Reviewerly/exHarmony huggingface dataset:
ANALISIS PENGARUH PEMBELAJARAN DARING TERHADAP PRESTASI BELAJAR SISWA DIMASA PANDEMI COVID-19 MENGGUNAKAN MODEL REGRESI BERGANDA (STUDI KASUS DI SMPN 33 BANJARMASIN)

The impact of the Covid-19 pandemic that occurred from 2019-2021 has had an impact on many sectors, one of which is the education sector. With this pandemic, learning takes place online which is possible for junior high school students is not an effective thing so that it has an impact on the final grades of student learning. This study aims to estimate the parameters of the multiple linear regression model formed in the analysis of the effect of online learning on student achievement at SMPN 33 Banjarmasin during the Covid-19 pandemic, and to explain the factors that significantly influence student achievement at SMPN 33 Banjarmasin during the pandemic. Covid-19. This study uses the independent variables of learning media and teacher teaching methods while the dependent variable uses student achievement. The population of this research is students at SMPN 33 Banjarmasin with a total sample of 120 respondents. Analysis was performed using multiple linear regression analysis. The results of the study showed that there was a significant influence between learning media and teacher teaching methods on student achievement at SMPN 33 Banjarmasin during the Covid-19 pandemic. Other results are shown based on testing the hypothesis that the variable that has the most influence is the teacher's teaching method variable. The teacher's teaching method variable (X2) has the largest relative contribution compared to the learning media variable (X1). Keywords: Online Learning, Learning Achievement, Multiple Linear Regression
intuitR: A Theorem Prover for Intuitionistic Propositional Logic

A constructive proof proves the existence of a mathematical object by giving the steps necessary to construct said object.Proofs of this type can be interpreted as an algorithm for creating such an object.Intuitionistic Propositional Logic (IPL) is a propositional logic system wherein all valid proofs are constructive.intuitR is a theorem prover for IPL, that is, it determines whether a given formula is valid in IPL or not.In this paper, we describe how intuitR determines the validity of a formula and review its performance.When compared on a benchmark set of problems, intuitR was determined to solve more problems and to be of comparable speed or better than other IPL-provers.
Exploring Methods Used in Face Swapping

Face swapping involves replacing the face in one image (the target) with a face in a different image (the source) while maintaining the pose and expression of the target face. Previous methods of face swapping required extensive computer power and man hours. As such, new methods are being developed that are quicker, less resource intensive, and more accessible to the non-expert. This paper provides background information on key methods used for face swapping and outlines three recently developed approaches: one based on generative adversarial networks, one based on linear 3D morphable models, and one based on encoder-decoders.
Effects of Using the WhatsApp Application on Iranian Intermediate EFL Learners’ Vocabulary Learning and Autonomy

The current study was planned to find out if the use of blended learning as a combination of face-to-face instruction and mobile-assisted language learning using WhatsApp contributed to the vocabulary learning and autonomy of Iranian EFL learners compared to the traditional method. To assess their English skills, PET was given to 80 homogenous intermediate learners at the beginning of the study. The study's intended participants were fifty EFL learners with scores that were within the intermediate competency level. Then, the participants were divided randomly to experimental and control groups to see how successful blended learning vs traditional education is at improving learners' vocabulary knowledge. One-way between-groups analysis of covariance was run. On post-test scores, the findings indicated a statistically significant difference between the experimental and control groups. Another one-way between-groups analysis of covariance was performed to assess the impact of two distinct blended learning vs traditional teaching treatments on EFL learners' autonomy. In post-test results, a significant difference between the control and experimental groups' performance was observed. This study provided insights into how technology may be applied to teach language components and skills.
An Entity based Approach for Extracting Relationship between Chinese Characters

Knowledge extraction includes the extraction of entity relationships, which is crucial. Deep learning algorithms are increasingly prevalent in relation extraction tasks when compared to conventional pattern recognition techniques. The majority of current research on remotely supervised methods and kernel functions in relationship extraction strategies for Chinese language cannot disregard the detrimental effects of noisy data in the dataset on the experimental findings. A bidirectional GRU neural network, a two-layer attention mechanism, and a Chinese relationship extraction model are proposed. For the forgetfulness problem, a bidirectional GRU neural network is utilised to fuse the input vectors. Integrating the structural properties of the Mandarin language, word vectors are employed as the input. Sentence-level attention mechanisms are utilised to extract sentence features, and word-level feature information is retrieved from a sentence. About 1300 bits of data are extracted from news websites using a remotely supervised methodology for validation. According to the experimental findings, the neural network model with a two-layer attention mechanism is able to fully utilise all of the feature information in a sentence, and it performs significantly better than the neural network model without an attention mechanism in terms of accuracy and recall rates. Compared to the model without the attention mechanism, the accuracy and recall rates are noticeably greater.
Examinations and Modeling of Metal Mesh Surfaces for Antenna-on-Display (AoD) Technology

The metal mesh structures for Antenna-on-Display (AoD) are investigated in this paper. Their equivalent electromagnetic properties are extracted from the simulated results by applying the Floquet theorem. The equivalent homogeneous model is studied, provided and validated with measurements in this paper which can be employed to simplify AoD structures for the ease of simulation and antenna design.
A New Method for Image Tamper Detection Based on an Improved U-Net

With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious, and introduce the channel coordinate confusion attention mechanism (CCCA) in skip-connection to further improve the segmentation accuracy of the network. In this paper, extensive experiments are conducted on various mainstream datasets, and the results verify the effectiveness of the proposed method, which outperforms the state-of-the-art image forgery detection methods.
Comment on “Multi-Keyword Searchable and Verifiable Attribute-Based Encryption Over Cloud Data”

In recent article Zhang, et al. 2023, the authors presented an efficient and verifiable multi-keyword attribute-based search scheme over cloud data. In this comment, we show that the key equation design in their scheme has errors, leading to the inability to perform effective searches in the case of multi-keyword. Then we propose a correction to address this issue without compromising the original scheme’s security.
Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks.
Deep Neural Network Quantization Framework for Effective Defense against Membership Inference Attacks

Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. This paper exploits the implications of quantization on privacy leakage and proposes a novel quantization method that enhances the resistance of a neural network against MIA. Recent studies have shown that model quantization leads to resistance against membership inference attacks. Existing quantization approaches primarily prioritize performance and energy efficiency; we propose a quantization framework with the main objective of boosting the resistance against membership inference attacks. Unlike conventional quantization methods whose primary objectives are compression or increased speed, our proposed quantization aims to provide defense against MIA. We evaluate the effectiveness of our methods on various popular benchmark datasets and model architectures. All popular evaluation metrics, including precision, recall, and F1-score, show improvement when compared to the full bitwidth model. For example, for ResNet on Cifar10, our experimental results show that our algorithm can reduce the attack accuracy of MIA by 14%, the true positive rate by 37%, and F1-score of members by 39% compared to the full bitwidth network. Here, reduction in true positive rate means the attacker will not be able to identify the training dataset members, which is the main goal of the MIA.
Zk-SNARKs As A Cryptographic Solution For Data Privacy And Security In The Digital Era

This research brings the concept of zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) in the context of data security and privacy in the digital world. zk-SNARKs is a cryptographic technology that allows individuals to prove a statement or knowledge without having to reveal the actual details of the information. We illustrate this concept through a simple example of proving age over 18 without revealing the actual date of birth. This research highlights the importance of maintaining data privacy in various technological applications, including the use of zk-SNARKs in blockchain to maintain transaction privacy and personal data protection in increasingly sophisticated applications. However, the implementation of zk-SNARKs requires deep mathematical understanding and strong data security concerns. With great potential to support data privacy and security in the evolving digital era, zk-SNARKs is a highly relevant tool in addressing privacy-related challenges in the digital world. The conclusion of this research is that zk-SNARKs is an important tool in maintaining data privacy and security in the digital age. With its ability to allow individuals to prove knowledge or assertions without revealing actual details of information, this technology has wide applications in various sectors, including finance, data management, and data privacy protection. However, it should be emphasized that the implementation of zk-SNARKs requires extra care in securing the system and ensuring that the technology is used properly to maintain high data privacy and security. With further development and understanding in this technology, zk-SNARKs can be an integral component in building a safer and more private digital world.
Ensino eficiente de alunos com albinismo e com visão subnormal: uma cartilha para educadores

The World Health Organization (WHO, 2021) classifies albinism as a hypomelanotic disorder described as a group of hereditary disorders in which melanin skin production is reduced or absent, mainly because of defects in enzymes necessary for its biosynthesis. This context can contribute to physiological changes that contribute to increasing barriers in the educational (generating school, social (promoting difficulty access to the labor market) and economic (poverty or financial dependence) field. The objective of this work was to elaborate a booklet on teaching/learning methods to be used by the teacher, to provide the student with albinism with a significant and effective learning. To this end, reference materials were used in inclusive teaching, as well as aesthetic adequacy manuals that provide accessibility to the public with low vision. A booklet accessible to the entire faculty was elaborated, with direct language and comfortable visual design. The teaching-learning process of people with disabilities is not far from regular education, so that the albino person, framed as a person with visual impairment, needs to have his space guaranteed with the promotion of the active methodologies presented, inserting them in the construction of his own knowledge, which until then is denied to them. Thus, the preparation of the booklet allowed the presentation of reliable and easy-to-be-reproduced information, so that the insertion of such content during teacher training has the potential to enable the achievement of the proposed objectives, making it important for teachers and, consequently, for students and school institutions that have students with albinism.
Development of the method of general interpolation for Z-number-valued if-then rules

Rule interpolation-based methods are used when the rule base is sparse. This frequently being the case, as information relevant to real-world problems is not usually comprehensive. At the same time, relevant information is often characterized by both fuzziness and partial reliability. To deal with such kind of information, the concept of Z-number was introduced by Zadeh. This paper is devoted to an extension of the general interpolation method for fuzzy rules to the case of if-then rules with Z-number-valued antecedents and consequents. The proposed approach relies on the determination of the distance between the current observation vector and vectors of rules antecedents. By determining the distance between the current vector and the antecedents of the rules, decisions can be made based on the nearest antecedents. In this context, rule antecedents are vectors that represent certain conditions. The resulting output is computed as a weighted sum of rules consequents. Weighting factors are used to account for the importance of each rule in the interpolation. Weights of interpolations are found on the basis of mentioned distance values. The results of this study are aimed at developing an approach to decision-making in terms of Z-valued information. The method is characterized by relatively low computational complexith. Regarding the application of the proposed approach, the job satisfaction evaluation problem is considered. Consequently, the obtained results confirm the efficiency of the proposed approach. The proposed method can be a useful tool for decision-making in various applications, especially where high computational complexity is unacceptable or impractical
Penerapan data mining dalam analisa profil mahasiswa menggunakan metode support vector machines (SVM)

decision making. The Support Vector Machine algorithm is a method in supervised learning which is usually used for classifications such as Support Vector Classification and support Vector Regression regression. In this study the SVM algorithm was used in the classification of student data profiles at the Batam Institute of Technology, which can be used as material for consideration in determining and considering decisions to make an effective destination strategy promotion.&#x0D;
Metode Random Forest untuk Klasifikasi Penyakit Diabetes

Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.&#x0D; Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.
PRIMARY VOCATIONAL EDUCATION IN ORENBURG REGION IN THE POST-SOVIET PERIOD

Освещены важные преобразования в системе начального профессионального образования в Оренбургской области начиная с 1991-го по 2000-е гг. Показана специфика переходного периода, когда шла перестройка законодательного характера, заложившая нормативно-правовую основу новой российской государственности. Использованы материалы «Российского статистического ежегодника» (Федеральная служба государственной статистики). Основное внимание уделено вопросам преобразования стандартов госрегулирования образовательного функционирования, созданию рыночной институциональной среды, а также модернизации экономических систем в начальном профессиональном образовании региона. The article is devoted to important transformations in the system of initial vocational education in the Orenburg region from 1991 to the 2000s. The author shows the specifics of the transition period from 1991-2000s., when the legislative restructuring took place, which laid the legal basis for the new Russian statehood. In the article, the author uses quantitative characteristics that are indicative and are not of a principled nature, as there are discrepancies in the field of statistics in education, which is related to both different approaches in the calculation, and with a uniform understanding of a fixed phenomenon. The author uses materials of «Russian statistical yearbook» (Federal Service of State Statistics). The author focuses on issues of transformation of standards of state regulation of educational functioning, creation of market institutional environment, as well as modernization of economic systems in primary vocational education of the region.
Towards Low-light Image Restoration via Color Correction Matrix Learning

Images captured in low-light not only suffer from low visibility but also result in poor quality, which may significantly degrade the performance of multimedia and computer vision tasks. A large number of techniques have been proposed to enhance the low-light images. The majority of the low-light techniques use sRGB datasets and do not perform well in extreme low-light image enhancement. A few techniques use the raw data for extreme low-light enhancement but the output images suffer color correction and details loss. In this paper, to address this color correction problem a new dual MultiResUnet based network is proposed. In the proposed approach, raw and sRGB data is used as input, whereas sRGB images are generated from raw data by using the traditional pipeline. One branch of the dual CNN learns the structural details from raw data and the other branch learns a spatially variant color transformation matrix from sRGB data for color correction. The final result is produced by applying the learned color transformation matrix to the structural information produced by the first branch. The experimental results demonstrate that the proposed approach performs better and achieved well-adjusted color information for extremely low-light images.
Axiom Content: Delineating Broad Categories

In the previous chapter, we organized axioms in terms of format. Let’s now organize them in terms of content, economic content. In this chapter, I’ll introduce broad categories that I’ll refine progressively in the following chapters.