Reviewerly/exHarmony huggingface dataset:
Evaluation of alternatives of cryptocurrency accounting

The emergence and popularity of the cryptocurrency has caused a great deal of controversy in the scientific community over market development opportunities, further use of virtual money, and its integration into traditional accounting systems. The global financial market creates many opportunities to use electronic and virtual money for various types of transactions, and the question of how to record cryptocurrency and related transactions in accounting remains relevant. The aim of the research is to evaluate the alternatives of cryptocurrency accounting. For the research methods of scientific literature analysis, comparison, interpretation, information systematization, generalization were used. Although various authors, national and international accounting standards regulators provide their insights and recommendations on cryptocurrency accounting, a unified system has not been formed yet. Currently are analysed such alternatives of cryptocurrency accounting as financial asset, intangible asset or inventory.
Cyber Security Awareness Among Secondary School Students in Malaysia

&#x0D; &#x0D; It is people’s choice to go online without worrying about their presence being at the stake. However, there are too many unknown cyber risks and threats of attack in different forms coming ahead. These cause their presence along with personal data are dangerously at risk. Moreover, the access to the cyber spaces are unlimited and unrestricted to all levels of ages. If this situation is ignored, it will create more unpredicted and new cybercrimes as well as more increasing the existing ones. This paper explores on understanding the level of cyber security situational awareness among secondary school students, their teachers as well as their parents in Malaysia. Both physical and online survey methods were deployed to administer the data collection exercise. The target groups were divided into three categories: students (secondary students aged 13-16 years old), teachers and parents. A different questionnaire set was designed for each category. The survey topics/areas included Internet and digital citizenship knowledge. Respondents were selected from certain areas throughout the Klang Valley area in Malaysia. The results show most of the respondents are aware of the cyber threats and risks of being in cyber space, but a very few of them take actions on security measures of being online. The findings and recommendations from this awareness study are fundamental to develop a model for secondary school students to understand the security risks and threats associated with the Internet throughout their years in school. Early exposure and awareness will help ensure healthy cyber habits among millennials and their environment in Malaysia. &#x0D; &#x0D;
Metaphor-Based Research For Studying Russian And Chinese Students’ Perception Of The University

The potential competition between the emotional and cognitive components of marketing communications is an important issue within the process of ad’s content designing and planning of brand communication system both for a commercial company, and for a modern university. Traditional research as a part of educational marketing is almost exceptionally verbal and linear, although there is often a serious mismatch between the verbal data and the multisensory language of the university’s brand communications. Therefore, a need for a technique to elicit students’ feelings, emotions and attitudes toward the university is significant. The modern projective and psychosemantic research techniques in education could provide vital information on students' perception of the university’s image. The article discusses the results of the Metaphor-Based research of Russian and Chinese students’ perception and attitude toward the university. The study was carried out on a base of the modified Zaltman Metaphor-Based technique at Peter the Great St. Petersburg Polytechnic University. To process the results, the semantic differential technique was used to reveal the unconscious attitudes of the respondents. The study showed that Russian and Chinese respondents perceive the university’s image as strong, active and evaluate it positively. The modified Zaltman Metaphor Elicitation Technique (ZMET) helps to reveal the visual and synonyms and to understand students’ thinking and behavior toward the university and its brand activities. The study results can be applied to the process of designing and honing communication activities of the university, aimed at the various groups of the students within the multicultural educational environment.
Evidence Based Education e didattica efficace: come integrare conoscenze metodologiche e tecnologiche nella formazione degli insegnanti

The health emergency caused by Covid-19 offers new opportunities to implement possible changes in schools to improve learning-teaching processes beyond the current situation. One of the most significant space concerns the possibility to rethink teacher training by integrating organically methodological and technological dimensions. Traditionally these dimensions have been treated as separated. In this paper the authors intend to bridge this gap by making use of the recent acquisitions of educational research, confirmed in the Evidence Based Education perspective. This research allows identifying fundamental principles of effective teaching working in the physical classroom as well as in the virtual one. In this context, the theory of cognitive load provides a useful connection for a valid didactic approach. The question the authors want to answer is the following: to integrate methodological and technological knowledge and skills adequately, how can a training course for teachers be organized and implemented? If on the one hand, the fundamental principles of effective teaching are the same, on the other the different implications in terms of risks and opportunities offered by the different communicative settings must be considered. Therefore it is necessary to look for a solution that highlights the common fundamental principles as well as the the specific criticalities and the potentialities that arise in the transition from teaching in the physical classroom to teaching in the virtual one.
Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles' path planning problem.The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints.The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem.A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem's resolution.To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature.These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem.An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones.Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.
Item Analysis of English Final Semester Test

Numerous studies have been conducted on the item test analysis in English test. However, investigation on the characteristics of a good test of English final semester test is still rare in several districts in East Java. This research sought to examine the quality of the English final semester test in the academic year of 2018/2019 in Ponorogo. A total of 151 samples in the form of students’ answers to the test were analysed based on item difficulty, item discrimination, and distractors’ effectiveness using Quest program. This descriptive quantitative research revealed that the test does not have good proportion among easy, medium, and difficult item. In the item discrimination, the test had 39 excellent items (97.5%) which meant that the test could discriminate among high and low achievers. Besides, the distractors could distract students since there were 32 items (80%) that had effective distractors. The findings of this research provided insights that item analysis became important process in constructing test. It related to find the quality of the test that directly affects the accuracy of students’ score.
Fostering Professional Competencies Of Students With The New Approaches In Higher Education

The article discusses the problem of the competency gap between employers' expectations and the actual competency profile of university graduates by focusing on the case of the Ural Federal University (UrFU, Russia). It is shown how this problem can be addressed through modern teaching practices aimed at enhancing the quality of education and increasing student motivation. The authors analyze the existing legislative framework of higher education in Russia and the current priority tasks of the university development. To meet these tasks, UrFU management devised a set of measures to modernize the education process and ensure adequate support to the faculty and students. The university is currently developing its own education standards to make its programs more flexible, promote cross-disciplinary interaction and adjust students' competency profile to the demands of the job market. The study puts a special emphasis on the use of online technologies and project-based learning to develop professional competencies and encourage a proactive approach to mastering new knowledge and practical skills. A special incentive program is implemented to maximize personal and professional growth of the university's faculty. One of the instruments applied to address the competency gap problem is the creation of the so-called 'Boiling Point' - a space for free exchange of ideas and collaboration between students and external experts from the business community. A conclusion is made that UrFU has been successful in developing a fundamentally new approach to organizing the education process and adjusting students' competency profile to the demands of the modern job market.
Privacy Preserving Data Publishing for Heterogeneous Multiple Sensitive Attributes with Personalized Privacy and Enhanced Utility

In recent years, personal data availability has become vast, which leads to the concept of Privacy-preserving. Privacy-Preserving is an essential issue in all research fields. Many privacy methods are available for privacy-preserving data publishing (PPDP); however, it suffers from a few drawbacks i) couldn't use on heterogeneous multiple sensitive attributes; ii) Customized sensitivity requirements are ignored. To make the model satisfy both criteria, we have proposed a Quasi-Identifier-Multiple heterogeneous sensitive attribute (QI-MHSA) generalization algorithm. Our first work in this paper is to apply vertical partitioning in the microdata and partitioning it into i) Quasi-identifier bucket (QIB) ii) Multiple heterogeneous sensitive attribute bucket (MHSAB). Second, we have applied k-anonymity in QIB to anonymize the quasi-identifiers and l-diversity in MHSAB to anonymize the different sensitive attributes (categorical and numerical). A Top-down generalization method is adopted to generalize the categorical and numerical attributes. Finally, a new approach has been implemented in the personalized privacy of sensitive attributes. A flag is set for both categorical and numerical sensitive attributes based on their sensitivity requirements in MHSAB. The generalization approaches differ according to the level of sensitivity requirement. Extensive implementation is done on two datasets to compare the algorithm's efficiency and prove that our model has a better balance between privacy and utility.
Application of knuth-morris-pratt algorithm on web based document search

The rapid development of information technology makes a number of documents in a university need to be kept in web-based storage to be utilized by the entire academic community. One example is a thesis document of Universitas Maarif Hasyim Latif students who do not have a web-based storage system so that the academic community has difficulty in finding lecture references at the university. System management is needed to be able to handle student thesis document reference searches. In searching for lecture references, a document search algorithm is needed, the algorithm that can be applied to the system made is Knuth-Morris-Pratt and the development of the system using Waterfall. The results of system development show that the system that has been made successfully knows the position of the words entered by the user so that it is very useful for the entire academic community in searching for thesis documents for students, especially students who are currently undergoing a thesis. has been guided by a supervisor so that the University can find out the ratio of a supervisor in conducting guidance to students.
Decentralizing Large-Scale Natural Language Processing with Federated Learning

Natural Language Processing (NLP) is one of the most popular and visible forms of Artificial Intelligence in recent years. This is partly because it has to do with a common characteristic of human ...
BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning

Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence.SemEval 2020 Task-11 aims to design automated systems for news propaganda detection.Task-11 consists of two sub-tasks, namely, Span Identification -given any news article, the system tags those specific fragments which contain at least one propaganda technique and Technique Classification -correctly classify a given propagandist statement amongst 14 propaganda techniques.For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach.For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features.Our results reveal that the linguistic features are the reliable indicators for covering minority classes in a highly imbalanced dataset.
Students’ creative abilities development in higher educational institution

main purpose of vocational training in higher education is training of a competitive highly qualified graduate. The requirements for his professional and creative level of development increase with increasing role of creative abilities in professional activities implementation. The purpose of the article is to describe experience of students’ creative abilities development. The authors identified the stages of students’ creative abilities development: organizational and preparatory; activity-evaluation; designing, during which students develop original projects, develop creative abilities and readiness for creative activity. The article describes the experience of project training in the branch of Tyumen industrial University in Surgut. The stages of project preparation Drilling rig, combined with installation of a rod depth pump (hereinafter –RSP) are analyzed in detail. The paper also provides examples of project tasks performed by students in the study of pedagogical courses. Criteria for evaluating creative abilities were developed. In order to achieve high results, students plan their own individual activities and team activities in detail, make non-standard decisions during the project, show flexibility in resolving conflicts and originality of ideas. Modern high school, acting in terms of competence approach, using project activities, contributes to developing an independent and creative personality of future professional.
DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction

We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction.We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model.Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1).
Robust Machine Reading Comprehension by Learning Soft labels

Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.We argue that hard labels limit the model capability on generalization due to the label sparseness problem.In this paper, we propose a robust training method for MRC models to address this problem.Our method consists of three strategies, 1) label smoothing, 2) word overlapping, 3) distribution prediction.All of them help to train models on soft labels.We validate our approach on the representative architecture -ALBERT.Experimental results show that our method can greatly boost the baseline with 1% improvement in average, and achieve state-of-the-art performance on NewsQA and QUOREF.
Controllable Abstractive Sentence Summarization with Guiding Entities

Entities are the major proportion and build up the topic of text summaries.Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries.In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities.Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary.Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations.Our model can also generate more novel entities with them incorporated into outputs directly.To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives.We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics.
Serverless Elastic Exploration of Unbalanced Algorithms

In recent years, serverless computing and, in particular the Function-as-a-Service (Faas) execution model, has proven to be efficient for running parallel computing tasks. However, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. The main challenge of executing this type of algorithms in the cloud is the difficulty to account for the computing requirements beforehand. This places a burden on scientific users who very often make bad decisions by either overprovisioning resources or inadvertently limiting the parallelism of these algorithms due to resource contention. Our hypothesis is that the elasticity and ease of management of serverless computing can help users avoid such decisions, which may lead to undesirable cost-performance consequences for unbalanced problem spaces. In this work, we show that with a simple serverless executor pool abstraction one can achieve a better cost-performance trade-off than a Spark cluster of static size and large EC2 VMs. To support this conclusion, we evaluate two unbalanced algorithms: the Unbalanced Tree Search (UTS) and the Mandelbrot Set using the Mariani-Silver algorithm. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users - i.e., no need for users to understand non-obvious system-level parameters and optimizations.
Train Once, and Decode As You Like

In this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based nonautoregressive models.Our approach works by repeatedly selecting positions and generating tokens at these selected positions.After being trained once, our approach achieves better or competitive translation performance compared with some strong task-specific baseline models in all the settings.This generalization ability benefits mainly from the new training objective that we propose.We validate our approach on the WMT'14 English-German and IWSLT'14 German-English translation tasks.The experimental results are encouraging.
Topic-driven Ensemble for Online Advertising Generation

Online advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users into visiting a corresponding webpage. Professional generation of banners requires creative and writing skills and a basic understanding of target products. The great variety of goods presented in the online market enforce professionals to spend more and more time creating new advertisements different from existing ones. In this paper, we propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. The important part of the approach is training on open data available online, which allows avoiding costly procedures of manual labeling. Collected open data consist of multiple subdomains with high data heterogeneity. The subdomains belong to different topics and vary in used vocabularies, phrases, styles that lead to reduced quality in adverts generation. We try to solve the problem of identifying existed subdomains and proposing a new ensemble approach based on exploiting multiple instances of a seq2seq model. Our experimental study on a dataset in the Russian language shows that our approach can significantly improve the quality of adverts generation.
Summarization of COVID-19 news documents deep learning-based using transformer architecture

Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.