Reviewerly/exHarmony huggingface dataset:
Generation of Images with Handwritten Text in Russian

Automatic handwriting recognition is an important component in the process of electronic documents analysis, but its solution is still far from ideal. One of the main reasons for the complexity of Russian handwriting recognition is the insufficient amount of data used to train recognition models. Moreover, for the Russian language the problem is more acute and is exacerbated by a large variety of complex handwriting. This paper explores the impact of various methods of generating additional training datasets on the quality of recognition models: the method based on handwritten fonts, the StackMix method of gluing words from symbols, and the use of a generative adversarial network. A font-based method for creating images of handwritten text in Russian has been developed and described in this work. In addition, an algorithm for the formation of a new Cyrillic handwritten font based on the existing images of handwritten characters is proposed. The effectiveness of the developed method was tested using experiments that were carried out on two publicly available Cyrillic datasets using two different recognition models. The results of the experiments showed that the developed method for generating images made it possible to increase the accuracy of handwriting recognition by an average of 6%, which is comparable to the results of other more complex methods. The source code of the experiments, the proposed method, as well as the datasets generated during the experiments are posted in the public domain and are ready for download.
Cloud Development and Deployment

This chapter describes the challenges related to the software deployment and configuration activities, which could be addressed through AI techniques. Additionally, we illustrate the application of such techniques with two specific problems: the QoS-aware binding of web services, and the selection of the optimal configuration in highly-configurable systems. The AI techniques applied to solve such problems are GRASP with Path Relinking and Multi-Objective Evolutionary Algorithms, respectively. These techniques will be applied to deployment scenarios based on empirically obtained data from real-world web services and highly-configurable software systems. These examples are implemented in Java and C, respectively, providing the source code, build scripts and data sets for the sake of reproducibility.
Real Application of CNN Interpretation Methods: Document Image Classification Model Errors’ Detection and Validation

In this paper, we consider the case of applying convolutional neural networks interpretation methods to ResNet 18 model in order to identify and justify model errors. The model is used in the problem of classifying the orientation of text documents images. First, using interpretation methods, an assumption was made as to why the neural network shows low metrics on data that differs from training images. The alleged reason was the presence of artifacts on the generated training images, caused by the use of an image rotation function. Further, using the Vanilla Gradient, Guided Backpropagation, Integrated Gradients, GradCAM methods and the invented metric, we managed to accurately confirm the hypothesis put forward. The obtained results helped to significantly improve the accuracy of the model.
Testing the Satisfiability of Formulas in Separation Logic with Permissions

Abstract We investigate the satisfiability problem for a fragment of Separation Logic (SL) with inductively defined spatial predicates and permissions. We show that the problem is undecidable in general, but decidable under some restrictions on the rules defining the semantics of the spatial predicates. Furthermore, if the satisfiability of permission formulas can be tested in exponential time for the considered permission model then SL satisfiability is Exptime complete.
Human-centred feasibility restoration in practice

Abstract Decision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and determine how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical, and flexible user support. We evaluated the resulting framework on three real-world applications and conducted a qualitative expert user study with participants from different application domains.
Development of practicum-based mobile augmented reality through the group investigation model to improve students' creative thinking

Students' creative thinking skills need to be developed, one of which is through learning. This research was aimed to produce Augmented Reality (AR) media based on Group Investigation (GI) practice models on various living things to improve students' creative thinking skills. This research and development using the 4-D model. However, it only reached the Develop stage. The population of this study was students of SMA Muhammadiyah 10 Surabaya in the Science class, with 37 students in each class. This study collected data through validation tests, pre-tests, post-tests, observation sheets for creative thinking skills, questionnaires, and student responses. The data analysis technique used is qualitative and quantitative descriptive. The results of development research carried out by researchers related to Mobile AR media with display aspects with 17 indicators obtaining good category. Whereas in the programming aspect, seven indicators are good category. So, the Mobile AR media validation results are good category. Biological material in the application with AR with content aspects with eight indicators are very good category. Whereas in the learning aspect, which consists of 16 indicators, in the good/valid category. The material in this category is worthy of being tested in the field. Based on descriptive statistics, the pre-test and post-test proved to be higher. We discuss these results and its implications.
A Novel Robotic Path Planning Method in Grid Map Context Based on D* Lite Algorithm and Deep Learning

Mobile robot path planning has received more and more attention as an important technology in robotics. Based on the D* Lite algorithm, this paper constructs a robot path planning model in a grid map environment, and proposes a deep learning fusion algorithm for path planning under complex large maps. The D* Lite algorithm with excellent performance adopts the enhanced neural network algorithm with environment self-learning ability for local parts. The model introduces the gentle update method of the Q value in the D* Lite algorithm into the optimization target calculation, calculates the loss function and updates the network parameters, thereby solving the overestimation problem of deep reinforcement learning in the application of mobile robot path planning. In the simulation process, the idea of averaging is introduced into the e-greedy strategy, and the value function output by the previous generation parameter network is used to obtain the average result to determine the next action direction of the mobile robot. The experimental results show that in the simple environment and the complex environment, the planned path lengths in the environment are 44.21, 43.63 and 43.61[Formula: see text]m, respectively, reducing the number of collisions with obstacles during the training process of the mobile robot, and improving the superiority and effectiveness of the algorithm.
IMPACTOS DAS SEQUELAS DA COVID-19 NA QUALIDADE DE VIDA DOS SOBREVIVENTES: UMA ANÁLISE DOS ESTUDOS RECENTES

The emergence of COVID-19 and the associated pandemic outbreak demonstrated the need to educate and mobilize the public to adopt behaviors for community mitigation.In a context of rapid epidemiological worsening, the use of digital media to access information benefits the population and can contribute to the control of the impact of the COVID-19 pandemic.This study aimed to evaluate the most viewed Brazilian videos on YouTube as a source of information about COVID-19.The quantitative, observational, and crosssectional study was conducted on YouTube on 04/30/2020, with videos uploaded between 01/01/2020 and 04/ 30/2020.The descriptor "coronavirus" was used and the first 100 most viewed videos that were recorded in Brazilian Portuguese, related to the topic, unduplicated, and being less than 30 minutes long were selected.Two judges recorded the Uniform Resource Locator (URL) of the videos downloaded from YouTube for data analysis.The 100 videos analyzed showed the following results: 140,027,282 views; 7,201,187 "likes"; 165,624 "dislikes"; 352,541 comments; and a total duration of 12 hours, 28 minutes, and 12 seconds.In terms of content, death was mentioned in 71 videos, and the top Temas em Saúde Coletiva Covid-19 -Vol. 3 Capítulo 1 2prevention behaviors listed by the U.S. Centers for Disease Control and Prevention were addressed by less than one-third of the videos.Most YouTube videos could do a greater job of disseminating information about COVID-19 prevention behaviors, promoting population adherence.Educating, mobilizing, and engaging the public in adopting and practicing behaviors for community mitigation are differential aspects of successfully addressing the COVID-19 pandemic.Given the need for social distancing, YouTube could play an important role in interaction and communication about COVID-19 and be a significant complementary information strategy to combat the pandemic.
DESAFIOS E ESTRATÉGIAS NA GESTÃO DA SAÚDE DURANTE A PANDEMIA DA COVID-19: LIÇÕES APRENDIDAS E PERSPECTIVAS FUTURAS

The emergence of COVID-19 and the associated pandemic outbreak demonstrated the need to educate and mobilize the public to adopt behaviors for community mitigation.In a context of rapid epidemiological worsening, the use of digital media to access information benefits the population and can contribute to the control of the impact of the COVID-19 pandemic.This study aimed to evaluate the most viewed Brazilian videos on YouTube as a source of information about COVID-19.The quantitative, observational, and crosssectional study was conducted on YouTube on 04/30/2020, with videos uploaded between 01/01/2020 and 04/ 30/2020.The descriptor "coronavirus" was used and the first 100 most viewed videos that were recorded in Brazilian Portuguese, related to the topic, unduplicated, and being less than 30 minutes long were selected.Two judges recorded the Uniform Resource Locator (URL) of the videos downloaded from YouTube for data analysis.The 100 videos analyzed showed the following results: 140,027,282 views; 7,201,187 "likes"; 165,624 "dislikes"; 352,541 comments; and a total duration of 12 hours, 28 minutes, and 12 seconds.In terms of content, death was mentioned in 71 videos, and the top Temas em Saúde Coletiva Covid-19 -Vol. 3 Capítulo 1 2prevention behaviors listed by the U.S. Centers for Disease Control and Prevention were addressed by less than one-third of the videos.Most YouTube videos could do a greater job of disseminating information about COVID-19 prevention behaviors, promoting population adherence.Educating, mobilizing, and engaging the public in adopting and practicing behaviors for community mitigation are differential aspects of successfully addressing the COVID-19 pandemic.Given the need for social distancing, YouTube could play an important role in interaction and communication about COVID-19 and be a significant complementary information strategy to combat the pandemic.
AS MUDANÇAS NO ATENDIMENTO PSICOLÓGICO DURANTE A PANDEMIA DE COVID-19

The emergence of COVID-19 and the associated pandemic outbreak demonstrated the need to educate and mobilize the public to adopt behaviors for community mitigation.In a context of rapid epidemiological worsening, the use of digital media to access information benefits the population and can contribute to the control of the impact of the COVID-19 pandemic.This study aimed to evaluate the most viewed Brazilian videos on YouTube as a source of information about COVID-19.The quantitative, observational, and crosssectional study was conducted on YouTube on 04/30/2020, with videos uploaded between 01/01/2020 and 04/ 30/2020.The descriptor "coronavirus" was used and the first 100 most viewed videos that were recorded in Brazilian Portuguese, related to the topic, unduplicated, and being less than 30 minutes long were selected.Two judges recorded the Uniform Resource Locator (URL) of the videos downloaded from YouTube for data analysis.The 100 videos analyzed showed the following results: 140,027,282 views; 7,201,187 "likes"; 165,624 "dislikes"; 352,541 comments; and a total duration of 12 hours, 28 minutes, and 12 seconds.In terms of content, death was mentioned in 71 videos, and the top Temas em Saúde Coletiva Covid-19 -Vol. 3 Capítulo 1 2prevention behaviors listed by the U.S. Centers for Disease Control and Prevention were addressed by less than one-third of the videos.Most YouTube videos could do a greater job of disseminating information about COVID-19 prevention behaviors, promoting population adherence.Educating, mobilizing, and engaging the public in adopting and practicing behaviors for community mitigation are differential aspects of successfully addressing the COVID-19 pandemic.Given the need for social distancing, YouTube could play an important role in interaction and communication about COVID-19 and be a significant complementary information strategy to combat the pandemic.
Alternative Sources of Financing and the Sustainability of Cameroonian Start-ups

The objective of the paper is to assess the contribution of alternative sources of financing to the survival of SMIs through an analysis of start-ups in Cameroon.Our study employs a qualitative multisite case study methodology.Data was collected from both documentary and primary sources.For the primary data, we conducted semidirected interviews with five start-ups operating in four fields of activity (agriculture, health, finance and ICTs) to assess in depth the behaviour of various promoters who have received alternative financing at least once.The results of our manual and automated analysis led to two major findings: firstly, it is possible to identify alternative financing in the environment of Cameroonian start-ups in the form of social capital (help from loved ones, support from elites and families, community fundraisers, tontines) on the one hand, and crowdfunding on the other.Secondly, these two means of alternative financing are significant sources of added value for the survival of start-ups not only through the preparation and precreation activities (social capital) but as important levers in improving the organizational strategies of start-ups (fundraising among individuals and the possibility of marketing through platforms).
Simpus and Promem as Library Management System: the Quality Measurement

Many libraries have used technology-based management services extensively. However, not all of them can live up to the standards stipulated by the library. This study will use the PIECES method to assess Simpus and Promem's level of excellence. This technique is one of the approaches for evaluating the program's library information system's level of quality. The population of this descriptive research, which uses a quantitative methodology, is 905 people, with a sample size of 90 respondents. The data is collected via online survey as a research tool and accidental sampling. The data analysis procedure used the PIECES analysis method and descriptive statistical analysis. The study's findings show that Simpus and Promem's quality level employs the PIECES analysis method for each indicator, with the performance indicator receiving a score of 4.96 (very high), the information indicator receiving a score of 3.92 (high), the economic indicator receiving a score of 3.98 (high), the control indicator receiving a score of 3.78 (high), the efficiency indicator receiving a score of 3.94 (high), and the service indicator receiving a score of 4.0 (high). Thus, this demonstrates that performance has the best quality of the six indicators, and control has the lowest rate.&#x0D;
The Spanish experience of future classrooms as a possibility of smart learning environments

Developing advanced technologies and their integration into educational spaces generates new training opportunities. Examples of these are Future Classrooms (FC) and Smart Learning Environments (SLE), two concepts linked to educational innovation through technology but also have their differences. This paper aims to identify the similarities and differences between FC developed in the Spanish formal educational context concerning the theory of SLE. This study follows an exploratory research methodology using an ad hoc questionnaire applied transversally to a representative sample of teachers involved in the SLE (N = 66). SPSS V. 28 software was used. In relation to the results, some statistics (mean, standard deviation, kurtosis and skewness) were used to analyse the developed FC practices and professional satisfaction. In conclusion, the FC are at an initial stage of development in Spain, and although they could represent an approximation to what would be the practical basis of SLE, there are still several aspects to be developed, especially in relation to the technologies used and the dimension of attention to diversity. In relation to SLE technologies, they are related to the automation of certain processes and include artificial intelligence, learning analytics and sensors, among others. On the other hand, these technologies are not widely used in the FC, and the use of other more conventional technological resources, such as interactive whiteboards, online collaborative environments, LMS platforms, etc. In terms of attention to diversity, despite the existence of initiatives to personalise the learning experience in FC, assistive technologies are not considered, nor is automatic personalisation through certain SLE technologies.
Information Support System for the Concept of Educational and Methodological Material of the Module “Fundamentals of Russian Statehood”

This article analyzes the possible content of the information support system of the concept of educational and methodological material of the module “Fundamentals of Russian statehood”. It is concluded that it is necessary to create a single information portal, which will host video materials, information resources for scientific conferences and Olympiads of students.
The Analysis of the Mathematical Training Content Using Cluster Analysis: Didactic and Technological Aspects

The article deals with the application of combining the methods of hard and fuzzy clustering to establish the frequency of interdisciplinary relationships. These relationships are defined by the main concepts of the mathematical analysis for students of mathematical specialties of a classical university. The training program provides for a deep mastering of mathematical content. Identifying the most in-demand concepts, properties, and relationships is the basis for optimizing the program and the content of the mathematical training to integrate the provisions of knowledge engineering, teaching theory, and educational practice.
Artificial Nociceptor Based on Short-Term Memory TFT with Controllable Sensitization Level

In this work, we proposed and fabricated a novel artificial nociceptor based on short-term memory (STM) TFT with fully BEOL-compatible process. The proposed device well emulates four essential characteristics of nociceptors and exhibits adjustable sensitization level controlled by gate voltage. We further demonstrated a pain perception system composed of the nociceptor and a leaky integrate-and-fire (LIF) neuron.
The Basic Principles of the Development of the Elearning Program in the Communities of the Schoolstudents

The article deals with the issues of the implementation of the e-learning programs in the communities of the schoolstudents. There is specified the concept of “online community”, there is considered the classification of the online communities. The authors describe the theoretical approaches to the development of the online course of Computer Studies in the context of the introduction of the Federal State Educational Standards and the digital transformation of education at the example of the online course “Training of schoolstudents to the Basic State Examination of Computer Studies”, implemented in Volgograd State Socio-Pedagogical University.
Olympiad Programming Movement Features in Higher Education

Olympiad programming also called algorithmic programming or sports programming involves writing computer code in an arbitrary programming language (C++, Python, Java, C# and so on) that is the solution to a given problem. The solution must be efficient (i.e. fast) in time and efficient (i.e. small) in memory consumption during execution. Many schoolchildren participating in school Olympiads in Informatics continue to improve their skills and participate in student competitions. This article is devoted to features and practices of such Olympiads.
Garbage Classification with Deep Learning Techniques

With growing populations across the world and high consumption of goods and services, many problems arise with respect to waste management and dumps. The waste, if burned emits harmful fumes, and if not sorted properly, can lead to avoidable pollution due to loss of recyclable material. There is no active mechanism geared towards sorting and sifting through the waste generated, proper treatment and recycling of which can be beneficial to both the environment and governments. In order to provide a viable solution to the problem of manual classification and its costs, both human and monetary, we propose an automation system for detecting, sorting and classifying recyclable waste. If scaled for practical use, this can have a long-lasting impact on our relationship with the waste we generate. In this paper, we try to identify waste objects and classify them into categories by making use of Deep Learning techniques, Convolution Neural Networks, ResNet50, MobileNetV2, DenseNet, and other viable techniques and comparing the final results thus achieved.