Reviewerly/exHarmony huggingface dataset:
Linguistic verbal intelligence in welding practice lectures

Abstract This study aims to determine student linguistic intelligence, the level of student linguistic intelligence, and measure the relationship between linguistic intelligence and learning outcomes of welding practice lectures in the Undergraduate Program of Mechanical Engineering Education, Faculty of Engineering, Yogyakarta State University. The method used is a survey. The research was carried out in the Undergraduate Program of Mechanical Engineering Education, Faculty of Engineering, Yogyakarta State University. The research was conducted from April to July 2020. The research subjects were students of the 2018 batch of Undergraduate Program of Mechanical Engineering Education, Faculty of Engineering, Yogyakarta State University. The research variables are students’ linguistic intelligence (independent) and the learning outcomes of welding practice lectures (dependent). The results showed that the students’ verbal-linguistic intelligence was at an average value of 72.01%. 7.79% or as many as 6 students made verbal-linguistic intelligence the highest score compared to other intelligence. The significance value (Sig.) Is 0.450 and greater than 0.05, so it can be concluded that Ha is rejected, and H0 is accepted, that is, there is no significant influence between the verbal-linguistic intelligence of students (X) on student learning outcomes of welding practice (Y). The value of R Square is 0.009, which means that the effect of verbal-linguistic intelligence on students (X) on the learning outcomes of students in the Undergraduate Program of Mechanical Engineering Education, Faculty of Engineering, Yogyakarta State University (Y) is only 0.9% while 99.1% is influenced by other variables.
Science + Education = ( ≠ ) Education + Science

Based on a comparative analysis of the Prussian and Anglo-Saxon models, as well as the implementation of the concepts of didactic encyclopedism and utilitarianism in the education system, the author demonstrates the changes taking place in social institutions – education and science. It is shown that in countries that have long developed under the influence of the public administration system that implemented the model of compulsory education for all, science as a sphere of strategic development of the country determined the content and level of training of graduates. The attempt to include research institutions in universities in the context of the formation of a new Russian system of administration in the field of science and education, as a stage of transition to the implementation of the Anglo-Saxon model, not only violates the logic of the established system of training, but also is a way to substitute real research activity for compliance with the formal criteria of international ratings.
Building of knowledge bases of decision support systems using the directed networks of terms during information operations research

The relevant issue of information operations research is considered, which includes issues of detection, recognition, analysis, and counteraction. This relevance is due to the significant impact of the information environment on society, social groups and individuals who are in it. The construction of knowledge bases of decision support systems in the information operations research takes place within the method of hierarchical goal dynamic estimating of alternatives. A new approach for the building knowledge bases of decision support systems during information operations research is proposed, which allows reducing the number of appeals to experts through the use of directed weighted networks of terms. Its application saves time and financial resources by reducing expert information usage and also provides an opportunity to identify gaps in knowledge bases. The concept of an information-analytical system for information operations research is developed, which uses directed networks of terms, within which this approach will be implemented. This system allows making recommendations that can be used to analyze and counteract information operations. During automated processing of texts related to the subject domain of the information operation object, there are conducted the formation of text corpora, based on which the construction of networks of terms. Processing of text corpora is performed by computational linguistics, and the construction of a directed network of terms is conducted using an algorithm for constructing a graph of horizontal visibility. It is proposed to determine a sufficient number of texts for representative coverage of the subject domain by stabilizing the ranks of terms when constructing thematic text corpora. An approach for determining the weights of links in the subject domain's directed networks is proposed. The application of the proposed approaches was tested on the example of texts corpora on current topics “Brexit”.
Fuzzification of Feynman Path Integral and Its Effect on Field Theory and Quantum Gravity—Reformation and Redevelopment of Quantum Theory

The quantum probability theory of fuzzy event is suggested by using the idea and method of fuzzy mathematics, giving the form of fuzzy event path integral, membership degree amplitude, fuzzy field function, Green function, physical quantity and fuzzy diagram.This theory reforms quantum mechanics, making the later become its special case.This theory breaks unitarity, gauge invariance, probability conservation and information conservation, making these principles become approximate ones under certain conditions.This new theory, which needs no renormalization and can naturally give meaningful results which are in accordance with the experiments, is the proper theory to describe microscopic high-speed phenomenon, whereas quantum mechanics is only a proper theory to describe microscopic low-speed phenomenon.This theory is not divergent under the condition of there being no renormalization and infinitely many offsetting terms, thereby it can become the theoretical framework required for the quantization of gravity.
Predicting Organizational Cybersecurity Risk: A Deep Learning Approach

Cyberattacks conducted by malicious hackers cause irreparable damage to organizations, governments, and individuals every year. Hackers use exploits found on hacker forums to carry out complex cyberattacks, making exploration of these forums vital. We propose a hacker forum entity recognition framework (HackER) to identify exploits and the entities that the exploits target. HackER then uses a bidirectional long short-term memory model (BiLSTM) to create a predictive model for what companies will be targeted by exploits. The results of the algorithm will be evaluated using a manually labeled gold-standard test dataset, using accuracy, precision, recall, and F1-score as metrics. We choose to compare our model against state of the art classical machine learning and deep learning benchmark models. Results show that our proposed HackER BiLSTM model outperforms all classical machine learning and deep learning models in F1-score (79.71%). These results are statistically significant at 0.05 or lower for all benchmarks except LSTM. The results of preliminary work suggest our model can help key cybersecurity stakeholders (e.g., analysts, researchers, educators) identify what type of business an exploit is targeting.
Rancang Bangun Aplikasi Rekapitulasi Obat dengan Menerapkan Tanda Tangan Digital

The current condition experienced by the XYZ Polyclinic is that there is no application built to simplify the service process contained in the polyclinic so that all service processes are still manual. An example is the calculation of the number of drugs that are in and out of use. This calculation takes a long time, is prone to errors in accounting, and consumes many papers. To solve this problem, we develop an application to recap drugs. The drug recapitulation application is based on a website with the WebML method using the CodeIgniter framework and MySQL database. The application produces the result in the form of a PDF file of the drug recapitulation report per month and day. As a security feature, the monthly drug recapitulation report results include a digital signature as proof of authentication, integrity, and anti-denial. The application built has been tested using web testing methods and user acceptance testing to be applied.
Implementing Picture Series To Improve Speaking Skill

This research is aimed to improve the English speaking skills by using picture series at the Tenth grade of SMK Darussalam Blokagung Banyuwangi in the academic year of 2019/2020. This is action research. This research consisted of two cycles. In doing the research, the researcher involved 15 students of Class TKR X 1, one English teacher as the collaborator, and the researcher herself. The data were obtained from the observation during the implementation of the actions, interviews with the students of Class TKR X 1, discussion with the collaborators, and conducting speaking tests: test, daily meeting tests, and the test. The data were in the form of planning, observation, implementing, reflecting, interview and scores. The results of this study indicate that there is an increase in student involvement and student speaking skills. This is indicated by the students' grades in four aspects of speaking: accuracy, fluency, pronunciation, and vocabulary. The student's mean score on the accuracy aspect increased after the application of the image. The student's accuracy score improved from the initial total score of 775, 925the total score in cycle 1, 1182.5 total scores in cycle 2. It is concluded that the series of pictures applied in this study can solve problems in the speaking class and also improve the teaching and learning process of speaking.
Ethical Component of Architectural Education (International Experience)

The paper aims to reveal the ethical component of architectural education. Architectural profession involves artistic and engineering elements and imposes special moral requirements, so ethical issues acquire importance. Scientific originality of the research lies in the fact that relying on an analysis of the educational strategies used in teaching ethics, the author examines ethical education influence on the process of professional ethics formation and on professional activity quality. The research findings are as follows: the author reveals the role of ethical knowledge as a component of an architect’s professional competence and identifies its influence on the formation of a system of ethical and normative standards.
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition

Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation.However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions.To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user's historical answers grouped by DA labels as additional clues to recognize the DA label of utterances.To handle the noise caused by introducing the user's historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process.We evaluate the proposed method on two benchmark datasets MSDialog and MRDA.The experimental results verify the effectiveness of integrating user's historical answers, and show that our proposed model outperforms the state-of-the-art methods.
Efficient Radial Distortion Correction for Planar Motion

In this paper we investigate simultaneous radial distortion calibration and motion estimation for vehicles travelling parallel to planar surfaces. This is done by estimating the inter-image homography between two poses, as well as the distortion parameter. Radial distortion correction is often performed as a pre-calibration step; however, accurately estimating the distortion profile without special scene requirements may make such procedures obsolete. As many modern day consumer cameras are affected by radial distortion to some degree, there is a great potential to reduce production time, if properly implemented. We devise two polynomial solvers, for radially distorted homographies compatible with different models of planar motion. We show that the algorithms are numerically stable, and sufficiently fast to be incorporated in a real-time frameworks. Furthermore, we show on both synthetic and real data, that the proposed solvers perform well compared to competing methods.
Application of high frequency filtration to remove cloudiness in Earth remote sensing images

At present, methods of digital processing of Earth remote sensing images are widely used to improve the image quality. For example, many images are discarded due to high clouds in the images, which obscure objects of interest. In this paper, the possibility of using high- frequency global filters to reduce cloudiness in the image is considered, and the results of image enhancement are shown.
Data based Analysis, Modeling and Forecasting of Novel Coronavirus in infected regions using Extreme Learning Machine algorithm

Abstract In current development in science and technology, Machine learning algorithms play an essential role for prediction, classification, data analysis and data visualization. With this efficient algorithm, we can solve many real-world problems in all domains like education, healthcare, banking, geographical analysis, etc., in the current scenario; much research work is going on with the new virus's infection called the corona. This Corona virus is a comprehensive unit of virus this cause illness in humans or animals, now in East Asian countries, this virus affected more people. In India, the first case was found in January month, originated from China. The entire world is focusing on the disease, and day by day, the infection and death rate is increasing. In this, we intended to focus on the spread of this deadly disease and to demonstrate which countries are the most affected by doing statistical analysis. On December 2019, As of 10 February 2020, China reported overall of 40,235 cases 909 deaths, evoking local and foreign terror. Here we provide estimates of the major epidemiological parameters, Based on the epidemiological data available to the public for Hubei, China, 11 January to 10 February 2020. In particular, we give an estimate Fatality and case recovery rates, along with their 90 per cent confidence levels as the epidemic progresses. For this work implementation, Extreme Learning Machine algorithm used. ELM is a feed-forward network and its learning rate also fast when compare to normal neural network. No need to provide any weights and bias values. This algorithm will give a promising result with the best accuracy.
Modern approaches to polytechnic education

Abstract In modern society there is a growing demand in high-quality specialists of engineering sphere as a result of science, technique and technology development. In this context a new concept of polytechnic education is important, in which there is a special significant place for the humanitarian segment. Mastering the above mentioned competences implies a high cultural and educational level of a future specialist of engineering sphere. The aim of this article is to study the modern condition of humanitarian subjects in a technical university and offer the ways of solving the existing problems by means of modeling the academic process in a new demanded context of a high school. The actuality of this article consists in an insufficient study of the humanitarian segment and a growing need in changing approaches to its studying in a polytechnic university.
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index

Automated ranking of pre-trained Deep Neural Networks (DNNs) reduces the required time for selecting optimal pre-trained DNN and boost the classification performance in transfer learning. In this paper, we introduce a novel algorithm to rank pre-trained DNNs by applying a straightforward distance-based complexity measure named Separation Index (SI) to the target dataset. For this purpose, at first, a background about the SI is given and then the automated ranking algorithm is explained. In this algorithm, the SI is computed for the target dataset which passes from the feature extracting parts of pre-trained DNNs. Then, by descending sort of the computed SIs, the pre-trained DNNs are ranked, easily. In this ranking method, the best DNN makes maximum SI on the target dataset and a few pre-trained DNNs may be rejected in the case of their sufficiently low computed SIs. The efficiency of the proposed algorithm is evaluated by using three challenging datasets including Linnaeus 5, Breast Cancer Images, and COVID-CT. For the two first case studies, the results of the proposed algorithm exactly match with the ranking of the trained DNNs by the accuracy on the target dataset. For the third case study, despite using different preprocessing on the target data, the ranking of the algorithm has a high correlation with the ranking resulted from classification accuracy.
Functional-voxel Modeling of Navigation Algorithm ORCA

The article considers an example of modeling the ORCA algorithm using the functional-voxel method. An analytical description of the permissible collision zone using the set-theoretic apparatus of Rvachev functions (R-functions) is proposed. Based on graphical image models, an approach to constructing the area of permissible robot speeds on the plane and in space has been developed.
A Simple Text-based Relevant Location Prediction Method using Knowledge Base.

In this paper, we propose a simple method to predict salient locations from news article text using a knowledge base (KB). The proposed method uses a dictionary of locations created from the KB to identify occurrences of locations in the text and uses the hierarchical information between entities in the KB for assigning appropriate saliency scores to regions. It allows prediction at arbitrary region units and has only a few hyperparameters that need to be tuned. We show using manually annotated news articles that the proposed method improves the f-measure by > 0.12 compared to multiple baselines.
Collusion Resistant Trace-and-Revoke for Arbitrary Identities from Standard Assumptions.

A traitor tracing scheme is a multi-user public-key encryption scheme where each user in the system holds a decryption key that is associated with the user’s identity. Using the public key, a content distributor can encrypt a message to all of the users in the system. At the same time, if a malicious group of users combine their respective decryption keys to build a “pirate decoder,” there is an efficient tracing algorithm that the content distributor can use to identify at least one of the keys used to construct the decoder. A trace-and-revoke scheme is an extension of a standard traitor tracing scheme where there is an additional key-revocation mechanism that the content distributor can use to disable the decryption capabilities of compromised keys. Namely, during encryption, the content distributor can encrypt a message with respect to a list of revoked users such that only non-revoked users can decrypt the resulting ciphertext.
ВІРТУАЛЬНИЙ ОБРАЗ ТА ЙОГО ВІДПОВІДНІСТЬ СТВОРЕНИМ МАТЕРІАЛЬНИМ АРТЕФАКТАМ

The purpose of the article is to study the concept of a virtual image created in the artist’s imagination and its correspondence to the material invariant. The research methodology based on a system approach to the analysis of the virtual dimension of art. The analytical method is applied to the study of philosophical, art, history and cultural literature for a topic. The theoretical and conceptual method is to characterise the research vocabulary; the comparative-typological method is to find out the correspondence of the virtual image, which is formed in the imagination of a person, with the recovered material artefacts. The article covers Plato’s concept “on the specifics of creating images and simulacra”, Thomas Aquinas’ regulation “on the category of virtuality” and Serhii Bezklubenko’s (2015) concept of “psychological ultrastructure of art”. The scientific novelty of the study is to research the differences between the virtual image and its material invariant, using the examples of the works of Ludwig van Beethoven, Leonardo da Vinci, and Rembrandt van Rijn. Conclusions. We have proven that the art piece making begins with the formation of a virtual image (concept statement) in the imagination of the creator and ends with the materialised form of the original plan. The art piece making is a complex psychological ultrastructure of art starting from the time when its image is born in the artist’s imagination to its embodiment (materialisation) into a certain product, and then the search for its completion in the minds of consumers. Using the examples of well-known artworks, we have learnt that the end image often is out of keeping the original virtual concept.
ANALYSIS AND PROSPECTS FOR USING IMMERSIVE TECHNOLOGIES IN THE SYSTEM OF TRAINING OFFICERS OF ENGINEERING SPECIALTIES

Improving the quality of training military specialists at the command level is undoubtedly an urgent problem for law enforcement agencies and the state as a whole. This fact is directly related to the maintenance in a combat-ready state of military units deployed both in the territory of the Russian Federation and abroad. The article analyzes the views of domestic and foreign scientists on the problem of digitalization of education. Its goals, objectives, current state and prospects for further development are revealed. The ways of improving the quality of the organization of the educational process in terms of learning in a distance (digital) format are presented. The current state of integration of immersion technologies in higher education, including military education, is analyzed. A method of giving military education a practical orientation is presented, which consists in the use of immersive technologies. The main provisions of the immersive approach in military education are analyzed. Within the framework of this article, the ways of further development of immersive technologies in the military education system are presented. The parallel development directions of immersion technologies are highlighted: virtual reality (VR) and augmented reality (AR). The possibilities of using these technologies in the perspective of the development of military education are revealed. The necessity of re-profiling the role of the teaching staff of military educational institutions of higher education due to the further integration of immersion technologies into the training system of officers of the Russian army is proved.
Automatic Modulation Classification Using Dense Memory Fusion Network

Automatic modulation classification (AMC), as a key technology of cognitive radio (CR), aims to identify the modulation format of the received signal.In this paper, we propose a novel dense memory fusion neural network(DMFN) based AMC method where grid constellation matrix (GCM) extracted from the received signals with low computational complexity are utilized as the input of DMFN.In DMFN, densnet with densely connected structures is designed to extract high representative feature of GCMs, the unit of long short-term memory (LSTM) and fully connected layer are used to make classification decisions.Extensive simulations demonstrate that DMFN yields significant performance gain and takes higher robustness comparing with other methods.In addition, DMFN based AMC scheme achieves 90% classification accuracy at 4dB when the symbol length is set as 512, which illustrates its outstanding performance.