Reviewerly/exHarmony huggingface dataset:
Training as a means of successful professionalization of applicants for higher education in quarantine

The article presents a model of training as an innovative pedagogical technology for successful professionalization of future professionals in quarantine and distance learning. The pedagogical conditions and features of educational and professional training to ensure the formation of professional competencies in students are determined. The principles and methods of application of training pedagogical technology in the context of integration approaches to the organization and content of distance learning in higher education institutions are analyzed. The role of development of pedagogical skill of the teacher-trainer as a factor of qualitative preparation of future experts in unpredictable situations of their professional formation is noted.
What Should Robots Do? A Comparative Analysis of Industry Professionals, Educators and Tourists

While robots have been used extensively for many years in manufacturing, many robots are fairly new arrivals in tourism-related industries. This paper delves into how the general public and stakeholders in the travel, tourism and hospitality industries view the incorporation of robots into tourism-related industries. Based upon a sample of over 1,000 respondents to an international online survey, it analyses which tasks are deemed as most appropriate for being delegated to a robot. In addition, it segments the respondents to determine whether the professions of those responding view the delegation of tasks to robots differently. The results indicate that in general, the occupation of respondents did not seem to play a significant role in perceptions of which tasks are most appropriate for robots in tourism-related industries, although respondents employed in robotics seem to be more receptive towards robots, while tourism educators – more pessimistic.
A Provincial University at the Times of the World Pandemic: Preservation or Development?

The authors describe the issues a Russian provincial university has attempted to adjust during the Covid-19 pandemic, and the significant challenges as well as the opportunities that faculty members and students continue to encounter during these times of uncertainty.
SOCIAL MEDIA IN ACADEMIC LIBRARIES: A SWOT ANALYSIS

Social media offers libraries new ways to communicate, identify, and satisfy the information needs of library users. Although social media has several benefits, some challenges might be associated with its use. This study aims to make a SWOT analysis to help academic libraries identify their strengths, weaknesses, opportunities, and threats regarding social media usage. Managers and employees of four academic libraries in Ho Chi Minh (Vietnam) were asked for the current use and the factors influencing social media use in their libraries. The results shows that the surveyed libraries adopted Facebook and YouTube to promote resources and services, and to extend the reach of the university community. However, there are several weaks and challenges around the use of social media. Among them, lack of written policy, human resources, and social media skills are considered the significant challenges. Based on these findings, this study may assist libraries in creating an action plan to ensure growth and overcome challenges.
An extraocular muscle stimulation system based on EOG and FES

A device and system for treating misaligned eye positions sourced for any type of eye movement disease lead to checking for performance errors, by which the extraocular muscles or nerves are electrically stimulated when the eye movement of the controller&apos;s eye is detected non-invasively. The stimulation signal is sent to the electrode via a wireless system while the electrode is implanted in the stimulation selected area.
Pragmatic Analysis Of Written Directives In English Examinations

The study analyzed the directives in the English major examinations collated from Batangas State University. It covered the instructors extent of use of examination types. At the end of the study, a set of guidelines in the construction of directions was proposed. The descriptive method of research was utilized with the questionnaire as the main data gathering instrument. To get the pragmatic implications, the qualitative method was employed in analyzing the forms of directives under the lenses of Searleâ€™s Speech Acts, Hylandâ€™s Classification of Directives, indices of power, and directness. The results revealed that there were 97 forms of directives embedded in the tests with write, read, and identify as the top three most frequently used. In terms of power and directness, most directives were deemed firm and moderately direct. Using Hylandâ€™s classification, the majority of the directives fell under cognitive acts, followed by the physical acts.
Studi Komparatif Metode Pembelajaran Team Assisted Individualization dan Cooperative Integrated Reading and Composition terhadap Hasil Belajar IPS

This research aims to compare the differences in students learning outcomes in IPS subjects in grade VIII of SMP Negeri Tumijajar using cooperative learning models such as Team Assisted Individualization (TAI) and Cooperative Integrated Reading and Composition (CIRC). This study use pseudo-experimental research with Nonequivalent Control Group Design. Population in this study is all grade VIII students and samples are 58 students divided into 29 students in class VIII C and 29 students in class VIII D. Collecting data technique using observation, pre and post test, and documentation literature while for the analyzing data using t-test. The results showed that there were a significant differences in learning outcomes for student who learned cooperative TAI type compared to CIRC type with t-test results obtained 2,486 &gt; 2,003 with a significant rate of 0.009 &lt; 0.05&#x0D; &#x0D;
Predict depth constraints for lithospheric modelling by machine learning&amp;#160;

&amp;lt;p&amp;gt;Machine learning applications in geophysical studies are often used to predict geophysical observations in areas with sparse or not data or recognize patterns and similarities in data. In our study, we test different techniques to improve the information of constraining data by machine learning and to improve strengthen the modelling of lithospheric structures with potential field data. Constraining data like seismic information, surface geology, rock classifications etc. is often used during the interpretation step of lithospheric modelling to aid the qualitative interpretation. Consensus between additional data and the own model is assessed by comparison and used to describe the model goodness consistency. First Wwe test, how this additional data can be used before the modelling by using machine learning techniques to quantify the data. We focus on supervised learning to predict crustal structure in areas with little constraints, on trained learning in data-rich areas. Second, we test the spatial analysis of surface data to determine lithospheric boundaries in depth. These tests are performed in North America and the Central Asian Orogenic belt (CAOB) to compare the results in areas with respectively good and spare data coverage. That approach can be used to link the large variety of surface and deep information in the CAOB region.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The combination of the different geophysical data available with the geological data should improve our tectonic modelling.&amp;lt;/p&amp;gt;
The Main Factors Influencing The Development Of Logical Thinking Skills Of Students In Physics Lessons

As a comprehensive understanding of the concept of intelligence in the world is a global concept that focuses on research in the development of natural and specific Sciences, which focuses on the logical thinking of students, as well as expanding the requirements for the quality and competence of content based on the level of logical thinking. The main focus is the development of intellectual and intellectual abilities of students in higher education institutions in the United States, England, France, Japan, South Korea, and Russia.integration of technologies used in the teaching of natural Sciences, virtual and experimental projects using interactive learning technologies and logical thinking is important for development. The article considers the main factors (physiological, psychological, pedagogical and methodological factors) that affect the development of logical thinking skills of students. Also new approaches and innovative methods for development
Performance Evaluation of Components of the Hadoop Ecosystem

The Hadoop framework allows distributed processing of large sets of data on standard computer clusters. MapReduce is the main programing language of the Hadoop ecosystem that processes data stored in the Hadoop Distributed File System (HDFS). It is difficult for nonprogrammers to work with MapReduce. Hadoop supports HiveQL (SQL statements) that translates queries into one or more MapReduce jobs implicitly and immediately. To help developers of procedural languages, Hadoop supports the language Pig Latin. In this chapter, we present an overview of the Hadoop ecosystem; we perform sentiment analysis based on tweets text data generated using Apache Flume. Apache Flume is a tool that is used to recover data from the social media site Twitter. Creating an automated process allows one to isolate tweets based on a hashtag and store them in HDFS. The processing of tweets was done using MapReduce, Hive, and Pig on a single-node Ubuntu platform, and we compared the performance of these solutions. Experiments showed that MapReduce is an efficient paradigm for the analysis of Twitter data. The Pig and Hive instructions simplify the syntax of the queries and decrease the Java MapReduce code. MapReduce programing was faster than Pig and Hive in performing sentiment analysis based on the polarity of the tweets.
Designing a mobile medical expert system using the Yandex.Alice voice assistant

Abstract Solving the problem of making a primary diagnosis in the absence or shortage of qualified doctors. The main task is to reduce the risk of incorrect assessment of the health status of people who do not have the opportunity to receive prompt qualified medical advice and assistance. We offer a project solution – Alice’s skill (voice assistant from Yandex) “Pocket Doctor” with a voice interface, which is a diagnostic expert system containing rules for logical inference based on symptoms, the assessment of which does not require special laboratory tests and can be performed by people who do not have a medical education. Based on the collected information from the user, the primary diagnosis and recommendations for emergency medical care are determined.
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

This paper focuses on cooperative value-based multi-agent reinforcement learning (MARL) in the paradigm of centralized training with decentralized execution (CTDE). Current state-of-the-art value-based MARL methods leverage CTDE to learn a centralized joint-action value function as a monotonic mixing of each agent's utility function, which enables easy decentralization. However, this monotonic restriction leads to inefficient exploration in tasks with nonmonotonic returns due to suboptimal approximations of the values of joint actions. To address this, we present a novel MARL approach called Universal Value Exploration (UneVEn), which uses universal successor features (USFs) to learn policies of tasks related to the target task, but with simpler reward functions in a sample efficient manner. UneVEn uses novel action-selection schemes between randomly sampled related tasks during exploration, which enables the monotonic joint-action value function of the target task to place more importance on useful joint actions. Empirical results on a challenging cooperative predator-prey task requiring significant coordination amongst agents show that UneVEn significantly outperforms state-of-the-art baselines.
Cloud Based Property Management System in Integration with IoT

Cloud based Property Management System (CPMS) is one of the best data management software that assist in managing the hospitality sector by undertaking reservations, providing discount allocation and handling capacity management for generating revenue, it also assist in gaining the loyalty of guest by communicating with sensors for guest security and comfort using Internet of Things (IoT). Nevertheless the knowledge of employees by being as source of information in hotel Industry, CPMS stores the data in a cloud server in order to keep this data safe. The main objective of this study is to find the association between CPMS with IoT for business and trust perspective.  Secondly it focuses on the deployment of sensors and actuators in the infrastructure facilities that are integrated with Property management System (PMS) in luxury hotels, which simultaneously increases the hotel room revenue with consequent future concerns. Thus this investigation provides an effective operational decisions has to be taken by authorities on installation of cloud based PMS that can coordinate with OTA using Application Programming Interface (API) to increase the business with safety and security.
Principal Bit Analysis: Autoencoding with Schur-Concave Loss

We consider a linear autoencoder in which the latent variables are quantized, or corrupted by noise, and the constraint is Schur-concave in the set of latent variances. Although finding the optimal encoder/decoder pair for this setup is a nonconvex optimization problem, we show that decomposing the source into its principal components is optimal. If the constraint is strictly Schur-concave and the empirical covariance matrix has only simple eigenvalues, then any optimal encoder/decoder must decompose the source in this way. As one application, we consider a strictly Schur-concave constraint that estimates the number of bits needed to represent the latent variables under fixed-rate encoding, a setup that we call \emph{Principal Bit Analysis (PBA)}. This yields a practical, general-purpose, fixed-rate compressor that outperforms existing algorithms. As a second application, we show that a prototypical autoencoder-based variable-rate compressor is guaranteed to decompose the source into its principal components.
MRI Brain Image Enhancement Using an Improved Contrast Enhancement Method

The uncontrollable cells growth in the brain portion is the main reason for cancer deaths nowadays. So, effective detection of brain tumors is more important in the medical field to analyze the tumor portion. Detecting tumors prior and diagnosis of tumors can play a major role in preventing human death due to brain tumors. To detect the tumor portion, many segmentation and classification methods have been proposed. For the effect segmentation process, enhancing brain images is necessary. In this present paper, Magnetic resonance imaging (MRI) brain images have been taken as test images. The proposed enhancement method has two major phases. The first phase contains a regularization process in two steps to equalize the test images' intensities, and the second phase contains a mapping process of two steps to enhance the contrast of the image and remap their intensity values to the natural dynamic range.
On the distribution of the incentive fund at the university

The article highlights two approaches to solving the problem of the distribution of the incentive fund, one of which is based on the calculation of the cost of one point; the second one is based on elementary incentive payment in the system with weight indices. The latter approach makes it possible to single out a small number of clusters of successful personalities. Comparison of the considered approaches at the stage of their implementation allows us to draw the conclusion about the different aspirations of teachers in the performance assessment systems. A mathematical solution is proposed for the distribution of the incentive fund in the framework of the second approach. The proposed method allows dividing fund resources in an extremely short period of time.
Tools for monitoring the spatio-temporal evolution of seismic sequences: An application to&amp;#160;the Azores triple junction

&amp;lt;p&amp;gt;In the last decades, the discovery of seismic signals other than those known as classical&amp;amp;#160;earthquakes have changed our understanding of the dynamic process of lithosphere fracturing&amp;amp;#160;with implications for seismic monitoring. These signals are hypothesized to be generated by slow&amp;amp;#160;slip on faults and/or by the motion of fluids in the crust. The earthquakes that occur in the Azores&amp;amp;#160;archipelago are thought to result from the interaction between a tectonic triple junction and a&amp;amp;#160;low-velocity (possibly hot) anomalous mantle. Although most of the seismic activity in this&amp;amp;#160;region is tectonic, there is also evidence of seismic activity related to hydrothermal and&amp;amp;#160;magmatic activity, which makes the Azores region a privileged natural observatory for studying&amp;amp;#160;different types of seismic signals. In this work we will then focus on the spatio-temporal&amp;amp;#160;evolution of the February 2018 seismic sequence which occurred in the island of S&amp;amp;#227;o Miguel. We&amp;amp;#160;will carry out detection and preliminary location of seismic events using Lassie, an open-source&amp;amp;#160;software for earthquake detection. We will also perform waveform similarity and clustering&amp;amp;#160;analysis to understand the detailed spatio-temporal evolution of the crisis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work is funded by FCT through projects UIDB/50019/2020 &amp;amp;#8211; IDL and PTDC/CTAGEF/&amp;amp;#160;6674/2020 (RESTLESS).&amp;lt;/p&amp;gt;
Difficult Points of Chess Metaphor in the Theoretical Questions of Language and Mind

This review article reveals the structural components of the chess metaphor, which represent in an unusual perspective the properties of a linguistic sign and its connection with mental processes. Strict rule-following and the conventionality of the material plane of expression turn chess into a convenient illustration of a universal linguistic structure. Particular attention is paid to the computer profile of the chess metaphor, since it reflects a whole complex of philosophical problems of computer science about mind, thinking and intelligence. In the proposed paper, the presentation of most of the material is based on the works of F. de Saussure, L. Hjelmslev, L. Wittgenstein and their interpreters, in whose texts an obvious important place is occupied by chess analogies and theoretical conclusions initiated by them. First we investigate chess metaphor in the context of language and speech structures. Next, we analyze the "chess track" in the problem of individual language and the rule-following problem. The final part is devoted to the technical elements of computer chess and the influence of this area of computer science on some of the points of cognitive theories of language and mind. The author of the article emphasizes a nontrivial transformation of the conceptual content of the chess metaphor, which indicates the evolution of computational tendencies in modern theories about language and mind. The article substantiates the thesis that the traditional chess metaphor used in the XX century in the philosophical investigation of language and mind, today it takes on a realization in the framework of computer models of the chess game. Machine deep learning can significantly expand the horizon of computability. Game interaction makes it possible to ascribe the elements of intentional content to machine functions. Nevertheless, all the argumentation in the work is aimed at proving that the rules governing language and mind are rules different from the rules of computer intelligence.
Mobile voice recording and analysis: Uncertainty and reproducibility

Mobile voice recording has been a staple of voice research, providing an authentic representation of speech outside a controlled environment; however, mobile recordings increase uncertainty with potential environmental sound contamination, as well as speech production differences due to variations in environment, purpose, or communication partner. Several studies will be reviewed to give context to uncertainty and reproducibility issues to be considered when conducting mobile voice recording.
Attributes of the Criminal Copyright Infringement Phenomenon

Criminal copyright infringement is a multi-faceted phenomenon. This article discusses aspects regarding the infringement of copyright from the criminal law perspective. The article explains the need for effective criminal provisions and, based on the content analysis of a comprehensive corpus of data, consisting of cases brought to United States courts, law enforcement press releases, and intellectual property agencies and industry groups reports, identifies, analyzes, and groups the attributes of the phenomenon under four main categories: type of works infringed, methods of infringement, enforcement aspects, and loss. An important contribution of this article is the presentation of the findings in a table format. The table could be used to improve the protection of copyright in a number of ways: to analyze or assess the effectiveness of the current legal framework; to develop educational materials; to improved the prevention policies or mechanisms; in conjunction with cost-benefit analysis tools, to prioritize the enforcement efforts and efficiently allocate resources; and to encode and report offenses.
Research on face recognition technology based on KPCA and SVM under convolutional filtering

Face recognition is an important and difficult problem in the field of artificial intelligence and image vision. This paper studies the feature face principal component analysis (PCA) based on statistical features, which is used to reduce the gray dimension of face image extraction. Aiming at the problem that PCA can't handle the nonlinear reduction of face image information and the classification of prediction results well, the multi-classification voting algorithm of KPCA and SVM is adopted. Due to the limitations of the feature face technology, the feature extraction of the local sensitive area of the face is insufficient. The convolutional filter is designed to preprocess the image to enhance the extraction of the contour features of the face and the feature of the key area. Experimental results show that this method can improve the recognition accuracy.
K-MEANS CLUSTERING ANALYSIS FOR ELECTION PREDICTION USING PYTHON -

The opinion of the public for a candidate will impact on the potential leader. Public in sequence information andtheir opinions on candidates are used to obtain the large diverse data set representing the present public opinionsabout the candidate. The collected data set is analyzed using the k-means algorithm, which helps us in perceptiveuser opinion for a exacting candidate and this clustering algorithm is used to make comparisons among thecandidate. The feature analysis is done by Classification where the Logistic regression classification algorithm andSVM are used to predict, for which candidate users cast their vote. In our experiment, the best result was forpredicting the election by taking people's opinions from the village. The efficiency and effectiveness of theclassification methods are verified by the obtained results.