Reviewerly/exHarmony huggingface dataset:
Spatial-Temporal Autoencoder with Attention Network for Video Compression

Deep learning-based approaches are now state of the art in numerous tasks, including video compression, and are having a revolutionary influence in video processing. Recently, learned video compression methods exhibit a fast development trend with promising results. In this paper, taking advantage of the powerful non-linear representation ability of neural networks, we replace each standard component of video compression with a neural network. We propose a spatial-temporal video compression network (STVC) using the spatial-temporal priors with an attention module (STPA). On the one hand, joint spatial-temporal priors are used for generating latent representations and reconstructing compressed outputs because efficient temporal and spatial information representation plays a crucial role in video coding. On the other hand, we also added an efficient and effective Attention module such that the model pays more effort on restoring the artifact-rich areas. Moreover, we formalize the rate-distortion optimization into a single loss function, in which the network learns to leverage the Spatial-temporal redundancy presented in the frames and decreases the bit rate while maintaining visual quality in the decoded frames. The experiment results show that our approach delivers the state-of-the-art learning video compression performance in terms of MS-SSIM and PSNR.
Unifying Structural and Semantic Similarities for Quality Assessment of DIBR-Synthesized Views

Multi-view 3D content is subject to distortions during the process of depth image-based rendering (DIBR). Studies have shown the unreliable performance of the well-established image quality assessment (IQA) models for evaluation of DIBR-synthesized views which surge the need for more effective IQA methods. Existing objective methods generally rely on the pixel-wise correspondences between the reference and distorted images, while view synthesis can introduce pixel shifts. Moreover, local DIBR distortions in disoccluded regions have different visual impacts from those of the conventional distortions, challenging the available IQA models. Here, we developed a Full-Reference (FR) objective IQA metric for synthesized views that perform significantly better than 2D IQA and the state-of-the-art DIBR IQA approaches. While the pixel misalignment between the reference and synthesized views is a big challenge for quality assessment, we deployed a Convolutional Neural Network (CNN) model to acquire a feature representation that inherently offers resilience to the imperceptible pixel shift between the compared images. Therefore, our model does not need accurate shift compensation. We deployed a set of quality-aware CNN features representing high-order statistics, to measure the structural similarity which is combined with a semantic similarity measure for accurate quality assessment. Moreover, prediction accuracy is improved by incorporating a visual saliency model acquired using the activations of the higher CNN layers. Experimental results indicate a significant performance gain (14.6% in terms of Spearman&#x2019;s Rank-order Correlation) compared to the top existing IQA model. The source code of the proposed IQA metric will be publicly available.
Methodical aspects of improving pedagogical skills of the teacher in a modern preschool institution

Науковий часопис НПУ імені М. П. Драгоманова this method.Assessment of the performance of situation analysis by trainee teachers can be carried out according to the following criteria: completeness of the analysis (selection of information useful for the analysis); infallibility of analysis (scientific validity of characteristics and estimates); systematic analysis (logical sequence of its implementation).Modeling allows in the walls of agricultural institutions of higher education to create situations close to those that take place in real professional activities, and thus prepare students for it, to develop various professional skills, to form not only cognitive but also professional motives and interests, to give a holistic ideas about future activities and its individual fragments.Training of psychological and industrial orientation involves techniques of psychodrama -an attempt by trainee teachers to play the same role as a teacher of agar disciplines, but in a different way -according to their own methodology; analysis -the teacher analyzes the problems with the training group, notes the points in which the participant departed from the planned plan, analyzes the reasons for improvisation and its performance; Teachers' self-study -learning tasks are embodied in self-taught classes when the trainee teacher performs these tasks under the guidance of a guardian, who thus confirms the gradual integration of the trainee into the professional activities of the teacher.Teachers of partial disciplines (partial modules) evaluate the results and give a partial assessment, these partial assessments are calculated as a percentage of the total assessment according to their participation in the whole module.
Comparative analysis of biometric measurements of face geometry obtained with the use of direct measurements, photoanthropometry and 3D scanning

The article presents methods of acquiring face morphometric data using direct measurements, 2D photoanthropometry and 3D scanning.The aim of the work was to compare the accuracy of the dimensions.In the study 15 students at the age of 22-24 years were examined.Subsequently 13 linear dimensions of face were measured with the use of a caliper on the test persons, as well as on 2D photos and on 3D models in the scanner software.Percentage differences of the obtained measurements were calculated in order to check the occurring deviations.In addition, Bland-Altman plots were created for a few selected parameters to assess the compliance of the measurement methods.Three-dimensional scanning has been found to be the optimal method of obtaining reliable anthropometric data.
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model

Coreference resolution such as for anaphora has been an essential challenge that is commonly found in conversational machine reading comprehension (CMRC).This task aims to determine the referential entity to which a pronoun refers on the basis of contextual information.Existing approaches based on pre-trained language models (PLMs) mainly rely on an endto-end method, which still has limitations in clarifying referential dependency.In this study, a novel graph-based approach is proposed to integrate the coreference of given text into graph structures (called coreference graphs), which can pinpoint a pronoun's referential entity.We propose two graph-combined methods, evidence-enhanced and the fusion model, for CMRC to integrate coreference graphs from different levels of the PLM architecture.Evidenceenhanced refers to textual level methods that include an evidence generator (for generating new text to elaborate a pronoun) and enhanced question (for rewriting a pronoun in a question) as PLM input.The fusion model is a structural level method that combines the PLM with a graph neural network.We evaluated these approaches on a CoQA pronoun-containing dataset and the whole CoQA dataset.The result showed that our methods can outperform baseline PLM methods with BERT and RoBERTa.
Cognitive investigation of the complex systems behavior using medical objects as an example

The article gives a brief review of mathematical methods developed to study the behavior of open and nonequilibrium physical systems. Some results of these methods are used in medical field. Three-valued logic, nonparametric and nonnumerical statistics applied to medical data processing. To compare nonnumerical sets, the authors have introduced a measure of their proximity, called indistinguishability. The proposed data processing method in open systems, the “logical symptoms method”, is provided by the author's computer programs complex and can be used in other fields of science.
Structure-From-Motion With Varying Principal Point

We consider the problem of structure-from-motion (SfM) for images with fixed calibration but varying principal point. This scenario occurs for archival imagery taken using historic glass plate and film cameras without fiducial markers, when images have been inconsistently cropped or when image plates are broken into multiple fragments.We derive initialisation and pose estimation methods and regularisation penalties tuned specifically for this scenario leading to a complete archival imagery SfM pipeline. We illustrate the performance of our methods on challenging real world examples from image archives. Specifically, we use archival images of the East coast of Greenland from the British Arctic Air Route Expedition (BAARE). This is of particular glaciological interest for measuring historic ice loss. We use a modern digital elevation model (ArcticDEM), masked to stable regions, as ground truth to evaluate our method.
Blockchain Privacy Data Access Control Method Based on Cloud Platform Data

With the improvement of digital informatization and openness of the smart grid, the security of all kinds of sensitive and private data in the power grid is inevitably facing severe threats and challenges.In this paper, we propose a privacy protection scheme for multidimensional data aggregation and access control in the cloud Internet of Things for smart grid.The scalable access control based on attribute encryption is used to determine the data security of power user data in the process of data information sharing in the blockchain under the large data traffic of the cloud platform, which is to achieve privacy protection and fine-grained access control for demand-side multidimensional data.By using the EBGN homomorphic encryption algorithm, the multidimensional data is encrypted, and each dimension can be decrypted separately using the corresponding private key.The multidimensional data aggregation at the gateway can aggregate the multidimensional data into cipher-text, and the control center does not need to decrypt the cipher-text data of each dimension, thereby simplifying the operation of the gateway and the control center and improving the security and privacy of the data.By encrypting the EBGN private key of each dimension through the cipher-text policy attribute encryption algorithm, the finegrained access control at the dimension level is realized.The experimental results show that the proposed method can effectively improve the security of private data in the aspect of multidimensional data privacy protection, thus reducing the security risk of multidimensional data being illegally accessed.The research in this paper can effectively reduce the communication overhead and computational complexity, reduce the computational cost, and is suitable for data security and privacy protection of smart grid cloud Internet of Things.
Exploring Deep Learning of English Vocabulary Acquisition in a Mobile Learning Environment

With the continuous development and extension of mobile learning technology, English vocabulary learning has become more extensive, personalized and communicative with the support of the mobile learning environment; at the same time, the creation of this environment has gradually become one of the important ways to learn English vocabulary. However, there are two sides to everything, and mobile learning is no exception. In the learning process, we find that English vocabulary learning becomes more fragmented in the mobile learning environment, and the relevant information is not effectively accessed by learners, lacking some deep learning thinking skills. Deep learning in education is a way of learning that aims to construct more meaningful learning, to understand, generalize, master and apply on the basis of memory, to critically accept and learn new knowledge in conjunction with pre-existing knowledge structures, to establish interconnections between knowledge, and through analysis, to make decisions and solve problems through learning. Based on the research on English vocabulary learning in mobile learning environment, this paper makes corresponding elaborations and suggestions on the problems and effective strategies for deep English vocabulary learning in mobile environment in order to improve learners' efficiency in English vocabulary learning, enhance the depth of learning and improve the thinking ability of learning.
Designing Mobile Learning Lesson Plan for English as a Foreign Language (EFL) Learners: ADDIE model

Mobile learning is the continuation development of e-learning.In mobile learning, teaching and learning activities are facilitated by mobile, handheld, and wireless connectivity to promote constructive learning behaviour both for teachers and students.This study describes how EFL teachers design and promote mobile learning in their lesson plan.designbased research was conducted to make this exemplary plan by using ADDIE model with a prototype paradigm.In this lesson plan design, the formative evaluation was repeatedly conducted to firmly conclude the best design result.From this evaluation, it is suggested that the use of mobile learning with ADDIE model in language learning can promote students' attention, motivation, and learning engagement.
CORRELATION OF THE CONCEPTS OF «PROFESSIONAL DIRECTION» AND «PROFESSIONAL PATRIOTISM»

Changes in the system of education actualize the problems of professional direction of students. New trades appear and bring new tasks for the teachers promoting the need to find new mechanisms of value approach to training future specialists and new ways to solve education problems. One of the tasks is to develop faith and dedication to the chosen profession. In this respect the article analyses essence of the concept of «professional direction» and «professional patriotism». The attitude of students to the chosen profession and understanding of the definitions of the above concepts are revealed.
THE ROLE OF BIOLOGY TEXTBOOKS IN THE FORMATION OF STUDENT’S ABILITY TO ESTABLISH CAUSE-AND-EFFECT LINKS

The article presents the results of the analysis of school biology textbooks for 9th grade, related to their ability to form cause – effect relationships in students of general secondary education in biology lessons. Causality is presented as a connection between events, where one event is the cause and the other is the consequence. The analysis of biology textbooks on the possibility of forming cause-and-effect relationships was carried out according to the following criteria: structuring the main content of the textbook, its systematization and classification; the use of terms and concepts that form the foundation of each subject, act as the main form of knowledge and thinking of students in the subject area; types of tasks as mandatory components of the methodological apparatus of the biology textbook. Analysis of biology textbooks gives grounds to conclude that each of them has certain features, forms, methods that contribute to the formation of students’ cause-effect relationships. However, in no textbook we have not observed a comprehensive construction of content based on the disclosure of cause and effect relationships of biological processes, natural phenomena. The problem of selection and selection of the main, establishing causal links between the structure and function of objects and processes in biology textbooks needs to be studied and solved. Building the content of the textbook on the basis of causal relationships will contribute to the formation of causal thinking, more effective study of the subject, its unloading, the ability of students to correlate causal relationships in natural processes with their own lives.
Building a Tontine Simulation in R

Abstract In this chapter I explain the core of the (basic, version 1.0) modern tontine simulation algorithm and provide R-scripts that can be used to generate forecasted values for what I have called the Modern Tontine (MoTo) Fund.
A Brief Concept on Machine Learning

Machine learning is a subset of AI. It’s a research project aimed at gathering computer programs capable of performing intelligent actions based on prior facts or experiences. Most of us utilize various machine learning techniques every day when we use Netflix, YouTube, Spotify recommendation algorithms, and Google and Yahoo search engines and voice assistants like Google Home and Amazon Alexa. All of the data is labeled, and algorithms learn to anticipate the output from the input. The algorithms learn from the data’s underlying structure, which is unlabelled. Because some data is labeled, but not all are, a combination of supervised and unsupervised techniques can be used.
Condition Assessment of Cultural Heritage Sites Using Semantic Bim Models

The paper reports the results of a research project funded by the University of Ferrara (FIR 2020 Program) and aimed at developing and testing some achievements of the project "INCEPTION - Inclusive Cultural Heritage in Europe through 3D semantic modelling" (H2020 EC funded research project).The tests carried out on the INCEPTION platform functions, outcome of the research project and subsequently implemented by the homonymous spin-off, have shown the potential of semantic-BIM technologies in the field of Cultural Heritage sites maintenance.Novel and innovative applications grounded on the platform core functions include:tools and protocols for the management of inspection procedures supported by information and instructions provided by the platform;assessment, based on inspection data, of the condition of the site and/or individual parts based on a "condition scale";access to constantly updated condition information, shown on the virtual model as linked to the objects of the BIM model;remote analysis and evaluation of site condition data through interaction, in a virtual environment, with 3D model "objects".The paper presents the main elements of the ongoing experimentation: the matrixes for the automatic analysis of the detected data and the assignment of the "condition score", the structure of the ontology with which the semantic tools of the platform operate, the workflows for data loading phases such as: in situ inspections, remote monitoring, planning of interventions.The tests on features and procedures reported in the paper have been carried out on the 3D model of the church of Santa Maria delle Vergini in Macerata, as part of a wider collaboration between the INCEPTION spin-off company and the ICCD (Istituto Centrale per il Catalogo e la Documentazione) of the MIC (Ministero della Cultura).
Analysis of user interaction on web pages in organizations located on the border between Ecuador and Colombia

Communication through web pages has created a change in the interaction with users. This change in the financial institutions of a region and those located on the border between Ecuador and Colombia, arouses the interest of this study, since it was hypothesized that the best-known institutions in the city of Tulcán do not have significant differences in their web pages. A survey was carried out with clients of the two institutions that have used the website, organizing the survey in four aspects: personalization flow, participation functionality, Prediction feedback and Peer to Peer loyalty. The results using the student's t test show that the two institutions analyzed do not present significant differences in their Web pages.
Early Literacy Practice in Kindergartens in West Sumatra Indonesia

This study examines the practice of introducing literacy in Kindergarten in rural areas in the city of Solok, West Sumatra province, Indonesia.From the literature study, it shows that there is still limited research on literacy development practices in rural areas in West Sumatra.The research question that will be investigated is: "how do teachers involve and introduce and engage literacy to children''.This study uses a qualitative approach and the data were collected by observation, document analysis and teacher interviews.Data from the study revealed that in the rural area of South Solok Regency in West Sumatra, it was found that teachers developed literacy in Kindergarten with various methods and media.The study shows that literacy activities are carried out dominantly through playing and storytelling activities.The media used in literacy development are printed media such as books and pictures.However, using technology in developing literacy is still limited.Teachers prefer to use printed media such as books, picture cards in introducing literacy to children rather than using technology.Lack of availability of ICT technology is shown as the obstacle to using ICT for literacy activities in the kindergartens.The Movement of Ministry of Education with literacy program (Gerakan literasi Sekolah) are less supported by computers and ICT facilities lead to limited taught technology literacy in Solok region therefore it is recommended that the stakeholders prioritize rural areas to be considered in the provision of assistance for the development facilities to support literacy programs since early childhood by providing aid and facilities for ICT technology in kindergartens.
Development of a CPU-GPU heterogeneous platform based on a nonlinear parallel algorithm

Abstract In order to seek a refined model analysis software platform that can balance both the computational accuracy and computational efficiency, a CPU-GPU heterogeneous platform based on a nonlinear parallel algorithm is developed. The modular design method is adopted to complete the architecture construction of structural nonlinear analysis software, clarify the basic analysis steps of nonlinear finite element problems, so as to determine the structure of the software system, conduct module division, and clarify the function, interface, and call relationship of each module. The results show that when the number of model layers is 10, the GPU is 210.5/s and the CPU is 1073.2/s, and the computational time of the GPU is significantly better, with an acceleration ratio of 5.1. For all the models, the GPU calculation time is much less than that of the CPU, and when the number of model degrees of freedom increases, the acceleration effect of the GPU becomes more obvious. Therefore, the CPU-GPU heterogeneous platform can more accurately describe the nonlinear behavior in the complex stress states of the shear walls, and is computationally efficient.
Application for a Waste Management via the QR-Code System

This research aims in developing an application for the waste management via the QR code system: 1) to study the quality of the application and 2) to study the satisfaction of users of the application by using the system development life cycle (SDLC) principle.There were 388 people of sample groups in this research which consisted of community leaders, village health volunteers, youth and the general public of Ban Yang Subdistrict, Mueang Buriram District, and Buriram Province.The research instruments were the application for a waste management via the QR code system, Application Quality Assessment Form, and the application satisfaction questionnaire.The statistics used in the data analysis were mean and standard deviation.The results of the research revealed that there were three main functions of the application for a waste management via the QR code system as follows: 1) The quality assessment of the application in all aspects at a high level (Mean = 4.41, S.D.=0.10).2) Study of satisfaction of the application users in all aspects at a high level (Mean = 4.42, S.D. = 0.45).3) The application of waste management application via the QR code system allowed group members in the community to reduce the process of managing household waste more conveniently and create a positive attitude in using waste to elevate through the work of the group members in the community.