Reviewerly/exHarmony huggingface dataset:
Collaborative General Purpose Convolutional Neural Networks

Recently, deep neural networks (DNNs) have made outstanding progress and have been applied for solving various real-world problems. Among DNNs, convolutional neural networks (CNNs) are especially well known for their high performance as image classifiers. In this study, we generalize the CNN as a general classifier, propose a collaborative framework for neural networks and humans, and test the proposed collaborative model for medical diagnosis problems. Here, the CNN is not used as a stand-alone classifier but as a recommendation system, while humans make the final decision intuitively. To make this idea possible, a high-dimensional sample is first converted into a two-dimensional topological map before being subsequently given to the CNN as an input. The conversion of a general high-dimensional sample to a two-dimensional map allows the CNN to process it as an input while giving intuitive visual information for humans to be used in the final decision making. In this study, we attempt to expand the usage of CNNs for non-image inputs and propose a collaboration system for human and neural networks with the primary objectives of generating viable synergy between humans and AI. The paper is supported by some preliminary experiments to show the viability of our idea.
Competitive complex particle swarm optimization: An efficient algorithm for optimization of linear-rotational sun-tracking control parameters

Particle swarm optimization (PSO) is a well-established heuristic global optimization algorithm inspired on the social behaviors observed in flocking birds. Premature convergence is major issues in PSO that may converge prematurely around the local optima. In this paper, control parameters of a new dual axis sun tracking system are optimized by an improved variant of particle swarm optimization, named as competitive complex multi-leader particle swarm optimization (CCPSO). Good properties of the proposed algorithm such as clustering, dispersion control, enhancement fitness in clustering and competitive complex evolution approach not only enable the algorithm to have an enhanced and systematic global search in high dimensional problems but also can make balance between the exploration and extraction and as a result prevent from premature convergence. These characteristics make the algorithm robust, effective and fast. The proposed algorithm results in more efficiency for the power generation of a new dual-axis sun-tracker by seeking the optimal solution of control parameters for single-axis and dual axis modes (e.g.: 823.2 Wh surplus energy in 7 h effective sunlight, 49% energy production using linear-rotational with respect to fixed-axis, and etc). Finally, to show the performance and the efficiency of the proposed method, the tracking speed, and energy consumption of motors are compared with algorithms in different climates.
Neighbouring Constraint Deep Matrix Factorization for Sequential Multi-view Clustering

Multi-view clustering (MVC) aims to partition a set of multi-source data into their underlying groups. To boost the performance, how to explore the better representation is important. In this paper, we present a deep matrix factorization model with the features fusion to deal with sequential multi-view clustering problem. In this method, neighbouring constraint is embedded to find the cluster boundary information in each view layer by layer, and an aggregated output representation can be obtained for MVC. Experiments have shown that the proposed model improve the clustering performance greatly and can be used in applications such as motion segmentation.
Cloud Computing security classifications and taxonomies: a comprehensive study and comparison

Cloud Computing is an evolving term that is subject to security threats, vulnerabilities, and attacks. Latterly, various classifications and taxonomies have been suggested to characterize and classify cloud security issues. Some of them are based on general security factors, such as the CIA triad (confidentiality, integrity, and availability), while others specify cloud security classes. Most of these classes are determined by the cloud's attributes, such as Cloud service models, cloud deployment models, and cloud actors. In this paper, we explore the already existing criteria and dimensions considered in the development of cloud computing security classification/taxonomy. Then, we study and compare their strengths and characteristics. Thereafter, our objective is to provide and develop exhaustive cloud security taxonomy and push researchers to better comprehend the nature of any newly introduced threat or attack, categorize them, and explain the relationship between threats and other categories or subcategories.
A Feature Extraction Method Based on Few-shot Learning

The current deep model networks are all proposed for tasks with large scale datasets. Therefore, this paper proposes some improvements for mining the most essential features of data in few-shot learning problem. Firstly, the multi-channel feature maps are important parts of the network model. Whether there is a dependency between the channels of the feature maps is what we are going to consider. On this basis, we propose to weight the feature maps of different channels to highlight the information that contributes better to the subsequent learning tasks and suppresses the more useless feature information. In addition, we present new feature maps that combine the location information of the shallow layers and the semantic information of the deeper layers in an example by fusing the feature maps of the same picture with different scales to generate a more expressive feature map of the example.
Demonstration of Forward Collision Warning System Based on Real-Time Computer Vision

This paper demonstrates the software and hardware of a forward-collision warning system using techniques of realtime computer vision which helps self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems. The software approach of the paper consists of five parts: car detection, depth estimation, lane assignation, the relative speed of other cars and their corresponding speed limit and finally ultrasonic sensors which completes the front of the vehicle as the camera can't cover it alone. Besides these five objectives, a real-time notification system exists which interacts with the driver. In this paper, a hardware approach is proposed which implements the software approach techniques on a NVidia Jetson TX2 development kit and AVR microcontroller to prove the feasibility of the proposed collision warning system. The results show the reliability and promptness of the proposed approach by achieving up to 23 FPS (frame per second).
Penmanship and signal acknowledgment utilizing inertial pen

This paper exhibits an inertial-sensor based advanced pen, connected with Dynamic Time Traveling calculation for penmanship and signal acknowledgment. With favoured dealt with speed, client holds inertial pen to compose numeral or lowercase literatures and make indicator signals. The signs from accelerometer and gyro sensor are coordinated and gathered into a quaternion-based reciprocal channel to lessen the vital blunders from the gyro sensor which causes the inherent commotions and sign float are here and there diminishes the exactness of the direction estimation. This uses DTW based acknowledgment calculation with which it comprises of different structures like preparing, recognizable proof and afterward the recognition part. To get a prevalent class detachment for improved acknowledgment we have created to negligible between class to maximal class-based format determination technique. In the Trial results, the adequacy of the DTW-based calculation for web-based penmanship and motion utilizing the inertial pen is effectively approved.
Research on Network Communication Security Based on PKI

Based on the analysis of the connotation and function of PK, combined with the realization of network communication security, this paper puts forward the methods of improving network communication security from three aspects of network equipment physical security, network communication operation security and network communication management security, so as to provide reference for people who pay attention to this topic.
Text Analysis for Honeypot Misuse Inference

Transformation of raw text is required for computational text analysis using Natural Language Processing methods. Computational text analysis leverage on human brain limitations to automatically index documents for retrieval and topic generation for topic distribution correlations in corpus of voluminous documents. Natural language non-parametric and parametric Topic modeling with Expectancy Maximization and Gibbs sampling render technique to build Machine Learning models for evaluation with log-likelihood, topic coherence and coefficient of determination of held-out document. This research extends the concept of Natural Language Processing to automate analysis of High interaction honeypot system call documents to deduce system resources misuse by malcode during real-time engagement with the user-space applications of the deployed honeypot.
Retaining Semantic Data in Binarized Word Embedding

Natural Language Processing(NLP) has emerged as a crucial research area in Machine Learning domain. NLP concerns itself with understanding human language and deriving meaningful inferences from the textual data. In most NLP models, word embeddings are a good starting point. Word embedding is a vector representation of words in a predetermined space. These represented word vectors follow typical vector rules, and depict similarity or dis-similarity of words in a vector space. GloVe, Word2Vec and FastText are some of the most popular pre-trained word embeddings. However, since these embeddings are trained on a very large corpus, they tend to be very large in size. The large size makes it difficult to use such embeddings in a memory constrained environment. The real values of these embedding also makes downstream tasks slow. In this paper we present a novel approach to convert the continuous pre-trained embeddings to its binary representation, without degrading the semantic information they carry. The binary conversion drastically reduces the size of the resultant embedding and facilitates porting to memory constrained devices. Experiments have shown comparable results to original embeddings in downstream tasks, with 20 to 40 times reduction in file size.
Digital competences in the aspect of sociocultural education

The purpose of this paper is to examine different social and academic activities related to the development of a digital culture of bachelors and masters of culture and arts. It analyses the achieved results of the research project aimed at the study of educational and pedagogical opportunities and resources for the more effective development of digital competencies of students and graduates in the life-long system of sociocultural education and realized in the Moscow State University of Culture (Khimki, Moscow region), the Petrozavodsk State University (Petrozavods, Republic of Karelia) and in the Ulyanovsk State Agrarian University named after P.A. Stolypin (Ulyanovsk) in 2010-2019. The authors consider digital competences as a core of a digital culture of graduated in arts and culture is a multi-level, complex, interdisciplinary phenomenon, and therefore it needs a comprehensive analysis from the standpoint of several sciences (philosophy, sociology, psychology and pedagogy, etc.). The results of the implementation of educational technologies orientated to values and competences of a digital culture are discussed. The paper focuses on the importance of the educational process orientated on creation of values of digital culture which are significant for the organization of social and cultural activities in the frame of the state policy on digitalization of education and culture.
Domain-Agnostic Context-Aware Assistant Framework for Task-Based Environment

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only. This paper presents a domain-agnostic framework that creates natural language interfaces for computer-controlled systems that have been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.
Research on Ancient Chinese Learning Mode Under the Mobile Internet Environment

Mobile learning is an emerging product of the rapid development of the Internet. It is a learning behavior that uses the concept of “Internet +”, and relies on mobile terminals such as smartphones and tablets to perform online self-learning through wireless networks. The ancient Chinese mobile learning model can break the time and space limitations of the traditional Chinese classroom-teaching model, making the ancient Chinese teaching model more flexible, convenient and efficient, which helps to stimulate students’ curiosity and interest in learning, guide students to learn independently, and solve problems by themselves. Cultivate students’ comprehensive ability to use Chinese.
Diagnosing the formation of an environment-oriented component of competencies in higher education

The article examines the diagnostic tools for evaluating the formation of an environment-oriented component of competencies in students within the system of higher education. The authors present the set of diagnostic tools following their long-term work on identifying the experience of emotional and values-based attitude to nature as the most difficult element of the environment-oriented component of competencies to assess. The article substantiates the selection of diagnostic tools taking into account available environmental knowledge, skills, abilities and behavioural stereotypes of students. It focuses on determining the level of adapted social experience combining the experience of environment-oriented cognitive activity, environment-oriented work methods, environment-oriented creative activity, emotional and values-based attitude. The use of diagnostic tools is illustrated by experimental work within the educational process at A.V. Khrulev Penza Branch of the Military Academy of Logistics and Penza State University.
Sentiment Analysis of News Articles Using Deep Learning Methodologies

This research explores recent studies on the implementation of deep learning methodologies such as CNN, RNN and LSTM for news sentiment analysis and examines the methods that provide qualitative results. The various literature in this research gives its own architecture in order to address specific issues that are generally encountered while analyzing news sentiments. It asserts various methods for feature representation like simple encoding of words, word2vec-based encoding, etc. and various other word embedding techniques like word2vec, FastText and GloVe, whereas word2vec is the popular and frequently used technique. The other types of embedding methods like sentence embedding and event embedding are considered to be productive than word embedding; however, event embedding is more potent among all. This research focuses on the combination of deep learning methodologies manipulated for sentiment analysis.
Comparison of Sentiment Analysis on Auto-Summarized Text &amp; Original Text using various Summarization Techniques

In today's day & age Text Summarization and Sentiment Analysis add lot of value to businesses and other organizations. Sentiment Analysis can help a business get an idea about their product and gather meaningful feedback from customers. And auto-text summarization helps in articulating the important points from a large data-set, doing so can make the viewers/readers get a quicker idea about that data-set, this data-set can be a large document, a blog or an article. This paper presents a new method of combining the concepts of Sentiment Analysis and Auto-Text Summarization so that content-writers can enhance the quality of their manuscript. In this research work, certain observations have been made which can help in analyzing the polarity and subjectivity of the summarized text using various summarizers. Businesses and other organizations can use this technique to enhance their online content and intrigue viewers or readers by creating a better digital content ecosystem.
(β, α)-Fuzzy Magnified Translations of AT-algebra

Abstract Magnified translation of fuzzy AT-subalgebras and fuzzy AT-ideals on AT-algebras are discussed. Relations among magnified translations, them are investigated.
Translating additive connectors from English into Spanish and vice versa

Abstract This contrastive study aims to analyze and compare the usages of one type of sentence connectors, reinforcing additive connectors, in English and in Spanish through a corpus-based approach, which relies on bidirectional translation data. The analysis includes behavioral profiles of each of the list of connectors in each language, comparisons among them, comparisons between original texts and translated texts in both directions, cross-register differences, translational options, and the connectors’ mutual correspondence, which shows the degree of equivalence of each pair of connectors based on how often they are translated into each other. The results show important differences in the use of reinforcing additive connectors, mainly connected with (a) the more even distribution in English as compared with the great predominance of one connector in Spanish, además ; (b) a more marked tendency for explicitation in Spanish and for the use of zero translation in English; (c) generally low mutual correspondence values, which seem to reflect high language variation, richness, and complex mapping of resources utilized to connect sentences.
Research and design of low noise broadband frequency source based on FPGA and DDS technology

In this paper, FPGA is used to control DDS to design a broadband frequency source from 10MHz to 1200MHz with high integration degree, flexible design, high frequency resolution, high stability., low stray, fast switching speed and low phase noise. Through reasonable design, the phase noise reaches -127dBc@lkHz., -130dBc@10kHz., and the stray suppression is better than -73dBc when f≤500MHz. When 500MHz <; f≤1200MHz., it is better than -58dBc.
On the Prospects of Pedagogical Technology “Flipped Classroom” in Teaching History at University in Conditions of Digitalization of Educational Environment

This study is devoted to such an urgent issue as the development of a concept for improving the teaching system of the discipline “History (Russian History, General History)” at the State University of Management, using the “inverted class” pedagogical technology as an example. In the modern world, recently there has become a need for a significant expansion of the digitalization of education. Russian educational institutions, acting in accordance with Russian legislation, are forced to introduce digital technologies in the organization of training. In these circumstances, the necessary transformation of the model of classical education becomes the primary task of every teacher and teacher. It’s very important to create a completely new educational environment using the latest digital technologies that meets the needs and requirements of students. Mostly, teachers and educators need to pay attention to the development of content for various platforms and also update tasks on each topic of the course. In the new conditions, teachers should pay special attention to conducting video conferences, video lectures, webinars, and other classes online. The concept of improving the system of teaching the discipline “History”, which is proposed in the article, is based on practical experience identified in the analysis of the questionnaire of teachers and students, which was carried out in February of the current school year. The scientific novelty of the study lies in the fact that earlier at the State University of Management and, especially for the teaching of the discipline “History”, the teaching technology, which is called the “inverted class”, wasn’t applied. The results obtained during the implementation of this research work are mostly recommendatory in nature and should be tested in the near future.
Parameter Estimation Method of Experimental Data Based on Grey Distance Measure

It is a new idea to use Grey theory to solve the parameter estimation problem of small sample data. However, in the calculation of grey distance measure, there exists the problem that the resolution coefficient is fixed, so the calculation result may not be optimal. To solve this problem, a new method to select the resolution coefficient is proposed. On this basis, an improved weight calculation method is proposed. Finally, the effectiveness of the proposed algorithm is verified by simulation, and the accuracy of parameter estimation can be improved.
TESTING AS A METHOD OF CONTROL AND ASSESSMENT OF THE FORMATION OF COMPETENCIES

At the present stage, pedagogical tests are one of the most accessible and developed methods for assessing students' knowledge. Evaluation of the results of global research on testing problems showed that the developers of test items pay special attention to the form and their standardization, the processing of test results and the interpretation of the data obtained, methods of automatic test formation. The goal is to study the possibility of using testing to assess the formation of students' competencies. Materials and methods. Theoretical: a review of psychological and pedagogical literature on research issues, analysis, synthesis, generalization, systematization. Results. Testing characterizes a number of properties, the main of which is the objectivity of the assessment of results, the definition of "gaps" in knowledge. The result of testing is obtaining objective information about the quality of knowledge and skills, determining the sections that are the worst learned by the students. All of the above makes it possible to adjust the course of training. Test control can be external, when the subjects are offered a dichotometric range, which contains both completed and unfulfilled tasks on a wide range of issues. During the testing process, students have the opportunity to independently identify gaps in their own knowledge. The study confirms the possibility of using testing in assessing the formation of students' competencies and ultimately provides an increase in the level of knowledge. Conclusion. At the present stage, the introduction of tests into educational practice is an inevitable process, therefore, efforts should be directed to the development of a theoretical platform for the testing system, which will entail an increase in the efficiency of using tests as a form of control.