Reviewerly/exHarmony huggingface dataset:
Psychological and pedagogical conditions for the development of professional abilities of future teachers

The article describes features of the development of professional abilities of future teachers. Different views of scientists on the definition of “professional abilities” are considered, the development of which, in turn, affects the effectiveness of professional pedagogical activity. The role of the educational process in higher education as a platform for the development of professional and pedagogical abilities is noted. The pedagogical conditions that contribute to the effectiveness of the development of students' professional abilities are highlighted and substantiated. The article describes the results of experimental work to identify the current level of development of professional abilities of future teachers. At the initial and control stages, monitoring and a number of diagnostic techniques were used, as well as self-diagnostics. The formative stage of the experimental research is represented by the testing of the selected pedagogical conditions. According to the results of the control section, the dynamics of the level of development of the professional abilities of future teachers in a positive direction was observed.
Teachers’ perceptions of the role of technologies for inclusion. Results from a special needs teacher training course

The study focuses on the Information and Communication Technology course run within the “Special Needs Teacher Training Specialization  Course” developed by the University of Macerata (Italy) in the academic year 2018-2019. Specifically, the case, framed in a qualitative approach, aims at analysing how special needs trainee teachers (henceforth referred to as trainee teachers) approach the  use of technology with inclusive perspectives and what factors can  influence their decision making process in using technology. The  case highlights what connections can be drawn between the design and  development of the Information and Communication Technology course  and the trainee teachers' perceptions in direction of the UDL principles.
Variational Inference for Learning Representations of Natural Language Edits

Document editing has become a pervasive component of production of information, with version control systems enabling edits to be efficiently stored and applied. In light of this, the task of learning distributed representations of edits has been recently proposed. With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. We achieve this by introducing a latent variable to explicitly model the aforementioned features. This latent variable is then combined with a document representation to guide the generation of an edited-version of this document. Additionally, to facilitate standardized automatic evaluation of edit representations, which has heavily relied on direct human input thus far, we also propose a suite of downstream tasks, PEER, specifically designed to measure the quality of edit representations in the context of natural language processing.
Electronic Voting—Cloud Storage—Smart and Collaborative Transportation—Blockchain and International Trading—Blockchain Business Models

New technology and innovation have always been the top priority in any business. Many organizations and industries were searching for new technologies that will improve not only the efficiency but also aid in the growth of the organization. Earlier industries have used artificial intelligence in their businesses, which does increase the efficiency and growth, but security and privacy are still needed in this growth. So, a business process, if carried out faster, more secure and with privacy preserved, will automatically result in the performance improvement of any business. The blockchain technology that came with the concept of cryptocurrency has progressed in the last decade. The technology is not just limited to cryptocurrency as many organizations and industries are moving on to blockchain technology because of the benefits of the security and privacy features provided by this technology. Organizations and industries are now working on business models based on the blockchain technology. These business models will definitely help in the growth of not only the industries but their respective country as well. Blockchain technology has allowed the solution to rapid increase in size and scale, and many solutions can be adapted to perform multiple tasks across industries. The blockchain technology allows many innovative ways that will boost economic growth and reduced the costs and time associated with many intermediaries.
Utilization of QR Code for Tracking Digital Expenses

Today, a lot of us have a lot of digital wallet services that we use. Because of that tracking our spending has become significantly harder because most people use more than one digital wallet. This study aims to find out how can we utilize QR code in the payment receipt at out digital transaction, to keep track on our transaction on various digital wallet. To help keep track of the expenses from the various digital wallet, we use QR code that is generated from the receipt of the transaction that contain the encrypted data of the transaction that occurs. This data can be scanned so it can be added to a personal budget tracker app so the user can manage their digital expenses in one view.
Securing the Blockchain Network from Cyberattacks

Blockchain is built upon the distributed mechanism where the database comprises a record list that grows incessantly with each record called a block. Each record needs to be protected against tampering as well as modification efforts. Nowadays it is gaining more popularity because of the promised alleviation of cybersecurity. Organizations believe that blockchain is one of the most secure technologies as information hacking is not easy because stored information on blockchain uses cryptographic technologies. They are used for secure transactions. The goal of cybersecurity is to provide a secure environment where hackers and third parties cannot enter. In blockchain, DDoS attacks overload the blockchain with bits of data, which can force the blockchain security toward the server to utilize processing power. For every organization regardless of size, it can be small or it can be a big industry, development of a cybersecurity risk-management plan is necessary for every organization. Currently different applications like industry, academia, and many more are attracted to blockchain for the for secure environment it provides.
APLIKASI MOBILE LEARNING PADA MATA PELAJARAN EKONOMI KELAS X BERBASIS ANDROID SEBAGAI MEDIA PEMBELAJARAN EFEKTIF

Economics subject is one of the subjects in school that studies the behavior of individual societies in fulfilling their daily needs. During a pandemic like this, teaching and learning activities in every school, especially high school, have begun to be converted into online learning. Therefore, an effective mobile learning application is needed with the aim that every high school student can use this application anywhere and anytime. By using the literature study research method, the researcher collected market material on economic subjects from various book sources as material for making applications. This mobile learning application is made using the waterfall method and uses use case diagrams and activity diagrams in the design process. This mobile learning application for market material on economic subjects can make it easier for every high school student to study anywhere and anytime.
Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study

Preserving the edges and information is one of the main purposes of edge-preserving filters. That is, they’re employed to smooth a picture, and minimize halos, phantoms, and edge blur over the edge. They have a nonlinear relationship between one thing and another. Bilateral filters, anisotropic diffusion filters, directed filters, and trilateral filters are all types of example filters. The filter family may be used in a wide range of image processing tasks, such as denoising, video abstraction, demosaicing, optical flow estimation, stereo matching, tone mapping, style transfer, relighting, and others. The paper gives a clear description of edge-preserving filters, from the heat diffusion equation in ancient times to the present, explaining their numerous applications and detailing their numerous uses. Additionally, mathematical analysis is included, as well as efficient and optimized implementations. The focus is on preserving the boundaries, spikes, and canyons, and the information is given clearly and in detail. Finally, it offers a realistic representation of efficient implementation, as well as a comprehensive research scope for future hardware implementation.
ΔRLE

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.
Mining of High Utility Item sets using Genetic Algorithm

A high utility item set mining system using genetic algorithm has been proposed in this article. The proposed system mines high utility item sets by parsing the database only once. The high utility item set mining problem has been reformulated as a constrained optimization problem. Genetic algorithm has been used to mine the high utility item sets. The entities such as gene, chromosome, population and fitness function, required to apply genetic algorithm for mining high utility item sets has been defined. In the proposed system, due to the requirements imposed by the high utility item set mining problem, a non binary representation of the genetic algorithm has been proposed. The operators of genetic algorithm such as cross over and selection operation have been customized to efficiently operate in the mining problem. To reduce the explosion of candidate generation, an upper bound based on the sum of the item set utility and remaining utility is used.The item set utility is compared with the threshold to select the high utility item sets. The remaining utility is used to select prospective candidates for superset generation. The algorithm starts with an initial population comprising of single item set. The fitness function computes the sum of the item set utility and the remaining utility and selects the prospective item sets for breeding. The children are generated by merging two prospective item sets. The selection and breeding process are repeated until there are no more chromosomes in the population for further breeding. The proposed system was able to identify high utility item sets in a single scan of the database. The genetic algorithm was able to converge from an initial population of item sets to high utility item sets. Extensive testing of the proposed system showed the optimal potential of the system verified to other similar state of the art system.
Systematic Design and Evaluation of a Citation Function Classification Scheme in Indonesian Journals

Classifying citations according to function has many benefits when it comes to information retrieval tasks, scholarly communication studies, and ranking metric developments. Many citation function classification schemes have been proposed, but most of them have not been systematically designed for an extensive literature-based compilation process. Many schemes were also not evaluated properly before being used for classification experiments utilizing large datasets. This paper aimed to build and evaluate new citation function categories based upon sufficient scientific evidence. A total of 2153 citation sentences were collected from Indonesian journal articles for our dataset. To identify the new categories, a literature survey was conducted, analyses and groupings of category meanings were carried out, and then categories were selected based on the dataset’s characteristics and the purpose of the classification. The evaluation used five criteria: coherence, ease, utility, balance, and coverage. Fleiss’ kappa and automatic classification metrics using machine learning and deep learning algorithms were used to assess the criteria. These methods resulted in five citation function categories. The scheme’s coherence and ease of use were quite good, as indicated by an inter-annotator agreement value of 0.659 and a Long Short-Term Memory (LSTM) F1-score of 0.93. According to the balance and coverage criteria, the scheme still needs to be improved. This research data was limited to journals in food science published in Indonesia. Future research will involve classifying the citation function using a massive dataset collected from various scientific fields and published from some representative countries, as well as applying improved annotation schemes and deep learning methods.
Adoption of Off-Line Signature Verification and Forgery Detection System Using Additive Fuzzy and TS Modelling Technique in Financial Auditing and Forensics Investigation﻿

This paper presents a robust signature verification and forgery detection system using Additive fuzzy and TS modeling technique. The features of various handwritten signatures are sampled with proper analysis and encapsulated to devise an effective verification system. Grid method was used to extract features angles for detection of forgeries and verification of genuine signatures. In financial Accounting, Auditing and Forensic Investigation, signature forgery could occur in various ways. This could be carried out on papers, sales documents such as invoices or inventory procurement requisition paper, title documents on landed property or other tangible assets. It is also perpetrated on payment authorization such as cheques, payment vouchers both in cash and on bills. During this exercise, the fraud perpetrators perfect their concentration on the surface paper, and trace the original signature from the mandate given earlier. It has been difficult to use accounting and auditing professions to track down financial fraud in Nigeria mostly with the problem of unearthing ingenious fraud. Exponential membership function was used to fuzzified the derived functions, and modified into structural parameters suitable to adapt to any possible variations that may result from handwriting styles and also to reflect any other factors due to scripting of a signature. The proposed system is tested on a large database of signatures obtained from 40 subjects.
Realization of author’s program of developing artistic abilities of schoolchildren on basis of continuity

The definition of abilities and their correct development act as one of the most important pedagogical tasks that require special attention, solution, approach, especially during the period of changes in the content of the educational process connected with its reform. According to the author, to update educational approaches related to improving the effectiveness of the educational process, it is necessary to pay attention to the development of author’s training programs. They will control planned and systematic activities of all participants in the educational process, regardless of the form of its organization. The article considers the main provisions of the author’s program that contribute to the development of the artistic abilities of schoolchildren in the conditions of modernization and reform of modern Russian education, including the changes caused by COVID 19. The author’s program is designed to expand the content of the concept of the continuity of secondary vocational and higher education. Its structure includes content components of general education and training of future designers and teachers of technology and design in multilevel professional education as potential directions for its development in the context of global challenges.
THE USE OF COMMUNICATIVE LANGUAGE TEACHING TO INCREASE THE STUDENTS’ MOTIVATION

This research aimed to improve the students’ motivation by using Communicative Language Teaching (CLT) Method in teaching English at the Third Semester of Dental Nurse Department Poltekkes Kemenkes Makassar. The teaching material was English for Specific Purposes and the subjects of the research consisted of two classes, they were class A as a control class and class B as an experimental class. This research used quasi-experimental design with nonequivalent control group design. The research indicated significant increasing of the students’ English learning motivation by using Communicative Language Teaching method for experimental class than applying Grammar Translation Method in control class. The score of Intrinsic, Integrative and Instrumental motivation, indicated it. The mean scores of students before treatment was 47.05 and after treatment was 81.78 in experimental, while in control class mean scores of students was 51.16 and after treatment was 51.89. The results of the research showed that Communicative Language Teaching (CLT) was effectively improving the students’ English Learning Motivation than using Grammar Translation Method (GTM). The table shows the different students’ mean score for both two Classes. For Experimental class, the students’ mean score improved significantly from 47.05 with standard deviation 7.491 to 81.78 with standard deviation 6.386. For Control class, the mean score of the students increase from 51.16, standard deviation 10.602 to 51.89, standard deviation 10.671.
Multiple object tracking and trajectory recognition based on deep learning

In automobile plastic parts factory, the workers need to check every automotive plastic assembly whether the fastening buckle is missing. This manual method is inefficient. To solve this problem, the space and channel attention mechanism is applied to YOLOv3-tiny network, which uses K-means clustering to find the best suitable size of anchor box. This algorithm has significantly improved detection accuracy in fastening buckles in comparison with original YOLOv3 algorithm, and the speed has increased by 3 times. Threshold segmentation and Hu comparison method are adopted to correct the confidence of the detection target with weight, so as to filter out the false target. Finally, the Lucas-Kanade optical flow method and the Delay-precision mechanism are used to realize the tracking and recognition of the fastening buckle of the automobile plastic assembly. In the experiment of fastening buckle video sequence, the MOTA(Multiple-Object Tracking Accuracy) of this algorithm is about 3.9%~7.6% higher than other algorithms. In 200 experiments, the recognition accuracy of automotive plastic components and fastening buckles reaches 100%.
Creating community and empowering underrepresented geospatial professionals at Women in Geospatial+

&amp;lt;p&amp;gt;Women in Geospatial+ is a professional network to promote gender-equality and diversity in the geospatial industry and academia. We are a vibrant and active community with more than 2,200 registered members from all over the world and over 14,000 followers on Twitter &amp;amp; LinkedIn. We have a range of activities all with the aim to inspire, unite and empower us as individuals but also us as a community.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This community brings together women and other people from underrepresented gender backgrounds in the geospatial field by providing a safe platform on Slack for open communication and exchange, which is especially crucial for networking during the COVID-19 era. We promote and foster the professional development of our members by sharing geospatial news and job vacancies as well as articles about diversity and tips about leadership and career development. We regularly run online career development workshops and panel discussions. Each week, we post a profile on LinkedIn to feature the work and achievements of women geospatial leaders. In 2020, our second international mentorship programme cohort grew to over 110 participants. We also launched a global speakers database which allows event organisers to search for and invite speakers from underrepresented gender backgrounds.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This presentation will highlight key activities of the Women in Geospatial+ network, tapping into some current facts and figures on diversity in the geospatial field. We will explain how to join the community and how you can contribute to its success and expansion. Let us change the status quo together by creating a strong network of Women in Geospatial+ leaders and changemakers.&amp;lt;/p&amp;gt;
Research on the Strategies of English Writing Teaching Reform under the Background of Big Data

With the further development of the new curriculum reform, English writing is required to be better. At the same time, with the rapid development of information technology, intelligent English writing class has become an inevitable trend. The reform of English writing teaching should be based on the background of information technology, and keep pace with the times, so as to inject new elements into the traditional English writing mode. This paper explores the strategies of English writing teaching reform under the background of big data. Firstly, this paper analyzes the influence of big data on English writing teaching. Secondly, it analyzes the reform measures of English writing teaching in the context of big data from various angles. It is hoped to offer some reference and help.
Activation of Cognitive Activities of 5-6 Grade Students on Lessons of Labor Learning

The article is devoted to the problem of activation of the cognitive activity of 5-6 grade students in the lessons of labor training.The purpose of the study was to substantiate the pedagogical conditions and develop methods for enhancing the cognitive activity of students in grades 5-6 in the process of studying the technology of household activities and self-service, as well as develop experimental testing of the methods' effectiveness.The experimental method of revealing the effectiveness of the introduction of pedagogical conditions for the activation of cognitive activity of secondary school students was used.The results allowed to clarify the essence of the concept of activation of cognitive activity, and to study the application of certain pedagogical conditions by teachers of labor training and students' attitudes to learning the basics of everyday life and self-service.The importance of the availability of educational material on household activities and self-service for its assimilation by students, the degree of importance of pedagogical conditions for activating cognitive activity for teachers of labor education, and the dynamics of changes in levels of cognitive activity of students during the study of technology of household activities and self-service were determined.The basic model of activation of the students' cognitive activity has been proposed.