Reviewerly/exHarmony huggingface dataset:
The Essence of the Professional Training of Elementary School Teachers in the Context of Society Informatisation

This article studies the problems of the essence of the professional training of elementary school teachers in the context of society informatisation. It is specified that the development of the scientific and technological progress incites to the information and communications technology onrush. It is noted that the educational reform is currently being implemented in Ukraine. The definitions of the concepts ‘competence’, ‘responsibility’ and ‘informational competence’ are established. The author outlines the publications of the foreign and national researchers of the problem raised. It is found out that digital competence is supported by digital literacy. It is accentuated that a future elementary school teacher should use the modern technology, have a high level of digital competence formedness. It is studied that the foreign higher educational institutions had experiments conducted on the use of platforms for online meetings and websites development at the beginning of the XXI century. It is indicated that the skill to interact in different environments, to share the knowledge, to form a new communication, to correct the information is one of the advantages of the information and communications technology from the students` viewpoint. The author emphasises that the outbreak of COVID-19 incited lecturers and teachers at higher educational institutions to arrange the distance education as an alternative to the intramural form of study. It is ascertained that students appeared to be ready to develop their own pedagogical competence during their online education. The article shows that digital competence includes three measurers: technical skills, cognitive skills and ethical knowledge. The author outlines the ways for forming digital competence in attendees of educational institutions, presents some educational programs, e-sources that an elementary school teacher can use when organising the educational process. It appears that the factor of successful use of the information-communications technology is an adequate number of computer laboratories and appliances, broadband Internet access.
The Authority of the Teaching Profession in the Opinion of Teacher Education Students

The article presents the results of own research on the perception of teacher authority by students of teacher studies. The theoretical part presents a definitional approach to the concept of authority and indicates the factors conditioning the perception of a teacher by students as an authority. The empirical part presents the results of own research. Fifty essays written by students of teaching faculties (aged 19–23) were analyzed. The selection of the research sample was convenient. The analysis of the essays’ content made it possible to specify the teacher’s qualities, which are perceived by the respondents as the features of authority. These features included, among others, moral values, openness, commitment to the teaching process, motivation to act, openness to other people. In the analyzed essays, students often indicated that on their educational path they had met many anti-authorities, teachers who demotivated them to act and subjected them to excessive criticism. The summary presents practical conclusions for preparing students to perform the teaching profession.
LIBWAY-2022: results and perspectives

Brief information is presented on the results of the International scientific and practical conference «Science, Technology, Information in Libraries (Libway-2022)», which continued the traditions of the 2018–2020 forums. This year, the participants discussed topical issues, practices and research in the field of scientific communications and the organization of science, book culture and library and information activities. The papers and messages presented at the plenary session, section sessions, round tables and panel discussions are characterized. The moderators of the events provided information about the work of the meetings.
A Bayesian Surprise Approach in Designing Cognitive Radar for Autonomous Driving

This article proposes the Bayesian surprise as the main methodology that drives the cognitive radar to estimate a target's future state (i.e., velocity, distance) from noisy measurements and execute a decision to minimize the estimation error over time. The research aims to demonstrate whether the cognitive radar as an autonomous system can modify its internal model (i.e., waveform parameters) to gain consecutive informative measurements based on the Bayesian surprise. By assuming that the radar measurements are constructed from linear Gaussian state-space models, the paper applies Kalman filtering to perform state estimation for a simple vehicle-following scenario. According to the filter's estimate, the sensor measures the contribution of prospective waveforms-which are available from the sensor profile library-to state estimation and selects the one that maximizes the expectation of Bayesian surprise. Numerous experiments examine the estimation performance of the proposed cognitive radar for single-target tracking in practical highway and urban driving environments. The robustness of the proposed method is compared to the state-of-the-art for various error measures. Results indicate that the Bayesian surprise outperforms its competitors with respect to the mean square relative error when one-step and multiple-step planning is considered.
Low-Light Face Recognition and Identity Verification Based on Image Enhancement

After years of development, face recognition is now a relatively perfect technology. It is non-contact, intuitive, simple, accurate, and applicable to complex practical environments. To a certain extent, the application of deep learning has enhanced the accuracy of face recognition. But there are some defects with deep learning in detecting face objects of different types in different environments, calling for further explorations. Therefore, this paper explores the low-light face recognition and identity verification based on image enhancement. Specifically, light processing and Gaussian filtering were adopted to suppress and eliminate the low-light effect of low-light face images. The basic framework and objective function of the existing generative adversarial network (GAN) were modified. By learning the mapping of side and front faces in multi-pose face images in the image space, a cross-pose GAN was established to turn faces of different poses into front faces. The proposed model was proved effective through experiments.
The Smartphone Connects Us with the Strangers and Disconnects from the Fellow Men

The current digest is focused on the issue of the individual’s relations with the information milieu provided by modern digital media. We present the results of the foreign scientific studies of the Internet’s influence on mental wellbeing. We also cite a philosophical understanding of the global processes stemming from the effect of the digital stimuli on the individuals. (Translated by Elena Mozhaeva).
A Novel Encryption Algorithm by Fusion of Modified Blowfish Algorithm and Fermat’s Little Theorem for Data Security

Nowadays, protecting sensitive factual information from hackers when it is transmitted over the broad network to communicate around the world is a huge task. To provide secure online transactions, communication, and business, researchers are insisted to develop novel security techniques. One of these approaches is on the basis of encrypting a plaintext will be in ciphertext by applying some mathematical models. This paper suggested a modified Blowfish algorithm with Fermat’s Little Theorem to provide an additional layer of security for sensitive information while sending over a non-secure channel. The performance analysis of Modified Blowfish Algorithm and Fermat’s Little Theorem (BFLT) encryption algorithm reveals consumption of memory, time consumption for encrypting and decrypting by comparing with the standard encryption algorithm. According to the analysis, this proposed encryption algorithm provides significantly upper level of performance and security and BFLT can be used for real-time implementations.
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

Framing has significant but subtle effects on public opinion and policy.We propose an NLP framework to measure entity-centric frames.We use it to understand media coverage on police violence in the United States in a new POLICE VIOLENCE FRAME CORPUS of 82k news articles spanning 7k police killings.Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved.Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim's criminal record.Liberal sources focus more on the underlying systemic injustice, highlighting the victim's race and that they were unarmed.We discover temporary spikes in these injustice frames near highprofile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions. 1
Towards Realistic Single-Task Continuous Learning Research for NER

There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.Meanwhile, there is still a lack of academic NLP benchmarks that are applicable for realistic CL settings, which is a major challenge for the advancement of the field.In this paper we discuss some of the unrealistic data characteristics of public datasets, study the challenges of realistic single-task continuous learning as well as the effectiveness of data rehearsal as a way to mitigate accuracy loss.We construct a CL NER dataset from an existing publicly available dataset and release it along with the code to the research community 1 .
An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value
PENGARUH VIDEO ANIMASI TERHADAP KEMAMPUAN BERHITUNG ANAK DI TK PERTIWI IV TALAWI

This study aims to determine the effect of using animated videos on the numeracy skills of early childhood in Kindergarten Pertiwi IV Talawi. This study uses a quantitative approach with a quasi experimental research type. The data collection technique used a test with 10 statement instrument items. The analysis technique compares 2 average values, using the t test (t-test). But before carrying out the normality test and homogeneity test. Based on the results of the study, the average value of the experimental class using animated video was 35.93 with a standard deviation of 2.25 and the average value of the control class using non-animated counting learning videos was 29.53 with a standard deviation of 1 , 40. Based on the calculation of the t-test with the help of SPSS, the significance value (Sig.) Is obtained of 0.000 0.05, which means that there is an effect. Thus it can be concluded that the use of animated videos affects the development of children's numeracy skills in TK Pertiwi IV Talawi.
Sentiment Analysis of Text using Rule Based and Natural language Toolkit

Analysis of sentiment or opinion is one of the computational studies of the human kind's written opinions, feelings, attitudes, emotions. It is one of the most active fields of research in the area of natural language processing and text mining in the past few years and it has been popular for its wide range of applications because opinions are central to almost all human activities and are key drivers of our behavior. Part of the reason for the lack of the previous study was that in digital forms there was little opinionated text. So it's no surprise that the field coin starts and grows swiftly. Coincide with the web social media. In fact, research has expanded beyond computer science into management science and social sciences, given its importance to the enterprise and society in general. This paper shows the sentimental analysis process by Natural Language Toolkit and Python Libraries to find the hidden meaning in the unstructured data..
MOBILE LEARNING IN HIGHER EDUCATION

Mobile technologies are an important part of our daily life and we carry them with us all the time.Mobile learning is already used in informal and non-formal learning but in the context of formal learning, it is not much used, yet.The constant availability of learning content and integration in digital networks inspire new methods and concepts in the field of learning and teaching.Furthermore, more emphasis on learners is required, to understand how they are already using mobile devices to support their learning and to encourage their active participation in mobile learning in formal settings.This paper presents some of the work of the ERASMUS+ funded MINE project which has brought together lecturers and learners from a range of European institutions to help develop competencies for active mobile learning in higher education.
Spam Detection using NLP Techniques

Natural Language Processing is a vital field of research having applications in different subjects. Text Classification is a part of NLP where the text is converted into a machine-readable form by performing various methods. Tokenizing, part-of-speech tagging, stemming, chunking are some of the text classification methods. Implementing these methods on our data gives us a classified data on which we will train the model to detect spam and ham messages using Scikit-Learn Classifiers. We proposed a model to solve the issue of classifying messages as spam or ham by experimenting and analyzing the relative strengths of several machine learning algorithms such as K-Nearest Neighbors (KNN), Decision Tree Classifier, Random Forest Classifier, Logistic Regression, SGD Classifier, Multinomial Naive Bayes(NB), Support Vector Machine(SVM) to have a logical comparison of the performance measures of the methods we utilized in this research. The algorithm we proposed achieved an average accuracy of 98.49% with SVM model on ‘SMS Spam Collection’ dataset
Optimized Mechanism for Resource Sharing in Cloud

The cloud computing paradigm has settled to a stable stage. Due to its enormous advantages, services based on cloud computing are getting more and more attraction and adoption by diversified sectors of society. Because of its pay per use model, people prefer to execute various data crunching operations on high end virtual machines. Optimized resource management however becomes critical in such scenarios. Poor management of cloud resources may affect not only customer satisfaction but also wastage of available cloud infrastructure. An optimized resource sharing mechanism for collaborated cloud computing environments is suggested here. The suggested resource sharing technique solves starvation issue in inter cloud load balancing context. In case of occurrence of starvation problem, the suggested technique resolves the issue by switching under loaded and overloaded virtual machines between intra cloud and inter cloud computing environment.
A study to enhance candidate screening process using similarity analysis

Recruitment process is always continuous and open positions mostly have short time to get filled thereby for any such open position, organisations generally have to deal with higher incremental costs.Many advanced recruitment processes/methodologies are directly associated with the risk of failure, loss of effort, loss of time and money.Every organisation in any industry always aims to hire the best suitable candidate to the open position with right skillsets in the shortest possible time and with the lowest costs.Therefore, there is a need to experiment and explore the existing approaches, which can help in reducing the time and the screening effort in initial stage of recruitment practices.Consequently this paper attempts to implement the similarity analysis approach for a random sample of resumes from IT sector in National Capital Region (NCR), India to provide the best match of candidates' profile as per the required job description.The latent Dirichlet allocation (LDA) is used to find the similarity between job description and candidate's profile.The study will help the IT industry in identifying and selecting the best matched candidates' profile based on the key features in job description.
Association rule mining algorithm based on Spark for pesticide transaction data analyses

With the development of smart agriculture, the accumulation of data in the field of pesticide regulation has a certain scale. The pesticide transaction data collected by the Pesticide National Data Center alone produces more than 10 million records daily. However, due to the backward technical means, the existing pesticide supervision data lack deep mining and usage. The Apriori algorithm is one of the classic algorithms in association rule mining, but it needs to traverse the transaction database multiple times, which will cause an extra IO burden. Spark is an emerging big data parallel computing framework with advantages such as memory computing and flexible distributed data sets. Compared with the Hadoop MapReduce computing framework, IO performance was greatly improved. Therefore, this paper proposed an improved Apriori algorithm based on Spark framework, ICAMA. The MapReduce process was used to support the candidate set and then to generate the candidate set. After experimental comparison, when the data volume exceeds 250 Mb, the performance of Spark-based Apriori algorithm was 20% higher than that of the traditional Hadoop-based Apriori algorithm, and with the increase of data volume, the performance improvement was more obvious. Keywords: Spark, association rule mining, ICAMA algorithm, big data, pesticide regulation, MapReduce DOI: 10.25165/j.ijabe.20191205.4881 Citation: Bai X N, Jia J D, Wei Q W, Huang S Q, Du W C, Gao W L. Association rule mining algorithm based on Spark for pesticide transaction data analyses. Int J Agric & Biol Eng, 2019; 12(5): 162–166.
Automated Assistance for Breast Cancer Identification on Mammograms Using Computer Vision Algorithms

One of the greatest health problems in the world is breast cancer. If these breast cancer abnormalities are identified early, there is a maximum chance of recovery. We can go for this early prediction. It is one of the most effective detection and screening strategies and is widely used. The basic goal of CAD systems is to support physicians in the process of diagnosis. CAD systems, however, are very expensive. Our emphasis is on developing a CAD system that is low-cost and effective. To categorize breast cancer as either benign or malignant, a computer-aided detection approach is suggested. The standard mammogram image corpus, Digital Database used for Screening Mammography, images are used for enhancement, segmented and GLCM, intensity and histogram methods are used to extract features. The work is carried out by effective multilayer perceptron classifier (MLP) and support vector machine (SVM). Compare the performance of the classifiers. The proposed approach achieved 96 % accuracy and 8% improvement in accuracy compared to previous approaches with same dataset [4].
Video Saliency Detection Using Motion Distinctiveness and Uniform Contrast Measure

Saliency detection has been deeply studied in the last few years and the number of the designed computational models is increasing.Starting from the assumption that spatial and temporal information of an input video frame can provide better saliency results than using each information alone, we propose a spatio-temporal saliency model for detecting salient objects in videos.First, spatial saliency is measured at patch-level by fusing local contrasts with spatial priors to label each patch as a foreground or a background one.Then, the newly proposed motion distinctiveness feature and gradient flow field measure are used to obtain the temporal saliency maps.Finally, spatial and temporal saliency maps are fused together into one final saliency map.On the challenging SegTrack v2 and Fukuchi benchmark datasets we significantly outperform the state-of-the-art methods.
PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed.
NLP: Text Summarization By Frequency And Sentence Position Methods

In today's fast-growing online information age we have an abundance of text, especially on the web. New information is constantly being generated. Often due to time constraints we are not able to consume all the data available. It is therefore essential to be able to summarize the text so that it becomes easier to ingest, while maintaining the essence and understandability of the information. The summarizer basically uses the combinations of term frequency and sentence position methods with language specific lexicons in order to identify the most important sentence for extractive summary. We aim to design an algorithm that can summarize a document by their performance both objectively and subjectively in Afan Oromo Language. The performance of the summarizers was measured based on subjective as well as objective evaluation methods. The techniques used in this paper are term frequency and sentence position methods with language specific lexicons to assign weights to the sentences to be extracted for the summary.