Reviewerly/exHarmony huggingface dataset:
Retraction: On the Thanksgiving Value of Confucian Culture based on the Big Data (J. Phys.: Conf. Ser. 1744 042027)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
The Relationship between efficiency of education and economic development: A comparative study

In the last years governments are working towards improving their economic development stance among countries, while also working on achieving their set sustainable development goals in different sectors. One of those sectors that plays a vital role in enhancing and improving economic development ranks is education. However, countries are not paying enough attention on improving and developing their education system through neither increasing their spending on developing and improving their education sector nor paying attention on assessing their efficiency performance in education. Therefore, the aim of this paper is to study the impact of efficiency of education on economic development through on selected Asian and North African countries from the period of 2000-2021. In which the results of this study have showed that the efficiency of education determinants has a positive impact on spurring economic development. The outcomes of the study have also indicated that the countries who recorded higher efficiency of education and thus higher economic development rate are the countries that are concentrated in Asia. On the other hand, North African countries had low rates of education efficiency and lagged behind in the economic development ranks compared to Asian Countries this was due to the characteristics of their education system which was acknowledged by being underdeveloped infrastructure, traditional learning tools, and low government spending.
Sample Density Clustering Method Considering Unbalanced Data Distribution

The data distribution of the multidimensional array sensor is unbalanced in data sample collection. To improve the clustering ability of data samples, a data density clustering method of sparse scattered points and multisensor array sensor samples based on the analysis of unbalanced data distribution characteristics is proposed. The sparse scattered multisensor array network’s sample data collection structure is created using the Voronoi polygon topology. By analyzing the unbalanced parameters between data classes and reconstructing the characteristic space of data sample sequence, the time series of sample data collected by sparse scattered multisensor array is reorganized, and the statistical characteristic quantity and high-order cumulant of sample data collected by sparsely scattered multisensor array are extracted. Combined with the learning algorithm of unbalanced data distribution sample feature fusion, the fuzzy clustering of sample data information flow collected by sparse scattered multisensor array elements is realized. According to the feature clustering and convergence analysis, the sparse scattered feature detection method is adopted to realize the data density clustering and data structure optimization configuration of sparse scattered multisensor array elements. The test results show that the method in this paper has good convergence, strong spectrum expansion ability, and low error rate of data clustering when collecting samples with sparse scattered points and multisensor arrays.
Penerapan Metode Belajar Sambil Bermain Dalam Mengembangkan Minat Belajar Anak-Anak Gang Perwira Medan

Interest is a sense of preference, interest, attention, focus, persistence, effort, and the result of a person's or individual's interaction with certain content or activities. One of them is learning while playing to foster one's interest in learning, seeing children's playing spirit is very high, therefore it is combined so that learning is not left behind. Children get bored quickly if it is not combined with methods like this, in addition to increasing interest in learning, it also increases creativity for the officers of Gang officers. The process of helping to increase interest in learning with the PKL I mini project using the casework method through the individual intervention stages of Engagment Intake Contract, Assessment, Planning / Planning, Intervention, Evaluation, and Termination. The focus of the client problem solving process is the use of the Learning While Playing method as a jointly designed strategy to increase the client's interest in learning. The purpose of this program is stated to have been achieved, proven, and an increase in the enthusiasm for learning and a great curiosity towards mathematics and other sciences
Attention Multi-scale Fusion Hashing for Image Retrieval

With the rapid development of the Internet and the dramatic increase of image data, image retrieval become a research hot topic. Deep hashing is a binary retrieval method for large-scale data, which combines the powerful feature learning capabilities of neural networks and the inherent advantages of small storage and fast retrieval of hash codes and has become a powerful tool for solving multimedia retrieval problems in recent years. Image feature extraction is the basis of image retrieval and has an important impact on the results of image retrieval. To obtain better image features, this paper proposed an attention multi-scale fusion module to enhance the global and local features and introduced SoftPool to retain more critical features. This algorithm can better solve the problem of gradient disappearance through the SoftSign function. The experiments show that this algorithm performs well in hashing retrieval, which verifies the effectiveness of the algorithm.
Single Image HDR Synthesis Using a Dense Reverse Connection Network

In order to better improve the image quality of high dynamic range (HDR) scenes, the single-exposure image is used to generate HDR images without increasing the hardware cost. In order to achieve more effective feature learning, a deep learning model based on dense reverse connection network is proposed. Compared with continuously learning redundant features, feature reuse through dense connections is a better way to acquire features. On the basis of dense connections, local feedback and residual connections are added to further improve the information flow, increase the network representation ability, and achieve better performance. During the training process, the high-level features delivered by the residual reverse connection modules of each layer all contain some of the optimal weights and rich local feature information required for image reconstruction. Global reverse connection adds the high-level features of each layer to the input, and uses the high-level features to guide the low-level features to train through the hidden state. Compared with other forward transfer networks, it avoids the loss of optimal results, obtains higher image brightness and contrast, reduces gradient disappearance, and enhances high-frequency details. The model is an end-to-end training network. The experimental results show that the method in this paper has higher SSIM and PSNR indicators than existing methods, and achieves better subjective vision results. The brightness and details of the LDR image are better restored.
Certificateless Blind Aggregation Signature Scheme in the Blockchain Electronic Auction

In the age of Internet, the commodity transaction is characterized by some new forms, such as e-transaction, e-auction, etc. The advanced internet technology can bring us several convenience and economic benefits, but it also poses a lot of risks and hidden danger in online transaction. In the process of e-transaction or e-auction, the theft of personal private data is also increasing. In order to protect the privacy of bidders in electronic auctions, this paper proposes a blind aggregation signature scheme that can be applied to the blockchain-enabled electronic auctions. Combining the characteristics of blind signatures and aggregated signatures, this scheme ensures the privacy of bidders and allows the electronic auction system to generate only one blind aggregated signature, which makes the system more optimized. The smart contract is invoked as a trusted third party to build an electronic auction system to prevent malicious internal members from attacking by combining the blind aggregation signature scheme. The security analysis shows that the proposed scheme can provide security protection for the electronic auction system, such as anonymity and non-forgery. Meanwhile, in the performance, the proposal enjoys a higher improvement in computing speed, which confirms the applicability of blockchain technology in the field of electronic auction.
Comparing the Denoising Effect of NMF with Different Auxiliary Variables on Face Images

This paper evaluates the denoising effect of NMF with different auxiliary variables on face images. Firstly, the data is preprocessed, and then different degrees of Gaussian noise or Salt-pepper noise is added, respectively. Finally, the denoising effects of NMF, L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -NMF, L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -NMF, CIM-NMF, and Huber-NMF are evaluated by three evaluation methods. The results show that all NMFs show good robustness in dealing with Gaussian noise; however, it is not ideal for dealing with Salt-pepper noise, and CIM-NMF is better than other NMFs in dealing with Salt-pepper noise.
Problem Identification of Learning Chinese Proverbs at Faculty of Teacher Training and Education Tanjungpura University

This research aims to reveal some problems in learning Mandarin Chinese Proverbs in the Mandarin Language Education Study Program FKIP Tanjungpura University. The research method used in this research is qualitative descriptive study. Researchers use documentation techniques and interview techniques in gathering the necessary data. The documentation technique is used to collect data from non-human sources, which consists of learning curricula, textbooks, reference books in the library of mandarin language education programs FKIP UNTAN. The interview technique is done indirectly through Google forms and then analyzed descriptively. The results of the study indicate that the problem of learning Mandarin proverbs are related to the curriculum, teaching materials and students. The curriculum applied by the FKIP mandarin language study program in Tanjungpura University is in accordance with what was proclaimed by the government, namely the Curriculum Based Indonesian National Qualification Framework (KKNI)-based Higher Education Curriculum but in the translation of the available courses there is still not enough attention to the learning of Mandarin proverbs, because learning Mandarin proverbs is only an insert in the middle of learning another new vocabulary. The availability of teaching materials is still an obstacle in learning Mandarin Chinese proverbs because textbooks are still very dependent on grants from the Chinese government, so the available teaching materials are still very limited. The textbooks used in learning Mandarin proverbs explanations are also incomplete. From the viewpoint of students, they realize the importance of learning Mandarin proverb towards improving Mandarin language skills. 72.2% of students thought that by mastering Mandarin proverbs, it can indicate person's Mandarin language skills were already very high and 96.7% of students claimed to have difficulty in learning Mandarin proverbs.
Credit Card Fraud Detection using Various Machine Learning Algorithms

Abstract: Detecting credit card fraud is probably the most typical problem in the modern day. This is due to a growth in digital shopping as well as electronic-commerce platforms. As digitalization gains popularity in this current world, people are choosing online payment methods for time and transportation ease, among other reasons.Credit card fraud has increased significantly as a result of the tremendous growth in e-commerce use. Fraudsters attempt to exploit the card and the transparency of online payments. Credit card fraud often occurs whenever the card is taken/stolen or when the details of the card are known. As a result, countering fraudsters' activities has become critical. In the modern world, people are facing loads of credit card issues. There are various techniques and Machine Learning Algorithms to overcome this problem such as using Logistic Regression, XgBoost, Naïve Bayesian, K- Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) etc.The central goal is to safeguard electronic payments, allowing people to use e-banking safely and easily.
Augmented reality technology for the scientific publication on the history of the higher education libraries

The article highlights the experience of using infographics and augmented reality elements (QR code markers) to visualise information in the Scientific Library of the Kyiv National University of Culture and Arts: Times of Change and Augmented Reality anniversary scientific publication.
Deep Label Prior: Pre-Training-Free Salient Object Detection Network Based on Label Learning

Due to the excellent semantics extraction capabilities, deep learning methods have significantly progressed in salient object detection (SOD). However, these methods often require time-consuming pre-training and large training datasets with ground truth. To address these issues, by referring to the framework known as “deep image prior (DIP),” we propose a SOD method called deep label prior network (DLPNet), which consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal A$</tex-math></inline-formula> -stream and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal B$</tex-math></inline-formula> -stream. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal A$</tex-math></inline-formula> -stream includes two cascaded UNets and a simple CNNs module to extract the initial saliency map, while the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal B$</tex-math></inline-formula> -stream contains only two cascaded UNets, which refines the extracted initial saliency map. Unlike most of the current deep learning methods, DLPNet views the SOD task as a conditional image generation problem, relying on only the internal prior of the input itself to generate the saliency map. Hence, our DLPNet does not require pre-training or large annotated / unannotated datasets. Furthermore, we propose a morphology operation scheme, which creates rich pseudo-labels for facilitating the updating of network weights. Extensive experiments demonstrate that our method outperforms state-of-the-art unsupervised techniques and is even comparable to state-of-the-art supervised and weakly supervised methods on different evaluation metrics.
Feature Interaction Prototypical Network for Few-Shot Fine-Grained Image Classification

In recent years, although the task of fine-grained image classification has achieved remarkable results, these algorithms need to be trained on large datasets in order to obtain good results, otherwise it is easy to cause model overfitting and reduce the effect. However, fine-grained image datasets need to distinguish different subcategories under the same category. It is difficult to collect and label fine-grained images in practical application, so it is not easy to obtain a large number of good training data. Few-shot learning provides a solution for this. However, the traditional few-shot learning algorithm has limited feature extraction ability, and it is difficult to effectively classify fine-grained images with large intra class differences and small inter class differences. To this end, this paper utilizes the idea of model fine-tuning to extract features using a pre-trained model. In addition, the SimAM non-parametric attention mechanism is introduced to enhance features and filter backgrounds while reducing model parameters. Secondly, feature maps of different convolutional layers are fused using hierarchical bilinear pooling (HBP), thus effectively utilizing information from different convolutional layers to further enhance feature representation. Finally, the classification is completed by calculating the distance between the embedding vectors of the test samples and the well-distributed prototypes generated using the fused features. Experiments show that this algorithm performs well on few-shot fine-grained image classification tasks and achieves excellent performance on traditional few-shot image classification.
Effect of Using Quizlet Toward Students’ Vocabulary Mastery

The aim of this research was to know if there is significant effect of using Quizlet toward student vocabulary mastery. This research was using quasi-experimental research with two types of variable. First variable was the application of Quizlet as the independent variable while the second variable was the vocabulary mastery as the dependent variable. The samples in this research are students of eleventh grade at SMA Negeri 2 Trenggalek they were MIPA 2 as experimental group and MIPA 5 as control group. The experimental group was using Quizlet as learning media while the control group was not. The instrument was to collect the data of this research by pre-test and post-test. After the data was collected, the researcher analyzed the data by using independent sample t-test. Based on test the Sig. (2-tailed) of post-test were 0.000 &lt;0.05, than as the basic rules of making independent sample t-test, it can be concluded that H0 is rejected and Ha is accepted. Thus, it can be concluded that there is a significant difference between the average student learning outcomes in the experimental group and the control group. So, Quizlet application is an effective learning media for students' vocabulary mastery
An Algorithm for Detecting the Discontinuous Areas in Interferometric Phase Images

Interferometric measurements have been widely used in medical diagnosing and geographic mapping. The detection of discontinuous areas in interferometric phase images is very significant for subsequent processing (such as denoising, unwrapping, and so on). In this paper, the Wrapped Laplace Transform is proposed to adapt the edge detection methods to the characteristics of the interferometric phase image. Thresholding is committed to the transformation results to generate the preliminary detection results of the discontinuous area. Then the detected discontinuous areas are restored by dilation and erosion. The experimental results show that the proposed algorithm in this paper can effectively detect discontinuous areas in the interference phase image, while avoiding the effects of interference fringes.
DEEPFAKE DETECTION USING EYE-BLINKING PATTERN

Deep learning algorithms have become so potent due to increased computing power that it is now relatively easy to produce human-like synthetic videos, sometimes known as &amp; quot; deep fakes. &amp; quot; It is simple to imagine scenarios in which these realistic face switched deep fakes are used to extort individuals, foment political unrest, and stage fake terrorist attacks. This paper provides a deep learning strategy novel for the efficient separation of fraudulent films produced by AI from actual ones. Automatically spotting replacement and recreation deep fakes is possible with our technology. To combat artificial intelligence, we are attempting to deploy artificial intelligence. The framelevel characteristics are extracted by our system using a Res-Next Convolution neural network, and later these features are applied to train an LSTM-based recurrent neural network to determine if those submitted video is being altered in any way or not, i.e. whether it is a deep fake or authentic video. We test our technique on a sizable quantity of balanced and mixed data sets created by combining the different accessible data sets, such as Face-Forensic++[1], Deep fake detection challenge[2], and Celeb-DF[3], in order to simulate real-time events and improve the model's performance on real-time data.
A Survey of Text Detection Algorithms in Images Based on Deep Learning

Nowadays, text detection has infiltrated various industries like banking, education, criminal investigation, network public opinion, and more. However, the traditional way of text detection is largely dependent on the characteristics of manual design, which is time-consuming, laborious and inaccuracy. With the emergence of deep learning technology, text detection has already been transformed from text detection in static documents to more practical text detection in natural scenes. To address such challenges as complex background, improper acquisition, and multilingual text in natural scene images, there have been various improved neural network structures constructed. Especially, the performance of text detection in various scenes has been significantly improved. In learning in different scenes are summarized in detail. According to the different text objects in detection, the existing methods are classified into two categories as top-down and bottom-up, and the logic structure, advantages and disadvantages of various algorithms are summarized. In addition, the commonly used data sets and performance evaluation indexes are analyzed and explained, the practical application of text detection in different fields is introduced, and the future trend of research and development are indicated.
Peningkatan Kompetensi Teknologi Informasi dan Komunikasi Bagi Guru Paud Dengan Pelatihan Microsoft Powerpoint dan Access

The role of Information and Communication Technology (ICT) in the learning process in kindergarten is significant, especially in assisting teachers in evaluating learning and making interactive and communicative learning media. However, the PERAK Foundation, which has 9 PAUD in Petukangan Utara, South Jakarta, does not yet have a computer laboratory facility or provide computer facilities in each class or provide computers to PAUD teachers to assist teachers in the teaching and learning process. In addition, most of the PAUD teachers at Yayasan Perak are over 40 years old and technologically savvy. So that some of the things above hinder the application of ICT to PAUD teachers at the PERAK Foundation. Facing this problem, FTI proposed computer application training activities for PAUD teachers at the PERAK Foundation. The computer application training program aims to improve ICT skills or competencies for PAUD teachers, especially in using learning media. Based on the evaluation results of training activities carried out by the FTI Lecturer Team at Budi Luhur University in collaboration with the PERAK Foundation. It can be concluded that Microsoft PowerPoint and Access training activities have positively impacted efforts to improve information and communication technology competencies, especially for PAUD teachers at the PERAK Foundation