Reviewerly/exHarmony huggingface dataset:
FORMATION OF COMMUNICATIVE COMPETENCE OF STUDENTS OF TECHNICAL UNIVERSITIES IN THE PROCESS OF STUDYING THE DISCIPLINE «PSYCHOLOGY OF SOCIAL COMMUNICATIONS»

The process of formation and development of social and communicative competence of students in the process of studying the discipline "Psychology of social communications" is considered. The role of humanitarian academic disciplines in the process of formation and development of social and communicative competence of students is emphasized. The essence of "social and communicative competence" is analyzed. The components that make up this competence are discussed in detail. It is emphasized that the most effective way to form social and communicative competence can be directly at the university in the process of professional training. The conditions for the effective formation and development of this competence are listed. It is concluded that students in the humanities have a higher level of formation of social and communicative competence, since their curriculum includes disciplines of the socio-psychological cycle, aimed the formation of competencies included in the researched competence. It is noted that in the curricula of technical specialties, developed on the basis of Federal State Educational Standard 3 ++, many universities of the Russian Federation began to include disciplines of the social and humanitarian cycle, which make it possible to form social and communicative competence at a high level. The experience of the Samara State Technical University on the inclusion in the curriculum of all technical specialties the discipline "Psychology of social communications", aimed the formation and development of universal competencies, which are components of social and communicative competence. Methods and forms used by teachers in the process of teaching this discipline, contributing to an increase in the level of formation of social and communicative competence, are analyzed in detail. The urgency of the problem of the formation of social and communicative competence is noted. It is concluded that the discipline "Psychology of Social Communications" contributes to the effective formation and development of social and communicative competence of students of technical specialties.
Fine-grained Typing of Emerging Entities in Microblogs

Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation.To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film).We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog.The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity.To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision.We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts.To type 'homographic' emerging entities (e.g., 'Go' means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity.Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
A Robust User Authentication Technique in Online Examination

User authentication in the online environment is promoting a hugely challenging issue. This has contributed to the realization of a user authentication where the exams can be performed over the Internet at any time and from any place and by using any digital device. Consequently, further investigations are required to focus on improving user authentication methods to enhance online security mechanisms, especially in the field of e-exams. This research proposes a new user authentication technique based on the user interface (UI). The novel idea is created based on the design preferences of candidates who are taking the e-exams. Several design features are used to design a special user interface for e-exams, for example, the font attributes, back colour, number of questions per page, group categories for questions based on difficulties, and timer setting. The introduced technique can be used to support the user authentication process in the e-exams environment. Furthermore, the proposed technique provides the ability to login to the e-exam without the need to remember the login information, but to select what the student prefers according to his/her personal information. Based on the literature review, a primary evaluation claiming that the students have differences in their preferences and that each user has stable design preferences within different sessions is revealed. In regard to these facts, they become the resource and essence of this research. The security performance of the proposed method is evaluated. The results of the experiments show a false positive (FP) rate of 0.416% and a false negative (FN) rate of 0%.
Web-Based Health Monitoring System and Textual Mining

The goal of the Health Monitoring System (HMS) and Textual mining is a large area to discuss. Web based health monitoring systems provide a massive number of benefits to the patient and normal healthy people to know about health issues and discuss the condition with doctors through a web portal. HMS is a process that has been used since past decades to monitor patients at hospitals in urgent situations, with the development of technologies we can use devices to check health updates of a person anytime. So, we built a prototype which is a heat sensor connected with a wireless network process. The device gives the temperature update on a real time basis, where we can add many more devices to add with the web portal and compare the data through our textual mining algorithm procedure. We believe our proposed methodology works in a larger benefit which will give a better version of the Health Monitoring System to the world for individuals. Our proposed work consists of a large amount of data that we examined to make our health monitoring system effective
Influencing Choices by Changing Beliefs: A Logical Theory of Influence, Persuasion, and Deception

We model persuasion, viewed as a deliberate action through which an agent (persuader) changes the beliefs of another agent’s (persuadee). This notion of persuasion paves the way to express the idea of persuasive influence, namely inducing a change in the choices of the persuadee by changing her beliefs. It allows in turns to express different aspects of deception. To this end, we propose a logical framework that enables expressing actions and capabilities of agents, their mental states (desires, knowledge and beliefs), a variety of agency operators as well as the connection between mental states and choices. Those notions, once combined, enable us to capture, the notion of influence, persuasion and deception, as well as their relation.
Alinha-PB: A Phonetic Aligner for Brazilian Portuguese

Phonetic alignment is the task of finding the limits of phones and higher units in an audio file. This has been reliably done in many languages such as English, French and German, but, so far, no available Brazilian Portuguese aligner had a performance comparable with the ones used for these other languages. Thus, the main goal of this work was to implement a useful tool for forced alignment for Brazilian Portuguese. The implementation was done in two steps, the grapheme-to-phoneme conversion and the alignment itself. The Converter is responsible for receiving the input transcription in graphemes and converting it to its equivalent in phonemes and allophones, and was implemented using computational rules derived from the analysis of regular grapheme-phoneme relations in Brazilian Portuguese and an exception dictionary, for words to which no regular rules could be applied. The Aligner was responsible for aligning the phonemes/allophones of the previous module to the corresponding acoustic intervals of the audio file, called "phones". This module was implemented using hidden Markov models. Results for the Converter have an accuracy of over 99%, where the main mistakes involved mid vowels /e/ and /ɛ/ and /o/ and /ɔ/. As for the Aligner, the best model has 87% of the alignments with errors below 25 ms.
Hate Speech and Blasphemy on Social Media in Indonesia: Forensic Linguistic Studies

The development of technology and information as well as the very rapid use of social media today makes it easy for someone to write a status or comment about what he thinks.Not infrequently, someone's status or speech on social media which is considered to contain elements of language crime can have legal implications if other peoples feel insulted by the utterances.This study aims to analyze hate speech that contains elements of blasphemy on social media and the legal impact of these utterances.The research method used is qualitative with a forensic linguistic approach.The data source is taken from the decision of the Medan District Court with decision number 931/Pid.Sus/2019/PN.Mdn has permanent legal force.Based on data analysis, the results of this study are: (1) the expressive speech act committed by the defendant contains utterances of hatred or contempt for a particular religion and (2) based on the appraisal system analysis, the defendant's utterance of hatred towards religion was evident from the use of language which was reflected in her attitude.The results of this study are that there is a criminal act intentionally showing hatred towards certain individuals and or groups of people based on religion through social media based on Article 28 paragraph (2) of Law No.19 of 2016 about the changes of the Law Number 11 of 2008 on Information and Electronic Transactions (ITE) in terms of speech act theory and appraisal system.
Influence of Knowledge Sharing on Students’ Learning Ability under the Background of “5G+AI”

The integration of 5g and AI realizes the intelligent communication and deep interaction between educators and learners, and promotes the reconstruction of various education and teaching resources in space and time dimensions. With the wide application of 5g and AI technology, knowledge sharing has become ubiquitous. The willingness and behavior of knowledge sharing provide a huge space for students to improve their learning ability. Taking 5g + AI new technology as the intermediary variable, this study analyzes the impact of knowledge sharing on students' learning ability. The results show that the overall reliability coefficient is 0.787. The reliability of the questionnaire is high, and the Kaiser Meyer Olkin value is 0.756, ranging from 0.7 to 0.8. The validity of the research data is good. The willingness to share knowledge has a positive role in promoting the improvement of students' learning ability. Knowledge sharing behavior plays a positive role in promoting the improvement of students' learning ability. "5g + AI" new educational technology plays an intermediary role in actively promoting knowledge sharing behavior and improving learning ability. The research results have important reference value for fully understanding the impact of knowledge sharing on the improvement of students' learning ability under the background of 5g + AI.
The Effectiveness of the Blended Learning Model to Increase the Learning Motivation of Primary School Students

This study aimed to examine the effectiveness of blended learning on students' learning motivation at primary schools.The method used was descriptive research with a qualitative approach.The study was carried out by observing the blended learning process in the class without making changes to the observed class.The population in this study was all fourth graders, totaling 61 students.The research samples were all students of Class IVA, totaling 30 students.Blended learning was employed in this class.Questionnaires were distributed to the students who participated in blended learning to collect the research data.There were four indicators in the motivation questionnaire, namely persistence, tenacity in the face of difficulties, interest in learning, and confidence.The data from the questionnaires were then analyzed using a Likert scale.The results by the average indicator show that the student learning motivation is 94, which belongs to a very high category.The average value of each indicator is the persistence indicator scoring 97; the tenacity indicator in the face of difficulties is 90; the interest in learning indicator is 96; and the confidence indicator is 95.The results of the overall questionnaire analysis belong to the high category.Thus, it can be concluded that blended learning is effective in increasing the students' learning motivation at SDN 1 Lambheu, District of Aceh Besar.As blended learning provides opportunities for students to learn independently, the students can study anywhere and broaden their horizons by accessing the learning resources globally.
WORLD RANKINGS OF HIGHER EDUCATION QUALITY ASSESSMENT: PROBLEMS AND PROSPECTS OF USE

This article examines the influence of the main world rankings of higher education (using the example of the QS rating) on the change in the quality of higher education in Russia. The question of the applicability of these ratings for Russian universities, possible problems and prospects of changing the quality of higher education in Russia with the active application of this rating is considered. The analysis of the indicators affecting the places of Russian universities in the QS rating was also carried out, and the places of Russian universities were calculated without taking into account the most controversial indicators for them. As an alternative rating, RAEX-100 was considered and the change in the places of universities in it (relative to the results of the QS rating) was evaluated.
Comparison of High-performance Computing Approaches in the Python Environment for a Five-point Stencil Test Problem

Several of the most important high-performance computing approaches available in the Python programming environment of the LNCC Santos Dumont supercomputer, are compared using a specific test problem. Python includes specific libraries, implementations, development tools, documentation, optimization and parallelization resources. It provides a straightforward way to program using a high level of abstraction, but the parallelization features for exploring multiple cores, processors, or accelerators such as GPUs, are diverse and may not be easily chosen by the user. Serial and parallel implementations of a test problem in Fortran 90 are taken as benchmarks to compare performance. This work is a primer for the use of HPC resources in Python.
The Regularization Effect of Pre-activation Batch Normalization on Convolutional Neural Network Performance for Face Recognition System Paper

Face recognition is of pronounced significance to real-world applications such as video surveillance systems, human computing interaction, and security systems.This biometric authenticating system encompasses rich real human face characteristics.As such, it has been one of the important research topics in computer vision.Face recognition systems based on deep learning approaches suffer from internal covariate shift problems that cause gradients to explode or gradient disappearance, which leads to improper network training.Improper network training causes network overfitting and computational load.This reduces recognition accuracy and slows down network speed.This paper proposes a modified preactivation batch normalization convolutional neural network by adding a batch normalization layer after each convolutional layer within each of the four convolutional units of the proposed model.The performance of the proposed model is validated with a new dataset, AS-Darmaset, which is built out of two publicly available databases.This paper compared the convergence behavior of four different CNN models: the Pre-activation Batch Normalization CNN model, the Traditional CNN without Batch Normalization, the Post-Activation Batch Normalization CNN model, and the Sparse Batch Normalization CNN Architecture.The evaluation results show that the recognition performance of Pre-activation BN CNN has training and validation accuracies of 100.00% and 99.87%, the Post activation Batch normalization has 100.00% and 99.81%, and the traditional CNN without BN has 96.50% and 98.93%.The sparse batch normalization CNN has 96.25% and 97.60% success rate, respectively.The result shows that the Pre-activation BN CNN model is more effective than the other three deep learning models.
Pedagogical Strategies for the learning of medicine students

With the epidemiological contingency, the learning processes were modified by teaching with virtualization. The teaching staff implemented pedagogical strategies structuring methodologies, interaction dynamics, fulfilling the objectives set out in the educational curriculum. Objective: To implement pedagogical strategies involved in virtualization during the learning process in medical students. Methodology: A descriptive, observational study with a qualitative approach over a period of 8 months. The sample was 225 students from the 5th semester of the Surgeon Degree. Results: It was shown that group activities, synchronous online conferences have 68% approval, dynamics such as discussion forums have 85% approval. Video recording has 45%, essays 25%, and mind maps 30%. Conclusion: Implementing educational strategies allows assessing efficiency and effectiveness promoting learning for the development of professional intellect.
Teknik Uji Penetrasi Web Server Menggunakan SQL Injection dengan SQLmap di Kalilinux

&lt;p&gt;&lt;em&gt;In recent years cases of cyber attacks that lead to website security have increased. The most widely used website hacking threat is sql injection. By using the sqlmap tool that runs on the Kalilinux operating system, attackers can easily take over very important user authentication data with their passwords. Attackers only use a special SQL query script using the python programming language will force the web server to output database information, tables, columns and data contents. This sql injection technique is not difficult, knowing how sql injection works is expected to be useful for web admins and web application developers to be able to secure user access from attackers. This attack simulation uses a virtual machine, by creating two virtual computers that are scripted as the attacker and the target server. By testing through this simulation, we can find out how the attack process and the consequences of attacks carried out by attackers.&lt;/em&gt;&lt;/p&gt;
APPROACH TO THE GENETIC ALGORITHM FOR SEGMENTING AND ANALYSING MEDICAL IMAGES

Ultrasound is required for producing medical pictures of the mammalian anatomy.Diagnostics imaging had represented the humongous leap further by embracing the workplace on picture capture.Image detection was used to combine the vast bulk of medical information contained in photos taken at different temporal periods and from diverse perspectives.For the study, 2 image methods are enrolled using a density true sequence approach.So, each angle of
Sign Language Interpreter using Deep Learning

Abstract: Sign Language is invaluable to hearing and speaking impaired people and is their only way of communicating among themselves. However, it has limitations with its reach as the rest of the people have no information regarding sign language interpretation. Sign language is communicated via hand gestures and visual modes and is therefore used by hearing and speaking impaired people to intercommunicate. These languages have alphabets and grammar of their own, which cannot be understood by people who have no knowledge about the specific symbols and rules. Thus, it has become essential for everyone to interpret, understand and communicate via sign language to overcome and alleviate the barriers of speech and communication. This can be tackled with the help of machine learning. This model is a Sign Language Interpreter that uses a dataset of images and interprets the sign language alphabets and sentences with 90.9% accuracy. For this paper, we have used an ASL (American Sign Language) Alphabet. We have used the CNN algorithm for this project. This paper ends with a summary of the model’s viability and its usefulness for interpretation of Sign Language. Keywords: Sign Language, Machine Learning, Interpretation model, Convoluted Neural Networks, American Sign Language
Crime of Encouraging the Use of Narcotics on Social Networks

Social networks influence people and users effect each other with interesting posts.• Broadcasting on social networks that encourage the use of drugs or stimulants seriously threatens especially the health of children and young people.• It is obvious that there is a need for regulations to prevent the crime of encouraging the use of drugs or stimulants on the internet for the protection of public health.
Discrete facility location in machine learning

Facility location problems form a wide class of optimization problems, extremely popular in combinatorial optimization and operations research.In any facility location problem, one must locate a set of facilities in order to satisfy the demands of customers so as a certain objective function is optimized.Besides numerous applications in public and private sectors, the problems are widely used in machine learning.For example, clustering can be viewed as a facility location problem where one needs to partition a set of customers into clusters assigned to open facilities.In this survey we briefly look at how ideas and approaches arisen in the field of facility location led to modern, popular machine learning algorithms supported by many data mining and machine learning software packages.We also review the state-ofthe-art exact methods and heuristics, as well as some extensions of basic problems and algorithms arisen in applied machine learning tasks.Note that the main emphasis here lies on discrete facility location problems, which, for example, underlie many widely used clustering algorithms (PAM, affinity propagation, etc.).Since the high computational complexity of conventional facility location-based clustering algorithms hinders their application to modern large-scale real-life datasets, we also survey some modern approaches to implementation of the algorithms for such large data collections.Bibliogr.138.
Fast Multi-sensor Image Matching Algorithm Based on a Multi-scale Dense Structure Feature

A fast image matching algorithm based on a multiscale dense structure feature has been proposed for matching multi-sensor images.In this method,the Gabor filter is employed for generating the structure response of the image.Then,the multiscale structure feature is combined on the basis of the major orientation response.Subsequently,fast Fourier transform is employed to calculate the convolution for each feature component image in the frequency domain.Finally,the similarity between images is estimated based on the sum of the convolutions,and the position with maximum similarity is outputted as the matching result.The proposed algorithm can effectively adapt to non-linear intensity variation between a multi-sensor image and noise distortion.In the experiments,a dataset consisting of optical,infrared,and synthetic aperture radar images was used for evaluating the proposed algorithm and other existing algorithms.The results indicate that the average error matching rate of the proposed algorithm is the lowest among the investigated algorithms and it has a distinct advantage in terms of computational performance.