Reviewerly/exHarmony huggingface dataset:
CONCEPTUALIZATION OF THE PHILOSOPHICAL TEXT IN THE STRUCTURES OF FOREGROUNDING

The foregrounding of the text goes from a potential, not yet existing text to its relevance. In this march, the philosopher coincides with the philosophical text. This coincidence can be called a fore-text, which allows you to "enter" a philosophical text. The Internet considers hypertext as an informational formless set of texts, which is predetermined for the subject. The posed problem concerns the possibility of the presence of the subject in a digital hypertext. The aim of the study is to set the philosophical text as a concept, showing the subjectivation of the text with the help of the developed concept of a fore-text. To achieve the expected result, it is necessary to consider the history of philosophy as a chronological coherent sequence of texts, which is assumed as a context or common meaning. The problem is complicated by the fact that a single history of philosophy does not exist in digital space. It resides as a plurality of authors' histories of philosophy. The "introduction," "preface," etc. to a philosopher's text then becomes a "substitute" for the lost context. The development of the subjectivity of philosophy shows that the fore-text becomes the text itself. This development corresponds to the conceptualization of philosophy and its liberation from the authority of antiquity as traditionalism. Under the conditions of virtual reality and hypertext information, only conceptualization preserves the human in man - thinking. The author of the article poses the questions: "So what have we lost by asserting the end of the history of philosophy? Isn't the "new" pre-structure of the philosophical text the actualized "hyper-conceptual" space of the Internet?"
Study of Intelligent Search Engine of Energy Industry Based on BERT Semantic Model

Based on a variety of heterogeneous data of energy enterprises, this paper provides an intelligent search method based on the Bert preprocessing model. This paper provides a method of intention recognition based on the combination of template matching and text classification, which involves machine learning, deep learning and other fields of artificial intelligence. First, the preprocessing model Bert is used to rewrite the natural language into a vector based on syntax. Then, the information extraction technology is used to extract the structured and machine understandable information from the problem to provide parameters for intention processing. The intention recognition technology used in this paper first uses template matching method, and then uses text classification method. Finally, the effects of various methods are compared through experimental examples.
Thesaurus of Russian adjectives, organized into an array of linearly ordered groups of semantically close words, densely packed on the flat surface

We split thesaurus of Russian adjectives into an array of semantic categories including words with close meaning.For compilation of the semantic categories we used Russian-English translations as the measures of proximity of word sense.The words can be divided into three domains, nearly equal in size, containing adjectives describing positive, neutral and negative qualities.Words in the groups are arranged in ordered lists, and these groups can be packed densely side by side.The whole thesaurus can be mapped onto a nearly circular patch of surface divided into three sectors.We conjecture that the emerging structure reflects representation of words in the human cortex.
Digital Media as COVID-19 Disaster Information Literacy

Information about COVID-19 in digital media circulating must be confirmed because there are many fake news (hoaxes). The purpose of this study was to evaluate the digital literacy curriculum of Tular Nalar Internet and Disaster Preparedness in order to reduce hoaxes among PKK RW 69 women in Perum MGA Yogyakarta. This study uses an evaluation method oriented to the program's objectives that apply Tyler's model. Evaluation is carried out in the following steps: 1) Establishing program objectives; 2) Classifying objectives; 3) Identify structured objectives; 4) Data collection through: observation, interviews. Data analysis was carried out by assessing the conditions that occurred before, during and after the implementation of the digital literacy curriculum and drawing conclusions. The results of the study show that the Tular Nalar digital literacy curriculum in the PKK RW 69 Mitra Griya Asrita community provides awareness in critical thinking. PKK women become “Tahu (know), Tanggap (respond), Tangguh (resilient)” in responding to information in disaster emergency situations.
Ontology-based syntactic analysis of domain-specific texts

The paper compares three methods for parsing of patients’ chief complaints extracted from electronic medical cards. We propose two methods which are based on usage of an ontology: either as a method for correction of mistake made by a parser, or for constructing syntactical dependencies according to this ontology and a limited set of rules of syntactical governance. As a control test, we use existing natural text parsing libraries. The paper demonstrates that such a simple approach could achieve a high accuracy, which is comparable to modern parsers.
Machine learning methods solving an inverse problem in spectroscopy: comparison of efficiency and noise resilience

Problems of indirect measurements arising in experimental science belong to the class of inverse problems (IP), which are often ill-posed, non-linear, and have high dimensionality. Machine learning methods are used to solve IP due to their ability to resist these unfavorable properties. In this study, we solve an IP of determination of concentrations of components in multi-component solutions by their Raman spectra. The results demonstrated by various machine learning methods are compared by the solution error and by their resilience to various types of noise encountered in experimental spectroscopy. The best results were demonstrated by multi-layer perceptrons with two hidden layers and by convolutional neural networks with one convolutional layer. For multiplicative noise with high noise level, the most noise resilient algorithms were random forest and gradient boosting.
Teaching English Vocabulary for Young Learners Using Electronic Word Gueesing Game

Teaching English in this Pandemic period requires an interesting method and teaching Vocabulary using Educational games for young learners becomes interesting. This study aims to describe the application of electronic word guessing game, students' vocabulary mastery, and achievement motivation after the implementation of educational games. Researchers collect research data through observation, interviews, questionnaires, and tests. The results showed that: the benefits of implementing electronic word guessing game were: Making the learning process fun; it activates students to engage; it integrates pronunciation and meaning; it helps students to memorize vocabulary. This research was also conducted to find out the studentsâ€™ vocabulary mastery before and after electronic word guessing game implementation. The result of the pre-test and post-test show that the studentsâ€™ mean score on the pre-test was 6.00 and it improved to 7.50 on the post-test. While the problems are: the need for adequate facilities and some technical obstacles that occur during its implementation. And most of the students are very interested in learning vocabulary using electronic word guessing game.
The system of training a new generation of mathematics teachers based on project and heuristic activities

Introduction. In recent years, the problem of training a new generation of mathematics teachers who have fundamental mathematical knowledge in combination with formed methodological competence and fluency in digital technologies has become topical. The purpose of the article is to present a scientifically based system for training future mathematics teachers, created at Donetsk National University, based on a comprehensive program for managing the project and heuristic activities of students for their productive work in a modern school. Methods. The study involved 125 people studying at Donetsk National University (65 students of the direction of training "Pedagogical education" (Profile: mathematics and computer science) – an experimental group, 60 people of the direction of training "Mathematics" – a control group). The effectiveness of a comprehensive program for managing students' project and heuristic activities was assessed according to three criteria: heuristic and didactic (HD); methodological and mathematical (MM); project and activity (PA). The meters selected: author's texts of control papers with heuristic tasks and computer testing systems (for HD and MM criteria), assignments for the development of author's educational projects using IT, students' term papers and theses (for PA criterion). To analyze the effectiveness of the complex program, the Student's t-test was used. Results. During the experiment, it was revealed that for each criterion, the average values of their indicators (in fractions of one) in the experimental group increased significantly. For the HD criterion, the level of heuristic skills formation among students of the experimental group increased from 0.51 to 0.65, for the MM criterion, the levels of mathematical and methodological competence formation increased from 0.67 to 0.753, for the PA criterion, the level of mastering the methods of project activities changed from 0.57 to 0.756. The empirical value of the statistics of the Student's criterion t = 2 (p &lt; 0,05) obtained in the assessment procedure proves that changes in the results of training are not random, which means that the proposed comprehensive program is more effective than the traditional one. Conclusions. The preparation of a future mathematics teacher based on a comprehensive program for managing the project and heuristic activities of students ensures the formation of heuristic and didactic, methodical and mathematical, project components of the teacher's professional activity, which are necessary for teaching schoolchildren of the Z generation.
The Role of Knowledge Management in Improving Performance of Human Resources – A Field Study on the General Organization for Technical and Vocational Training, Riyadh Region –: دور إدارة المعرفة في تحسين أداء الموارد البشرية – دراسة ميدانية على المؤسسة العامة للتدريب التقني والمهني بمنطقة الرياض –

This study aimed to identify how knowledge management plays a role in improving human resources, it also aimed to identify the realities of knowledge updating of the institution, and knowledge disseminating and distributing among workers. The research sample consists of the employees of the top departments 11.419 of the general organization for vocational training in three departments: the national training department, the general administration of quality and training, the general administration for development and curriculum design, in the Riyadh region, the study used a descriptive and analytical approach to address the research subject. knowledge management has been also mentioned as an important and positive impact on the efficiency of the organization's internal processes and the quality of performance. The results have been as follows: there is a positive statistical significant correlation between the knowledge diagnosis and measuring the quality of human resource performance, the value of the correlation between them was (0.577), at a significant level (0.00), which is a value less than (0.01),there is a positive statistical significant correlation between updating knowledge and measuring the quality of human resources performance, as the value of the correlation between them reached (0.682), at a significant level (0.00), which is a value less than (0.01),there is a positive statistical significant correlation between the disseminating and distributing of knowledge and the measuring of the quality of human resource performance (0.705), at a significant level (0.00), which is a value less than (0.01), as the higher the diagnosis of updating, disseminating and distributing knowledge, the higher the quality of human resource performance. The most important recommendations of researchers: The necessity for the institution to have the ability to identify its employees who possess knowledge related to its fields of activities. The necessity for the institution to provide material and moral rewards to its employees for their efforts to gain new knowledge that supports its activities. Reliance on objective and fair standards in order to truly reveal the levels of employee performance, which leads to avoiding problems among workers.
THE IMPORTANCE OF EDGE COMPUTING IN DATA MANAGEMENT WORKFLOW

Similar to other innovation alternatives brought forward before, Edge Computing can be incorrectly distinguished as the arrangement when the necessities of the business are ineffectively characterized, or the advantages of the clever design are inadequately perceived.Edge Computing should be effectively characterized and perceived.Anyway, what is Edge Computing?The term edge is a relative term.The computing model likens to figuring in one (focal) area, regardless of whether that is in the cloud or in your own datacenter.In this way, one part of edge computing is that it is entirely different than the approach where all information is pushed to a solitary ingest or capacity endpoint.Edge computing is exceptionally decentralized.Yet, what subtlety about "edge" makes it unique?"Edge" suggests that something is situated as close as possible to a reference point.Consider the two universes we work inone world is our actual world and the second is the advanced world.The actual world is the regular world we live in that contains all that we can see with our five senses.The computerized world is a world wherein we are only guests.We can't contact, feel, or smell information or computerized signals.Also, for quite a while, the advanced world was kept extremely isolated from our actual world.It's anything but a distant land known as the Data Center.Then, that point it moved, apparently, much farther away to a further-off land known as the Cloud.This paper will try to exemplify what Edge Computing actually is and how it can benefit the future of data management.
Investigation of Students’ Perception of Sustained Silent Reading (SSR) on Reading Comprehension in SMPN 1 Abang

Assisting students to have good reading comprehension is essential to be accomplished during learning. Sustained Silent Reading (SSR) comes as the most used strategy to promote students’ literacy and build their reading comprehension based on the pre-observation in junior high schools in Karangasem Regency. This study aimed to investigate students’ perception of using SSR to enhance students’ reading comprehension in SMPN 1 Abang. The design of the study was mixed-method. The setting of this study is SMPN 1 Abang, located in Abang district, Karangasem regency, Bali province. This study used a questionnaire as the instrument of this study. The type of questionnaire used is a mail survey, specifically the questionnaire created in scale items by using Likert-type questions. As the study results, the mean score was categorized as very high because it was 4.50 and the interval was 4.00&lt;M&lt;5.00. In other words, the student's perception of using SSR to enhance students’ reading comprehension in SMPN 1 Abang showed very positive qualifications. Thus, it implies that SSR is helpful to improve students’ reading comprehension.
High Fluorescence Lymphocyte Count and Immature Platelet Fraction; “Can these Two Parameters Reliably Distinguish between Dengue and ITP?”

Abstract: Objective: Thrombocytopenia is a frequent finding in Dengue fever. Immune Thrombocytopenia (ITP) is a diagnosis of exclusion and is not associated with febrile illness. Ignoring fever, low platelet count and clinical signs may be similar in both conditions. Peripheral film finding in Dengue fever shows many reactive lymphocytes. Enumeration of these reactive lymphocytes as high fluorescence lymphocyte count (HFLC) and computing immature fraction of platelets (IPF) may be useful in differentiating these two disorders as soon as the blood sample is analyzed on XN-1000 hematology analyzer. Materials and Methods: A cross-sectional study was conducted at National Institute of Blood Disease and Bone Marrow transplantation from January to July 2019 during (Dengue season); blood samples from emergency room were analyzed on XN-1000 hematology analyzer for complete blood count and IPF. Samples with thrombocytopenia were checked for IPF count and HFLC from the extended research parameter data of the analyzer. Patients presenting with fever, had Dengue NS-1 tested. Detailed history &amp; examination was recorded from the patients. Results: Out of the 124 patients, 62 (50%) patients of Dengue and ITP respectively. Male to female ratio was 2:1. Mean age in Dengue was 25.52±10.46 years while 34.44 + 20.82 years in ITP group. Mean platelet count was significantly higher in dengue than ITP patients (120.59 ± 80.28 x109/L versus 41.84 ±38.62 x109/L) (p&lt;0.001). HFLC was 11.71± 7.17% in Dengue fever while 0.198±0.25% in ITP patients whereas IPF in ITP group was 21.91 ± 16.09% while 8.79± 4.39%in Dengue patients were found to be significant i.e. (p&lt;0.001). Conclusion: Excluding fever, thrombocytopenia, presence of increased number of HFLC and low or normal IPF can reliably a predictor of a diagnosis in Dengue season. Keywords: High fluorescence lymphocyte counts, Immature platelet functions, Dengue, Immune thrombocytopenia, Resource constraint Laboratories, Research parameters.
Gaming argumentation framework (GAF): Pfizer or AstraZeneca Vaccine of The COVID-19 as a case study

Dung's argumentation frameworks (AF) were introduced in the last century it works with the justification of the argument.This framework analyzes attacks of arguments, it works away on the characteristics of arguments structures and words was used in the attack between each other, etc.These properties make this model attractive as it decreases most of the complexities included when applying the argumentation system.This system can be applied to different states such as to evaluate the arguments or with the supported argument to be defense and attacked arguments.In addition, the group of experts may be making argumentation about some cases.In the latter scenario, agents with potentially dissimilar arguments and/or opinions are used to evaluate the arguments, allowing for the consideration of several sets of arguments and attack relations.This framework is extended to propose a new system called gaming argumentation framework (GAF).It helps to make a decision about the current problem by making claims and attack determination to the arguments, then putting the result of these claims and attack determination to the game theory with two players to achieve the final results that help the decision-maker to decide about the current problem.Finally, compare this framework with other frameworks, and provide an example to explain how the proposed framework performs its intended purpose, where decision making is very important in the medical field therefore this paper taking the confusion on the COVID-19 vaccines as a case study to solve Pfizer or AstraZeneca problem and make the decision about this case.
TLBIONER: TRANSFER LEARNING BASED NAMED ENTITY RECOGNITION ON MEDICAL LITERATURE DOCUMENTS

Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.
EFFICIENT METHODS FOR FACIAL MICRO-EXPRESSIONS DETECTION & CLASSIFICATION

Micro-expressions (ME) are a form of facial expression that lasts just a few milliseconds.Due to its use in revealing implicit human intentions, particularly in high-stakes situations, ME detection and classification is a burgeoning field of research.In recent studies, it has been found that the apex frame contains maximum facial movement conveying expression information.But they last for very few milliseconds, hence detecting the apex frame and classifying the ME is a very challenging task.This paper addresses this problem by proposing a training-free method for ME detection and an ME classifier using hybrid features.For ME detection, video frames are preprocessed to remove head movement and align faces.Then, from five local facial regions, Local Binary Patterns (LBP) are extracted.These regions are selected to reduce the effects of head eye blinks and changing eye gaze.Then the apex frame is detected using the Frobenius norm on extracted LBP features.For ME classification, hybrid features: Frame Difference (FD), Optical Flow Magnitude (OF-Mag), and Optical Flow Histogram of Oriented Gradients are extracted from regions around facial landmarks and are used to classify micro-expressions.These regions are selected to reduce the effects of head movement, eye blinks, and changing eye gaze.The proposed methods are tested on publicly available benchmark datasets CASME II.On the CASME II, the ME detection method achieved 0.1137 Normalized Mean Absolute Error (NMAE), 0.0720 Normalized Standard Error (NSE), and the ME classifier achieved 67.07 percent and 0.69, accuracy and a f1-score respectively.The results reveal that the proposed methods excelled the existing methods.