Reviewerly/exHarmony huggingface dataset:
Post COVID-19 Global Macrotrends in the pedagogical practice to achieve Student Outcomes-“ICACIT”

The suspension of the on-site educational process due to the temporary lockdown of Educational Institutions provoked scenarios with their due reactions and repercussions of emergency against the Global Crisis as a result of the COVID-19 virus spread.Since Perú is in the midst of this current crisis, the Education sector asked some questions based on ICACIT accreditation model -such as: Which global macrotrends can be applied in the context of a Post COVID-19 pedagogical practices, in order to achieve Student Outcomes (SOs) from ICACIT, the accreditation model?Three categories were identified based on the previous development: I) Global macrotrends related to the Education Sector; II) The Post COVID-19 pedagogical practice, as a role assumed by the professor involved in the different education spaces in accordance with the different didactive approaches (academicist, technological, cultural interpretative, sociocritical and socio-formative); and III) Student Outcomes (SOs) from ICACIT, the accreditation model.Global macrotrends were selected to be applied in The Post COVID-19 pedagogical practice that contribute in the accomplishment of 12 ICACIT SOs and were consequently classified into three areas: i) Crosscutting Macrotrend (public awareness), ii) the Macrotrend of Post COVID-19 pedagogical practices (Disrupting Education: the assessment of progress, harnessing innovation, multiple senses, co-creation, instant entrepreneurship, the User Experience business-focused model approach for education, and gamification), and iii) the Macrotrend of Support (Networking & Technology).
Teacher Learning through Technology-Enhanced Curriculum Design Using Virtual Reality

Background/Context The rapid development of virtual reality (VR) technology increases expectations regarding the potentials of using VR in the classroom and requires teachers to engage in professional development. Although there are recent studies that explore how this new technology can facilitate learning in classroom settings, little research has explored what constraints and affordances emerge during VR curriculum design and implementation and how a teacher's knowledge and beliefs change during this process. Purpose/Objective/Research Question/Focus of Study The purpose of this study is to explore one exemplary teacher's curriculum design processes and changes in knowledge, beliefs, and practices during the design and implementation of VR lessons in an elementary classroom. Population/Participants/Subjects The participant of this study is one teacher who works in a private elementary school in a metropolitan area of South Korea. The participant is an exemplary teacher with a unique combination of experience and training in pedagogy and technology. Research Design We used an intrinsic case study approach to collect and analyze data around the development of the participant's reflective practice, beliefs, and knowledge. Data Collection and Analysis During two iterations of design and implementation of VR lessons, we collected qualitative data in the form of the participant's written reflections, video recordings of his classroom teaching, field notes of observations, and follow-up interviews after each observation. Data were open-coded based on the interconnected model of teacher professional growth (IMTPG) and the framework of technological pedagogical content knowledge (TPACK). Findings/Results We observed changes occurring in the participant's TPACK, his beliefs about students, and his dispositions toward the use of VR. Most of the changes in the design phase appeared to inspire the participant's development of TPACK and ended with curriculum design using newly developed knowledge. The participant's enactment brought changes in instructional outcomes for both himself and his students, which then either altered his practice while teaching or changed/reinforced his knowledge and beliefs. Conclusions/Recommendations Our findings support a nonlinear, ongoing, and iterative approach to teacher change, as emphasized by the IMTPG. We add to this line of research by offering a detailed description of one teacher's learning process and development of TPACK. More important, this study focused on an exemplary teacher's design and implementation of VR and explored how certain barriers or enablers affected his design decisions by changing his knowledge, beliefs, and instructional practices. The findings of this intrinsic case study allow us to offer advice for teachers interested in integrating VR into their instruction.
Towards building a Robust Industry-scale Question Answering System

Industry-scale NLP systems necessitate two features.1. Robustness: "zero-shot transfer learning" (ZSTL) performance has to be commendable and 2. Efficiency: systems have to train efficiently and respond instantaneously.In this paper, we introduce the development of a production model called GAAMA (Go Ahead Ask Me Anything) which possess the above two characteristics.For robustness, it trains on the recently introduced Natural Questions (NQ) dataset.NQ poses additional challenges over older datasets like SQuAD: (a) QA systems need to read and comprehend an entire Wikipedia article rather than a small passage, and (b) NQ does not suffer from observation bias during construction, resulting in less lexical overlap between the question and the article.GAAMA consists of Attention-over-Attention, diversity among attention heads, hierarchical transfer learning, and synthetic data augmentation while being computationally inexpensive.Building on top of the powerful BERT QA model, GAAMA provides a ∼2.0%absolute boost in F 1 over the industry-scale state-of-the-art (SOTA) system on NQ.Further, we show that GAAMA transfers zero-shot to unseen real life and important domains as it yields respectable performance on two benchmarks: the BioASQ and the newly introduced CovidQA datasets.
Formation of the personal meanings of the study of mathematics among students of agricultural areas of training by means of contextual learning

The article presents the results of a study of the influence of contextual teaching methods on the development of personal meanings of teaching university students. Since it is education that provides real opportunities for the development of citizens, their self-realization and demand in modern society, which is being formed today under the influence of the processes of informatization and intellectualization of all spheres of public life, the problem of the formation of value orientations, the meanings of learning is especially relevant. The aim of the research is to find ways and means of forming the personal meanings of students in the study of non-core subjects for their direction, in particular mathematics. The process of teaching mathematics is modeled, providing a semantic orientation of teaching. Three stages of meaning formation with the characteristics of their content are considered. The effectiveness of using a contextual approach to the process of teaching mathematics to students of agricultural areas of training is shown.
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing

In this paper, we present a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing.In this architecture, we use an adversarial training method to align the space of different languages and use sentence level and word level parallel corpus as supervision information to align the semantic of different languages.Finally, we jointly train the multi-level alignment and semantic parsing tasks.We conduct experiments on a publicly available multi-lingual semantic parsing dataset ATIS and a newly constructed dataset.Experimental results show that our model outperforms state-of-the-art methods on both datasets.
Predictive and Causal Implications of using Shapley Value for Model Interpretation

Shapley value is a concept from game theory. Recently, it has been used for explaining complex models produced by machine learning techniques. Although the mathematical definition of Shapley value is straight-forward, the implication of using it as a model interpretation tool is yet to be described. In the current paper, we analyzed Shapley value in the Bayesian network framework. We established the relationship between Shapley value and conditional independence, a key concept in both predictive and causal modeling. Our results indicate that, eliminating a variable with high Shapley value from a model do not necessarily impair predictive performance, whereas eliminating a variable with low Shapley value from a model could impair performance. Therefore, using Shapley value for feature selection do not result in the most parsimonious and predictively optimal model in the general case. More importantly, Shapley value of a variable do not reflect their causal relationship with the target of interest.
Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Abstract Marine renewable energy has made significant progress in the last few decades. Even after making substantial progress, the cost of electricity produced by tidal turbines is high. Therefore, the current paper concentrated on reducing the cost of transportation and installation of the turbine by performing a model. Extreme Learning Machine and Support Vector Machines as well as Genetic Programming were applied to predict the performance of the turbine model by creating short‐term, multistep‐ahead prediction models to compute the performance of the H‐rotor vertical axis Folding Tidal turbine. The performance of the turbine was verified by a numerical study using the three‐dimensional approach for the viscous model with the unsteady flow. Statistical evaluation of the outcomes pointed out that advanced Extreme Learning Machine simulation made the assurance in formulating an innovative forecasting strategy for investigating the performances of the tidal turbine. This study shows that the application of the new procedure resulted in confident generality performance and learns faster than orthodox learning algorithms. In conclusion, the assessment indicated that the advanced Extreme Learning Machine simulation was capable as a promising alternative to existing numerical methods for computing the coefficient of performance for turbines.
Blockchain technology in elections: past, present and future

The purpose of the research is to use blockchain technology in the electoral process. The study was conducted on the example of blockchain technologies used in elections in the United States, Canada, Australia, Sierra Leone, Switzerland, Spain, Russia and other countries. The methodological basis of the research is based on the methods of comparative analysis and component analysis of definitions. Empirical methods of expert assessment and interviewing were used. The pros and cons of blockchain technologies and the possibility of using this innovative technology in elections of different levels are revealed. The advantages of inclusive blockchain technology include the mobility and accessibility of voting; minimizing the costs of organizing and conducting elections; de-bureaucratization by reducing the staff of election commissions; the possibility of excluding the impact on voters from participants in the electoral process; reducing the time for processing ballots and determining the results of voting; increasing the level of trust in electoral procedures on the part of citizens who usually do not participate in voting. Among the disadvantages of blockchain technologies, we can highlight: technical failures and hacker cyber-attacks; the possibility of hackers using data about voters, in case of hacking electronic databases; violation of the secrecy of voting.
Neural Networks on an FPGA and Hardware-Friendly Activation Functions

This paper describes our implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset-the Modified National Institute of Standards and Technology (MNIST) database.We also propose a novel hardware-friendly activation function called the dynamic Rectified Linear Unit (ReLU)-D-ReLU function that achieves higher performance than traditional activation functions at no cost to accuracy.We built a 2-layer online training multilayer perceptron (MLP) neural network on an FPGA with varying data widths.Reducing the data width from 8 to 4 bits only reduces prediction accuracy by 11%, but the FPGA area decreases by 41%.Compared to networks that use the sigmoid function, our proposed D-ReLU function uses 24 -41% less area with no loss to prediction accuracy.Further reducing the data width of the 3-layer networks from 8 to 4 bits, the prediction accuracies only decrease by 3 -5%, with area being reduced by 9 -28%.Moreover, FPGA solutions have 29 times faster execution time, even despite running at a 60× lower clock rate.Thus, FPGA implementations of neural networks offer a high-performance, low power alternative to traditional software methods, and our novel D-ReLU activation function offers additional improvements to performance and power saving.
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution

Now that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora.One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors.Plural anaphors with multiple antecedents-so-called split-antecedent anaphors (as in John met Mary.They went to the movies)-have not been widely studied, because they are not annotated in ONTONOTES and are relatively infrequent in other corpora.In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors.We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its performance by addressing the sparsity issue.To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks.Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline. 1(1)The Joneses i went to the park.They i had a good time.
Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.
Professional Action Competences Through Experiences With Augmented Reality

This empirical study presents university students‟ perceptions of professional competences through the use of such technologies as Augmented Reality (AR). The possibilities offered by this technology allow designing educational environments with different contexts that involve non-traditional practices. An online questionnaire was used to gather the data about their opinions of the use of the AR and the factors supporting the development of good professional practices. The sample consisted of 1920 students from the departments of Early Childhood Education, Primary Education and Pedagogy in the 2015/16 and 2016/17 academic years. To analyse the data, the statistical software SPSS 23.0TM was used. Descriptive, inferential and multilevel analyses were conducted. Three researchers analyzed and categorized the students‟ comments, suggestions, opinions and critics before and after the teaching intervention with the AR. Then, Cohen‟s kappa of 0,70 coefficient was used to calculate interrater reliability value. The results indicate that we must improve our learning design by incorporating simplicity and flexibility to it. On the other hand, we have managed to activate the professional skills of our students. We propose improvements in relation to the activity that should be intuitive, comfortable and easy. We add that personalized work is a very relevant aspect.
Semantic Segmentation for Aerial Images: A Literature Review

Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the world of research by conducting a review of Semantic Image Segmentation which aims to provide a big picture related to the latest developments related to Semantic Image Segmentation. In addition, we also provide the results of performance measurements on each of the Semantic Image Segmentation methods that we discussed using the Intersectionover-Union (IoU) method. After that, we provide a comparison for each semantic image segmentation model that we discuss using the results of the IoU and then provide conclusions related to a model that has good performance. We hope this review paper can facilitate researchers in understanding the development of Semantic Image Segmentation in a shorter time, simplify understanding of the latest advancements in Semantic Image Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future
Fluctuations of text complexity: the case of Basic State Examination in English

Text complexity as a research problem is equally relevant in linguistics and education since its solution provides an algorithm to match readers of certain categories to texts. Numerous studies have been conducted to identify quantitative and qualitative parameters that affect text complexity in ESOL. However, text complexity range within one proficiency level remains a research niche. The current study is aimed at identifying the range of text complexity fluctuations within one proficiency level and their appropriateness for readers. We conduct a multi-factor analysis and contrast 66 English texts for Basic State Examination (OGE) in readability, average sentence length, word length, number of verbs and nouns, cohesion and lexical diversity. The text features computed with the two online services, Text Inspector and Coh-Metrix, provide slightly different quantitative results though consistent in qualifying the range of text complexity fluctuations as high. The research findings refute the hypothesis of a linear nature of text complexity growth in the textbooks designed to increase students’ proficiency and confirm the lack of correlation between the revealed and claimed complexity of texts. The algorithm suggested by the authors can be useful for textbook writers and test developers selecting reading material for any proficiency level.
Image Recognition Based Autonomous Driving: A Deep Learning Approach

Autonomous vehicle (AV) is a broad field in artificial intelligence which has seen monumental growth in the past decade and this had a significant impact in bridging the gap between the capability the intelligence of human and the efficiency of machines.With millions of people losing their lives, or have being a victim of road traffic accidents.There is a need to find a suitable algorithm for a navigation system in an autonomous vehicle with the purpose of help mitigate the traffic rule violation that most human drivers make that lead leads to traffic accidents.With both researchers and enthusiasts developing several algorithms for AVs, this field has been split into several modules which continually broaden the scope of AV's technology.In this paper, we focus on the lane navigation which has an important part of the AV movement on the road.Here lane decision making is optimized by using deep learning techniques in creating a Neural Network model that focuses on generating steering commands by taking an image the road mapped out with lane markings.The navigation aid is a frontfacing camera mounted and images from the camera are used to compute steering commands.The end to end learning scheme was developed by Nvidia cooperation to train a model to compute steering command from a front-facing camera.The model does not focus on detecting the lane but only generating the appropriate command for steering AVs' on the road.This focus on one objective of the model helps in maximizing the potential of better accuracy in lane navigation of our AVs.The modeled car navigates through the designed lanes accurately with the level of intelligence the car shows in maneuvering through the lanes shows this method is more suitable in lane navigation.
INNOVATIVE TEACHING AND BEST PRACTICES: UKRAINIAN UNIVERSITIES

The purpose of this study was to identify the most used, popular innovative technologies, best pedagogical practices; comparison with the experience of EU universities and the creation of recommendations that will enable Ukrainian partners in the future to prepare applicants for higher education competitive in the labor market. This study was conducted to implement one of the goals of the project - effective cooperation between the pedagogical sector of higher education in Ukraine and the EU, school teachers and their associations. The Ukrainian participants of the project faced the task: to formulate an understanding of the necessary aspects for creating a profile of student competence in new disciplines, to build profiles of digital competence of teachers and lecturers based on the exchange of knowledge gained from EU partners. As part of the project tasks, an analysis of the level of educational programs and best pedagogical practices, issues of community importance, exchange of pedagogical experience between university teachers and university departments, technological features of university leadership support for teachers' innovation, student involvement in research and project activities, use of technical means by teachers of Ukrainian universities - project partners, current assessment systems and priority areas of educational culture.