Reviewerly/exHarmony huggingface dataset:
Advanced content-based retrieval for digital correspondence documents with ontology classification

The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using a combination of convolutional neural network (CNN) and regular expression method), archiving (document archiving with automatic classification), and retrieval (content-based search by displaying ontology relationships from the document classification). The archiving of 100 documents using the automatic hierarchical classification was found to be 79% accurate as indicated by the 99% accuracy for CNN and 80% for Regex. Moreover, the search results for classified content-based documents through the display of ontology relationships were discovered to be 100% accurate. This research succeeded in improving the quality of search results for digital correspondence documents as indicated by its higher specificity, accuracy, and speed compared to conventional methods based on file names, annotations, and unclassified content.
Analisis Performa Algoritma Machine Learning pada Prediksi Penyakit Cerebrovascular Accidents

Cerebrovascular Accidents (stroke) are a disease that threatens and causes death and disability and disability in the world, in Indonesia the number of people affected by stroke is increasing every year. Stroke can be prevented by adopting a healthy lifestyle, eating nutritious food, and doing physical activity. The purpose of this study is to create an effective stroke prediction model, the system uses parameters from lifestyle factors, controllable factors such as medical risk factors, and uncontrollable factors. Four classification algorithms are proposed, namely multi-layer perceptron, KNN, Decision Tree, and Random Forest. The results show that the classification algorithm can work effectively by producing a perfect score of 99.99% accuracy at the 10K-Fold Validation level of validation.
A Comparison of Deep Learning Architectures for the 3D Generation Data

There is a need to identify the best artificial images for each use case faced with several Deep Learning architectures for generating them. Twelve models with different hyperparameters were created to compare several networks with the generative architectures Autoencoder, Variational Autoencoder, and Generative Adversarial Networks in the 3D MNIST dataset. After training, the models were compared with loss functions to assess the difference between the original and artificial data, so that greater complexity did not translate into better performance, indicating the Autoencoder models as the best cost-benefit.
SparseFormer: Attention-based Depth Completion Network

Most pipelines for Augmented and Virtual Reality estimate the ego-motion of the camera by creating a map of sparse 3D landmarks. In this paper, we tackle the problem of depth completion, that is, densifying this sparse 3D map using RGB images as guidance. This remains a challenging problem due to the low density, non-uniform and outlier-prone 3D landmarks produced by SfM and SLAM pipelines. We introduce a transformer block, SparseFormer, that fuses 3D landmarks with deep visual features to produce dense depth. The SparseFormer has a global receptive field, making the module especially effective for depth completion with low-density and non-uniform landmarks. To address the issue of depth outliers among the 3D landmarks, we introduce a trainable refinement module that filters outliers through attention between the sparse landmarks.
Tolerating Inconsistencies: A Study of Logic of Moral Conflicts

Moral conflicts are the situations which emerge as a response to deal with conflicting obligations or duties. An interesting case arises when an agent thinks that two obligations A and B are equally important, but yet fails to choose one obligation over the other. Despite the fact that the systematic study and the resolution of moral conflicts finds prominence in our linguistic discourse, standard deontic logic when used to represent moral conflicts, implies the impossibility of moral conflicts. This presents a conundrum for appropriate logic to address these moral conflicts. We frequently believe that there is a close connection between tolerating inconsistencies and conflicting moral obligations. In paraconsistent logics, we tolerate inconsistencies by treating them to be both true and false. In this paper, we analyze Graham Priest’s paraconsistent logic LP, and extend our examination to the deontic extension of LP known as DLP. We illustrate our work, with a classic example from the famous Indian epic Mahabharata, where the protagonist Arjuna faces a moral conflict in the battlefield of Kurukshetra. The paper aims to avoid deontic explosion and allows to accommodate Arjuna’s moral conflict in paraconsistent deontic logics. Our analysis is expected to provide novel tools towards the logical representation of moral conflicts and to shed some light on the context-sensitive paraconsistent deontic logic.
Modern Trends In The Management Of Russian Universities Competitiveness

The topically of the chosen paper is proved by the increase in the competitiveness of Russian universities in the global educational market.It either enhances the attractiveness of the national education system or increases the country's position in the world community.The educational services market is a complex system, remaining one of the most dynamically developing areas, which has recently undergone significant transformations.The purpose of the paper is to study current trends in higher education development and draw conclusions about the competitiveness of Russian universities in the educational services market.The main methods of the research were logical, monographic, deduction, induction, analogy, comparison, and analysis.Research results show that the positions of Russian universities in international rankings are analyzed, and the authors have proved that they can compete with other global universities.At the same time, it still is essential to make several management decisions to increase the competitiveness among the world's leading educational institutions.The authors concluded that the modernization of Russian education can be realized by complex projects accompanied by the creation and implementation of cutting-edge models, programs, and technologies.Thus, it is likely to increase the level of competitiveness of Russian universities not only at the national level but also in the international educational market more effectively.
Latent Dirichlet Allocation for Double Clustering (LDA-DC): Discovering patients phenotypes and cell populations within a single Bayesian framework

Abstract Background : Current clinical routines rely more and more on ``omics'' data such as flow cytometry data from host and sometimes microbiota. Cohorts variability in addition to patients' heterogeneity make any underlying structure of these high-dimensional difficult to understand. In order to patients stratification and diagnostics, there is an acute need to develop novel statistical machine learning methods that are robust with respect to the data heterogeneity, efficient from the computational viewpoint, and can be understood by human experts Results : We propose a novel approach to stratify cell-based observations within a single probabilistic framework, i.e., to extract meaningful phenotype from both patients and cells simultaneously. We define this problem as a double clustering problem that we tackle with the proposed approach. Our method is a practical extension of the Latent Dirichlet Allocation and is used for this Double Clustering task (LDA-DC). We first validate the method on artificial datasets, then we apply our method to two real problems of patients stratification from cytometry and microbiota data. We observe that the LDA-DC returns clusters of patients and simultaneously clusters of cells related to patients’ conditions.We also construct a graphical representation of the results which can be easily understood and a great help for human experts.
Sentiment Analysis Using Natural Language Processing

Abstract: In recent years, there has been an increasing interest in using natural language processing (NLP) to perform sentiment analysis. This is because NLP can help to automatically extract and identify the sentiment expressed in text data, which is often more accurate and reliable than using human annotation. There are a variety of NLP techniques that can be used for sentiment analysis, including opinion mining, text classification, and lexical analysis. Each of these methods has its own advantages and disadvantages, and the choice of technique will often depend on the type and quality of the text data that is available. In general, sentiment analysis using NLP is a very promising area of research with many potential applications. As more and more text data is generated, it will become increasingly important to be able to automatically extract the sentiment expressed in this data.
Machine Learning Techniques in Public Safety: An Intelligent Surveillance System for Recognising the Faces of Wanted Criminals by Studying Facial Features Through Neural Network Mechanisms

Detection recognition and analysis of human faces are some of the most interesting subjects of computer vision, especially in a video stream. In the last few years, the algorithms of learning facial features have evolved in many ways, and since the capacity of detecting and reading the human facial features is extremely powerful and is becoming more and more efficient, it will lead us to detect a certain specific data that can be used for security purposes. On the other hand, the appearance of the special bases of command and coordination that enter in the political safe cities projects makes it a huge deal of interest to use these models for empowering those cameras to detect and identify target people. In This article, we propose a real-time intelligent monitoring system that has as objective to detect a specific accuracy of figures of wanted people that had already been studied and learned by a pre-trained model that describe the data in the learning stage through extracting feature vectors from The electronic identity pictures available in the database given by the new generation of the national electronic identity card and to set an alarm for the bases of command and coordination to take the necessary measures in the stage of positive inference.
A quantum algorithm for solving eigenproblem of the Laplacian matrix of a fully connected weighted graph

Solving eigenproblem of the Laplacian matrix of a fully connected weighted graph has wide applications in data science, machine learning, and image processing, etc. However, this is very challenging because it involves expensive matrix operations. Here, we propose an efficient quantum algorithm to solve it based on a assumption that the element of each vertex and its norms can be effectively accessed via a quantum random access memory data structure. Specifically, we adopt the optimal Hamiltonian simulation technique based on the block-encoding framework to implement the quantum simulation of the Laplacian matrix. Then, the eigenvalues and eigenvectors of the Laplacian matrix are extracted by the quantum phase estimation algorithm. The core of our entire algorithm is to construct the block-encoding of the Laplacian matrix. To achieve this, we propose in detail how to construct the block-encodings of operators containing the information of the weight matrix and the degree matrix respectively, and further obtain the block-encoding of the Laplacian matrix. Compared with its classical counterpart, our algorithm has a polynomial speedup on the number of vertices and an exponential speedup on the dimension of each vertex. We also show that our algorithm can be extended to solve the eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.
The Development of Private Digital Currency in China and Its Enlightenment to the Promotion of Legal Digital Currency

With the rapid development of global financial technology, legal digital currency will become an important form of currency in the future. At present, the central bank is committed to the development of legal digital currency in China, and has carried out experiments on relevant platforms. Legal digital currency is a new thing and lacks successful experience that can be used for reference. Therefore, the analysis and Research on the potential risks of private digital currency and a clear understanding of the shortcomings of China's current response to the risks of private digital currency are important prerequisites for the steady development of China's legal digital currency.&#x0D; Firstly, it expounds the concept and classification of digital currency, then analyzes the development and supervision status of private digital currency in China and other countries around the world, points out the shortcomings of China's response to the risk of private digital currency, and finally puts forward the enlightenment and suggestions for the issuance of legal digital currency in China.&#x0D; There are some problems in private digital currency, such as unclear legal attributes and tax policies, non-standard trading platforms, lack of consumer protection, and weak international cooperation. China should pay attention to the above problems when promoting the development of legal digital currency. Specifically, we should clarify the legal attribute of digital currency; Build a complete digital currency supervision system; Improve the digital currency tax policy; Standardize the digital currency trading platform; Building an anti-money laundering mechanism; Strengthen the protection of consumers' rights and interests; Strengthen international digital currency cooperation.
Parametric Optimal Design System of Construction System Based on Distributed Optimal Algorithm

With the rapid development of science and technology and the continuous expansion of network scale, traditional centralized control and optimization techniques have been difficult to solve large-scale complex network problems, and distributed optimization algorithms with more robustness and flexibility have attracted more and more attention. In view of the irreplaceable advantages of multi-intelligent systems in distributed computing, many researchers have used them as carriers of distributed optimization for theoretical research and application promotion. For the distributed optimization problem in the undirected network environment, this article outlines and summarizes the existing gradient-based distributed simultaneous optimization algorithms, based on which a novel distributed simultaneous momentum acceleration optimization algorithm is proposed. Under the premise that the local objective function is strongly convex and Lipschitz continuous, the algorithm uses heterogeneous steps to precisely drive each node to converge asymptotically to the global optimal solution, and we reduce or compensate the effect of the original error on the pairwise error by introducing an adjustable symmetric matrix. The distributed resource allocation problem under undirected graphs is studied, in which the local cost function of the intelligences is unknown. Using sinusoidal signal excitation, the input and output of the cost function are utilized, and the first-order and second-order polar search algorithms are designed, respectively. In addition, the algorithm innovatively introduces the dual acceleration mechanism combining the Nesterov gradient method and the Heavy-Ball method to greatly improve the convergence speed of the algorithm. Based on the interrelationship of the agreement error, the optimal distance, the state difference, and the tracking error, the step size and the range of the momentum parameters for the linear convergence of the algorithm to the optimal solution are analyzed using the linear matrix inequality technique.
Techniques to Enhance the Performance of DBSCAN Clustering Algorithm in Data Mining

Abstract: Clustering is a form of learning by observations. It is an unsupervised learning method and does not require training data set to generate a model. Clustering can lead to the discovery of previously unknown groups within the data. It is a common method of data mining in which similar and dissimilar type of data would be clustered into different clusters for better analysis of the data. In this paper the DBSCAN algorithm has been applied to compute the EPS value and Euclidian distance on the basis of similarity or dissimilarity of the input data. Also back propagation algorithm is applied to calculate Euclidian distance dynamically and simulation study is conducted that shows improvement to increase accuracy and reduce execution time. Keywords: Clustering; DBSCAN; Back-propagation; Accuracy; Execution time
ON THE PROBLEM OF THE COMPONENT STRUCTURE OF ECOLOGICAL CONSCIOUSNESS OF THE FUTURE TEACHERS AND FACTORS THAT DETERMINE IT

The structure of ecological consciousness of a teacher-to-be is revealed in the article. The author considers the components of ecological consciousness as well as the factors which determine the formation of those components.
INNOVATE AND ENTREPRENEUR IN THE CLASSROOM: DEVELOPING SKILLS FOR THE 21st CENTURY ENG MEIO DE ELECTRONIC GAMES

This article reports the application of a school practice with the use of simulation and strategy electronic games.The objective is to present the possibility of working on the development of interpersonal and intrapersonal competences required for the professional of the 21st century.The application took place with 76 high school/ technical students in a state public school.More than 65% of these students were able to satisfactorily assimilate theoretical concepts in a practical way.
A blockchain-based model to support student activities management

The fourth industrial revolution, digital transformation, and blockchain technology are those the terms get most interested in recently. The application of new technology to obtain the accuracy, convenience, and speed in managing work in general always attract much attention from the community. In the field of education, the exploitation of new technologies to promote the development of education and training is not out of the common trend. Current studies in blockchain-based application for education often focuses on exploiting features such as security, immutability, independence, etc., and applying them to verify the correctness and anti-counterfeiting of diplomas/certificates. Researches related to support student management is still very limited. In this paper, we study and propose a blockchain-based application to support the management of students' activities in general, such as managing code of conducts, extracurricular activities or soft skill courses for instance. The aim is to study the application of smart contracts of blockchain technology to support automatic, transparent and unbiased student reward or discipline through conduct point assessment. Along with that, the system will assist in verifying the career skills that students have accumulated to enhance the strength and reliability of the resume or portfolio of students.
Research on the Reform Countermeasures and Implementation Path of Primary and Secondary School Language Education in Hong Kong SAR, China

According to a questionnaire and interview survey of 689 students, teachers, and parents from six primary and secondary schools in Hong Kong, China, both public and government-funded schools use traditional Chinese teaching materials and Cantonese as the medium of instruction, while private schools use original English textbooks and English as the medium of instruction. All schools' self-selected teaching materials and teachers have a major colonial education legacy, in that they do not objectively introduce the growth process of Chinese culture and socialism, affecting students' ability to acquire a correct world outlook and ideal. In everyday life, students primarily speak Cantonese, and their Putonghua proficiency is poor. In Hong Kong, the competition for college entrance exams and jobs is fierce, and nearly half of students see no hope and expect to migrate to the mainland for university and employment in order to advance. This study proposed specific reform strategies and implementation methods for language teaching in Hong Kong primary and secondary schools to increase patriotism, university enrolment, and employment rates among Hong Kong students.
R. L. Pritchard's classic mutual impedance contribution

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling

Domain adaptation (DA) aims to transfer knowledge learned from a labeled source domain to an unlabeled or a less labeled but related target domain. Ideally, the source and target distributions should be aligned to each other equally to achieve unbiased knowledge transfer. However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source domain, leading to adapting unnecessary source specific knowledge to the target domain, i.e., biased domain adaptation. To resolve this problem, in this work, we delve into the transferability estimation problem in domain adaptation and propose a non-intrusive Unbiased Transferability Estimation Plug-in (UTEP) by modeling the uncertainty of a discriminator in adversarial-based DA methods to optimize unbiased transfer. We theoretically analyze the effectiveness of the proposed approach to unbiased transferability learning in DA. Furthermore, to alleviate the impact of imbalanced annotated data, we utilize the estimated uncertainty for pseudo label selection of unlabeled samples in the target domain, which helps achieve better marginal and conditional distribution alignments between domains. Extensive experimental results on a high variety of DA benchmark datasets show that the proposed approach can be readily incorporated into various adversarial-based DA methods, achieving state-of-the-art performance.