Reviewerly/exHarmony huggingface dataset:
Social media as the main means of communication in public relations

Bu məqalədə ictimaiyyətlə əlaqələrin əsas vasitələrindən olan sosial medianın istər təşkilat imicini qorumaqda, istərsə də, təşkilatın bazarda davamlı fəaliyyət göstərə bilməsi üçün nə qədər kritik əhəmiyyətə sahib olmasından bəhs edilmişdir. Araşdırmada gündəlik sosial mediadan istifadə edən kütlənin yaş ortalaması, ən çox istifadə edilən sosial şəbəkələr və ölkəmizdə sosial media ilə bağlı vəziyyət qeyd edilmişdir. Həmçinin statistik məlumatlar əsasında sosial media istifadəçiləri haqqında məlumatlara araşdırmada yer verilmişdir. İctimaiyyətlə əlaqələr ilk vaxtlar propaqanda vasitəsi kimi istifadə olunmuşdur. Günümüzdə ictimaiyyətlə əlaqələr, qurumlarla ictimaiyyət arasında körpü rolunu oynamaqdadır. İctimaiyyətə əlçatanlığı isə sosial media təmin etməkdədir. Texnologiyanın irəliləməsi nəticəsin XX əsrin sonlarından qəzet və jurnalların yerlərini soial media almaqdadır. Geniş kütləyə sahib olan sosial media nə qədər önəmli olduğunu göstərməkdədir. Açar sözlər: sosial media, ictimaiyyətlə əlaqələr, sosial şəbəkə, internet, facebook, twitter, bloqlar, instagram, YouTube Ulvi Vugar Gurbanzade Social media as the main means of communication in public relations Summary This article discusses the critical importance of social media, one of the main means of public relations, both in maintaining the image of the organization and for the organization to be able to sustain itself in the market. The study noted the average age of the population using daily social media, the most used social networks and the situation with social media in our country. The study also included information on social media users based on statistics. Public relations was first used as a means of propaganda. Today, public relations plays the role of a bridge between institutions and the public. Social media provides access to the public. As a result of advances in technology, newspapers and magazines have been replaced by social media since the late twentieth century. Social media, which has a wide audience, shows how important it is. Key words: social media, public relations, social network, internet, facebook, twitter, blogs,instagram, YouTube
Analogue modelling of subduction: yesterday, today and tomorrow.

&amp;lt;p&amp;gt;The use of experimental tectonics (also known as analogue-, laboratory, or physical modelling) to study tectonic processes is not a novelty in Earth Science. Following Sir James Hall&amp;amp;#8217;s pioneer work (1815), many modellers squeezed, stretched, pushed and pulled a wide range of materials &amp;amp;#8211; e.g., sand, clay, oil, painters&amp;amp;#8217; putties, gelatins, wax, paraffin, syrups, polymers &amp;amp;#8211; to unravel a wide range of tectonic processes to determine parameters controlling their geometry, kinematics and dynamics. However, only recently experimental analogue modelling has definitively transformed from a qualitative to a quantitative technique, thanks to appropriate scaling relationships, the improvement in the knowledge of the rheology of both natural and analogue materials and the use of high-resolution monitoring techniques to quantify morphology, kinematics, stress, strain and temperature.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, I specifically review the experimental work performed to study one of the most intriguing aspects of plate tectonics: the subduction process. Subduction provides the dominant engine for plate tectonics and mantle dynamics. Moreover, it has also societal importance playing a key role on hazard at short (i.e., earthquakes and mega-earthquakes, tsunami, effusive and&amp;amp;#160;explosive volcanic activities with impact on aviation safety) and long time scales (i.e., local and global climate change). Over the last decades, a noteworthy advance in the quality and density of global geological, geophysical and experimental data has allowed us to provide systematic quantitative analyses of global subduction zones and to speculate on their behaviour. These constraints have been integrated into a mechanical framework through modelling.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;I will bring you to a journey through the past, the present and the future of analogue modelling and related efforts, results and perspectives for the study of the subduction process. It will be shown how analogue models, with their inherent 3D character and behaviour driven by simple and natural physical laws, contribute to successfully unravelling the subduction process, inspiring new ideas. Challenging ongoing perspectives of analogue models imply the possibility to compare time and space scales, allowing to merge, within the same model, both short- and long-term and shallow and deep processes.&amp;lt;/p&amp;gt;
TEACHERS’ SELF-ASSESSMENT OF COMPETENCE IN THE APPLICATION OF CONTEXTUAL TEACHING AND LEARNING

S. Kovacevic, J. Barbir, A. AlajbegUniversity of Split, Faculty of Humanities and Social Sciences (CROATIA)
L*: A First-order Polarity Logic for Generalized Quantification

In order to show that there is a tight relationship between logical polarity, logical inference, and natural language quantification, this chapter describes a language (L*) in which these relationships can be formalized and in which the properties can be studied in detail. It concludes with concerns as to whether the proposed formal language is too complex and alien from natural language to be used in an account of natural logic.
A Study on the Relationship between Self-leadership, Self-efficacy, Communication Competency, and Class Satisfaction: Focusing on Undergraduate Students Majoring in Tourism

In a situation where the COVID-19 pandemic continues, non-face-to-face and online learning are becoming more common, and in this learning environment, learning outcomes vary greatly depending on each student
Research on polarization imaging detection and tracking method of low-speed and small UAV targets

Aiming at the problem of the low contrast between target and background in the detected UAV target intensity images, a low-speed and small UAV targets detection and tracking method based on polarization imaging detection is proposed. Based on the analysis of the polarization imaging characteristics of low-speed and small UAV targets, through polarization image analysis, single-frame detection based on spatial filtering and adaptive threshold segmentation, continuous frame target trajectory association based on spatiotemporal information, and improved KCF algorithm Target tracking and other processing processes have realized the effective detection and tracking of low-speed and small UAV targets.
Detecting Unsatisfiable Conjunctive Property Path Queries under Shape Expression Schema

Shape Expression (ShEx) is a novel schema language proposed for RDF/graph data. For a ShEx schema S and a query q, q is said to be unsatisfiable if for any valid data D under S q reports an empty answer over D. In general, the size of RDF/graph data is very large, and thus it is inefficient to perform unsatisfiable queries on such data. Therefore, it is desirable that we can detect unsatisfiable queries efficiently before executing them. In this paper, we consider Conjunctive Property Path, a generalization of Property Path defined in SPARQL 1.1, as the query language. First, we propose an algorithm for determining satisfiability of Conjunctive Property Path queries under ShEx schema. Then we conducted a preliminary experiment, which results suggest that the proposed algorithm determines if a given Conjunctive Property Path query is satisfiable efficiently.
Forster, Andrea G. (2021). Navigating educational institutions. Mechanisms of educational inequality and social mobility in different educational systems. ICS-dissertation, Universiteit van Amsterdam. ISBN/EAN: 978-94-6416-281-3

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
NHK STRL’s trials for media services using AR and VR technologies

Various HMDs and AR glasses have currently been available in the market. Because they enable 3D, life-sized, and frameless presentation, they have a good potential to be a third terminal to watch video contents after TV set and smartphone. Science & Technology Research Laboratories (STRL) belongs to public broadcaster NHK and has been making some trials for media services using AR/VR technologies. In this talk, parts of our trials including 360° VR image system for comparative views, capture/display system for VR images with extremely high resolution, and space-sharing content viewing system using AR/VR are introduced to find the possibilities to make AR/VR technologies for the future media.
References

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Semantic Generalization Means Based on Knowledge Graphs

The article proposes approaches to text documents semantic content scaling, presented in a knowledge graph form, in order to reduce a cognitive activity working space. Two scaling types operations on graphs are considered - enlargement as aggregation based on inclusion (part-whole relationship) and generalization based on generic relationships. Examples of declarative means use for the scaling operations implementation - a relationships classification and thesaurus are given.
Computational Swarm Intelligence

Swarm Intelligence (SI) is about a collective behavior of a population of individuals. The main properties of such populations are that all of the individuals have the same simple rule from which the global collective behavior cannot be predicted.
THE USE OF VIRTUAL ASSISTANTS’ VOICES TO ENHANCE THE ONLINE EDUCATION CLASSES

Typically, in an online course class, the monotony at some point might arrive. Naturally, in a classical room, the words emphasis may be amplified in a pleasant manner by the echo capacities of that room. This is not the case when you’re talking to a microphone, and by contrary, in this situation, un unpleasant microphony might appear if some people from the audience have the microphones opened and to closely to their speakers. No matter what you do (presenting a PowerPoint slide, drawing on a whiteboard, drawing/writing using another software) your voice could sound too boring for the students. Trying to create an animated movie to present to the audience, might be a good alternative to fight with the monotony, but again has the same disadvantage; your voice that is pretty much the same. Of course, the next thought might be heading to a special software that can change your voice. This could be a pretty good solution, but you or your audience might perceive this voice as being too “robotic”. Another alternative might be to have 2 professors presenting that course, like in television with 2 presenters in the news studio, but typically there is just one single professor assigned for a course class and only for the labs might be assigned 2 assistants.In conclusion, the audience would love to have 2 or 3 presenters like in a tv show, but the management of that school will strongly disagree to pay 2 or 3 persons for the same job. Apparently, looks like there is no feasible solution to this problem.This paper wants to give a solution for the above-mentioned situation. It will present a research, done among the faculty’s students to highlight the main causes that can lead to a poor involvement of the students in an online course class and the perception of the students regarding the ingenious idea to use 2 virtual assistants (2 virtual characters and/or 2 virtual voices that would sound exactly like a human being) involved in an animated movie or presenting definitions, situations and even putting live questions to the audience. Before answering to the research questionnaire, the students will be given a short demo video in which will be simulated the same course class in both ways (the classical way and the one involving the virtual assistants), so that they can make a real comparison between the 2 approaches and can then provide a real feedback.
An optimized online version of NESTORE software package for the forecasting of strong aftershocks: an application to Italian clusters&amp;#160;

&amp;lt;p&amp;gt;NESTORE (Next STrOng Related Earthquake) is a recently developed algorithm (Gentili &amp;amp; Di Giovambattista 2017, 2020) to recognize clusters in which a strong mainshock is followed by an aftershock of similar magnitude. Specifically, NESTORE labels clusters as type A if the magnitude difference between the mainshock and its strongest aftershock is less than or equal to 1, otherwise as type B. After an intense earthquake, the prediction of strong following events is strategic for civil protection purposes. In fact, already weakened structures may suffer further damage, increasing the risk of collapse and casualties. The goal of NESTORE is a near real-time estimation of the probability that the ongoing cluster is type A. The software is based on a set of parameters (features) of seismic clusters calculated at increasing time intervals after the mainshock. In particular, the algorithm exploits a training procedure with a feature-based machine learning approach. The features are related to the evolution of the number of events and their space-magnitude distribution over time. To make NESTORE a suitable software for online sharing, we optimized its structure. Specifically, some functions have been improved, further ones have been added, and a new name structure has been introduced to better characterize the three independent modules of NESTORE (cluster identification, training, and testing). This software renovation has been developed in the frame of project &amp;amp;#8220;Analysis of seismic sequences for strong aftershock forecasting&amp;amp;#8221; funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation within the collaboration in science and technology between Italy and Japan. We applied this new version of NESTORE to Italian seismicity and in particular to North-Eastern Italy, and obtained information on the features with best performances in terms of type A and B cluster discrimination.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation&amp;lt;/p&amp;gt;
Deep Face Mask Detection: Prevention and Mitigation of COVID-19

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. In order to safely live with the virus while effectively reducing its spread, the use of face masks has become ubiquitous. Indeed, several countries enforced compulsory face mask policies in public areas. Therefore, it is important to provide automatic solutions for masked/not-masked faces detection. In this work, we proposed a face mask detection method based on deep convolutional neural networks (CNNs) in an uncontrolled environment. In fact, the proposed method aims to locate not-masked faces in a video stream. Therefore, we performed a face detection based on the combination of multi-scale CNN feature maps. Then, we classified each face as masked-face or not-masked face. The main contribution of the proposed method is to reduce confusion between detected object classes by introducing a two steps face mask detection process. The experimental study was conducted on the multi-constrained public dataset “Face Mask Dataset” and the Simulated Masked Face Dataset (SMFD). The achieved results reveal the performance of our face mask detection method in an uncontrolled environment.
Application of Lightweight CNN Model for Detecting Defects of Substation Insulators using Drone EO/IR Camera

This paper proposes a monitoring system of defected substation insulators based on drone EO/IR images and a light-weight deep learning model. The defected substation insulators generate corona discharge and excessive heating. Thus, this study used RGB-thermal blended images from drone EO/IR cameras to monitor insulator failures, which is not visible to naked eyes. Also, this paper compared several light-weighted object detection models to select the most suitable model to deploy on the embedded processor of drones. The applied compressed CNN model, YOLOv4 backbone with group convolution and channel shuffle operations, is sufficiently fast and light-weighted for an embedded GPU processor. Experiments with mockup insulators with corona discharge showed that the proposed system resulted 99.50% mAP, 5.9fps on embedded GPU(Jetson Nano), and has 3.9MB memory that is 37.99 times lighter than YOLOv4.
A comparative analysis of linguistic features in the texts between high school graduate equivalency tests and practical English 1 textbooks

This study aims to compare linguistic features in the texts between high school graduate equivalency tests and practical English 1 textbooks for high school students in Korea. The text data obtained from ten sets of tests from 2016 to 2020 and six English 1 textbooks were analyzed by using Coh-Metrix, a linguistic computational program. The results of the analysis on linguistic features reveal that the levels of difficulty were not consistent between two texts. The difficulty levels of the sentence length, the syntactic complexity and the readability in the tests were significantly lower than those in the textbooks. On the contrary, the difficulty levels of the lexical diversity and the cohesion in the tests were significantly higher. The findings suggest that the difficulty of texts in the test needs to be adjusted to be equivalent with that in the textbook when test designers write the test items.
Decompositions

The objective of this chapter is to connect the concepts of conditional independence with the separation in graphs. Both can be represented by a ternary relation on either the set of attributes or nodes and it seems to be promising to investigate how to represent the probabilistic properties of a distribution by the means of a graph. The idea then is to use only graph-theoretic criteria (separations) to draw inferences about (conditional) independences because it is them what enables us to decompose a high-dimensional distribution and propagate evidence.
OBS noise reduction using music information retrieval algorithms

&amp;lt;p&amp;gt;Since the ocean covers 71% of the Earth&amp;amp;#8217;s surface, records from ocean bottom seismometers (OBS) are essential for investigating the whole Earth&amp;amp;#8217;s structure. However, data from ocean bottom recordings are commonly difficult to analyze due to the high noise level especially on the horizontal components. In addition, signals of seismological interest such as earthquake recordings at teleseismic distances, are masked by the oceanic noises. Therefore, noise reduction of OBS data is an important task required for the analysis of OBS records. Different approaches have been suggested in previous studies to remove noise from vertical components successfully, however, noise reduction on records of horizontal components remained problematic. Here we introduce a method, which is based on harmonic-percussive separation (HPS) algorithms used in Zali et al., (2021) that is able to separate long-lasting narrowband signals from broadband transients in the OBS records. In the context of OBS noise reduction using HPS algorithms, percussive components correspond to earthquake signals and harmonic components correspond to noise signals. OBS noises with narrowband horizontal structures in the short time Fourier transform (STFT) are readily distinguishable from transient, short-duration seismic events with vertical exhibitions in the STFT spectrogram. Through HPS algorithms we try to separate horizontal structures from vertical structures in the STFT spectrograms. Using this method we can reduce OBS noises from both vertical and horizontal components, retrieve clearer broadband earthquake waveforms and increase the earthquake signal to noise ratio. The applicability of the method is checked through tests on synthetic and real data.&amp;lt;/p&amp;gt;
Ancient Chinese coins rating method based on colour and continuity features

The artificial rating accuracy of ancient Chinese coin is low, the cycle is long and the labor cost is high. At present, computer image processing technology is mostly used for coin denomination recognition or coin corrosion area recognition, and there is little research on the rating of ancient Chinses coins by image processing method. Therefore, this paper proposes a method of ancient Chinese coins rating based on the combination of image color and continuity features. The text adopts k-means algorithm to recognize the ancient Chinese coin region of the image, and extracts the variance and skewness characteristics of the H and I channel in the HSI color space of the ancient Chinese coin region. In this paper, Canny algorithm is adopted to extract the characters and edges of ancient Chinese coins, and the continuity characteristics of the raised characters and edges are quantitatively measured, and jointly constitutes the feature vector of ancient Chinese coins with color features. The rating score of ancient Chinese coins image is predicted by BP neural network model. Experimental results show that this method can accurately and quickly classify ancient Chinese coins by image information.
Elements of Evolutionary Algorithms

Evolutionary algorithms are not fixed procedures, but contain several elements that must be adapted to the optimization problem to be solved. In particular, the encoding of the candidate solution needs to be chosen with care. Although there is no generally valid rule or recipe, we discuss some important properties a good encoding should have. Then we turn to the fitness function and review the most common selection techniques as well as how certain undesired effects can be avoided by adapting the fitness function or the selection method. The final section is devoted to genetic operators, which serve as tools to explore the search space, and covers sexual and asexual recombination and other variation techniques.