Reviewerly/exHarmony huggingface dataset:
Formation of digital literacy of future teachers by means of cloud technologies

The problem and the aim of the study. The implementation of tutoring ideas that are extremely popular in the modern socio-economic situation in the digital educational space requires the teacher to move to a new level of professional skills, communication and information culture, network etiquette and experience in using software and hardware. To ensure the appropriate training of teachers, the development of the necessary personal qualities, improving the quality of education in higher education, new digital technologies are actively used: cloud services, interactive resources, etc. The purpose of the article is to investigate the features of the formation of digital literacy of future teachers by means of cloud technologies. Research methods. Theoretical analysis and generalization of scientific literature were used to identify the features of the training of future teachers, to determine the components of digital literacy. The method of pedagogization of information technologies, group interaction, mutual assessment and self-assessment are used to design and fill the "virtual classroom" with cloud service tools. The experiment involved 64 students of the Vyatka State University of the training program 44.03.01 Pedagogical education (bachelor level). The software tool is Google Classroom. As a method of statistical processing of the experimental data, the χ2 (chi-square) Pearson test was used. Results. In the experimental group, future teachers used Google Classroom tools to differentiate user access rights to content, organize and manage learning in a "virtual classroom" environment. The assessment of the levels of digital literacy formation was made and statistically significant differences in the qualitative changes that occurred in the pedagogical system were revealed, χ2obs. 2 &gt; χ2crit0.05 (7,07 &gt; 5,99). In conclusion, the features of the design and use of "virtual classrooms" implemented by Google Classroom tools for the formation of digital literacy of future teachers are summarized.
Evidence Selection as a Token-Level Prediction Task

In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim.Intuitively, the retrieval of the correct evidence plays a crucial role in this process.Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim.In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once.We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER.Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.
ON THE IMPLEMENTATION OF CERTAIN DIDACTIC PRINCIPLES IN THE ELECTRONIC INFORMATION AND EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY

The article defines the role of the electronic information and educational environment (EIEE) of the university in modern education. The advantages of the implementation of didactic principles in modern university training using integrative and competent approaches are considered. It is indicated that the electronic information and educational environment provides access to high-quality educational e-resources that have passed scientific and methodological expertise and creates conditions for solving the problem of searching for reliable educational data. ICT included in the electronic information and educational environment allow conducting all types of tutorials and attestations using remote educational technologies and e-learning. The content of the digital educational product, which is determined by pedagogical goals and didactic principles of higher professional training is discussed. The educational process in the EIEE should be carefully planned and provided with methodically proved educational content and contain a valid complex of methodological and assessment materials. It is underlined that the effectiveness of the EIEE application in the educational process depends on the quality of the electronic educational and methodological complex. The article deals with the didactic principles of training, which realization is facilitated by using electronic information and educational environment. It is noted that the most significant digital technologies for studying at the university are: virtual and augmented reality, artificial intelligence and the technology of electronic identification of the trainee. It is indicated that chat bots are effective to provide feedback and assistance in mastering the content of educational disciplines. Advantages of the visibility principle implementation in digital education with the use of means of virtual and augmented reality, as well as the principle of individualization of training using programs for determination of cognitive abilities level of trainees are described. It is noted that the EIEE allows the use of the educational technology of “flipped classroom or flipped learning”.
A Matrix Effect Correction Method for Portable X-ray Fluorescence Data

Portable X-ray fluorescence spectrometry (pXRF) is an analytical technique that can be used for rapid and non-destructive analysis in the field. However, the testing accuracy and precision for trace elements are significantly affected by the matrix effect, which comes mainly from major elements that constitute most of the matrix of a sample. To solve this problem, many methods based on linear regression models have been proposed, but when extreme values or outliers occur, the application of these methods will be greatly affected. In this study, 16 certified reference materials were collected for pXRF analysis, and the major elements most closely related to the elements to be measured were employed as correction indicators to calibrate the analysis results through the application of multiple linear regression analysis. Some statistical parameters were calculated to evaluate the correction results. Compared with the calibration data obtained from simple linear regression analysis without taking major elements into account, those corrected by the new method were of higher quality, especially for elements of Co, Zn, Mo, Ta, Tl, Pb, Cd and Sn. The results show that the new method can effectively suppress the influence of the matrix effect.
A data flow process for confidential data and its application in a health research project

The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data.We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK.We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK.Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.
Strategic academic leadership programs in Russia: Past and future

The article discusses the projects aimed to develop the national higher education system: “5-100” supporting regional flagship universities and the “Priority 2030” program, as well as the problems of their implementation and opportunities for participating universities to improve their results in terms of ensuring the constitutional right of the citizens for affordable and quality education. It has become evident that although the concentration of resources in the group of leading universities contributes to their entry into prestigious international academic rankings, it widens the gap between them and the bulk of other universities as regards the infrastructure, procurement and human resourcing that deteriorates the development of the educational system as a whole. The authors emphasize the necessity to consider the experience of implementing the “5-100” project in the future, in particular, when carrying out the “Priority 2030” project launched in 2021. The authors outline positive aspects of this project which can facilitate the development mechanisms for the regions in the Russian Federation as a result of the government funding and the creation of consortia with leading universities. The article also covers the key points during the tendering procedures of the Priority 2030 project, which in the future can play a significant role in the process of implementation of development programs for individual universities. The authors conclude that the use of the “5-100” project experience can improve the performance of the entire system of the national higher education.
Exploiting User Friendship Networks for User Identification across Social Networks

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.
The Impact of Covid 19 on Agile Software Development: A Systematic Literature Review

In 2020, the world changed due to the Covid 19 pandemic.Containment measures to reduce the spread of the virus were planned and implemented by many countries and companies.Worldwide, companies sent their employees to work from home.This change has led to significant challenges in teams that were co-located before the pandemic.Agile software development teams were affected by this switch, as agile methods focus on communication and collaboration.Research results have already been published on the challenges of switching to remote work and the effects on agile software development teams.This article presents a systematic literature review.We identified 12 relevant papers for our studies and analyzed them on detail.The results provide an overview how agile software development teams reacted to the switch to remote work, e.g., which agile practices they adapted.We also gained insights on the changes of the performance of agile software development teams and social effects on agile software development teams during the pandemic.
Knowledge-Guided Paraphrase Identification

Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem.Recently, BERT-like pretrained language models have been a popular choice for the frameworks of various PI models, but almost all existing methods consider general domain text.When these approaches are applied to a specific domain, existing models cannot make accurate predictions due to the lack of professional knowledge.In light of this challenge, we propose a novel framework, namely Knowing, which can leverage the external unstructured Wikipedia knowledge to accurately identify paraphrases.We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.After retrieving related outline knowledge, Knowing makes predictions based on both the semantic information of two sentences and the outline knowledge.Besides, we propose a gating mechanism to aggregate the semantic information-based prediction and the knowledge-based prediction.Extensive experiments are conducted on two public datasets: PARADE (a computer science domain dataset) and clinicalSTS2019 (a biomedical domain dataset).The results show that the proposed Knowing outperforms state-ofthe-art methods.
Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit

We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a "next hop prediction module" whose output is matched against page elements in a predicted article.Seeking evidence with the next hop prediction module continues to improve FEVEROUS score for up to seven hops.Label classification is trained on possibly incomplete extracted evidence chains, utilizing hints that facilitate numerical comparison.The system achieves .281FEVEROUS score and .658label accuracy on the development set, and finishes in second place with .259FEVEROUS score and .576label accuracy on the test set.
Informal Entrepreneurship Education: Overview of the Russian Field

This article analyzes the informal sector of entrepreneurial education — free “open” educational projects at the federal level in the context of broader trends in the development of education and society, including education’s ‘unbundling’. The search for information was carried out using the Internet, as a result 45 initiatives were discovered. The results show that the sector of entrepreneurship education is broad, but there are a large number of areas for improvement, in which universities can play an important role. In particular, this concerns elaborating and implementing a system for evaluating educational results, organizing monitoring of the effectiveness of such initiatives, including the analysis of success stories. In addition, a separate task is to expand the set of targeted programs for specific audiences (for example, unemployed), as well as to improve the content of such initiatives more deeply according to the specifics of the relevant target groups (for example, young mothers or older people).
PENGEMBANGAN APLIKASI ANDROID ‘AYO CUCI TANGAN’ UNTUK MENGAJARKAN KEBERSIHAN ANGGOTA TUBUH PADA MUATAN PELAJARAN IPA DI SEKOLAH DASAR

This study aims to (1) produce an android application 'let's wash your hands' to teach body hygiene in science lesson content in elementary schools (2) determine the value of product feasibility. This type of research is research and development. This study uses a 4d model. This model consists of four steps; (1) define, (2) design, (3) development, and (4) disseminate. Product validation is calculated based on the results of expert assessments. The results of this research and development are (1) an android application with the topic of 'washing hands' (2) the media developed has a good feasibility value. Based on these findings, it shows that the 'let's wash your hands' android application is feasible to use in learning.
Human identification and tracking using ultra-wideband-vision data fusion in unstructured environments

&lt;p class="Abstract"&gt;Nowadays, the importance of working in changing and unstructured environments such as logistics warehouses through the cooperation between Automated Guided Vehicles (AGV) and the operator is increasingly demanded. The challenge addressed in this article aims to solve two crucial functions of autonomy: operator identification, and tracking. These tasks are necessary to enable an AGV to follow the selected operator along his path. This paper presents an innovative, accurate, robust, autonomous, and low-cost operator real-time tracking system, leveraging the inherent complementarity of the uncertainty regions (2D ellipses) between ultra-wideband (UWB) transceivers and cameras. The test campaign shows how the UWB system has higher uncertainty in the angular direction. In contrast, in the case of the vision system, the uncertainty is predominant along the radial coordinate. Due to the nature of the data, a sensor fusion demonstrates improvement in the accuracy and goodness of the final tracking.&lt;/p&gt;
Modeling scientometric indicators using a statistical data ontology

Abstract Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data as a mean for providing flexible and quick access knowledge representation that supports indicator discovery, enquiring and composition. The semi-automatic generation and further storage of this linked data in the Neo4j graph database enabled an updatable and quick access model.
Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness

Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves.
On the algebraic structure of polycyclic codes

In this paper, we are interested in the study of the right polycyclic codes as invariant subspaces of Fnq by a fixed operator TR. This approach has helped in one hand to connect them to the ideals of the polynomials ring Fq [x]/?f)X)?, where f (x) is the minimal polynomial of TR. On the other hand, it allows to prove that the dual of a right polycyclic code is invariant by the adjoint operator of TR. Hence, when TR is normal we prove that the dual code of a right polycyclic code is also a right polycyclic code. However, when TR isn?t normal the dual code is equivalent to a right polycyclic code. Finally, as in the cyclic case, the BCH-like and Hartmann-Tzeng-like bounds for the right polycyclic codes on Hamming distance are derived.
The Contribution of Informatics to Overcoming the Covid-19 Fake News Outbreak by Learning to Navigate the Infodemic

Daily, people are being exposed to an enormous amount of Covid-19 information, but not all of it is reliable. This phenomenon seriously affects the public health policy effectiveness, because there is a lot of misleading or inaccurate information, which is spreading rapidly and makes it more difficult to restrict the pandemic. Healthcare informatics has emerged as a diverse and important new field. Healthcare informatics applications are becoming more and more popular and are providing easy access to new sources of knowledge. This way, the quality of patient care will improve and productivity will increase. However, people should also learn how to navigate this infodemic properly.