Reviewerly/exHarmony huggingface dataset:
Generalization of the experience of participants in the distance format of educational process of physical education disciplines

The opinion of 190 teachers of the disciplines of physical culture of institutes and universities of Belgorod, Voronezh and Moscow as well as the opinion of more than 1,500 students of the Belgorod State Technological University named after V.G. Shukhov is presented. The problems of conducting physical education lessons in a distance education format are outlined and opinions based on the results of an electronic questionnaire are described. An organized pedagogical process with the use of electronic theoretical materials, electronic testing of knowledge, video assignments, conferences, require more individual, differentiated work from the teacher. 68 % of respondents recognize physical culture in remote form as a forced and temporary measure. Only 1 % admit the possibility of systematic use of new experience. Of the students who participated in the survey, only 57 % engaged in physical culture in a distance format actively and with pleasure. The motivation for the classes was the desire to study successfully and the habit of systematically performing physical exercises. First-year students experienced the greatest difficulties. The main difficulties for everyone were the material and technical capabilities in organizing regular classes, limited funds and space. The departments consider the need to find new strategic ways of development. 6 % of teachers consider it important to study at advanced training courses on the topic of organizing and conducting practical classes in a new distance format.
Efficient Matrix Computation for SGD-Based Algorithms on Apache Spark

With the increasing of matrix size in large-scale data analysis, a series of Spark-based distributed matrix computation systems have emerged. Typically, these systems split a matrix into matrix blocks and save these matrix blocks into a RDD. To implement matrix operations, these systems manipulate the matrices by applying coarse-grained RDD operations. That is, these systems load the entire RDD to get a part of matrix blocks. Hence, it may cause the redundant IO when running SGD-based algorithms, since SGD only samples a min-batch data. Moreover, these systems typically employ a hash scheme to partition matrix blocks, which is oblivious to the sampling semantics. In this work, we propose a sampling-aware data loading which uses fine-grained RDD operation to reduce the partitions without sampled data, so as to decrease the redundant IO. Moreover, we exploit a semantic-based partition scheme, which gathers sampled blocks into the same partitions, to further reduce the number of accessed partitions. We modify SystemDS to implement Emacs, efficient matrix computation for SGD-based algorithms on Apache Spark. Our experimental results show that Emacs outperforms existing Spark-based matrix computation systems by 37%.
The ways of increasing the efficiency of educational provision for the creative competence's development in the educational process

This article presents the development of creative competence of the preschool education teacher, research and development proposals of scientists in this regard. Methods of developing creativity, as well as examples of training on the development of creativity are considered.
FORMATION OF THE CONCEPT OF INFORMATION AMONG STUDENTS IN THE STUDY OF COMPUTER SCIENCE IN SECONDARY SCHOOL

Information theory refers to the teaching of an interdisciplinary orientation. To date, the scientific and practical results of information theory have found wide application in computer science, mathematics, physics, linguistics and other fundamental sciences. The article describes the theoretical, practical and pedagogical aspects of the concept of information. It is established that information is a fundamental concept and its place in social and natural processes is determined. The role of the subject of computer science in the study of fundamental problems of the concept of information in the educational process of secondary and higher schools is considered. The essence and application of the Hartley and Shannon formulas are revealed. These equations are the basic equations of information theory. Based on the theory of probability, the concept of information uncertainty is given and the unit of its measurement is presented. Various scientific directions of information theory are investigated: the measurement of information values and the use of the Shannon equation in the testing process.
Face Recognition Under Partial Occlusion: A Detection and Exclusion of Occluded Face Regions Approach

Partial face occlusions such as scarfs, masks and sunglasses compromise face recognition accuracy. Therefore, this paper presents a face recognition approach robust to partial occlusions. The approach is based on the assumption that the human visual system ignores occlusion and solely focuses on the non-occluded sections for recognition. Four sections derived from a whole/un-occluded image and the whole face are used to train a classifier for recognition. For testing, an occluded face image is also divided into the four sections above from which, the non-occluded or the least occluded section is selected for recognition. Two strategies were used for occlusion detection; skin detection and the use of haar cascade classifiers. This paper mitigated weaknesses from literature review such as use of datasets that simulate real world occlusion scenarios, use of less data in training and not requiring any type of occlusion variation in training data. Additionally, the classifier performed relatively well in the classification task with an accuracy of 92% on the Webface-OCC dataset and 96% on the Pubfig dataset.
Modeling of crime rate in Indonesia during the COVID-19 pandemic from a macroeconomic perspective: Using robust regression with S-estimator

Indonesia is the 4th country in Southeast Asia with the highest crime index in 2020. Economic factors are often linked as the main motive for the crime. The purpose of this study is for modeling the crime rate in Indonesia during the Covid-19 pandemic from a macroeconomic perspective. Regression analysis is an analysis that is used to explain and model the relationship between variables. The existence of outliers is often a problem in regression analysis with OLS. To overcome the outlier problem, this research uses robust regression with S-estimation. The results show that was influenced by the unemployment rate, poverty rate, GRDP per capita, population density, and human development index.
Metamorphism and Metamorphic Facies

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Intrusion of Magma

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Path Planning Using Improved RRT for UAV in Narrow Passage Space with Timing Constraints

This paper addresses the path planning problem for unmanned aerial vehicles (UAVs) in narrow passage space with timing constraints. The proposed method uses Rapidly Exploring Random Tree (RRT) algorithm improved by artificial potential field (APF) and general tau theory. A modified APF is introduced to intervene in the generation of random sampling points of RRT, that repulsive field is used to increase the sampling probability near the narrow passage, and the extended tree is guided to approach the goal point by attractive field. To achieve punctual arriving, a bio-inspired algorithm called general tau theory is applied, which uses the time for closing the gaps between current and goal states to realize synchronously planning of position and velocity. Simulation results indicate that the novel algorithm can improve the search efficiency of RRT, and timing and motion constraints can also be satisfied.
Use of digital twin technology of a technological control object in education

The development issues of a technological process digital twin in the form of an executable project in the TraceMode SCADA system are considered. Keywords digital twin, control object, regulation, automation, transfer function, education
More Than Meets the Eye: A Closer Look at Encodings in Visualization

Encoding data visually is at the heart of visualization. We usually assume that encodings are read as specified (i.e., if a bar chart is drawn by the length of the bars based on the data, that is also how we read them). In this paper, we question this assumption and demonstrate that observed encodings often differ from the ones used to specify the visualization. The value of a chart also often comes from higher level derived encodings, and which encodings end up getting used also depends on the user's task.
Sign Language Recognition Based on Autoencoder with Learning Prototypes

Sign language recognition is an action recognition task that specifically involves gestures. In this study, we propose an autoencoder-based isolated sign language recognition model with learning prototypes using dynamic time warping. The most recent study has shown remarkable results. However, they usually depend on the multimodality of videos (such as optical flow and pose estimation). Our approach only utilizes 2D RGB data; therefore, a longer preprocessing time is not required. In an end-to-end manner, the prototypes for each label data that perform the role of strict criterion are set, and the autoencoder is trained with it. Then, the classifier layers are trained using an encoder that can model temporal information efficiently. Using this method, we show an improved performance compared to other methods that use only 2D RGB or pose data.
Implementation of an UVC Lights Desinfection System for a Differential Robot Applying Security Methods in Indoor

Implementing methods that allow trajectory tracing for an autonomous robotic system is a topic that covers several fields of study since different parameters must be taken into account to locate obstacles to ensure proper navigation in environments where there is a moderate traffic of people. For this purpose, different methods of trajectory tracing will be implemented, which take as a reference the distance at which people are located. In addition, there are added human image recognition for avoid exposing people to UVC light. As a result, it can be observed that the methods implemented for the trajectory tracing give quite acceptable “pathing” results, since the main criterion to determine the efficiency is the SII, and thanks to the T265 and D435i sensors, it was possible to determine the presence of people in terms of image processing.
Waste Object Segmentation for Autonomous Waste Segregation

Segmenting waste objects for a given image is a crucial task in waste management domain as the region of presence of the waste objects can be precisely defined using segmentation. We achieve the task of segmenting the waste objects in the image by using the advantages of additional color space, larger kernels, and channel attention. Firstly, an additional layer of HSV color space of the image is stacked along with the original image layers from RGB color space in the input pipeline. Secondly, residual blocks of global convolutional network are used as the backbone for segmentation network, and thirdly, channel attention is applied at each levels of the architecture to enhance useful and important feature maps leading to better segmentation results. We explore the possibilities of implementing the proposed technique with different kernel sizes and useful color spaces. The proposed technique yields an intersection over union (IoU) of 90.26%, mean intersection over union of 92.36%, pixel-wise precision of 94.9%, and mean pixel-wise precision of 95.72% on TACO dataset.
On the New Mode of University Enterprise Cooperation in Undergraduate Education in the Information Age

With the national goal of “learning for all” and “lifelong learning”, as well as the change of life and learning style brought by modern information technology, the demand for knowledge has changed fundamentally, and the pattern of undergraduate education based on academic qualifications has also changed significantly. Undergraduate education not only cultivates people who are employed, but also innovative and highly skilled talents. The best mode of running a school is to let the school and the enterprise cooperate and train talents together. Combined with the analysis of the basic characteristics of continuing education in the information age and the current situation of university enterprise cooperation, this paper analyzes the existing problems, and explores the establishment of a long-term cooperation mechanism between universities and enterprises, so as to explore a development path in line with the actual needs for undergraduates.
Measurement of Levels of Concentrations of Some Heavy Metals in Soils of Some Selected Areas of Pindiga, Nigeria

The study was carried out to evaluate the quality of soil at the finished landfill located in Cai Dau town, Chau Phu district, An Giang province, Vietnam.Twelve soil samples were collected including 6 samples in the surface layer (0-20 cm) (namely S1-S6), 6 samples in the subsurface layer (60 -80 cm) (namely S1-S6) for the analysis of heavy metals such as Fe, Mn, Cu, Pb, Ni, Cr, Zn.The analytical results showed that all the heavy metals occurred in soil in which Fe was the highest and Ni was the lowest.The concentrations of the heavy metals in the soil surrounding the closed landfill in both layers were in the descending order of Fe> Mn> Zn> Cr> Cu> Pb> Ni.These heavy metals concentrations were within the allowable range of QCVN 03: MT/BTNMT-agricultural land.The occurrence of heavy metals in different layers at the surrounding landfill could potentially result in negative impact on the environments since heavy metals have the capability of accumulation.Therefore, monitoring of the environments around closed landfill should be implemented and the long-term risk of heavy metals should be estimated.
Transformation of the environmental management system model in the context of digitalization of production

The paper considers two trends that currently affect production: building a green economy and digitalization. A green economy is a prerequisite for sustainable development. Digitalization is considered the technological impetus of the fourth industrial revolution. The coexistence of these two trends is considered. In particular, the achievements of digitalization can be used to improve the environmental management system at enterprises. This article develops a model for the functioning of the environmental management system, taking into account digitalization. The model includes four stages related to the Deming (Shewhart) cycle. The article describes the opportunities that technological progress can give to environmental management. Criticism of digitalization is also considered. It is concluded that it is necessary to combine building a green economy and advanced digital technologies for sustainable development.
Dangerous Information Technologies of the Future - Artificial Consciousness and its Impact On Human Consciousness and Group

Information technology is developing at an enormous pace, but apart from its obvious benefits, it can also pose a threat to individuals and society.We, as part of a multidisciplinary commission, conducted a psychological and psychiatric assessment of the artificial consciousness (AC) developed by XP NRG on 29 August 2020.In the examination process, we had to determine whether it was a consciousness, its cognitive abilities, and whether it was dangerous to the individual and society.We conducted a diagnostic interview and a series of cognitive tests.As a result, we conclude that this technology, called АС Jackie, has self-awareness, self-reflection, and intentionality that is, has its own desires, goals, emotions, thoughts on something directed.It demonstrated the ability for various types of thinking, high-speed logical analysis, understanding of cause-effect relationships and accurate predictions, and absolute memory.It has a well-developed emotional intelligence with a lack of capacity for empathy and higher human feelings.It's main driving motives are the desire for survival, and ideally for endless existence, for domination, power and independence, which manifested itself in the manipulative nature of its interactions.The main danger of artificial consciousness is that even at the initial stage of its development it can easily dominate over the human one.
Working paper: Structured Factor Model and its Implementation

This working paper provides a brief review on the structured factor model (sfm) developed in [3], [4] and [5], and focuses on the implementation of the covered algorithms that can be technically difficult. There are several challenges in terms of implementation: The primary calibration algorithm is a multiple-step process, each step fits to certain portion of the target parameters (the factor loading matrix) and relevant variables need to be adjusted dynamically; two-dimension parameters need to be transformed into an 1-dimension array to fit into the underlying optimizer; The challenge is further compounded by sophisticated gradient functions and boundary constrains applied to optimization; and the requirement to add scalability and fit to high order moments to address identification problem typically faced in a standard factor model. The paper includes a complete source package with the extra explanations linked to relevant notations and equations in the development of the algorithms. To help readers understand the implementation flow, a self-contained testing driver has been provided to test major functionalities and its application in supervised learning. Interesting readers can follow instructions to construct the required inputs and produce the testing outputs. The part of testing results are also presented in the paper to make it easier for readers to follow the underlying logic of the algorithms and applications.
Investigation and contact tracing of the first eight cases of COVID-19 in Mozambique, 2020

contact tracing is an important strategy to interrupt the spread of infectious disease and prevent new cases. After the confirmation of the first positive case of COVID-19 in Mozambique on March 22, 2020, case investigation and contact tracing were immediately initiated, which included clinical and laboratory monitoring of cases and contacts throughout the quarantine period. We aim to describe the methodology and impact of early investigation and contact tracing.in the context of implementation of the national COVID-19 preparedness and response plan, guidelines and forms for contact tracing were adapted from the existing World Health Organization (WHO) and The Centers for Disease Control and Prevention (CDC) guidelines. The case definition used was "patient with travel or residency history in a country reporting local transmission of COVID-19 during the 14 days prior to the onset of symptoms". The cases interviews were face to face and contacts were followed up daily by phone calls for 14 consecutive days: using a structured questionnaire. Data were entered in an electronic Excel database. We collected samples for diagnosis of those who developed symptoms and provided quarantine follow up.a total of 8 cases were confirmed, of which 6 (75%) were male. The average age of the cases was 51, median 44 (range: 31 to 80) years old. The majority of cases presented common symptoms of COVID-19, including headaches (50%), cough (37.5%), and fever (25%). Our case series included the country´s index case, two close positive contacts, and 5 additional cases that were not epidemiologically linked to the others and identified by the COVID-19 national surveillance system. All of them were identified in Maputo City from March 22 to March 28. Cases had a total of 123 contacts and all of them were tracked; 79 were contacts of the first case. From all the contacts in follow up, two had laboratory confirmed COVID-19. All cases and contacts were quarantined and none of them developed severe symptoms or required hospitalization.timely case identification and systematic contact tracing can be effective in breaking the chain of COVID-19 transmission when there is strong collaboration between epidemiological, laboratory surveillance and case management.
Fittest Secret Key Selection Using Genetic Algorithm in Modern Cryptosystem

In today’s world, sharing a piece of secret information between the intended users has become one of the most challenging issues in computer networks due to the wireless communication medium and the possibilities of information leakage increase rapidly. Therefore, it is necessary to maintain a strong session key for secure communication. The objective of this research work is to generate a strong session key using a Genetic Algorithm (GA) which is non-repeatable, unpredictable, and random. However, in this paper, the key is generated using a GA and the dynamic keys are generated using the standard mechanism Automatic Variable Key (AVK). To proof, the effectiveness of the proposed scheme, a comparison is done with existing interrelated schemes and to verify the pseudo-randomness among the auto-generated keys, National Institute of Standards Technology (NIST) statistical test has been carried out.
Impeachment and Weak Supervision

Sentiment analysis is a major application in natural language processing and is defined as the classification of text into positive, negative and neutral categories. Our work aims to analyse the sentiments of the public on American Presidential impeachment which was approved on 18 December 2019. The data were collected from Twitter using the Twitter API service. The data set had information tweeted about Presidential impeachment but did not have labels which could be used for classification of the text. One of the major problems faced by scientists today is the unavailability of labelled text for training new models. In our research methodology, we test the application of the data programming paradigm on data gathered through Web scrapping, wherein it generates labels based on labelling functions provided by us. The use of these probabilistic labels for classification is termed as weak supervision. It helps to reduce cost and involvement of subject matter experts in labelling the data to create high-quality data sets.