Reviewerly/exHarmony huggingface dataset:
THE ALPHABET AND SYLLABLE METHOD AS A TEACHER STRATEGY IN IMPROVING BEGINNING READING IN PHASE A CLASS I STUDENTS AT SEKOLAH DASAR NEGERI KALIPUCANG KULON BATANG

&lt;em&gt;A teacher is someone who plays an important role in education and tries various ways to overcome the difficulties experienced by students. One of the learning difficulties experienced by students, especially phase A, is difficulty in initial reading. If initial reading difficulties can be overcome well, it will be easy for students to take the next stage of reading. This research aims to describe the alphabet method as a teacher's strategy in improving initial reading skills in phase A class I students at the Kalipucang Kulon State Elementary School, to outline the teacher's strategy in improving initial reading skills using the syllabic method for phase A class I students in elementary school. Kalipucang Kulon State, and to explain the factors that influence initial reading difficulties in phase A class I students at Kalipucang Kulon State Elementary School. The type of this research is field research, namely research carried out directly to obtain data with a qualitative approach namely the approach used to gather information from reasonable conditions. Meanwhile, the data sources used are primary data sources and secondary data sources. Meanwhile, the data collection techniques used were observation, interviews and documentation. The data analysis techniques used are data condensation, data display, and conclusion drawing. The results of this research show that: (1) The alphabet method as a teacher's strategy in improving initial reading skills in phase A class I students at Kalipucang Kulon Batang State Elementary School, is considered quite successful in improving initial reading skills, there are changes in the students. (2) The teacher's strategy in improving initial reading skills using the syllabic method for phase A class I students at the Kalipucang Kulon Batang State Elementary School has not yet been achieved optimally, but little by little there has been an improvement in the students' reading skills. (3) The factors that influence initial reading difficulties in phase A class I students at Kalipucang Kulon Batang State Elementary School are caused by four factors, namely &lt;/em&gt; &lt;em&gt;physiological factors, intellectual factors, environmental factors and psychological factors.&lt;/em&gt;
Exploring the Impact of Blockchain Technology on the Financial System: A Case Study of Decentralized Finance (DeFi) in the European Banking Sector

This paper explores the transformative impact of blockchain technology on the European banking sector, with a specific focus on the dynamics of Decentralized Finance (DeFi). Beginning with an overview of the emergence and evolution of blockchain, the study delves into the integration of blockchain in European banking, examining its current landscape, opportunities, and challenges. A comprehensive exploration of DeFi principles, smart contracts, and cryptocurrency significance follows, highlighting European banking initiatives in this realm through case studies. The paper assesses the broader impact of blockchain and DeFi on financial services, emphasizing transparency, security enhancements, cost reduction, and advancements in financial inclusion. The regulatory framework and legal considerations in Europe provide a crucial backdrop, with a detailed analysis of smart contract legality and the delicate balance required for consumer protection and privacy in the DeFi landscape. A case study evaluates the implementation of DeFi in selected European banks, scrutinizing blockchain integration, impact assessment, and strategies to overcome challenges. The journey concludes with a comprehensive examination of the interconnected elements, emphasizing the transformative potential of blockchain and DeFi in reshaping the financial landscape.
PENERAPAN MODEL BLENDED LEARNING UNTUK MENINGKATKAN PEMAHAMAN KONSEPTUAL DAN KELANCARAN PROSEDURAL SISWA PADA MATERI STATISTIKA KELAS VIII

&lt;span class="ShortAbstract"&gt;&lt;em&gt;&lt;span&gt;This research aims to find the effect of applying the blended learning model to increase students' conceptual understanding and procedural fluency in statistics material in junior high schools. The Blended Learning model is used to with flipped classroom sub-model. This type of sub-model has two classes, named online and offline. For online's classes, use the help of the WhatsApp application and Google Classroom. The application of this blended learning model are expect to improve students' conceptual understanding and procedural fluency in learning mathematics, especially in statistical's subject matter to be researched in this research. This research is a type of quantitative research using experimental methods. Data collection techniques that used in this research is observation and tests. The observation in question is the teacher's observation using the observation sheet to observe the teacher's activities in teaching when the question test is a test of conceptual understanding and procedural fluency in the form of an essay test. Data analysis techniques were performed with calculating the results of the pretest and posttest in the experimental class and control class and then carrying out a Test of Normality, Test of Homogeneity of Variance, Paired Sample Test, Independent Sample Test, and concluding the results of the test. The results of the research are that both the blended learning model and the conventional learning model both have an increase. Even though there is an increase in both the blended learning model and the conventional learning model does not have a significant effect on students' conceptual understanding and procedural fluency even though the results show that there is a difference in the increase between the experimental class and the control class.&lt;/span&gt;&lt;/em&gt;&lt;/span&gt;
Designing the Decision Support System to Determine the Major and Higher Education Institution based on Preferences of Prospective Students

There are several factors considered by students toward determining the major and higher education institution. As a result, determining the best choices for further studies becomes difficult. Moreover, there are many choices of higher education. This study contributes to developing a decision support system (DSS) to help students determine the major and higher education institution considered several criteria based on Analytical Hierarchy Process (AHP). Several criteria and subcriteria were chosen by distributing 186 questionnaires to final grade students. Based on the result, criteria and subcriteria used were tuition fee, scholarship, infrastructure (library, classroom, laboratory, transportation, teaching staff), curriculum, quality (accreditation, study period, achievement), reputation, and international collaboration. According to these criteria and sub-criteria, a DSS was built to help students determine their major and higher education institution according to their preferences. The algorithm used was AHP, which was then built with Borland Delphi. Context Diagram, Data Flow Diagram (DFD), and Entity Relationship Diagram (ERD) were made as a guide to design the DSS. This DSS was tested using black box test, beta test and compatibility test with manual calculations. The black box test showed that the DSS has performed the functions according to the design. Later, the beta test resulted in a correctness score of 20.4, reliability of 16.8, usability of 25.5, which means they were in the very good category and integrity of 3.4, which means good. Meanwhile, based on manual calculations, the DSS provides output in accordance with the results of manual calculations. Finaly, he DSS was proven to be used as a decision-making tool in determining the major and higher education institution for students
CDP-Sim: Similarity Metric Learning to Identify the Fake Copy Detection Patterns

Due to development and broad availability of high-quality printing and scanning devices, the number of counterfeited products and documents is dramatically increasing. Therefore, different security elements have been suggested to prevent this socioeconomic plague. One of the most promising and cheap solutions is the use of Copy Detection Pattern (CDP), a maximum entropy image, generated using a secret key. This pattern takes full advantage of information loss principle during printing-and-digitization process to detect copies. Such an unpredictable pattern is highly sensitive to distortions occurring inevitably during production (printing), verification (digitization) and reproduction (duplication) processes. Initially, the detection of counterfeited CDP was devoted to evaluating the level of information loss using Pearson correlation. However, the security of CDP based authentication system was shown to be vulnerable to estimation attacks based on neural network that can infer a CDP after scanning. In this paper, we study how to increase the performance of a detector using a similarity metric learning approach.
A Transfer Learning Applied for Malaria Disease Detection on Blood Smear Images

In this work, we developed a convolutional neural network, which we used to diagnose malaria parasites in images of red blood cells in blood smear samples. A method was developed from the study of the sizes of the images to modify them with the minimum change to their original proportions to process them with a convolutional neural network previously trained using transfer learning techniques. In this paper, we also use data augmentation techniques to improve the efficiency of our predictor. The comparison of our method and the convolutional neural network architecture developed with the other twenty-one classification models reported in the literature is presented. Our proposal achieves an efficiency of 96% in image classification. This result is equal to the best result obtained with models reported in the literature, and it also has the advantage that it is a small convolutional neural network that does not consume much time and resources in its training.
Variational Voxel Pseudo Image Tracking

Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor. In this paper, we propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking. The Variational Feature Generation Network of the proposed Variational VPIT computes features for target and search regions and the corresponding uncertainties, which are later combined using an uncertainty-aware cross-correlation module in one of two ways: by computing similarity between the corresponding uncertainties and adding it to the regular cross-correlation values, or by penalizing the uncertain feature channels to increase influence of the certain features. In experiments, we show that both methods improve tracking performance, while penalization of uncertain features provides the best uncertainty quality.
Solar System Education Application Using Markerless Augmented Reality (AR) on The Android Platform

The solar system, a small component of the vast universe, encompasses celestial bodies including the Sun, its planets - Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune - along with 165 identified satellites. Many students find solar system studies engaging, as it enables understanding of the myriad celestial objects beyond our planet. Traditional learning methods, often limited to explanations and exercises by instructors, require students to be attentive and diligent in reading to master the subject. However, this can sometimes lead to monotony and disinterest. Previous research indicates that the use of educational media is a key factor in successful learning activities. Technology-based education systems make learning more dynamic and interesting by incorporating images, text, sound, and video. Augmented Reality (AR), which merges real objects with virtual elements in an interactive environment, allows virtual technology to appear seamlessly in the real world. This creates an illusion of virtual objects being present in front of the user, enhancing realism. Recent studies have resulted in the development of an AR application for learning and understanding the Milky Way galaxy's solar system. This application addresses the lack of comprehension in primary school students who traditionally use globes and pictures for solar system education. It operates without the internet, modernizing learning and capturing students' interest through its Android application format. The application functions optimally on Android devices with a minimum of 3GB RAM, performing more efficiently on devices exceeding these specifications.
Deep Learning for Digital Safety: Cyberbullying Detection in Arabic YouTube Content

In today's digital age YouTube is a primary platform for content sharing, communication, and entertainment. Yet, its growth has also seen the emergence of challenges like cyberbullying, which diminishes the platform's positive contributions. The challenge of addressing cyberbullying intensifies when dealing with the Arabic language, given its diverse dialects and distinct linguistic characteristics. In this study, we utilize a combination of deep learning and machine learning techniques, with a special focus on the Convolutional Long Short-Term Memory (CLSTM) model, to detect cyberbullying in Arabic YouTube comments. The primary challenge lies not only in the volume of data but also in its linguistic diversity. We curated a dataset of 365 comments from 16 YouTube videos using Python and applied the CLSTM model for analysis. Our model achieved an accuracy rate of 90%. These findings highlight the efficacy of deep learning in protecting Arabic-speaking YouTube users and present a framework for addressing similar linguistic challenges in other languages.
Blockchain-Based Secure Storage And Sharing Of Medical Data Using Machine Learning

Blockchain technology, combined with machine learning, is becoming increasingly relevant in healthcare for secure and transparent data management. This combination leads to smart applications that enhance patient data management, drug discovery, and supply chain management. For patient data management, it offers a secure, decentralized system for storing and sharing records, while machine learning analyzes this data, enabling personalized treatments that can lead to better patient outcomes. This approach is also instrumental in supply chain management where blockchain provides a secure, transparent method for tracking drug and supply movements, while machine learning helps in predicting and averting potential disruptions. In this study, we applied machine learning algorithms to predict and detect security breaches within a blockchain-based medical data system. Our experimental results indicated that the Random Forest algorithm was particularly effective in identifying security threats, as it recorded the highest performance among the various models tested.
Estimation of urban solar radiation for the City of Recife-PE

Urban Global Solar Radiation(RS) is one of the main inputs of urban climatology, and should already be considered from the conception of the construction project of green infrastructures, which mitigate the environmental impacts of urbanization. The objectives of this work were to calibrate and evaluate the performance in estimating the RS, for the capital of Pernambuco, of six existing models in the literature, namely: Hargreaves; Samani (1982), Bristow; Campbell (1984), Jong &amp; Stewart (1993), Chen et al. (2004), Hunt et al. (1998) e Annandale et al. (2002), referring to a rainy period in the year 2023, with data from an automatic meteorological station installed in one of the campuses of the University of Pernambuco, in the city of Recife-PE. The statistical criteria used for the analysis of the calculated data: the root mean square error (RMSE), the Nash-Sulcliffe coefficient (NSE) and the residual mass coefficient (CMR). The models, in ascending order of greater effectiveness are: Hunt et al. (1998), Bristow; Campbell (1984), Jong &amp; Stewart (1993), Chen et al. (2004), Hargreaves; Samani (1982), and Annandale et al. (2002). However, it should be noted that for the use of the model proposed by Hunt et al. (1998), it is necessary to have daily precipitation data, which may not be available, especially in historical series. If only temperature values are available, the best model for estimating RS is the one proposed by Bristow; Campbell (1984), which presented a satisfactory performance and very close to that obtained by the model proposed by Hunt et al. (1998).
An Evaluation of the Security and the Privacy of a Novel Single Sign-On System Based on Physical Unclonable Functions

In this work, we present a device-based approach for a Single Sign-On (SSO) system that can be used for user authentication to web services hosted by remote servers. Our approach is based on the utilisation of intrinsic Physical Unclonable Functions (PUFs) and each user's credentials in the context of the Operating System (OS) found on the device. We examine the security and privacy benefits and deficiencies associated with the employment of the proposed SSO system for web services in general, as well as those connected with its potential deployment to the public Internet. In particular, we note that the proposed scheme is highly flexible and secure, and can not only act in a privacy-friendly manner, but also provide for total anonymity, as long as trust can be adequately established.
Image Recovery Using Tensor Completion via Low-rank Augmentation

An image can be represented as a third-order tensor, and iteratively approximated using a low-rank tensor decomposition model. However, some images may not have a strong low-rank property. We need to lower their rank. In this paper we propose an effective tensor completion model via low-rank augmentation by integrating the low-rank property and local similarities of a tensor for recovering missing entries of an image. First, dividing an original image into sub-images and concatenating them to be a tensor. Second, utilizing a tenor completion algorithm based on the tensor nuclear norm to recover its missing entries, and extracting the recovered image from the tensor. Finally, filtering the recovered image with the bilateral filter. We fully utilize the local similarity of the image and obtain the third-order tensor composed of sub-images by sampling adjacent pixels of the image, making it have stronger low-rank characteristics. Experimental results demonstrate the advantages of our method compared with other tensor completion algorithms.
Face Image Inpainting Based on Mask Gridding Autoencoder and Global-Local Refinement Fusion

Face image inpainting is a technology that can accurately predict the texture and structure of missing areas in face images. In face image inpainting, it is crucial to consider both global and local context information for the missing region. However, existing methods tend to overlook the significance of this aspect. This work introduces a hierarchical framework for face inpainting, which is based on the fusion of global and local refinement using the mask griding autoencoder (MGAE).To begin with, the irregular mask in face inpainting is preprocessed using the grid strategy, which enhances by the pre-trained mask autoencoder (MAE). This preprocessing step helps in obtaining preliminary repair results. Subsequently, a global and a local context refinement network are designed to refine the preliminary repair results simultaneously. Finally, a fusion decoder is developed to effectively combine the global and local features of different scales, resulting in the final output of the face image inpainting process. The experimental results demonstrate that our proposed method is highly competitive when compared to the most advanced techniques on the face dataset.
Local Information fused Transformer model for Korean-Chinese Machine Translation

Chinese-Korean machine translation holds significant value. However, existing translation models face challenges such as vanishing and exploding gradients, as well as inadequate local information modeling. In order to overcome these issues, we present the LW-Transformer, an innovative Chinese-to-Korean machine translation model. This model is built on the foundation of pre-normalization and local information. To begin, we successfully mitigate the problems of vanishing and exploding gradients by incorporating pre-normalization layers. This approach ensures stable data distribution. Additionally, we implement a localized self-attention mechanism, which enhances the focus on local information within sentences. We conduct extensive experiments on the SWRC dataset and the Dong-A Ilbo dataset to validate the LW-Transformer model. The results clearly demonstrate the significant performance enhancement in Chinese-Korean machine translation. Compared to conventional models, the BLEU scores for Korean-to-Chinese translation improve by 4.7 and 1.4 on the SWRC and Dong-A Ilbo datasets, respectively. Similarly, Chinese-to-Korean translation achieves improvements of 5.1 and 1.5, respectively.
Automated data set construction system for clinical EEG research

In clinical EEG research, the construction of EEG datasets has an important impact on the research results. However, the needs of dataset construction are diverse, each of which involves multiple dimensions, and a single construction scheme cannot meet the needs of users. Meanwhile, when extracting samples, labels and requirements need to be compared one by one, and most datasets do not provide negative examples. Based on the above problems, a set of rules is proposed to describe the requirements of constructing EEG datasets, and a software system framework supporting rule-driven EEG dataset generation is designed and implemented. It solves the problem of too rigid rule making and lack of negative example labels when constructing the data set. It makes the construction of data sets more convenient and efficient. Finally, based on the system, a negative example construction scheme of active learning based on uncertain query strategy was designed. The model trained using the dataset constructed by the proposed scheme has better performance compared to the scheme where the negative examples are randomly selected.