Reviewerly/exHarmony huggingface dataset:
Transformation of Lifelong Digital Education in the Context of Sustainable Development of Society

The article examines the theoretical, methodological and analytical aspects of the transformation of lifelong digital education in the context of sustainable development of society. The authors summarized the main approaches to the transformation of digital education and indicated the peculiarities of digital education in Ukraine, and also revealed trends in the development of digital education in the EU and Ukraine. The proposed methodological provisions for the study of digital competences among students allowed to establish a strategy for acquiring digital competences as a partial assimilation of digital skills using the Monte Carlo simulation method. As a result, the study confirmed the need for continuous improvement and deepening of digital competences throughout life as a result of their partial assimilation.
Image Super-Resolution and FPGA Hardware Design

Super-resolution (SR) reconstruction technology refers to restoring a given low-resolution image to a corre-sponding high-resolution image through a specific method. This article proposes an FPGA-based image super-resolution method that reduces system runtime and computational resource usage while maintaining high-quality images. In terms of software implementation, this project uses a Resnet-based model and an SR Generative Adversarial Network (SRGAN) model for experiments. In hardware implementation, Squeeze and Excitation SR (SESR) is adopted and this project calls the Zynq DPU to generate a bit stream to cover the Field Programmable Gates Arrays(FPGA) and deploy the SR neural network model on the FPGA. The hardware platform used in this article is Ultra96V2. With optimized algorithm and model training and hardware compilation, the PSNR and SSIM of the software and hardware system can achieve considerable performance in power consumption.
Considering the optimal input for global horizontal irradiance forecasting based on Informer

Accurate global horizontal irradiance (GHI) forecasting is significant for the stability and economy of power system operation. This paper proposes an advanced model Informer to predict GHI and selects Root Mean Square Error (RMSE) as the primary evaluation metric to calculate the error. Through discussing the Pearson correlation coefficient, chooses five strong correlation parameters and selects the optimal input through the experiments of 2 and 3 inputs. The performance of the Informer model is evaluated by comparing it to five other machine learning (ML) models being used as reference, and the performance improvement is over 99% under optimal input, proving the proposed model's superiority. Finally, the Informer's long series prediction ability is verified, and the results showed that Inforemer can efficiently complete long series prediction tasks without losing accuracy, which has high practical value.
A Novel Dolphin Whistle Classifier Based on Attention-DenseNet

Traditional whistle classification algorithms need complex preprocessing operations to classify dolphin whistle signals. Moreover, conventional methods cannot fully extract features. To solve the above problems, this paper proposes an Attention-DenseNet for accurate dolphin whistle classification. This model fuses features to learn whistle characteristics from multiple perspectives and introduces the self-attention module and the central loss function to assist the network in improving the classification performance. The experimental results show that the proposed model can fully exploit the whistle signal features to avoid information loss and improve classification accuracy.
Stable Cloud Provider Selection via Group Role Assignment with KB4 Logic Extended

Although cloud manufacturing offers greater flexibility and diversity than traditional manufacturing, it also presents greater uncertainty and variability. How to select stable cloud providers for material procurement (SSCPFMP) has become a critical supply chain optimization issue in cloud manufacturing. By extending the Group Role Assignment (GRA) model, this paper formalizes the problem. Moreover, we propose a new method for evaluating cloud providers that incorporates stability as an important criterion. Additionally, in order to complete the stability assessment as quickly as possible, we propose using the KB4 logic instead of the commonly used KB5 to mine potential cooperative relationships between cloud providers. We prove by deduction that KB4 and KB5 are equivalent. Largescale simulation experiments indicate that the KB4 logic performs significantly better than the KB5 logic, which can be improved by up to 43.78%. By using this method, decision makers are able to find more stable cloud providers for material procurement within a shorter timeframe.
A Comparative Analysis of Single-Stage Detectors from the Perspectives of Anchor-Free and Anchor-Based Approaches

Since the emergence of deep learning in object detection in 2014, there has been rapid development and widespread application of detectors across various domains such as healthcare, road traffic, and industrial inspection. The evolution of convolutional neural network (CNN)-based detectors has reached a mature stage, with single-stage detectors playing a crucial role within CNN-based frameworks. This paper provides an analysis of the developmental process and distinctive features of classic single-stage detectors categorized into two types: Anchor-free and Anchor-based. Furthermore, the paper also provides a comparison of the accuracy trends for these two types of single-stage detectors over different years. Through this analysis and comparison, potential reasons behind the advancements in single-stage detectors during different time periods can be explored, such as network architecture optimization, increase in datasets, and algorithm improvements. Additionally, this paper offers insights into current research focal points and future prospects.
Research on Wordle Puzzle Game based on trend prediction model based on data index

World Puzzle is a word game that has exploded in popularity over the past year. For this kind of game phenomenon, this paper studies. First, the LSTM algorithm is used to describe the trend of their number and to fit a high-precision time model. At the same time, considering the rigor, this paper also establishes the ARIMA time series analysis model to predict the data again. On this basis, the data accuracy of the two models is compared, the model is selected, and five indexes such as the number of words and the number of meanings are defined. Second, Pearson correlation coefficient was used to investigate the relationship between the effects of these indicators on the percentage of reported scores in difficulty mode. Finally, BP iterative neural network was used to calculate the correlation between the indicators and the percentage of players attempting to solve puzzles each time, and GBDT machine learning method was used to quantify the relationship between the word difficulty attribute and the reported result distribution, so as to establish the correlation model between the word attribute and the percentage of passing rate. The results show that LSTM model is more suitable than ARIMA model to describe the trend of the number of reports over time. Meanwhile, when the word "EERIE" was inserted into the model, the average number of correct guesses was 5.399.
Real and Complex Analysis of Common Probability Density Functions

In this paper, the most common method of probability density function integration Gaussian Integration is analyzed in real and complex methods. This paper begins by clarifying the meaning and significance of integrating the probability density function. After this, it elucidates and derive the mathematical methods for the analysis of integrals, namely the multiple integral method in real analysis and the residue theorem method in complex analysis. At the same time, the paper also makes it clear to analyze the most typical Gaussian integral in the probability density function. Since then, this integration method has been used respectively for real analysis and complex analysis. Different means of analysis including Pinch criterion or Residue theorem have been applied comprehensively. In this session, different forms and formats of Gaussian integrals are explored to a certain extent. Finally, the benefits of complex analysis and real analysis for the synthesis of probability density function including Gaussian integral are summarized, and the possible applications and prospects of this new analysis method are prospected. However, the limitations of the current approach have also been mentioned.
Pelatihan Pemeliharaan Website Sistem Informasi Rukun Warga

The rapid development of technology has made information dissemination not only through offline media, but also online media contribute to the provision of information. One of the areas that use information technology in the service process is Rukun Warga (RW). RW 13 is one of 22 RW’s in Cipinang Melayu Village, which currently uses a manual system in the service process. In addition, the problem faced by the administrators of RW 13 is the lack of knowledge in managing and maintaining the website. As a form of service to the surrounding community, a group of lecturers from Universitas Nusa Mandiri conducted a community service activity in the environment of RW 13 in the form of training activities on website maintenance of the Neighborhood Information System. This activity aims to make the participants understand how important it is to maintain the website so that the website will continue to run optimally. Participants in this community service activity are RT administrators, RW administrators, and youth from RW 13 youth organizations. The method used in this community service activity is interactive theoretical training, while the practical method uses simulation and question and answer methods. The evaluation results of the community service activities conducted based on the completion of questionnaires by 20 participants can be concluded that the participants are satisfied with the implementation of this training activity so that it can increase the knowledge, understanding and skills of the participants on the importance of website maintenance. In addition, the participants argue that this community service activity in the form of training has met the expectations of the participants, can increase the knowledge and skills of the participants, and can help solve existing problems.
Real Time Face Mask Detection using Deep Learning Approach

In response to the need for face masks in preventing dangerous illnesses like COVID-19, this paper is centered around deep learning-based face mask identification. Leveraging powerful libraries such as TensorFlow, Keras, and OpenCV, our objective is to create a reliable and effective system capable of discerning individuals wearing face masks from those who are not. This innovative approach holds immense potential for applications in public health monitoring, security systems, and critical public spaces where adherence to face mask policies is of utmost importance.To ensure practical usability, we deploy the trained model in real-time scenarios, utilizing webcams or video streams. The system efficiently processes frames, swiftly detecting and classifying faces with or without masks, and promptly providing feedback or alerts to users or surveillance systems.This paper highlights how well TensorFlow, Keras, and OpenCV work together to create a reliable and accurate face mask detection system. The amalgamation of deep learning, image processing, and real-time capabilities facilitates seamless monitoring of face mask adherence, significantly contributing to public safety and health initiatives across various domains.
Uncertainty-Aware Deep Learning for Segmenting Ultrasound Images of Breast Tumours

Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU.
Exploring the Integration of a Patient Generated Health Data in a FAIR Digital Health System in Low-Resourced Settings: A User-Centered Approach

This article presents the initial user-centered research exploring the opportunities in the collection of Patient-Generated Health Data (PGHD) within the context of a project aimed at improving health management and outcomes among residents in African countries. Through interviews with a doctor, a patient and two data managers, the local status and opinions regarding PGHD collection, integration and use are investigated. The findings suggest that PGHD have only been encountered in paper forms - and are mostly patient driven, however opportunities for PGHD for the facility and patient were identified and included supporting the treatment of white-collar hypertension, treatment planning and self-management. One of the key findings is the observed formed opinion about the relationship between quality (reasonability and provenance) and trust of PGHD such that it appears there is a direct relationship between perceived quality and trust of PGHD by the healthcare personnel. Also, traditional digital home monitoring devices, such as blood pressure monitors combined with an Interactive Voice Response (IVR), offer a more practical approach to PGHD collection in the African (Nigerian) context compared to wearables, considering factors such as affordability, ease of use, and data extractability. The study highlights the importance of considering the specific circumstances and requirements of African countries while designing and implementing PGHD collection strategies.
Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology

Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.
Biostratigraphy of Eocene Sediments from Naopurdan Group, Chwarta Area, Kurdistan Region, NE Iraq: Paleogeographic Implication

The main purpose of the present work is to describe the microfossils and biostratigraphy of marine Eocene deposits, which described and studied in detail from carbonates of the Naopurdan Group, Chwarta area, Kurdistan Region, Northeastern Iraq, in order to define the main important index fossils and to give the biostratigraphy and age determination of the study area. A total of thirty-eight samples spaced ca 0.5 to 1 m apart, have been collected from the field, approximately 60 thin sections were examined and the microfossils have been identified by using polarized microscopes. Three main biozones were recorded in the studied section, these zones are: (Nummulites globulus - Nummulites planulatus zone, at the bottom of the section and assigned to the Early Eocene (Ypressian) age, Nummulites mamillatus - Nummulites alsharhani zone, which collected from the middle part of the section is assigned to the Middle Eocene (Lutetian) age and Nummulites fabianii - Assilina exponens zone which collected from the upper part of the section is assigned to the Early Bartonian age, so the studied section of the Naopurdan Group suggest to be Ypressian - Early Bartonian age, these three biozones were correlated with the other zones inside and outside Iraq
NAVIGATING GOVERNANCE: EXPLORING CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT STRATEGIES IN THE CONTEXT OF G20 SUMMIT

The paper examines India's impactful role during its G20 presidency in addressing climate change challenges and promoting sustainable development strategies. Focused on advocating for renewable energy adoption, climate resilience, and sustainable practices, India emphasized the urgency of transitioning to clean energy sources and highlighted the interdependence of sustainable development and climate action. Through discussions on green finance, knowledge-sharing, and public awareness initiatives, India's leadership aimed to mobilize global cooperation towards a more resilient and sustainable future. The analysis showcases India's pivotal contributions in steering the G20 community towards collective strategies that prioritize environmental conservation and inclusive growth.
INDIA'S DIGITAL GOVERNANCE ODYSSEY: NAVIGATING ECONOMIC TRANSFORMATION IN THE DIGITAL ERA THROUGH PROWESS AND LEGAL RESILIENCE

India’s economic digitalization transcends technological evolution; it’s a narrative of adaptability, resilience and a commitment to fostering an environment where innovation flourishes, individual rights are safeguarded and the nation emerges as a global frontrunner in the digital epoch. Integral to this digital odyssey are intellectual property laws and the Consumer Protection Act, serving as guardians of creators, consumers, and businesses in the digital realm. Key government programs, such as the National Digital Health Mission and the Unified Payments Interface, underscore India’s dedication to leveraging technology for societal well-being and financial inclusion. The imminent Digital Personal Data Protection Act establishes a comprehensive framework for responsible data processing and protection. The impending Digital India Act 2023, set to replace the Information Technology Act of 2000, is poised to bridge the gap between traditional legal frameworks and the complexities of the digital era. From online dispute resolution to intermediary liability, the Act signifies a progressive leap towards a comprehensive legislative landscape for the digital age. This Chapter navigates through the multifaceted dimensions of India’s economic digitalization, delving into the transformative impact of government initiatives such as Digital India and the impending Digital India Act. The legal underpinnings, including the Information Technology Act and cybersecurity laws have evolved in response to emerging challenges, focusing on data protection, privacy and the alignment of legal principles with the digitized landscape.
IMPLEMENTASI METODE RESITASI SEBAGAI UPAYA MENINGKATKAN HASIL BELAJAR SISWA PADA MATA PELAJARAN MATEMATIKA

The recitation method, namely the recitation method or assignment assignment is one of the teaching methods that can be chosen by educators, which in practice, educators demand that students play an active role in teaching and learning activities so that they are able to complete the tasks given by educators to do outside lesson hours. The assignments given are usually individual, but can also be given in groups. The focus of the problem in this research are: (1) How is the implementation of the assignment method (recitation) at Madrasah Ibtidaiyah Al Asy'ariyah Banjarsari Bandarkedungmulyo Jombang?, (2) How are student learning outcomes using the assignment method (recitation) at Madrasah Ibtidaiyah Al Asy'ariyah Banjarsari Bandarkedungmulyo Jombang?. The research in this thesis uses classroom action research (CAR) research which is carried out collaboratively. The data collection technique used is the test and observation technique (observation). The results of this study indicate that: (1) The first step in planning this action is to analyze the components and content of the items, determine the learning materials, examine the class III mathematics textbooks, develop a syllabus, develop a lesson plan, and make student activity sheets. The implementation process in the first cycle, the researcher succeeded in explaining to students about doing multiplication which resulted in three-digit numbers and division of three-digit numbers and learning by saying and doing would obtain high absorption of learning outcomes. The evaluation process is carried out at each meeting after the learning process takes place to determine how far the development of the method being developed has been successful as planned. (2) The results of class III Mathematics learning by applying the assignment method (recitation) are classified as very good. The results of this study can be seen from the average pre-test and post-test where the pre-test score is 76.19% while the Post-Test reaches a value of 95.23%, there is a very good increase.
Klasifikasi Hama Tanaman Padi berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network

This research aims to classify the types of pests on rice leaves using Tensorflow with the CNN method to make it easier for the public to know the types of pests that exist on rice plants. The research results showed that initially the accuracy was only 61% but was successfully increased to 99% through various variation tests. Accuracy is affected by the background of the object and the distance of the device. Results show this model provides accurate predictions, with an average accuracy of around 90%. In conclusion, the results of rice plant pest classification using the CNN model for detecting rice plant pests using the Android-based Tensorflow framework make it easier to classify pest types on leafy rice plants.&#x0D; &#x0D; Keywords: CNN, Padi, Tensorflow
The relationship of visual and aural perspective with decentering in virtual reality

Several areas of application for virtual reality technologies have seen significant growth recently, including mindfulness practice. This paper investigates the relationship between the mindfulness-related construct of decentering, i.e., detaching oneself from automatic thinking and observing one's thoughts and emotions, and body representation within virtual reality. Using a within-subjects approach, we investigated whether a third-person perspective, both in terms of visual and aural representation, affects decentering in virtual reality. This was done using a virtual reality application that presented a guided body-scan meditation in virtual reality and utilised these different forms of perspective. Our results do not show a significant effect of either form of perspective on decentering in virtual reality.