Reviewerly/exHarmony huggingface dataset:
Automated Script Outline Generator using TextRank Algorithm

The process of creating a condensed type of document that retains significant information and thus the general meaning of the source text is known as text summarization. Automatic summarization is a popular method for quickly locating significant information in vast texts with minimal effort. In today’s era information is the integral part of knowledge and it is too noisy and not in the precise manner for that both abstractive and extractive summarization techniques are used. The proposed model combines the summarization process for both text and image input type, which makes it easier for user to generate the precise summary regardless of the input type. The proposed model uses Textrank algorithm for the summarization process. Recall oriented understudy of gisting evaluation (ROUGH) score is used by the model to evaluate the accuracy of generated output. The model gives high ROUGH score which assures the accuracy of the generated summary.
Variability Index of Solar Resource Based on Data from Surface and Satellite

Characterizing solar resource variability is paramount for its thermal and photovoltaic use. Cloudiness is the parameter that modulates both the intensity and frequency of variations in solar irradiance on the ground surface. This work introduces an objective index to quantify the temporal variability of the incoming solar irradiance on the surface, defined as Cloudless Percentage Days (CPD). This index accounts for the number of days classified as clear and stable (i.e., high clearness coefficient and decreased frequency of ramps of incoming solar irradiance). Two methods were developed to quantify CPD, the first based on ground measurements and the other based on satellite data. This work compares the results achieved with the two methods to understand the correlation between them. Monthly and annual maps were drawn up for the Brazilian territory characterizing solar resource variability.
Translation Techies @DravidianLangTech-ACL2022-Machine Translation in Dravidian Languages

This paper discusses the details of submission made by team Translation Techies to the Shared Task on Machine Translation in Dravidian languages-ACL 2022.In connection to the task, five language pairs were provided to test the accuracy of submitted model.A baseline transformer model with Neural Machine Translation(NMT) technique is used which has been taken directly from the OpenNMT framework.On this baseline model, tokenization is applied using the IndicNLP library.Finally, the evaluation is performed using the BLEU scoring mechanism.
IMPROVEMENT OF TECHNOLOGY DEVELOPING PEDAGOGICAL RESPONSIBILITY FOR FUTURE TEACHERS

This article is about a type of pedagogical responsibility specific to prospective students, and general suggestions for learning about it.
IRON HYDROXIDES IS INDIRECT INDICATOR FOR THE DETECTION OF GOLDEN EPIDERMAL MINERALIZATION

The results of search and evaluation work on the Kremovaya license area (Central Chukotka) are demonstrated. In the course of field work, a stream was dis-covered, on the bottom and banks of which iron hydroxides are abundantly deposited, as well as outcrops of highly hardened rocks. The aim of the study is to study the rela-tionship between the presence of iron hydroxides and gold epithermal mineralization. The behavior of gold in the process of destruction and dissolution of the root source – epithermal ores is described. Brownstone formations are considered as an indirect indicator for the detection of golden epidermal mineralization.
SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language

Emotion analysis is the process of identifying and analyzing the underlying emotions expressed in textual data.Identifying emotions from a textual conversation is a challenging task due to the absence of gestures, vocal intonation, and facial expressions.Once the chatbots and messengers detect and report the emotions of the user, a comfortable conversation can be carried out with no misunderstandings.Our task is to categorize text into a predefined notion of emotion.In this thesis, it is required to classify text into several emotional labels depending on the task.We have adopted the transformer model approach to identify the emotions present in the text sequence.Our task is to identify whether a given comment contains emotion, and the emotion it stands for.The datasets were provided to us by the LT-EDI organizers Sampath et al. (2022) for two tasks, in the Tamil language.We have evaluated the datasets using the pretrained transformer models and we have obtained the micro-averaged F1 scores as 0.19 and 0.12 for Task1 and Task 2 respectively.
“PEDAGOGY OF THE OPPRESSED” IN RUSSIANVERSION: ALEXANDER BOGDANOV AND PAULO FREIRE

The article is devoted to a comparative analysisof the pedagogical ideas of Alexander Bogdanov and Paulo Freire. The author statesthat the principles of organizing education in the Proletarian University conceived by Bog-danov and in party schools for workers in Capri and Bologna, which he considered as a prototype of this university, are very close to the foundations of the “pedagogy of the oppressed”of P. Freire. At the same time, neither Freire himselfnor the researchers of his work mention Bogdanov among his predecessors or inspirers. The article notes that the starting points for formingthe basic principles of teaching proposed by Bogdanov and Freire were similar. Thesewere circles in which adult working people learned the basics of literacy –general (in Freire's case) and socio-economic (in Bogdanov's case). Bogdanov's pedagogical ideas followed from his theory of proletarian culture. Hebelieved that the first stage of the socialist revolution should be an ideological revolution amongthe workers, and only after that,the working class couldbecome the head of the social revolution. Freire reasoned in a similar way: proceeding from the fact that the subordinate position of the oppressed is determined by their objectivity, he believedthat “they should abandon the position of things and begin to fight as people”. At the same time, “they cannot join the struggle as objects in order to later become people”. The article also traces the affiliation of ideas underlying Bog-danov's and Freire's educational concepts,and shows thatdifferent pedagogical theorists and practitioners, suchas N.K. Krupskaya, A.S. Makarenko, and A. Gramsci,developed ideas similar, though not identical,toBogdanov's and Freire's views
Principles for personification of the educational process in post-nonclassical didactics (anthropological-axiological approach)

The paper addresses the problem of the transformation of didactic principles at the present stage of education development. Much attention is given to scientific research of this change which is conducted using the anthropological-axiological approach. The authors take it within the framework of the post-non-classical understanding of modern didactics and the critical consideration of postmodern ideas.Rethinking of the study process is seemed mainly as personalized educational process and concerns changes in basic didactic communication. The findings show that personalized learning is the process of becoming a lifelong learner which leads to raise his/her subjective experience as well as the independence of students and the formation of their responsibility for the learning process in general. Such a strong didactic transformation and personalized assumptions about learning made by researchers (shown in the form of teacher - student; teacher — student — information; teacher — student — educational and social environment) are presented within the framework of value interaction at any educational level. Therefore, the implementation of the principles of personification of the educational process at the university will make it possible to prepare students - future teachers - for the realization of the ideas of the post-nonclassical understanding of the development of modern didactics in education.
A Novel Morphological Analysis based Approach for Dynamic Detection of Inflected Gujarati Idioms

The Gujarati language is primarily spoken by Gujarati people living in the state of Gujarat, India.It is the medium of communication in the state of Gujarat.In the Gujarati language, 'rudhiprayog' i.e. idiom is a very much popular form of expression.It represents the real flavour of the Gujarati language.The idiom is a group of words saying one thing literally but means something else when we explored it in context.Like Gujarati verbs, idioms can be written in many forms.Due to different morphological forms of the same Gujarati idiom, Gujarati idiom identification is a challenging task for any machine translation system (MTS).Accordingly many forms also make idiom translation more complicated.In the current paper, Gujarati idioms in their different inflected forms are collected, analyzed and classified based on ending words.After classifying idioms, their base or root forms are identified.Base idiom form and their possible idioms forms are morphologically analyzed and rules are generated based on the relationship between base form and possible inflected forms of idioms.These rules are used to generate possible idiom forms from the base idiom form.Gujarati idiom in any valid inflected form can be dynamically detected from the Gujarati input text using the proposed novel morphological analysis based approach.The results are encouraging enough to implement the proposed model of rules for natural language processing tasks as well as a machine translation system for Gujarati language idioms.
Leveraging Modern Big Data Stack for Swift Development of Insights into Social Developments

Abstract Insights of social development, presented in various forms, such as metrics, figures, text summaries, whose purpose is to summarize, explain, and predict the situations and trends of society, is extremely useful to guide organizations and individuals to better realize their own objectives in accordance with the whole society. Deriving these insights accurately and swiftly has become an interest for a range of organizations, including agencies governing districts, city even the whole country, they use these insights to inform policy-makings. Business investors who peak into statistical numbers for estimating current economical situations and future trends. Even for individuals, they could look at some of these insights to better align themselves with macroscopical social trends. There are many challenges to develop these insights in a data-driven approach. First, required data come from a large number of heterogeneous sources in a variety of formats. One single source’s data could be in the size of hundreds of Gigabytes to several TeraBytes, ingesting and governing such huge amount of data is not a small challenge. Second, many complex insights are derived by domain human experts in a trail-and-error fashion, while interacting with data with the aid of computer algorithms. To quickly experiment various algorithms, it asks for software capabilities for infusing human experts and machine intelligence together, this is challenging but critical for success. By designing and implementing a flexible big data stack that could bring in a variety of data components. We address some of the challenges to infuse data, computer algorithm and human together in Zilian Tech company [20]. In this paper we present the architecture of our data stack and articulate some of the important technical choices when building such stack. The stack is designed to be equipped with scalable storage that could scale up to PetaBytes, as well as elastic distributed compute engine with parallel computing algorithms. With these features the data stack enables a) swift data analysis, by human analysts interacting with data and machine algorithms via software support, with on-demand question answering time reduced from days to minutes; b) agile building of data products for end users to interact with, in weeks if not days from months.
Research on the Digital Currency Price Synergy Based on VAR Model: Evidence from Bitcoin and Ethereum

Based on the VAR model, we use Bitcoin prices as well as the Ethereum price daily data between December 2019 and January 2022 for dynamic identification, and the empirical analysis results show that the Bitcoin price is the granger reason of Ethereum prices, but Ethereum price is not the Bitcoin prices granger reason, namely in the long run the Bitcoin price fluctuation can affect the Ethereum price fluctuations, But Ethereum price fluctuations do not affect bitcoin price fluctuations.Meanwhile, according to the impulse response diagram, when the Bitcoin price is impacted by one unit, the Ehereum price will fluctuate greatly and approach 0 in the 11th phase.Based on the above research conclusions, this paper will put forward corresponding policy suggestions.
Mask Detection Using IoT - A Comparative Study of Various Learning Models

Abstract Wearing a mask is an effective measure that prevents the spread of respiratory droplets into the air and thereby curtails the dissemination of coronavirus. Unfortunately, despite the proven effectiveness, the idea of wearing a face mask has difficulty being accepted by part of the population. To address this significant health concern, we present a monitoring system that automatically detects whether a mask is put appropriately over a face. The system annotates the videos that are provided by cameras. In this article, we present a comparative study of machine learning models (i.e., SVM, RNN, LSTM, CNN, auto-encoder, MobileNetV2, Net-B3, VGG-16, VGG-19, Resnet-152).
&amp; og "og"

This article deals with the use of ampersand in Icelandic. The sign has a long but not continuous history in written Icelandic. The oldest documented use is in manuscripts from the 12th century (e.g. AM 674 a 4to). In contemporary language, ampersand occurs mainly in company names and titles, probably due to English influence. The author does not recommend its use and considers it an unnecessary relic.
The New English Education College Students’ Rubric Evaluation on Their English Read-Aloud

Reading aloud has been very familiar for educational and English teaching study.As usual, read-aloud as the way to know students' competence in pronouncing words of English.A few research discussed about students' read-aloud competence in reading English text with rubric evaluation.Then, the evaluation can be used as the guidance for teaching intervention on students' reading skill.The aim of this study was to describe the read-aloud rubric evaluation on the aspects of pronunciation and clarity, fluidity, volume and intonation with expression for the new students of English education program.The rubric was adopted from i-Rubric.Both girl and boy students were engaged in this study.They were about 50 students all.As noted on the results of the study, the level of score in good categories was on the aspect of students' volume of reading aloud, but on the three other aspects was on the level of fair.The students' percentage on category of Excellent-Good read-aloud volume was 60% and Excellent-Good read-aloud pronunciation and clarity was only 44%.This was proved that new college students still need more intervention on their reading skill by read-
SOME ASPECTS OF THE MOTIVATION OF DISTANCE LEARNING

The presented article discusses the reasons for the low motivational activity of part-time trainees and the factors affecting the level of motivation. One of the ways to increase the level of motivation of part-time students by introducing tasks of professionally oriented content into the educational process is proposed. The authors propose to include professionally oriented tasks in test tasks in higher mathematics for students of distance learning in the specialty "Rolling stock of railways", specialization "Locomotives" of the Samara State Transport University. The article provides an example of professionally oriented tasks in higher mathematics for first-year students of correspondence courses. The proposed method of using tasks of professional content in the process of teaching part-time students has shown its effectiveness. The authors come to the conclusion that the use of professionally oriented mathematical problems in the process of distance learning of students of technical universities increases interest in studying the discipline, develops nonstandard thinking, and the desire to work independently. In addition, students accumulate mathematical knowledge that will be useful in their professional activities. The authors reserve the right, in the course of further research, to consider the application of tasks of professionally oriented content to other forms of education.
Three Tier Test in Analyzing the Misconceptions of Mathematics learning

The purpose of this research is to describe how students' misconceptions about the material of flat shapes in the fourth grade of SDN 050644 Bahorok on 2020/2021.Misunderstanding of the concept or the principle will have an impact on how students' ability to understand the learning material.Formal learning is a long continuous process must start with the right concept.This research was conducted by using descriptive methods.The research was carried out in the fourth grade of SDN 050644 Bahorok.The research instrument used the three-tier test or the test with three levels.The results showed that 29% of students have misconceptions, 12% of students have false positive misconceptions, and 14% of students have false negative misconceptions.
An Exploratory Study of the Relationship between Logistics Driver Competency and Work Performance by Using Occupational Competency Standard

The positive relationship between competency and work performance were revealed theoretically and practically.However, few studies demonstrated how to use or transform a competency model or job description to investigate the relationship.This study took a document called occupational competency standard of logistics drivers issued by Taiwan government as reference to develop the measurement of competency and work performance, and conducted a survey to investigate the relationship between competency and work performance as well.399 questionnaires sent to the logistics drivers of a famous logistics/express company in Taiwan were collected from Mach to April 2021.The research results supported the positive relationship between competency and work performance and identify a vital competency dimension.The managerial suggestions were proposed as well.
Low-Rank Correlation Analysis for Discriminative Subspace Learning

Linear dimensionality reduction is a commonly used technique to solve the curse of dimensionality problem in pattern recognition. However, learning a discriminative subspace without label is still a challenging problem, especially when the high-dimensional data is grossly corrupted. To address this problem, we propose an unsupervised dimensionality reduction method called Low-Rank Correlation Analysis (LRCA). The proposed model integrates the low-rank representation and the linear embedding together with a seamless formulation. As such, the robustness and discriminative ability of the learned subspace can be effectively promoted together. An iterative algorithm equipped with alternating direction method of multiplier (ADMM) and eigendecomposition is designed to solve the optimization problem. Experiments show that our method is more discriminative and robust than some existing methods.
Afc-Net: Adjacent Feature Complementary for Crowded Pedestrian Detection

In recent years, despite the significant performance improvement for pedestrian detection algorithms in crowded scenes, an imbalance between detection accuracy and speed still exists. To address this issue, we propose an adjacent features complementary network for crowded pedestrian detection based on one-stage anchor-free detector, which is called AFC-Net. Firstly, Deep Dilated Convolution (DDC) is invoked in the backbone to expand receptive fields, so that the feature map can remain its original size with feature spatial sensitivity enhanced. Secondly, Hierarchical Feature Extraction (HFE) is designed to extract feature information pertinently according to the feature properties from different layers. Specifically, multi-scale feature extractor and channel attention mechanism are employed to extract contextual information among features on high-level features. Spatial attention mechanism is applied to filter background information on low-level features. Finally, Adjacent Feature Integration (AFI) is proposed to aggregate the correlative features of adjacent layers so as to make expressive ability of features more comprehensive, thus improving the pedestrian detection results. In the challenging CityPersons dataset and CrowdHuman dataset, the crowded scene pedestrian detection network with complementary adjacent features has achieved great results in pedestrian detection. The result achieved from the experiment shows that the proposed algorithm can still maintain the comparability and stability of detection accuracy while the network parameters are greatly reduced and the speed is effectively improved.
On Piano Performing in Short Videos from a Semiotic Perspective

Inseparably connected, hands and musical instruments, piano as a well-known traditional one in particular, have been prevalent in short videos nowadays.Piano itself has descended from aristocratic culture to a part of popular culture, in which creative works are produced and shared.The short video authors, however, tend to hide their identities in the camouflage of nick names or by covering their face.Hands at this time become a sign focused to generate expressive form in this art semiosis with piano, and interact with the short video text and receivers.Semiologists including Charles Peirce and Susanne Langer try to build a system made up of three or four branches, from the object to signs, from the interpretant to the expositor, separately or interactively studied as a continuum.Short videos as a frame, in which handassociated works abode, and a sign at the same time give a window into fandom and youth subculture ecology as a macro-semiosis from a micro-semiosis perspective.
Heterogeneous-Graph Reasoning and Fine-Grained Aggregation for Fact Checking

Fact checking is a challenging task that requires corresponding evidences to verify the property of a claim based on reasoning.Previous studies generally i) construct the graph by treating each evidence-claim pair as node which is a simple way that ignores to exploit their implicit interaction, or building a fully-connected graph among claim and evidences where the entailment relationship between claim and evidence would be considered equal to the semantic relationship among evidences; ii) aggregate evidences equally without considering their different stances towards the verification of fact.Towards the above issues, we propose a novel heterogeneous-graph reasoning and finegrained aggregation model, with two following modules: 1) a heterogeneous graph attention network module to distinguish different types of relationships within the constructed graph; 2) fine-grained aggregation module which learns the implicit stance of evidences towards the prediction result in details.Extensive experiments on the benchmark dataset demonstrate that our proposed model achieves much better performance than state-of-the-art methods.
Anomalies in Higher Education as a Factor of Student Failure

The article analyzes in interrelation the social phenomena of students’ failure in the relationship with their anomalies (deviations) in higher education, which are quite common and widespread in the universities of the country. The nature and manifestations of the connection between academic deviations and educational failure of students are shown. Attention is drawn to the need to limit anomalies in the academic behavior of students and to search for its ways, as well as opportunities and ways to counteract them.