, precision and MMR. SVM is used to classify the online learning data, and hierarchical interpolation method is used to suppress the noise in the online learning data. This paper uses the multi-scale clustering algorithm to recursively process the data association rules, constructs the online learning data deep mining model through the regression classification tree, and solves the optimal solution of the deep mining model with the help of similar recursive function to complete the online learning data deep mining. The results show that this method has a high fitting degree with the ideal hierarchical result distribution, and the precision, recall and MRR of data mining are better than the traditional methods.
A Survey on Various Approaches for Support Vector Machine Based Engineering Applications

Support vector machines describe a system that uses a feature space with a hypothesis space of linear functions that is trained using various learning algorithms from optimization theory. This paper presents a brief introduction to SVM, and a survey with different methods applied for obtaining results using classifiers. The aim is to classify and obtain results for different classes of points with different SVM classifiers and to justify the results using various methods like Gaussian Kernel, Custom Kernel, Cross Validate functioning of SVM classifiers through Posterior Probability Regions for SVM classification models with various types of data.
Convolutional Neural Network Based Lossless Compression of Hyperspectral Images on FPGA Platform

Satellites used for remote sensing applications rely on substantial onboard storage capacity to accommodate the storage of hyperspectral images. These images, consisting of a three-dimensional data cube with multiple bands, are captured by high-resolution cameras capable of collecting a wide range of wavelengths. Compressing hyperspectral images not only reduces the required memory capacity but also optimizes communication channel bandwidth and download time. This paper focuses on finding a better way to compress hyper-spectral images, which contain a lot of detailed spectral information. Convolutional Neural Network (CNN) to compress these images without losing any of the important data is proposed in this paper. The Convolutional Shallow Neural Network(CSNN) algorithm here uses a smaller neural network which helps reduce the computation time. The proposed work achieves a peak signal-to-noise ratio (PSNR) of 25-27dB with DWT, 30–60 dB with JPEG-2000 and Convolutional Shallow Neural Network(CSNN) algorithm achieves a PSNR ranging from 30dB-100dB. Comparing the results obtained from CSNN algorithm with JPEG-2000 and DWT algorithm, the CSNN algorithm achieves better PSNR and maintains better image quality. To make it even more efficient, we implemented this compression method on an FPGA platform, which can process the images quickly. The CNN designed on FPGA provides onboard computational capabilities to process the hyperspectral image.
Problem-based learning in raising motivation when teaching English in a non-linguistic university

The article focuses on problem-based learning as one of the means of raising motivation of university students mastering English. It is highlighted that dealing with non-linguistic university students motivation is achieved when we refer to the topics relevant and challenging for their future profession. It is problem-based learning, treated as independent search activity of the students aiming at cognitive activity and strong motivation, that gives great opportunities to solve the problem under discussion. The main notions of problem-based learning, such as its distinctive features, functions and advantages, are further discussed. Realization of problem-based learning is exemplified on the method of projects used when teaching English to students majoring in Applied information in electronic economy. Based on the unit Job Interviews and Career from The Business 2.0 there was worked out an algorithm of project activity including preparation, organization and putting the project to action, role-play, evaluation of the results. Each of the stages contains the description of the students activity targeting at raising their cognitive function and motivation. In conclusion it is stated that method of projects as one of the technologies of problem-based learning is a relevant instrument in forming motivation to learn a foreign language in a non-linguistic university.
A case study on the development of an ontology for maintenance services of heavy machinery electronic components

Inadequate data organization within a company can result in decreased efficiency, increased costs, and longer delivery times. In the context of an electronic maintenance laboratory servicing a mining company internally, the lack of data organization hampers the conversion of information into actionable knowledge, affecting delivery efficiency. This study aims to address these issues by transforming existing data into structured knowledge using the Methontology and OntoForInfoScience methodologies. The ontology model was validated by both experts and a software plug-in and seeks to offers a systematic representation of the domain, facilitating data understanding and utilization, ultimately leading to cost and time savings in deliveries and ongoing process improvements.
Twitter Sentiment Analysis for Kurdish Language

Sentiment analysis of text data has received a significant attention throughout Natural Language Processing stages. However, most of the focus has been on English language depriving many other languages from taking advantage of the state-of-the-art techniques most suitable to a particular language especially the Kurdish Sorani language. This paper is an attempt to bridge the gap between English and Kurdish language in sentiment analysis for social media text.&nbsp; For this purpose, firstly a new Kurdish sentiment analysis dataset was curated and annotated then we tried different combinations of machine learning algorithms including classical machine learning algorithms such as Random Forrest, KNN, SVM, Naive Bayes bias and Decision trees and compared the results to Deep Learning techniques namely ANN, LSTM and CNN. In our experiments Naïve Bayes achieved the best results achieving an 78% accuracy.
Welcome to Optica Quantum: a new venue for high-impact photonics-related quantum information research

Editor-in-Chief, Michael G. Raymer, announces the first issue of Optica Quantum .
Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction.
E-Learning Satisfaction: Analysis of the Support Factors

E-learning satisfaction is an important factor in assessing the effectiveness and efficiency of distance learning. The use of Big Data technology can have a significant impact on e-learning satisfaction. The purpose of this research is to examine the implementation of big data in e-learning and the factors that can increase student e-learning satisfaction. This study uses a quantitative survey method with a questionnaire as a data collection tool. The research respondents were randomly selected e-learning students from several educational institutions that use Big Data technology in their e-learning platforms. There are 663 data collected through online questionnaires, then the data is processed through SmartPLS to test the research hypothesis. The results of the study show that the use of Big Data technology in an e-learning environment has a positive impact on student satisfaction. Several factors determine e-learning satisfaction including usability requirements, quality level, learning competence, and material relatedness. Relatedness to e-learning is more important in increasing student motivation in learning, as can be seen from the correlation value which is more dominant in forming student satisfaction. The e-learning platform must have navigation that is easy for users to understand and use. Learning content must be well-designed and follow established learning standards. The use of Big Data technology in e-learning can have a significant impact on student satisfaction and the quality of learning. Future research can develop a more personal and adaptive e-learning platform, using Big Data technology as the basis for its development.
Application Deployment Strategy Comparison at PT. XYZ

In software development, we can see the update or renewal of the version. The bearer does this for updating or improving the program. The process requires a transition time that causes downtime on the application. For some companies, if the application experiences downtime will cause large losses like PT. XYZ. The company buys and sells cars whose sources of strength in the application are available 24 hours to conduct transactions. The company wants to implement a safe development strategy to reduce downtime. This research was conducted to compare the availability of applications that use the Canary and Blue-Green development approach so that PT. XYZ can choose a strategy that suits the needs of its products. This study uses quantitative methods; the data will be analysed to see the application's effectiveness in each service. From the test results, the response time value of development using a canary shows a smaller value than the response time produced by action using blue-green. The spread using canary development has the advantage of maintaining the availability of applications and reliability without failing this test.