Reviewerly/exHarmony huggingface dataset:
Factors of successful work in school during COVID-19 pandemic in Latvia

During the COVID-19 pandemic, teachers faced the challenge of becoming good educators without face-to-face contact with their students. The main objective of this study was to explore the most effective teachers’ work methods by looking at technological resources and applied teaching methods used during the COVID-19 pandemic in spring 2020 in Latvia. We looked for differences in pedagogical approaches and methods to identify the best distance teaching practices. A self-assessment questionnaire was developed to assess six sub-domains of teachers’ success in distance education: digital skills and attitudes towards acquiring and using new technologies in the classroom; perceptions and attitudes towards distance teaching; necessary individual and institutional support; student engagement and communication; opinions on success factors in distance teaching; and teaching forms used in distance education. Teachers' answers were collected in May–June 2020 (N = 1,543). In addition, teachers were asked to evaluate their digital skills before and after distance teaching. Most of the respondents (60.8%) evaluated their own work in distance teaching as ‘very good/excellent’ or ‘rather good/good’ (30.4%). The number of teachers that evaluated their digital skills as ‘very good/excellent’ increased from 506 to 763 after their first experience of distance teaching. We observed statistically significant relationships of this parameter with better digital skills (p &lt; 0.01). Of the different methods that can be used in distance education, the most successful teachers frequently used students’ individual work and interactive methods of teaching. The article concludes that training in the use of technologies and in basic principles of digital pedagogy is essential for a positive attitude towards distance teaching.
Historical aspects of the problem of future teachers’ scientific-research culture formation

The article substantiates the topicality of the research problem, proves that a future teacher's scientific-research culture is becoming an important phenomenon in the context of modern ideas about culture under the conditions of fundamentalization of education, variable and polycultural character of educational systems, intensive growth of volumes of scientific and pedagogical information, frequent changes in scientific paradigms and pedagogical technologies, rapid renewal of scientific knowledge system.On the basis of narrative, historical-genetic and chronological methods the historical aspects of the problem of formation of future teachers' scientific research culture are covered.The coverage creates a methodological basis for its solution, makes it possible to trace the dynamics of the problem development.It is concluded that still in the eras of Ancient Civilizations and Classical Antiquity the requirement for educating a person capable of searching for the truth, who is able to "learn great-mindnedness", came to the fore.The Renaissance Period was characterized by the tendencies aimed at raising the status of an educated person capable of independent thinking, critical judgment, research, self-education, self-cognition.The main requirements of pedagogical education of the Enlightenment Period are the cultivation of scientifically meaningful knowledge, activation of a person's mental forces, his self-disclosure through scientific knowledge, the evolution of pedagogical training of teachers, at the core of which there is scientific work.The main slogan of pedagogical developments of the first half of the XX century was to apply heuristic, research, and experimental methods as the means of formation of thinking skills, intellectual activity, and self-education.The second half of the twentieth century was marked by the systematization and generalization of knowledge on the methodology and methods of pedagogical research, organizational and practical principles of research training in secondary and higher school.However, the systematic study of various aspects of teachers' scientific research culture, the issues of pedagogical science methodology become especially important only at the beginning of the XXI century.
Style as Sentiment Versus Style as Formality: The Same or Different?

Unsupervised textual style transfer presupposes that style is a coherent and consistent concept and that style transfer approaches will generalise consistently across different domains of style. This paper explores whether this presupposition is appropriate for different types of style. We explore this question by comparing the performance and latent representations of a variety of neural encoder-decoder style-transfer architecture when applied to sentiment transfer and formality transfer. Our findings indicate that the relationship between style and content shifts between these different domains of style: for sentiment, style and content are closely entangled; however, for formality, they are less entangled. Our findings suggest that for different types of styles different approaches to modeling style for style-transfer are necessary.
Recent Advances in Earthquake Engineering—An Introduction

This chapter summarizes the contents of the book volume on Recent Advances in Earthquake Engineering which discusses various relevant topics such as Seismic ground motion studies, Structural and geotechnical vulnerability assessment, Retrofitting and rehabilitation, Review of building codes, as well as more recent technological introduction of geospatial tools and geographical information systems. Cascading disasters such as fire hazards following an earthquake are also discussed in this volume.
Development of the methods for the class teacher effectiveness assessment

The problem of developing a methodology for assessing the effectiveness of the class teacher’s educational activity and the possibility of its implementation in the context of an educational establishment has been getting more vital. The developed methodology for assessing the class teacher performance efficiency was tested on the basis of four educational establishments in Nizhny Novgorod, which are the clinical internship bases to train future teachers and class teachers at Minin University (Nizhny Novgorod), who were experts carrying out the examination of the methodology for assessing the performance efficiency according to the proposed indicators. The developed methodology for assessing the class teacher performance efficiency can be used in the practice of educational organizations to develop a system for assessing the encouragement of the class teacher performance, supervising his professional growth, developing individual trajectories of professional improvement, analytical conclusions, and expertise.
Project Implementation Decision Using Software Development Life Cycle Models: A Comparative Approach

Selection of a suitable Software Development Life Cycle (SDLC) model for project implementation is somewhat confusing as there are a lot of SDLC models with similar strengths and weaknesses. Also, the solutions proffered among the researchers so far have been the qualitative comparative analysis of SDLC models. Hence, this paper proposes a comparative analysis of SDLC models using quantitative approach in relation to strengths and weaknesses of SDLC models. The study adapted comparative analysis and Software Development Life Cycle (SDLC) models features’ classification using ten characteristics such as project complexity, project size, project duration, project with risk, implementation/initial cost, error discovery, associated cost, risk analysis, maintenance and cost estimation. A quantitative measure that employs online survey using experts in software design and engineering, project management and system analysis was carried out for the evaluation of SDLC models. Purposeful Stratified Random Sampling (SRS) technique was used to gather the data for analysis using XLSTAT after pre-processing, taking into consideration both benefit and cost criteria. The overall performance evaluation showed that Spiral-Model is the best followed by V-Model and lastly Waterfall Model with comparative values of 38.63%, 35.76% and 25.61% respectively. As regards cost estimation, Waterfall Model is the most efficient with value of 41%, then V-Model with 31% and lastly Spiral Model with 28%. V-Model has great error recovery capability with value of 45% which is closely followed by Spiral Model with 37% and lastly Waterfall Model with 18%. The study revealed that, a model with efficient risk assurance does not guarantee efficient cost management. In the future work, more characteristics regarding SDLC models shall be considered.
Construction and Analysis of Scientific and Technological Personnel Relational Graph for Group Recognition

The key to the in-depth management of science and technology is to model the behavior characteristics of scientific and technological personnel and then find groups by analyzing the diverse associations among them. Aiming at the analysis of team relationship among scientific and technological personnel, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the entity and relationship recognition and extraction are performed on the structured and unstructured source data to construct a relational graph. An improved frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analyzing the data in the relational graph. In this paper, the proposed method was experimented on both open and private datasets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.
[Causal relationship between physical exercise and risk of ischemic stroke recurrence based on the potential outcome theory].

To explore the causal relationship between physical exercise and ischemic stroke recurrence using a multiple imputation approach based on the potential outcomes framework.We collected data from 636 patients who were diagnosed with first-ever ischemic stroke between July, 2010 and December, 2018 at West China Hospital, Sichuan University.All the patients had motor ability and were followed up at least for 1 year.According to the intensity of physical exercise, the patients were divided into low-level physical exercise (LPE) group (n=244) and moderate-to high-intensity exercise (MHPE) group (n=392).With both the result and the time of recurrence as the potential outcomes, a multiple imputation method was used to impute the missing values based on a predictive matrix.Based on the imputed datasets, a causal inference model was built to estimate the average causal effect of physical exercise on the risk of stroke recurrence.Among the enrolled patients, 148 experienced recurrent stroke with a median recurrence time of 24.0 months and a cumulative recurrence rate of 23.3%during follow-up.As there were no outliers and the marginal distributions of the potential outcomes were basically consistent, both the Strip plots and Kenel density plots indicated that the imputed values could have been plausible measurements if they had not been missing.The average causal effect (ACE) of physical exercise on stroke recurrence was 0.578 (95%CI: 0.186-0.970, P=0.012).There is a causal relationship between physical exercise and the risk of ischemic stroke recurrence, indicating the value of moderate or high-level exercise after stroke in reducing the risk of stroke recurrence.
Black-Box Audio Adversarial Example Generation Using Variational Autoencoder

Automatic speech recognition (ASR) applications are ubiquitous these days. A variety of commercial products utilize powerful ASR capabilities to transcribe user speech. However, as with other deep learning models, the techniques underlying ASR models suffer from adversarial example (AE) attacks. Audio AEs resemble non-suspicious audio to the casual listener, but will be incorrectly transcribed by an ASR system. Existing black-box AE techniques require excessive requests sent to a targeted system. Such suspicious behavior can potentially trigger a threat alert on the system. This paper proposes a method of generating black-box AEs in a way that significantly reduces the required amount of requests. We describe our proposed method and presents experimental results demonstrating its effectiveness in generating word-level and sentence-level AEs that are incorrectly transcribed by an ASR system.
Literature Review on Information Filtering Methods in Recommendation Systems

This article presents a review of the literature on the information filtering methods used in current Recommender Systems, with an emphasis on recommendation Systems based on content, and those based on collaboration. The study starts from the classic taxonomy of filtering methods: content-based and collaboration-based, their mechanisms are explored, advantages and areas of opportunity are cited, and knowledge-based filtering is also incorporated. It concludes with an easy-to-understand literary review that seeks to provide the reader with an overview of the classic mechanisms behind Recommendation Systems, and some of the techniques widely used in the models. The study of modern techniques that could be used to improve the recommendations in the different types of filtering, such as those that use mixed approaches and those that use knowledge graphs, is left in perspective.
Educational Resources for Inclusive Education

The article provides a theoretical grounding for the use of resources that can support educational activities at inclusive schools. We designate those techniques that help include students in the decision-making process, teach the principles of cooperation, and develop teamwork skills. We outline the features of the third space approach, which can be used to create a special environment for learning and development at the inclusive school.
Detection of Depression and Mental illness of Twitter users using Machine Learning

Today Micro-blogging has become a popular Internet-user communication tool. Millions of users exchange views on different aspects of their lives. Thus micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Due to the recent emergence of micro blogging, there are a few research works devoted to this subject. We concentrate in our paper on Twitter, one of the prominent micro blogging sites to analyze sentiment of the public. We'll demonstrate, how to gather real-time twitter data for sentiment analysis or opinion mining purposes, and employed algorithms like Term Frequency - Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes ( MNB). We are able to determine positive and negative sentiments for the real-time twitter data using the above chosen algorithms. Experimental evaluations below shows that the algorithms used are efficient and it can be used as a application in detection of the depression of the people. We worked with English in this article, but for any other language it can be used.
A Reproduction Study of an Annotation-based Human Evaluation of MT Outputs

In this paper we report our reproduction study of the Croatian part of an annotation-based human evaluation of machine-translated user reviews (Popovic, 2020). The work was carried out as part of the ReproGen Shared Task on Reproducibility of Human Evaluation in NLG. Our aim was to repeat the original study exactly, except for using a different set of evaluators. We describe the experimental design, characterise differences between original and reproduction study, and present the results from each study, along with analysis of the similarity between them. For the six main evaluation results of Major/Minor/All Comprehension error rates and Major/Minor/All Adequacy error rates, we find that (i) 4/6 system rankings are the same in both studies, (ii) the relative differences between systems are replicated well for Major Comprehension and Adequacy (Pearson’s > 0.9), but not for the corresponding Minor error rates (Pearson’s 0.36 for Adequacy, 0.67 for Comprehension), and (iii) the individual system scores for both types of Minor error rates had a higher degree of reproducibility than the corresponding Major error rates. We also examine inter-annotator agreement and compare the annotations obtained in the original and reproduction studies.
REALIZATION OF POTENTIAL OF PERSONALLY ORIENTED DIDACTICS IN FORMATION OF METHODOLOGICAL CULTURE OF FUTURE TEACHERS OF MUSIC ARTISTS

The article proves the importance of formation of methodical culture in future teachers of music art and the prospects of using the potential of personality-oriented didactics in this process.The distinctive features of personality-oriented didactics are identified and analyzed and the essence of phenomena (communication, pedagogical communication, interaction, pedagogical interaction) that determine its value essence and are the core for all its components is characterized.Methodological tools of personality-oriented didactics (project method, emotional stimulation of learning, "Case-stady", debates, method of "inverted learning", synectics, discussions, method of generative learning, heuristic conversations, method of self-reference, pedagogical council, etc.) are revealed and characterized.leading idea -to direct the professional training of students -future teachers of music to the formation of methodical culture.
Function to Flatten Gesture Data for Specific Feature Selection Methods to Improve Classification

Gestures are pieces of information with characteristics such as: multiple and chronologically linked samples with different length.The gesture characteristics mentioned before make classification, of this type of data, a challenging task.We studied the effects of flattening gesture data.We proposed a function to represent gestures in a flat format taking in consideration the evolution sense they possess.The function's main goal is to compare gestures intra class to spot differences.This function is described step by step and then its outcome is used as input to two feature selection methods (Bayesian network / Markov blanket and Logical Combinatorial to Pattern Recognition).After, with the subsets obtained, we trained Hidden Markov Models machines.We found that applying our methodology to gesture data, the subset of attributes obtained (feature selection) were able to classify with accuracies of 0.88 and 0.87 of a maximum of 0.90.The maximum accuracy was obtained from an exhaustive classification exercise we performed in order to compare our results.These findings suggest that our methodology can be applied over raw data (gesture data or any chronologically linked data) without the need of experts to transform data (i.e.feature extraction).
Electronic Glove for Troops to Interpret Hand and Arm Gestures in Close Combat Scenario

In a close combat situation several types of non-verbal communication are available. However these signals have limits of range and reliability, particularly when line of sight is disrupted. This paper proposes the system for troops to interpret hand and arm military gestures applicable in close combat scenario. In the proposed system, signals are transmitted through secured Bluetooth connections and interpreted at the receiver end. k-NN algorithm, Lookup Table (LuT) and Decision Tree algorithm are used to determine the exact classification of the gestures. This paper presents a system keeping only one fellow trooper in picture and reported 94.6 percent accuracy of the military gestures interpretation.
RETRACTED ARTICLE: Big data VR system oriented to cloud services in Spanish cultural video simulation application

The data volume of cloud service big data is huge, usually more than 10 TB, and it has even jumped to the petabyte level. Big data VR has a variety of data, not only structured, but also semi-structured, unstructured and interactive. In the Internet multimedia era, the output of images, sounds, videos and other data is amazing, so semi-structured and unstructured data are very important to a large extent. However, the main data of cloud services is not only a huge amount of data, but also has its diversity. The speed of big data VR is not only the speed of big data generation, but also the speed of processing. According to data, the amount of global data is growing at a rate of 50% per year, far exceeding the development speed of computer design. Therefore, processing and analyzing large data should be faster. Another way of spreading Spanish culture is non-governmental cultural exchanges. For example, some Chinese and Western art festivals held between China and Spain. China will also have some non-governmental cultural organizations about Spain, which can spread Spanish culture. Video simulation is a new type of video carrier, which has the characteristics of sound and image alignment, giving users a sense of in-depth immersion. Compared with traditional video images, virtual reality requires a higher bit rate, and also takes up more network bandwidth and storage space. While giving users a high-quality experience, how to optimize video quality and low bit rate video encoding is video simulation, one of the keys to rapid development, which is also the focus of this article.
Exploring Input Representation Granularity for Generating Questions Satisfying Question-Answer Congruence

In question generation, the question produced has to be well-formed and meaningfully related to the answer serving as input. Neural generation methods have predominantly leveraged the distributional semantics of words as representations of meaning and generated questions one word at a time. In this paper, we explore the viability of form-based and more fine-grained encodings, such as character or subword representations for question generation. We start from the typical seq2seq architecture using word embeddings presented by De Kuthy et al. (2020), who generate questions from text so that the answer given in the input text matches not just in meaning but also in form, satisfying question-answer congruence. We show that models trained on character and subword representations substantially outperform the published results based on word embeddings, and they do so with fewer parameters. Our approach eliminates two important problems of the word-based approach: the encoding of rare or out-of-vocabulary words and the incorrect replacement of words with semantically-related ones. The character-based model substantially improves on the published results, both in terms of BLEU scores and regarding the quality of the generated question. Going beyond the specific task, this result adds to the evidence weighing different form- and meaning-based representations for natural language processing tasks.
Extending Neural P-Frame Codecs for B-Frame Coding

While most neural video codecs address P-frame coding (predicting each frame from past ones), in this paper we address B-frame compression (predicting frames using both past and future reference frames). Our B-frame solution is based on the existing P-frame methods. As a result, B-frame coding capability can easily be added to an existing neural codec. The basic idea of our B-frame coding method is to interpolate the two reference frames to generate a single reference frame and then use it together with an existing P-frame codec to encode the input B-frame. Our studies show that the interpolated frame is a much better reference for the P-frame codec compared to using the previous frame as is usually done. Our results show that using the proposed method with an existing P-frame codec can lead to 28.5%saving in bit-rate on the UVG dataset compared to the P-frame codec while generating the same video quality.