Reviewerly/exHarmony huggingface dataset:
The possibilities of contemporary art in solving the problems of sociocultural identity of schoolchildren in the context of spiritual and moral education

Modern sociocultural characteristics and aspects of a growing person development are distinguished by the contradictory influences on the formation of his identity. The article focuses on the understanding of contemporary art as the content component and pedagogical means in the educational interaction of a teacher and schoolchildren. Possible ways of solving the problems of sociocultural identification of schoolchildren through the mechanism of assigning values to them within the framework of spiritual and moral education are proposed.
Professional ethics of the modern teacher: realities and transformations

As a result of globalization, paradigmatic changes have taken place in the modern world picture, which without exception have touched all spheres of social reality, including the sphere of education. Not only social have changed, but also basic subjects - participants of the educational process student and teachers. In moder reality, teacher's professional actyvity requires overcoming various contradictions. It just opened up to new, changes taking place in society, and in education, the necessary functions for exercises to use the function of calculating values, revisinf ethical norms are needed.
Market Analysis- Artificial Intelligence, IOT and Robotics

The After the successful completion of artificial intelligence conference series, we are pleased to welcome you to the “Artificial Intelligence, IOT and Robotics. The congress is scheduled to take place on July 27-28, 2020 in the beautiful city of Paris, France. This 2021 artificial intelligence Conference will give you exemplary experience and great insights in the field of research. Meetings LLC Ltd Organizes 1000+ Conferences Every Year across over USA, Europe, and Asia with assistance from 1000 progressively sensible social requests and Publishes 700+ Open access journals which contain in excess of 100000 well-known characters, assumed scientists as article board people. Artificial intelligence is becoming a crucial driving force behind innovation in robotics and Lot, with a range of advances including Machine Learning, Deep Learning, Robotics, Internet of Things. Universities also have begun to offer dedicated Internet of Things programs. Robotics will be getting to be progressively prevalent these times Around learners. Actually, if you follow again of the Inception about Artificial Intelligence, you will discover thatmany technologies likewise have robotics that fills done the individuals minor cracks more successfully Furthermore provides for you a shinier vehicle. There need aid likewise robotics technology items accessible with stay with your eyewear What's more different optical units What's more that's only the tip of the iceberg tough.
Penerapan Algoritma Backpropagation Dalam Memprediksi Jumlah Pengguna Kereta Api Di Pulau Sumatera

The purpose of this study is to analyze and test whether the number of train passengers in Indonesia can be predicted by using artificial intelligence techniques. In this study, the artificial intelligence technique used is the Artificial Neural Network Technique (ANN) with the Backpropagation method. Artificial neural network is a method that has been widely used to solve forecasting cases. The main difficulties in implementing neural network methods in forecasting are finding the right architectural combination, determining the appropriate learning rate parameter values and selecting the optimal training algorithm. The research data is secondary data sourced from the bps.go.id website from 2006 - 2019. The data in this study were computerized using the matlab application. From the 5 architectural models used, the best model based on computerized results with the Matlab application is 3-3-1 with an output value of 0.0215923 MSE. The accuracy of the truth obtained is 92%.
Algorithms for Basis Sparsity Non-negative Matrix Factorization

In this paper, we proposed a method called basis sparsity non-negative matrix factorization, for decomposing multivariate matrix into sparse nonnegative components. The algorithm combining the original NMF formulation with sparseness constraint to make the basis matrix reaches to a more sparseness degree, and with local features clearer. By controlling the parameter, we can obtain the results of the basis matrix with different sparseness degree. Experiments have been done on the ORL database and the results show the algorithm has a good sparse ability.
Forecasting for a Data-Driven Policy Using Time Series Methods in Handling COVID-19 Pandemic in Jakarta

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ФОРМУВАННЯ ЗМІСТУ КУРСУ «БАЗИ ДАНИХ ТА ІНФОРМАЦІЙНІ СИСТЕМИ» ДЛЯ МАЙБУТНІХ УЧИТЕЛІВ ІНФОРМАТИКИ

The article is devoted to the problems of content formation and software selection for the implementation of the course Databases and Information Systems as a necessary component of professional training of bachelors in the specialty 014 Secondary Education (Informatics).The current needs of the country in qualified IT specialists require the formation of computer science teachers' ability to work with popular programming environments and information and communication technologies. In particular, mastering the knowledge of such an important section of ICT as databases will allow pupils to implement the tasks of automating data processing and prepare them for future professional activities.Training of bachelors in the specialty 014 Secondary Education (Informatics) at the Volodymyr Vynnychenko Central Ukrainian State Pedagogical University is carried out on the basis of an educational and professional program, a mandatory component of which includes the discipline Databases and Information Systems as a necessary component of training in informatics. The discipline consists of the following thematic sections.Section 1. Basic concepts and architecture of databases and database management systems. Database design.Section 2. Creating and managing a relational database.Section 3. Use of CASE-tools and work in the database management systems terminal. Procedural SQL.Section 4. Database administration. Modern directions of database development.Theoretical and practical provisions of the course from sections 1 and 2, related to the issues of architecture, design and creation of databases, it is proposed to consider the example of Microsoft Office Access.Topics from sections 3 and 4 are offered to be considered on examples of non-educational database management systems. Thus, the study of system aspects of SQL, user security and authorization, data protection mechanisms, data backup and recovery, and database administration, it is proposed to carry out using the CASE-tool MySQL Workbench and the client terminal of the MySQL server This program was successfully tested in 2019-2020 academic year, when teaching 3rd year students majoring in 014 Secondary Education (Mathematics), educational program: Secondary Education (Mathematics, Informatics and Economics).
An Image Stabilization Technique Based on HHT (Hilbert–Huang Transform)

ABSTRACTImage stabilization has come to be a topic of massive interest and a lively research subject over the last years due to the huge use of digital imaging gadgets. The photo-stabilization method objectives at removing abnormal motion phenomena from picture sequences so that it will accomplish a compensated series that displays easy digital camera movements. An expansion of picture processing programs calls for motion-compensated picture sequences as inputs. The positional vacillations of the video sequence will affect the visible first-rate and obstruct the following techniques for several applications. A modern technique for virtual photograph stabilization (DIS) based at the Hilbert–Huang remodel (HHT) is studied. It exploits the simple functions of the HHT so that you can separate the local motion sign obtained from a photograph collection into special motion vectors. a ramification of embedded structures equipped with a digital photograph sensor, which include handheld cameras, mobile phones, and robots, can produce photo-sequences with an found motion resulting from special varieties of moves: the clean digicam motion (intentional) and the jitter. To verify the effectiveness of the proposed DIS method, several simulations have been accomplished, and the effects had been as compared with present stabilization methods. An attempt has been made for estimate mean square error (MSE), top sign to noise ratio (PSNR) and structural similarity index (SSIM) to study the overall performance of HHT-DIS and in comparison with other current strategies and showed that HHT-DIS outperforms the present strategies.Keywords: CCD, digital picture stabilization (DIS), Hilbert–Huang transform (HHT), photograph collection processing, jitter motion designationCite this Article: Yogita Rathi, Vikrant Singh, Mahesh Lamba. An Image Stabilization Technique based on HHT (Hilbert–Huang Transform). International Journal of Optical Sciences. 2020; 6(1): 1–5p
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack

Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not recognized by humans. This is an important issue for security or medical applications. In this paper, we propose an ensemble model training framework with random layer sampling to improve the robustness of deep neural networks. In the proposed training framework, we generate various sampled model through the random layer sampling and update the weight of the sampled model. After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method. To evaluate our proposed method, comprehensive and comparative experiments have been conducted on three datasets. Experimental results show that the proposed method improves the adversarial robustness.
РОЛЬ КОМП’ЮТЕРНОГО МОДЕЛЮВАННЯ У ФОРМУВАННІ СИСТЕМНОГО МИСЛЕННЯ СТАРШОКЛАСНИКІВ

The article analyzes the pedagogical research on the essence and content of the concepts of systems approach and systems thinking, substantiates the main features of the formation of systems thinking. It is noted that systematic thinking, in contrast to linear, is focused on seeing the integrity of an object or phenomenon, to identify not things but connections between them, not instantaneous states, but patterns of change. Systems thinking is needed to distinguish the structures that form the basis of complex situations. The implementation of integrative connections during the educational process is a necessary condition for the formation of a scientific worldview and competencies.It is determined that the formation of an environment for the effective formation of systems thinking is possible through the implementation of a competency approach and the introduction of active, activity forms of learning, in particular with the use of computer models.It is noted that the use of computer models in the educational process provides a high degree of clarity, and their interactivity allows to directly influence the course of the experiment, change the conditions of its conduct, which promotes motivation, interest and desire to experiment, conduct independent research in science, and thus promotes the development of creative skills and critical thinking. It is emphasized that individual research tasks based on computer simulations are an effective tool for implementing the activity approach. Students have the opportunity to something new, unknown to them, to observe the patterns of processes and phenomena, to discover a known law in physics by measuring physical quantities.
Remote Sensing Image Detection Based on FasterRCNN

The object detection algorithm can accurately detect the objects needed in remote sensing images. For the traditional object detection which adopts the sliding window method, both the detection accuracy and robustness are low and the generalization ability is weak. It is difficult to meet people’s demand for production and application. With the establishment of large-scale image database, the development of deep convolutional network has been promoted, which greatly improves the accuracy of object detection in images. In the field of target object detection, the algorithm based on depth of deep learning is the most popular and effective ones. Through a large number of experiments on the NWPU VHR-10 data set, we verified the remote sensing image detection based on FasterRCNN. The experiment results showed that the average accuracy of the ten types of targets in the NWPU VHR-10 data set reached 81.59%, which therefore proved the effectiveness of deep learning algorithm in remote sensing image detection.
Criteria, Indicators and Levels of the Readiness of Teachers of Natural Specialties To Develop the Entrepreneurship Competence of High School Students

The article determines the relevance of forming the of teachers of natural specialties to develop competence in the system of postgraduate pedagogical education, which ensures the improvement of education and vocational training of teachers by deepening, expanding and updating their professional knowledge, abilities and skills in accordance with current needs and education. Theoretical views of scientists to the definition of key concepts: entrepreneurial competence, readiness, indicators, of readiness are analyzed.By combining problem-oriented, system-generalizing methods and method of system-structural analysis of scientific literature, the author has achieved the goal and the criteria for determining the of teachers of natural specialties for the development of the competence of high school students, formed in the system of postgraduate pedagogical, technological, educational, personality-emotional, behavioral ones. Within the selected criteria, appropriate indicators are defined, which characterize them and the methods and means of diagnosis are defined for each criterion: conversation, observation, questioning, analysis, report, self-report, expert evaluation and introspection. The developed system of criteria and indicators makes it possible to determine the levels of of teachers of natural specialties for the development of the competence of high school students: low (reproductive), medium (reproductive-reconstructive), sufficient (reconstructive) and high (creative).The analyzed criteria, indicators and levels will allow to determine the realities and to predict the dynamics of forming the of teachers of natural specialties to develop the competence of high school students in the system of postgraduate pedagogical education.
International Exchange: The Adaptation of Foreign Students in the Learning Process in the Russian University System (a Case Study of Tomsk State University of Control Systems and Radioelectronics)

The article focuses on the multi-ethnic environment of the University, which reflects both multi-vector global and local processes and specificity of educational practices affecting the relationships in the University eco-environment. The authors consider the processes of adaptation of international exchange students in Tomsk State University of Control Systems and Radioelectronics (TUSUR) and the importance of intercultural interaction within these processes. Special attention is given to the harmonization of the University interethnic environment, the formation of friendly barrier-free interaction, which is possible because of the systemic work of educational process participants. The object of the research is the adaptation and intercultural communication in the student environment, with the focus on the specificity of adaptation of international exchange students. The authors draw on the theory of intercultural communication and employ methods of sociological survey and mathematical statistics. The survey, conducted in the fall of 2019 among 120 first-and second-year students from 36 countries with the use of a spontaneous sample type. The respondents were offered a questionnaire aimed at identifying the most urgent problems of adaptation in TUSUR. The survey resulted in a review of the challenges related to the adaptation of foreign students to new conditions in the host community. The results of the research bring to light new sociocultural approaches to the work with foreign students and can be used to optimize the interethnic interaction activities of educational system agents.
The Design of Automatic Rice Cooker 3 in 1 with Circuit Control Based on Solidworks as Three Dimensional Modelling Software

The development of technology allows all activities to be carried out automatically, including for rice cooking.The problems who still exist when we did it is about of the timeconsuming process, especially for among people who have a lot of activity.The automatic rice cooker 3 in 1 with circuit control is a solution for answer that problem.This rice cooker has three functions in the same appliance, which consists of a circuit control as a control center of the tool, a rice storage tank, a rice washing pan equipped with blade and cooking rice until it is ready for consumption.This study aims to design the automatic rice cooker 3 in 1 using Solidworks as a three-dimensional modeling software and secondary data from articles and journals to analyze bottle fiiling time, water discharge and selenoid valve power consumption.
Democratizing Evaluation of Deep Model Interpretability through Consensus

Deep learning interpretability tools, such as (Bau et al., 2017; Ribeiro et al., 2016; Smilkov et al., 2017), have been proposed to explain and visualize the ways that deep neural networks make predictions. The success of these methods highly relies on human subjective interpretations, i.e., the ground truth of interpretations, such as feature importance ranking or locations of visual objects, when evaluating the interpretability of the deep models on a speciﬁc task. For tasks that the ground truth of interpretations is not available, we propose a novel framework Consensus incorporating an ensemble of deep models as the committee for interpretability evaluation. Given any task/dataset, Consensus ﬁrst obtains the interpretation results using existing tools, e.g., LIME (Ribeiro et al., 2016), for every model in the committee, then aggregates the results from the entire committee and approximates the “ground truth” of interpretations through voting. With such approximated ground truth, Consensus evaluates the interpretability of a model through matching its interpretation result and the approximated one, and ranks the matching scores together with committee members, so as to pursue the absolute and relative interpretability evaluation results. We carry out extensive experiments to validate Consensus on various datasets. The results show that Consensus can precisely identify the interpretability for a wide range of models on ubiquitous datasets that the ground truth is not available. Robustness analyses further demonstrate the advantage of the proposed framework to reach the consensus of interpretations through simple voting and evaluate the interpretability of deep models. Through the proposed Consensus framework, the interpretability evaluation has been democratized without the need of ground truth as criterion.
Machine Learning with Provable Robustness Guarantees

Author(s): Zhang, Huan | Advisor(s): Hsieh, Cho-Jui | Abstract: Although machine learning has achieved great success in numerous complicated tasks, many machine learning models lack robustness under the presence of adversaries and can be misled by imperceptible adversarial noises. In this dissertation, we first study the robustness verification problem of machine learning, which gives provable guarantees on worst case performance under arbitrarily strong adversaries. We study two popular machine learning models, deep neural networks (DNNs) and ensemble trees, and design efficient and effective algorithms to provably verify the robustness of these models. For neural networks, we develop a linear relaxation based framework, CROWN, where we relax the non-linear units in DNNs using linear bounds, and propagate linear bounds through the network. We generalize CROWN into a linear relaxation based perturbation analysis (LiRPA) algorithm on any computational graphs and general network architectures to handle irregular neural networks used in practice, and released an open source software package, auto_LiRPA, to facilitate the use of LiRPA for researchers in other fields. For tree ensembles, we reduce the robustness verification algorithm to a max-clique finding problem on a specially created graph, which is very efficient compared to existing approaches and can produce high quality lower or upper bounds for the output of a tree ensemble based classifier. After developing our robustness verification algorithms, we utilize them to create a certified adversarial defense for neural networks, where we explicitly optimize the bounds obtained from verification to greatly improve network robustness in a provable manner. Our LiRPA based training method is very efficient: it can scale to large datasets such as downscaled ImageNet and modern computer vision models such as DenseNet. Lastly, we study the robustness of reinforcement learning (RL), which is more challenging than the problem in supervised learning settings. We focus on the robustness of state observations for a RL agent, and develop the state-adversarial Markov decision process (SA-MDP) to characterize the behavior of a RL agent under adversarially perturbed observations. Based on SA-MDP, we develop two orthogonal approaches to improve the robustness of RL: a state-adversarial regularization helping to improve the robustness of function approximators, and alternating training with learned adversaries (ATLA) to mitigate the intrinsic weakness in a policy. Both approaches are evaluated in various simulated environments and they significantly improve the robustness of RL agents under strong adversarial attacks, including a few novel adversarial attacks proposed by us.
Face Mask Detection using Deep Learning

In the Covid-19 Pandemic the government of every country warned the citizens to wear a mask whenever they are away from home. They ordered not to leave house without a face mask. In every public premise, offices, in transportation the government and office officials held a face mask compulsory poster. The police and others usually warn and alert a person who does not wear a face mask. In the offices at the entrance the people are examined individually for face mask manually. This manual checking whether a person wears a mask or not is a difficult process when the number of people increases or this will be difficult when there is a crowd, where the situation to wear a mask is mandatory. In the proposed system, the model is created using the deep learning. By this the system can detect whether a person wears a mask or not on the face.When the image is given as input to the system the model identifies every face in the image and it indicates whether a person wears a mask or not from the image. The system also works fine when there is more than one face in the image. By using this proposed model it is possible to detect whether a person wears a mask or not. This model can be used in the real world applications like Offices, Educational Institutions etc.
THE NEED FOR A NEW ONTOLOGY IN THE SUBSTANTIATION OF THE QUANTUM THEORY OF LIFE AND CONSCIOUSNESS

The article substantiates the need for the develop of a new ontology,which includes the common foundations of physical science and the science of life and consciousness. A hypothesis is put forward about the quantum-informational nature of matter, life, and consciousness. The basic concepts and principles of substance-information ontology are formulated.