Reviewerly/exHarmony huggingface dataset:
Professional Training of a Contemporary Teacher of Mathematics

This chapter focuses on designing an educational program for the bachelor’s degree in the direction of training “physics and mathematics education” based on the methodology of learning outcomes. The methodological foundations for designing and evaluating the learning outcomes of an educational program based on the competencies of graduates have not been sufficiently studied in the higher education of Kyrgyzstan. The relevance of this chapter lies in the design of the learning outcomes for the educational program for bachelors of physics and mathematics education training (the profile “Mathematics”) at the Osh State University based on the criteria of the EdNet Agency for quality assurance of educational field in Kyrgyzstan with the state educational standard in the pedagogical direction and international requirements. As a result of studying and analyzing the universal and professional competencies presented in the state educational standard and researching the opinions of the educational stakeholders, additional competencies were developed which consider the local demand of the labor market related to future mathematics teachers. To develop the learning outcomes of the educational program, we combined and grouped relevant universal, professional, and additional competencies into clusters of related competencies. This empirical work is important because it represents the first attempt to determine the main types of professional tasks of a mathematics teacher because a professional standard for a teacher has not yet been developed in Kyrgyzstan. This chapter defines clusters of related competencies as competently significant qualities. Based on the learning outcomes of the educational program, the content of the modular curriculum has been developed.
UTILIZATION OF MOBILE AUGMENTED REALITY IN A COURSE CONTENT: AN IMPACT STUDY

With the rapid evolution of interactive technology, the popularity of mobile augmented reality (MAR) as a learning aid has continued to grow. However, several studies have revealed that research on the impact of AR in the educational domain is both insufficient and in an early phase. More studies are required to evaluate the effectiveness of utilizing MAR in this domain. The purpose of this study was to measure the effect of a mobile training course designed using MAR on trainees’ motivation. We reviewed the associated concepts, highlighted the importance and effectiveness of MAR and explained the benefits and challenges of employing MAR in the educational domain. This study drew on John Keller’s motivational model components and emphasized the significance of intrinsic motivation. We used a quantitative approach and designed a mobile training course that uses MAR to train government employees in Oman. A total of 32 employees were randomly divided into an experimental group and a control group. The experimental group used the designed application, and the control group took a training course online via computers. A motivational survey was conducted, and SPSS statistical software was used for data analysis. The results revealed that there was a significant difference in the mean motivation value for the experimental group: the trainees from the experimental group were more motivated than those from the control group. This study confirms that learners are motivated to participate in mobile training courses designed using MAR, which can contribute to the development of human resources in various domains.
APPLICATION OF INTELLIGENT SYSTEM WITH BACKPROPAGATION MODEL IN CLOUD IMAGE CLASSIFICATION

The clouds have different patterns on each type and each type has different properties. The introduction of the type, shape, and nature of the cloud is indispensable in the weather forecasts so that the clouds can be classified. There are several methods used in the image classification process that is the method of the artificial neural network Backpropagation. The method of Backpropagation is one of the methods used for the classification process, in this research Backpropagation used on the training and testing process for the introduction of cloud imagery aimed at determining the type of cloud, before the second These stages are carried out imagery through the preprocessing process. From the training conducted using the Backpropagation method shows that this method generates the best weight value and saves that value into the database to do the testing process using a neural network Backpropagation. In addition, Backpropagation also has the ability to reduce errors by continuously correcting the weight until reaching the maximum target. Data used for training data as many as 92 cloud type image with each type of 10 imagery. In this study obtained a system success rate of 60.6%.
CDR2IMG: A Bridge from Text to Image in Telecommunication Fraud Detection

Telecommunication fraud has run rampant recently worldwide. However, previous studies depend highly on expert knowledge-based feature engineering to extract behavior information, which cannot adapt to the fast-changing modes of fraudulent subscribers. Therefore, we propose a new taxonomy that needs no hand-designed features but directly takes raw Call Detail Records (CDR) data as input for the classifier. Concretely, we proposed a fraud detection method using a convolutional neural network (CNN) by taking CDR data as images and applying computer vision techniques like image augmentation. Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of samples. Compared with the state-of-the-art method, the proposed method has achieved about 89.98% F1-score and 92.93% AUC, improving 2.97% and 0.48%, respectively. With the augmentation technique, the model’s performance can be further enhanced by a 91.09% F1-score and a 94.49% AUC respectively. Beyond telecommunication fraud detection, our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods.
Solving the Challenge of Volumetric Video Production and Streaming: An End-to-End Perspective of Technologies and Device Ecosystem Capabilities and Performance

Volumetric models and assets are great elements for content creators to use in the development of appealing immersive experiences. We can think about live sporting events where the entire scene is captured as volumetric elements and streamed to the mobile devices of thousands of consumers. However, distribution to consumers of high-quality immersive experiences using volumetric assets has been challenged by several factors including visual fidelity, bandwidth, latency, and device processing power. Content creators have been constrained from expressing the full potential intended for their original experience. In this article, you will learn about methods to stream volumetric video assets from an end-to-end perspective including an overview of production systems and suitable volumetric compression profiles (using point cloud compression) that can be used to render on any display for a wider reach: virtual reality (VR) headsets, augmented reality (AR) glasses, and flat screens. An ecosystem overview with device capabilities and performance is presented, and an analysis of content and end-user security functionality is further included. Finally, this article explores the evolution and opportunities of volumetric video streaming using 5G cloud/edge infrastructure to process and distribute volumetric content to any device, minimizing device processor power consumption, minimizing overall delay, and providing the best possible user experience.
A plan to improve the management system in society for high-risk Criminals: Focused on measures to strengthen cooperation between agency and support systems for high-risk criminals

In 2007, when cases of sexual violence and murder against children and women began to occur simultaneously in Korea, the government introduced and implemented new policies such as the 'electronic monitoring system' and 'chemical castration'. However, the release of Jo Doo-soon in 2021 and subsequent murders of those wearing electronic ankles has highlighted the need for better management of high-risk criminals in society. This study examines some meaningful policies that are being carried out to protect the safety of the people from high-risk crimes in foreign countries through literature research and examines their applicability to the current situation in Korea. The UK, having already experienced brutal sexual assault and murder cases targeting children and women, established the MAPPA (Multi Agency Public Protection Arrangement) in 2001 to manage about 80,000 dangerous criminals. The U.S.A is also trying to manage the high-risk criminals meticulously through the collaboration of criminal justice agencies. Meanwhile, apart from control-centered management of high-risk offenders through collaboration of institutions, it is also necessary to examine efforts to solve problems more fundamentally by realizing social reintegration through social support such as CoSA(Circle of Support &amp; Accountability) programs. In the case of Korea, I would like to discuss the applicability of cooperation between criminal justice agencies for the effective management of high-risk offenders and the lack of awareness of support in the society for high-risk offenders. Of course, according to the difference of social, economic, and cultural environment of Korea, foreign systems may not just fit the Korean situation, it will be difficult to introduce foreign systems right away. However, if the purpose of the system implemented in foreign countries is reasonable and desirable, it is necessary to consider actively the introduction of a new system in Korea.
Accuracy Improvement in Chondrosarcoma Detection using Decision Tree Algorithm (DT) and Comparing with Support Vector Machine (SVM)

This research is based on the improvement of accuracy in detection of chondrosarcoma the decision tree is taken and comparing the results with the support vector machine from deep learning algorithms. The total set of samples taken for these processes are 38 to analyze the two groups. Group 1 for the decision tree consisting of 19 samples and Group 2 for the support vector machine also containing 19 samples. The dataset has been imported and python code is implemented using Google Collab software. The sample has been calculated from the values obtained from the prior studies with help of some online statistical analysis tools with the pretest power of 80% and the alpha of 0.05 value. From simulation results of machine learning algorithms, the decision tree gives output accuracy of 95% which provides better output than the other used machine algorithm in this research support vector machine gives output of accuracy 81% (p < 0.05).For the above used dataset decision tree algorithm provides significantly more effective results than support vector machine from machine learning.
Simulation of Turn Alternation Strategy for Path Search

The behavior ‘Turn alternation’ that is seen in pull bug and others is investigated as one of intelligence of natural creatures in this paper. Some numerical simulations are carried out on the square lattice with some obstacles. As the number of obstacles increases, the reachable rate to the goal largely decreases. In order to investigate the effectiveness of this strategy, we compare it with that of random walk, it is clarified that it takes more time to reach the goal as obstacles increases. Moreover that tendency is more remarkable in the larger simulation space size. However under some obstacle allocation, the strategy of turn alternation enables us to move in the wider range. Therefore it has some possibility to be a promising search algorithm if we combine it with other algorithms.
Process knowledge distillation for multi-person pose estimation

Existing multi-person pose estimation methods show a tendency for the parameters in the model to become increasingly large in order to improve generalisation performance, requiring huge computational resources. Therefore, we propose a lightweight multi-person pose estimation method called PKDHP (Process Knowledge Distillation for Human Pose estimation), which applies knowledge distillation to pose estimation. PKDHP treats the training process of student models in knowledge distillation as a human learning process, specifically, PKDHP proposes the CABF module to replace the ABF module in ReviewKD aiming to guide the lower level feature through the model's higher level feature, and proposes the Transfer knowledge distillation method to force the student model to imitate the process of feature transfer from the teacher model, thus further simulating the human learning style. Compared with other knowledge distillation methods, we use the same model parameters on two multi-person pose estimation datasets (COCO and MPII) to achieve higher performance.
Nonclassicalities of hybrid coherent states

Abstract We address the non-classicality of hybrid coherent (HC) states, i.e. states expressed as a superposition of coherent state and single-photon-added coherent (SPAC) state. In particular, we evaluate their photon statistics, squeezing, and negativity of the Wigner function. Our results indicated that the HC states could exhibit larger non-classicalities than the SPAC state. We also suggest a generation scheme for HC states, which involves Kerr nonlinearity and postselection.
Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms

Microcalcifications clusters (MCCs) are relevant breast cancer indirect evidence and early detection can prevent death. In this paper, we carry out a comparison of the deep learning architectures (DL) InceptionV3, DenseNet121, ResNet50, VGG-16, MobileNet V2, LeNet-5 and AlexNet in classification of MCCs in digital mammograms, with the aim to select the best configuration and building blocks that yield a reduced number of parameters. We used the INbreast database to extract patches of size $$144 \times 144$$ pixels corresponding to 1 $$cm^2$$ . The networks were implemented and trained using four independent configurations that consisted of: training each architecture without any extra layer, by adding, after each convolutional and fully connected layer, a Batch Normalization layer, an $$L_2$$ regularization layer or a Dropout layer, respectively. The best overall accuracy was yielded by the MobileNetV2 with Dropout configuration. The network is built of residual blocks, depth separable convolutional blocks and $$1 \times 1$$ convolutional layers this allows a reduced number of parameters while yielding an accuracy of 0.99841. The comparison highlights the differences and similarities of the DL networks necessary to make the best decision possible to design and implement more optimal networks to classify MCCs. Furthermore, it demonstrates that for the classification of these lesions, the shallow architectures built with certain building blocks and trained with the right configuration, give similar results to their deeper and more complex counterparts.
Research on stereo matching algorithm of 3D reconstruction based on improved census transform

Stereo matching is a critical step in three-dimensional reconstruction. The conventional Census transform is excessively dependent on the central pixel and is susceptible to noise interference. Therefore, this study proposes a cost calculation method for multi-feature fusion based on the HSV color space and an improved Census transform. To address the problem of poor matching in regions with weak textures and depth discontinuities. In this study, a new window construction method is developed based on the length and variable color threshold to obtain the crossover region. After dividing the weights of the constructed windows, the cost aggregation is calculated. Finally, a dense parallax map is obtained via parallax calculation and optimization. Experiments were conducted based on the data provided by the Middlebury dataset. The experimental results showed that the improved cost calculation algorithm was significantly resistant to noise, and the average mis match rate of the algorithm was reduced to 8.03%.
Foreign object debris detection based on improved YOLOv5 algorithm

Aiming at the small target characteristics of Foreign Object Debris (FOD) on airport runways, a FOD target detection algorithm based on improved YOLOv5 is proposed. Based on the YOLOv5 network, this paper refers to the lightweight and efficient Convolutional Block Attention Module (CBAM), so that the model can focus on important features when detecting objects of different sizes and improve feature extraction ability. The PANet network is improved into BiFPN weighted bidirectional feature pyramid network, which integrates features and strengthens the extraction of deep features of images. Replace the border regression loss function EIoU to improve the regression speed and accuracy. The experimental results show that the improved YOLOv5 target detection algorithm achieves 98.2 % accuracy and 50.2 fps speed while meeting the real-time requirements. Compared with YOLOv5, it has higher detection accuracy and effectively solves the problems of low positioning accuracy and missed detection in the original YOLOv5. Thus, the effectiveness and engineering practicability of the improved YOLOv5 algorithm for the detection of foreign objects on the airport runway at night are verified.
Towards reliable medical image analysis based on deep learning with XAI

Deep learning-based medical image analysis (DLB-MIA) has achieved great success hitherto, but is in urgent need of model interpretability. Explainable artificial intelligence (XAI) can be used for improving model interpretability and is expected to help in providing crucial trustworthiness in DLB-MIA. To expedite the utilization of XAI technology for reliable DLB-MIA, the current status of XAI methods and toolkits is introduced firstly. The application of XAI technology in DLB-MIA tasks is then discussed with close attention in respect of method taxonomies and adversarial robustness respectively.
Design and implementation of automated organ analysis: AttUneXt

Histobiology, development and regeneration, disease modeling, organ transplantation technology improvement, drug discovery/efficacy evaluation, and pathology research can be carried out through the parameters such as morphology, size, and number of organ like organs. However, it takes a lot of time to manually count various parameters of organs. Therefore, here we propose a new semantic segmentation model, AttUneXt, for segmentation of adenocarcinoid organs. The dice of the model can reach 0.9. As can be seen, our proposed model achieves the effect of rapidly and precisely segmenting organoids.
Exploring Online College Students’ Satisfaction with Distance Education During the COVID-19

Because of the unexpected COVID-19 pandemic, most universities have successively launched emergency distance education. Consequently, this special project report will focus on the university students’ level of satisfaction with distance education during the COVID-19 pandemic. This is because the level of satisfaction among college students with online distance teaching and learning during the pandemic is likely to affect these students’ learning outcomes and academic performance. Therefore, this special project report aims to explore the college students’ satisfaction level with distance education due to COVID-19 and draw implications from the results. Firstly, it reviews several empirical studies and relevant theoretical discussions concerning students’ satisfaction with online distance education during the COVID-19 pandemic. Secondly, it reviews and analyzes the factors that influence students’ satisfaction with online distance education. Then, based on the review of the research results and views of previous scholars, this project will draw some implications on online distance learning. At the end of this paper, the limitations of this research will be described.
ANALISIS GAYA MENGAJAR GURU KELAS TINGGI PADA PEMBELAJARAN MENULIS NARASI DI KECAMATAN WADO

This study aims to analyze and determine the teaching style used by upper-class teachers in learning to write narratives in, with the respondents were teachers in 13 schools in the sub-district Wado District. The research approach used is a qualitative through descriptive methods with the type of research survey research, by distributing questionnaires through google form, then the data was analyzed by reduction and resentation data, and drawing conclusions. There are seven indicators that are used as a reference to see the sequence of learning carried out by the teacher. The results of the study were dominant teaching style used by 77.40% the teacher in learning to write narratives is an interactional teaching style. 84.6% of the application was mostly carried out by teachers was guiding students in determining themes, topics, and messages to be conveyed. Meanwhile, the lowest indicator is for determining the audience with around 30.7. To analyze the teacher's teaching style, it has transformed to 15 questions from all indicators of the teacher's teaching style. After collecting data, it was concluded that at the implementation stage of learning to write narratives, not all teachers applied these indicators.
Hand Symbol Classification for Human-Computer Interaction Using the Fifth Version of YOLO Object Detection

Human-Computer Interaction (HCI) nowadays mostly uses physical contact, such as people using the mouse to choose something in an application. However, there are certain problems that people face in using conventional HCI. The research tries to overcome some problems when people use conventional HCI using the computer vision method. The research focuses on creating and evaluating the object detection model for classifying hand symbols. The research applies the fifth version of YOLO with the architecture of YOLOv5m to classify hand symbols in real time. The methods are divided into three steps. Those steps are dataset creation consisting of 100 images in each class, training phase, and performance evaluation of the model. The hand gesture classes made in the research are ‘ok’, ‘cancel’, ‘previous’, ‘next’, and ‘confirm’, the dataset is made by the researchers custom. After the training phase, the validation results show 93% for accuracy, 99% for precision, 100% for recall, and 99% for F1 score. Meanwhile, in real-time detection, the performance of the model for classifying hand symbols is 80% for accuracy, 95% for precision, 84% for recall, and 89% for F1 score. Although there are differences, it still acceptable for the research and can be improved in future research.
Chunso Shin Choi's Study on 11 Won(十一原)

The purpose of this paper is to discuss 11Won(十一原) of Shin Choi, a writer in the mid-Joseon period. As a leading figure in writing in the mid-Joseon Dynasty, Shin Choi accepted various literary theories of China. 11Won(十一原) is an article expressing opinions in the style of circle on 11 topics across society. Won(原) is a style of writing that emphasizes the legitimacy of the author's claim through the process of 'chubonsowon (推本所原)', which traces the root in the process of introduction. As for the research method, after examining the origin of the original style and the situation of the Joseon literary world, 11Won(十一原) was analyzed. By setting three categories: customs and people's livelihood, politics centered on courtesy and music, and recruitment of talented people, the content and form were investigated. Each of the 11 writings has completeness and is organically connected, reflecting a high level of planning power. The sentences are very systematic and restrained, so the level of prose in the mid-Joseon period can be judged.