Reviewerly/exHarmony huggingface dataset:
Intellectualization of the assessment of the development of information and communication means of data processing

Abstract Given the weak formalization and insufficient information support in the early stages of data processing tools development in information and communication technologies, the use of intellectualization mechanisms and the creativity of the researcher is highly relevant. Mathematical models, decision support systems are actively used to introduce intellectualization into the process of modeling development prospects itself through the use of fuzzy set models, fuzzy logic, opportunity theory and other disciplines focused on unlocking the intellectual, creative potential of the developer. With this in mind, the paper aims to develop a decision support system scheme to evaluate the development of data processing tools in information and communication technologies by activating the researcher’s intellectual capabilities through the use of his fuzzy reasoning, fuzzy structural modeling of development motives, visualization and schematization of the successive development of data processing tools. In the process of developing such a decision support scheme, a procedure for calculating the coefficient reflecting the ability of the considered variant of the appearance of data processing tools to further improvement was obtained. A system of membership functions is proposed, which is used to determine the influence of development motives on the qualitative characteristics of the ability of prospective data processing tools to further improvement. Specific examples are given to illustrate the developed methodological apparatus, in which the spacecraft of the Earth remote sensing system is selected as a data processing tool.
Self-Attention Mechanism of RoBERTa to Improve QAS for e-health Education

This paper aims to discuss about the establishment of a health education system in the form of a Question Answer System (QAS) related to the current COVID-19 pandemic. QAS allows users to state information needs in the form of natural language questions, and then this system will return short text quotes or sentence phrases as answers. This is due to the tendency for recipients of information to more easily understand news/information when they can answer questions that may arise in their minds. The approach used was self-attention mechanism such as a Robustly Optimized BERT Pretraining Approach (RoBERTa), a method for question answering with span-based training that predicting the starting limit for answer start and the end limit for the answer index. The final results using 835 non-description questions, the best evaluation value on the training data showed the exact match of 91.7% and F1 value of 93.3%. RoBERTa tends to show the better results on non- description questions or questions with short answers compared to the description questions with complex answers.
Korean Traditional Document Translation Using Transformer In Bidirectional-CRF

This paper proposes a solution to solve the Out Of Vocabulary(OOV) problem in a framework built with a transformer-based machine translation algorithm. The translation input is a traditional Korean document written in Chinese characters, and the output is a decoding of modern Korean paragraphs written in Korean alphabet. We used the word2vec algorithm to represent symbolic characters as numeric vectors and used them as input to the converter. Also, to solve the OOV problem, Bi-Directional LSTM + CRF has been used. To show the validity of the data set, the Annals of the Joseon Dynasty were presented as translations prepared by experts. Another source was collected at Kyungpook National University (Diary dataset), which is much smaller than the Annals of the Joseon Dynasty. According to the BLEU score, after learning the Annals of the Joseon Dynasty, fine-tune with data collected at Kyungpook National University showed a lower BLEU score than general machine translation in the results of applying CRF When learning only with the dataset collected at Kyungpook National University, it can be seen that a slightly high BLEU score was obtained.
An individual-dependent differential evolution with dual information guidance

To enhance the effectiveness of differential evolution (DE) algorithm, in recent years, a number of DE variants have been proposed by employing the idea of multiple strategies. Although these DE variants have been shown competitive performance, there still exists a problem for them that the strategy selection mechanism mainly relies on the historical search experience. Unfortunately, the historical search experience may be suitable for the problems with a flat fitness landscape, while not for the problems with a rugged fitness landscape. To alleviate the issue, in this work, an individual-dependent DE variant, called IPDE, is proposed by using the dual information guidance, including the fitness information and spatial information. In the IPDE, the idea of multiple strategies is used as well, but its most salient feature lies in that the strategy selection mechanism is based on the individual role rather than the historical search experience. To better identify the individual role, the fitness information and spatial information are used simultaneously, which can roughly estimate the evolutionary statuses of different individuals in the fitness landscape. To validate the effectiveness of IPDE, 52 benchmark functions are used in the experiments, and four well-established evolutionary algorithms are included in the performance comparison. The final results show that IPDE can achieve promising performance.
Text Detection in Sign Images Using Maximally Stable External Regions and Stroke Width Transform

Sign images are widely used in daily life and contain a lot of important text information, but there is few text detection method for its characteristics. In view of different styles, sizes, and colors for its fonts, a text detection method based on Maximally Stable External Regions and Stroke Width Transform is proposed. Firstly, the candidate regions are extracted from Maximally Stable External Regions. Secondly, the candidate regions are optimized by Stroke Width Transform. Thirdly, the non-text areas are filtered according to several heuristic rules and CIELab color model. Finally, text regions are merged to text lines as the detected regions. Experimental results show that the proposed method is efficient on the horizontal text sign images, and achieves the F-measure of 87.43% under the ICDAR2013 evaluation index.
Adversarial Attacks for Deep Learning-Based Infrared Object Detection

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.
Behavior of Czech Teachers in the Techambition System during the COVID-19 Epidemic

In 2020, the global pandemic of COVID-19 significantly affected most human activities, including education. Many countries took anti-epidemic measures to reduce or eliminate the pandemic, which often included reduction or cancellation of full-time face-to-face school education. This, of course, went hand in hand with a number of technical, organizational, and pedagogical-didactic issues that had to be solved instantly. The presented study analyzes the behavior of more than 1000 secondary school teachers in the e-learning system named Techambition for mathematics education developed by the company with the same name. In the study, data from the spring and autumn 2020 waves of the pandemic are analyzed and presented (the period from 1 March 2020 until 18 March 2021). The main outcome of the study is a description of the basic types of approaches to teaching, based on cluster analysis of the collected data. The collected data show three basic types of teachers’ behavior during spring 2020 that were identified based on their characteristics: organized, hardworking, and seekers and three basic types of teachers’ behavior during autumn 2020: organized, seekers, and experimenters. As part of the findings, we present an interpretation of the data through the description of individual groups and monitor the changes in teachers’ behavior between the spring and the autumn 2020 period of the pandemic.
Computer Big Data Sentiment Intelligent Analysis Using Maximum Likelihood and Deep Learning

This With the field of machine learning continues flourishes, the rising demand of understanding human emotions brings attention of solving the issue of SA using deep learning. In this study, we want to utilize CNN techniques to evaluate a real dataset containing movie reviews with annotations either positive or negative. We will clean the data by removing html tags, removing non-alphabetical word, and removing stop words. Then we will first introduce one classification method based on statistical analysis based on maximum likelihood and discuss the usage of different smoothing methods. We will be able to construct a bag-of words model based on the statistical analysis. Lastly, we will convert the vector into multi-dimensional matrix and build a CNN model to categorize whether a movie review is positive or negative. As a result, we show that both approaches are effective proved by cross validation and discuss the differences between two methods and some common drawbacks.
Fusing image feature and title semantic regularization with adversarial networks for cross-modal retrieval

Abstract With the improvement of living standard, people pay more and more attention to their health. Food is the foundation of human life, modern society places more emphasis on a balanced diet, which controls fat, protein, carbohydrate and vitamin intake. So food computing is more and more important. Food retrieval is one of the important research directions in food computing. On the basis of food retrieval, we can predict ingredients and instructions in each dish, according to the ingredients and instructions to speculate on the visual effect of cooking, which can guide human reasonable diet, analyse human diet structure and diet culture and so on. In this paper, we focus on cross-modal retrieval between food image and recipe. Firstly, we analyze the problems existing in the present method. Based on the problems existing in the existing method, a fusion image feature and title regularization with adversarial network is proposed, which uses the idea of generative adversarial to align the modes, fuses the local features and global features of the image, and adds the semantic regularity of title to improve the accuracy of the retrieval.
Enhanced EVA with Threshold Limit of Similarity

In recent years, there has been a growing demand for efficient learning algorithms to apply reinforcing learning to real-world tasks. The algorithm, called EVA, utilized non-parametric Q-values without using neural networks to improve efficiency of learning. We pointed out the problems with EVA, and proposed the improved algorithm with even better performance. We conducted comparative experiments on several tasks to assess the efficacy of our proposed methodology.
Parallel lines in life, divergent lines in science: Complexity encompasses different worlds

Abstract This paper has two major parts. The first recounts the beginning and growth of the friendship between Siegfried Streufert and Peter Suedfeld, who in the mid‐1960s were graduate students in Harold M. Schroder's social psychology laboratory at Princeton University. Each continued to develop a different line of theory and research on cognitive aspects of personality and social psychology while maintaining their contacts and friendship. The second part describes the background, characteristics, and applications of the integrative complexity (IC) work of Peter Suedfeld and his collaborators and students.
Research on security of integrated energy management and control system based on trusted computing

China Energy Investment Corporation (China Energy) conducts research on energy management and control in photovoltaic buildings and building energy efficiency control, establishes a comprehensive energy control system, and has established demonstration pilots throughout the country. However, the research on the security of energy management and control systems is relatively weak. For this reason, this article analyzes the application background of trusted computing and establishes a comprehensive energy control system security architecture based on trusted computing, focusing on the realization of trusted guidance for embedded devices and trusted operation of system applications. And equipment status monitoring and other functions, provide a reference for the construction of safety protection measures for the integrated energy management and control system in the future.
Tutorial: Making C Programs Safer with Checked C

Despite its well-known lack of memory safety, C is still widely used to write both new code and to maintain legacy software. Extensive efforts to make C safe have not seen wide adoption due to poor performance and a lack of backward compatibility. Checked C is an open-source, safe extension to C that addresses these problems. This hands-on tutorial will introduce attendees to Checked C and provide guidance in the use of 3C, a semi-automatic tool that converts legacy C code to Checked C.
Expression Recognition and Classification Based on Jump-Layer Optimization of Convolutional Neural Network

In multi-layer neural networks, high-level abstract features are extracted by convolutional layers at the end, which lack low-level detailed features. Meanwhile, the requirements of training parameters are also higher because of the deepening of network layers, which increases the difficulty of training and is more prone to the problems of gradient disappearance or explosion. In this paper, an optimized jump-layer convolutional neural network (JCCN) structure is proposed to modify facial expression recognition classification network model. This method effectively combines low-level detailed features and high-level abstract features through jump-layer connections. Gradient disappearance caused by too much network layers and back propagation parameter transfer are modify via the approach. The proposed method can reduce the risk of network training overfitting and enhance the nonlinearity of the data. At the same time, the 1*1 convolution kernel introduced reduces the training cost and the number of parameters effectively. The experimental results show that the network has a good performance on the data sets FER2013 and CK+. It is anticipated that facial expression recognition and classification methods based on convolutional networks would benefit from this paper.
Research of the applicability of machine learning methods for assessment of departments’ performance

Currently, a number of educational organizations in Russia and abroad, including the National Research University “Moscow Power Engineering Institute” (MPEI), are introducing the European improvement model EFQM, designed to analyze and improve the educational, scientific and other activities of the departments. In accordance with this model, each university department is assigned a score equal to the sum of points for two groups of criteria: criteria from the group of opportunities and criteria from the group of results. To obtain such assessments, a commission consisting of external experts, EFQM assessors and university staff meets with heads of departments. Based on the results of the discussion of the results of the meetings, the commission determines the score and rating of the departments in accordance with the EFQM model. The purpose of the work presented in the article is to study the possibility of using machine learning to simplify the work of experts in terms of obtaining estimates according to criteria from a group of results. The article proposes a system for evaluating the activities of departments according to criteria from a group of results based on machine learning. A program in the Python programming language has been developed, which evaluates the activities of departments according to these criteria for each department of the MPEI. The program receives the initial data for such assessments from the monitoring system of key performance indicators implemented in MPEI.
Errors in machine translation from German into Russian (based on articles of German-language media and their translations)

The article is devoted to the study of errors and inaccuracies made by machine translation systems. The reasons for the appearance of errors of various types in the texts of machine translations of German-language articles of well-known mass media into Russian, made by popular translation programs, are analyzed. A classification of errors is given. The lexical-semantic and lexical-stylistic, normative-usual, grammatical, punctuation and spelling errors are highlighted. Typical “weaknesses” of machine translation from German into Russian are revealed, which should be paid attention to during post-editing of the text of such a translation. It is pointed out that the analysis of typical errors in automatic translation can also be taken into account in the process of improving the algorithm of their work by the developers of machine translation systems.
Design for learning in the third space: opportunities and challenges

Abstract Many educational agents offer paths that allow school to turn itself into a ‘third space’. Caritas Ambrosiana, based on a ‘pedagogy of facts’, proposes interventions to promote soft skills in schools. This non-formal education agency committed Research Center about Media Education, Innovation and Technology (CREMIT) of Catholic University for a project to improve their school programme and training effectiveness. We chose the participatory action research paradigm to verify how to design an educational path by applying third-space principles in the school context and how digital media can be embedded into the practice to enable a more porous exchange of experiences and expertise between students, educators and the school curriculum. The accompanying plan was designed on the basis of the initial questionnaire data analysis: sociomateriality was the main focus because it was considered by Caritas educators as one of the least important elements to include in the design process. The second reason is the need to rethink on-site training formats to face the challenges of the Covid-19 emergency. As expected, after the training intervention, sociomateriality had a significant growth in the design practices. The other third-space pedagogy elements (peering, experiential orientation, motivation, pleasure of making together) are maintained and reinforced, thanks to digital literacy.
Air Pressure Control on Ventilator using Fuzzy Logic Method

In a virus outbreak that attacks human respiration, there is an increase in the need for ventilators that is not proportional to their availability. A ventilator is a device that serves to assist or replace normal ventilation functions in people who have respiratory problems. The lungs are important organs in the respiratory system. The lungs can inflate if the atmospheric pressure is greater than the lung pressure (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alv</inf> ). Conversely, the lungs can deflate if the atmospheric pressure is less than P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alv</inf> . In the process of inspiration, air flows from outside to the alveoli, increasing oxygen levels. The faster oxygen is introduced into the alveoli from the atmosphere, the higher the oxygen concentration in the alveoli. So that the oxygen concentration in the alveoli is controlled by the speed with which new oxygen enters the lungs through the ventilation process. In people who have respiratory problems, the body is less able to supply the oxygen needs of the lungs so that the flow of air pressure that flows into the lungs must be controlled. A ventilator is made with the principle of pressure difference. Atmospheric pressure is set greater than P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alv</inf> so that air enters the lungs. In contrast, atmospheric pressure is set to be less than P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alv</inf> to force air out of the lungs. The outside air pressure can be increased by providing positive pressure air from the airflow drive device. With the Fuzzy Logic control method, the speed of the airflow drive device is adjusted to meet the patient's needs. Ventilators that have been designed and manufactured can provide air pressure in the pressure range of 0 to +50 cmH <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> O or the equivalent of 0 to 0.711167 psi. The basic pressure at the end of the breath is set to be equal to atmospheric pressure.
Entropic Laplacian eigenmaps for unsupervised metric learning

Unsupervised metric learning is concerned with building adaptive distance functions prior to pattern classification. Laplacian eigenmaps consists of a manifold learning algorithm which uses dimensionality reduction to find more compact and meaningful representations of datasets through the Laplacian matrix of graphs. In the present paper, we propose the entropic Laplacian eigenmaps (ELAP) algorithm, a parametric approach that employs the Kullback–Leibler (KL) divergence between patches of the KNN graph instead of the pointwise Euclidean metric as the cost function for the graph weights. Our objective with such a modification is increasing the robustness of Laplacian eigenmaps against noise and outliers. Our results using various real-world datasets indicate that the proposed method is capable of generating more reasonable clusters while reporting greater classification accuracies compared to existing widely adopted methods for dimensionality reduction-based metric learning.
Benchmarking of Feature Detectors and Matchers using OpenCV-Python Wrapper

This paper proposes a comparative analysis of AKAZE, BRISK, KAZE, ORB, and SIFT features detecting algorithms in combination with BF and FLANN feature matching algorithms. The comparative evaluation was implemented using the OpenCV-Python wrapper. In the first phase, for each pair of algorithms, we estimated the time of detection, the number of detected features, the time of matching, and the number of matched features. In the second phase, we estimated the time of feature detection depending on image resolution and the number of features. The results showed the best summary efficiency is achieved when using a pair of ORBFLANN algorithms. The performance of detectors and matchers is determined only by hardware features, image sets, and configuration while using the OpenCV-Python wrapper does not affect performance.
Comparison of Data Noise Filtration Methods for Casting Cooling Curves

A comparative analysis of different methods for filtering the measured noisy cooling curves of gray cast iron melts is carried out. Three different methods of noise filtering in the data are compared: simple moving average method as a baseline, method of adaptive smoothing as well as the proposed approximative polynomial filtration based on selection of the degree of the polynomial using GMDH. All this methods give a possibility to filter out noise in the data but the moving average and adaptive smoothing methods give only numerical filtration results. In contrast, the result obtained using the method of approximative filtration is a polynomial function which proved to be the most accurate and to keep all the critical points of the initial cooling curve.