Reviewerly/exHarmony huggingface dataset:
A VIEW ON THE LEVEL OF READING STRATEGIES OF THE SELECTED GROUP OF FIFTEEN YEARS OLD UKRAINIAN PUPILS

The paper presents results of the quantitative research on the level of reading strategies of fifteen years old pupils from Kyiv and Chernihiv region and their comparison with the results of Czech pupils of the same age (Vicherková, 2018). The research sample consisted of 121 subjects. The research data were obtained through the structured questionnaire. The main conclusion of the research is that Ukrainian pupils consider themselves reading literate. However, the fact contradicts the actual level of their reading strategies. The questionnaire items categorisation delimited the main factors influencing the fifteen years old Ukrainian pupils' level of reading strategies as well as the reading strategies level of the fifteen years old Czech pupils.
Understanding the perceived quality of video predictions

The study of video prediction models is believed to be a fundamental approach to representation learning for videos. While a plethora of generative models for predicting the future frame pixel values given the past few frames exist, the quantitative evaluation of the predicted frames has been found to be extremely challenging. In this context, we study the problem of quality assessment of predicted videos. We create the Indian Institute of Science Predicted Videos Quality Assessment (IISc PVQA) Database consisting of 300 videos, obtained by applying different prediction models on different datasets, and accompanying human opinion scores. We collected subjective ratings of quality from 50 human participants for these videos. Our subjective study reveals that human observers were highly consistent in their judgments of quality of predicted videos. We benchmark several popularly used measures for evaluating video prediction and show that they do not adequately correlate with these subjective scores. We introduce two new features to effectively capture the quality of predicted videos, motion-compensated cosine similarities of deep features of predicted frames with past frames, and deep features extracted from rescaled frame differences. We show that our feature design leads to state-of-the-art quality prediction in accordance with human judgments on our IISc PVQA Database. The database and code are publicly available on our project website: https://nagabhushansn95.github.io/publications/2020/pvqa.
The Image Annotation Refinement in Embedding Feature Space based on Mutual Information

The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed model use semantic embedding to properly map labels and visual features to a meaningful semantic space. To accurately measure the relevance between the particular image and its original annotations, the proposed model utilize all available information including image-to-image, label-to-label and image-to-label. Experimental results conducted on three typical datasets show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones. The improvement largely benefits from our proposed mutual information method and utilizing all available information.
ЗАСОБИ ФОРМУВАННЯ ЦИФРОВОЇ КОМПЕТЕНТНОСТІ МАЙБУТНІХ ВИКЛАДАЧІВ МИСТЕЦЬКИХ ДИСЦИПЛІН В ЗАКЛАДАХ ВИЩОЇ ОСВІТИ КНР

The article defines the essence of the concept of «digital competence» and the components of its formation: motivational-personal, cognitive, activity and reflection-evaluation. These components of digital competence of future teachers of art disciplines should be formed taking into account the specifics of professional activities and appropriate teaching aids.
The Need to Digitize Sukuk Issuance Amid COVID-19 Crisis

COVID-19 pandemic was a health crisis that plunged the world into economic turmoil due to its resultant national lockdowns across economies which brought business and market activities to a standstill. In order to adapt to ensuing restrictions owing to the pandemic, forge ahead in a new way of living, work and interactions with one another (new normal), digitizing business and market operations is considered a necessary option. Sukuk is an essential Islamic capital market product whose operations involve multiple parties/intermediaries alongside some technical financial, administrative and legal/shariah processes. On this note, this chapter aims to study and examine the need for digitizing and automating sukuk operations and related activities to pave way for innovation, development and better continuity of sukuk market. In conducting the study, a review of literature approach is employed where relevant works on sukuk and fintech were examined. Using content analysis, the chapter explored digitization of sukuk in the Islamic capital market via fintech and blockchain and associated benefits, including peculiar challenges therein. An interview was also conducted to better understand the Wethaq case study. The chapter reveals that digitizing sukuk issuance adds value to sukuk and remedies certain inadequacies associated with sukuk transactions; can increase transparency of underlying sukuk assets and cash flows and can reduce costs due to lesser number of intermediaries. Digitization is the future of sukuk issuance and will promote sukuk well through the COVID-19 crisis and beyond.
Multi-directional Feature Positioning Retrieval Method of Random Encrypted Images

Image encryption is an effective means to ensure image information security, but image encryption makes image features hidden, resulting in blurred image positioning features and cannot provide queryable rules. In this paper, the multi-direction feature retrieval method of random encrypted images is studied comprehensively. Multidirectional binary wavelet is used to decompose specific images, and multi-resolution analysis is used to extract multi-direction features of specific images. The method of image location optimization in random encrypted images is used to eliminate the excessive and repeated image features contained in the specific image by image verification, and the probability of image location errors is reduced. The specific image is retrieved by identifying frequent item sets in random encrypted images that are identical to the multidirectional features of a particular image. The results show that the method can locate the random encrypted image effectively. The accuracy of the image location and the average accuracy of the feature points are about 95 % and 97.3 % respectively, and the anti-noise ability is strong. It provides a scientific means for the rapid positioning of efficient images.
Knowledge Distillation-Based Multilingual Code Retrieval

Semantic code retrieval is the task of retrieving relevant codes based on natural language queries. Although it is related to other information retrieval tasks, it needs to bridge the gaps between the language used in the code (which is usually syntax-specific and logic-specific) and the natural language which is more suitable for describing ambiguous concepts and ideas. Existing approaches study code retrieval in a natural language for a specific programming language, however it is unwieldy and often requires a large amount of corpus for each language when dealing with multilingual scenarios.Using knowledge distillation of six existing monolingual Teacher Models to train one Student Model—MPLCS (Multi-Programming Language Code Search), this paper proposed a method to support multi-programing language code search tasks. MPLCS has the ability to incorporate multiple languages into one model with low corpus requirements. MPLCS can study the commonality between different programming languages and improve the recall accuracy for small dataset code languages. As for Ruby used in this paper, MPLCS improved its MRR score by 20 to 25%. In addition, MPLCS can compensate the low recall accuracy of monolingual models when perform language retrieval work on other programming languages. And in some cases, MPLCS’ recall accuracy can even outperform the recall accuracy of monolingual models when they perform language retrieval work on themselves.
Method for Diagnostics and Selection of the Type of Market Structure of Commodity Markets Based on Fuzzy Set Theory

This work is devoted to the issues of diagnostics and the choice of the type of market structure of the commodity market in the conditions of a free economy and global systemic changes, as a necessary component of economic growth and transformation of commodity markets. The paper concludes that the main and most difficult stage in diagnosing product markets is the process of selecting index indicators and coefficients for the study of product markets. It is proposed to use a complex of indices and coefficients that allow determining the level of the competitive environment in the commodity markets. The method for determining the type of the market structure of the commodity market based on the analysis of the state of competition is presented. The proposed method is based on the use of fuzzy inference capabilities, which is implemented on the basis of the MATLAB tool environment.
Plasma Chain and Blockchain Security Model

Open-source development has become popular in recent years. Many major technology companies are moving towards this form of development, where they can invite contributions from the community. The surge in recent years can be attributed to many solutions that have been adopted to overcome the problems that come with open-source development. Distributed versioning systems have allowed people to work collectively, and a variety of services enable the development to get organized despite the open nature. Still, open-source development heavily depends on early contributors which is a significant issue that needs to be tackled.
Students involvement into social and cultural activity: modeling and motivation

At present, higher education is undergoing major changes related to the implementation of the Bologna process ideas, education informatization, integrative processes in society, increasing competition, and globalization. In this regard, the problem of student involvement formation acquires exceptional importance in the university educational activity. The focus on student involvement formation in higher education's social and cultural activity process is conditioned by the necessity to improve professional personality development and raise social competencies. We consider the social activity as a person's integral quality which is realized in various types of self-activity, in the process of which the personality self-realization occurs, conditioned by objective and subjective factors. Also, the structure of motives for young people's social activity is considered. Students' involvement is seen as a person's property, which externalizes in activity and shows the person’s attitude to its various spheres. Particular importance is paid to the research of the students’ extracurricular organization. It is shown that the students free time is needed to organize in the process of educational activity to create conditions for self-realization and self-actualization of the student's personality. The submissions of the article can be useful for Faculty and Administration of higher education institutions.
Topic Detection on Spoken Documents Using Deep-Feature of Acoustic Model

State-of-the-art topic detection (TD) on spoken documents is typically completed by a topic detector based on the recognized text produced by automatic speech recognition (ASR). We propose to construct the spoken document representation without using recognized text in this paper. In the proposed algorithm, the conventional acoustic model of ASR is first used to extract the high-level deep feature from the acoustic feature, and then a hierarchical attention network (HAN) is applied on the extracted deep feature to construct the representation of spoken documents. Finally, the representations are clustered by agglomerative hierarchical clustering (AHC) algorithm. Experiments conducted on a Japanese speech corpus showed that the proposed method outperformed the TD systems based on recognized text. Furthermore, relative improvements of up to 6.74% in term of ACC could be obtained when combining the representations generated by both the recognized text and the deep feature.
Normalization and Attention

In this chapter, we will discuss very exciting and rapidly evolving technical fields of deep learning: normalization and attention.
Selective Search Segmentation Based Text Detection from Natural Images

Detecting and recognizing text from natural scenes has societal impact applications like wearable device for visually impaired persons, voice road signals for drivers (speed limit, school ahead etc.), number plate recognition etc. The present work shows fast segmentation with Resnet based approach for detection of text and OCR for text recognition. CPU with NVIDIA Getforce 2060 GPU is used for training a convolution neural network for detection and recognition of text from image. ICDAR 2015 data set is used for training purpose. This work is being completed under the JNTUH TEQIP-III Collaborative research scheme, project fund of 2.7 Lakhs.
ДО ПИТАННЯ ЕМОЦІЙНОЇ ВИКОНАВСЬКОЇ СТАБІЛЬНОСТІ ФАХІВЦІВ МУЗИЧНОГО МИСТЕЦТВА

The article highlights the issues of emotional stability. The approaches of psychologists to the definition of this definition are revealed, internal (personal) and external (interpersonal, social support) resources that support it are shown. The emotional performance stability of music specialists for their successful performance is characterized.
New Algorithm and Performance Analysis of Communication Signal Processing Under Stable Distributed Noise

The function of communication signal modulation recognition is to realize the effective processing and analysis of signals in the external environment with noise or multiple signals by determining the modulation types and parameters of different signals. Along with the rapid development of communication technology at home and abroad, how to use and analyze communication signals has attracted great attention from various related industries. In the academic field, more attention is paid to the modulation recognition of communication signals under α stable distribution noise. Because researchers think that real stable distribution is a more effective noise model.
The Role of Ubiquitous Computing in the Transformation of the Healthcare System

Currently, keeping the financial balance in health services has been a challenge for governments and private companies. Factors such as population aging, medical inflation and the lack of adherence to treatments raise costs and stimulate debates around the world on how to maintain systems’ budget viability. During the last decades, we could observe the phenomenon of computation needing less hardware, becoming more portable and migrating to our offices, pockets, clothing, and finally, to our body, thus allowing us to envision a range of solutions for these problems. The only way for society to offer such quality services and overcome these challenges is through the solutions that the convergence of new technologies brings to us now. The applications of ubiquitous computing have the potential to immensely benefit patients, managers and the society that finances the health system.
Combining Universal Speech Attributes into Deep Speaker Embedding Extraction for Speaker Verification

The universal speech attributes for x-vector based speaker verification (SV) are addressed in this paper. The manner and place of articulation form the fundamental speech attribute unit (SAU), and then new speech attribute (NSA) units for acoustic modeling are generated by tied tri-SAU states. An improved cross-stitch network is adopted as a multitask learning (MTL) framework for integrating these universal speech attributes into the x-vector network training process. Experiments are conducted on common condition 5 (CC5) of the core-core and the 10 s–10 s tests of the NIST SRE10 evaluation set, and the proposed algorithm can achieve consistent improvements over the baseline x-vector on both these tasks.
Guided Design for Efficient On-device Object Detection Model

The low-power computer vision (LPCV) challenge is an annual competition for the best technologies in image classification and object detection measured by both efficiency (execution time and energy consumption) and accuracy (precision/recall). Our Amazon team has won three awards from LPCV challenges: 1st prize for interactive object detection challenge in 2018 and 2019 and 2nd prize for interactive image classification challenge in 2018. This paper is to share our award-winning methods, which can be summarized as four major steps. First, 8-bit quantization friendly model is one of the key winning points to achieve the short execution time while maintaining the high accuracy on edge devices. Second, network architecture optimization is another winning keypoint. We optimized the network architecture to meet the 100ms latency requirement on Pixel2 phone. The third one is dataset filtering. We removed the images with small objects from the training dataset after deeply analyzing the training curves, which significantly improved the overall accuracy. And the forth one is non-maximum suppression optimization. By combining all the above steps together with the other training techniques, for example, cosine learning function and transfer learning, our final solutions were able to win the top prizes out of large number of submitted solutions across worldwide. Take-aways: Discusses the methods involved in the winning solutions over the years. Explains the impacts of each method (quantization, architecture search, hyperparameter tuning) Reduces the resolutions to improve performance
A Lightweight Dangerous Liquid Detection Method Based on Depthwise Separable Convolution for X-Ray Security Inspection

For personal safety and crime prevention, some research studies based on deep learning have achieved success in the object detection of X-ray security inspection. However, the research on dangerous liquid detection is still scarce, and most research studies are focused on the detection of some prohibited and common items. In this paper, a lightweight dangerous liquid detection method based on the Depthwise Separable convolution for X-ray security inspection is proposed. Firstly, a dataset of seven common dangerous liquids with multiple postures in two detection environments is established. Secondly, we propose a novel detection framework using the dual-energy X-ray data instead of pseudocolor images as the objects to be detected, which improves the detection accuracy and realizes the parallel operation of detection and imaging. Thirdly, in order to ensure the detection accuracy and reduce the computational consumption and the number of parameters, based on the Depthwise Separable convolution and the Squeeze-and-Excitation block, a lightweight object location network and a lightweight dangerous liquid classification network are designed as the backbone networks of our method to achieve the location and classification of the dangerous liquids, respectively. Finally, a semiautomatic labeling method is proposed to improve the efficiency of data labeling. Compared with the existing methods, the experimental results demonstrate that our method has better performance and wider applicability.
Implementing ACT-Based Modalities for Establishing Connectivity among Teachers and Students in the Context of COVID-19﻿

The government aims to make education accessible to all the people of the country can be fulfilled if the online education system is developed enough for all. The country may be benefited by the use of the right type of education system. Learners and teachers get advantages if the education system is reformed using digital technologies like Automatic Call Tree (ACT)-based modalities. Such types of education system has key significant in the pandemic situation which have been facing since two years. This study is assumed to examine the Automatic Call Tree (ACT) based modalities and their utilization for connection between students, teachers, parents and school management committee; and some recommendations are made concerning online education. The main information source for this study is primary. Instruments used are questionnaires, interview with video recording, phone call, and class room observation. The preliminary finding showed that there is a scarcity of orientations of online education to parents, students, teachers, and administrators, though all were found to realize its importance. Likewise, major problems found are lack of digital devices, regular power supply and backup; and network connectivity. Based on the opinions of the respondents and observations of the situations of the different parts of the country running online education during this pandemic period, many recommendations are made. The country needs strategic plans and policy to make online education accessible to all learners specially in urban areas of Nepal and this will ensure online education smooth. It concludes that the Automatic Call Tree (ACT)-based modalities became fruitful for some schools of Nepal during Corona Virus (COVID)-19 pandemic. But many of them are facing different problems while conducting online classes, examination and result processing. Providing practical knowledge and skills about engineering, mathematics, health science, account, computer science subjects are very difficult and to examine them.
Applying weak supervision to classify scarce labeled technical documents

The digitalization in the construction industry, the number of project relevant documents. It becomes a challenge to organize documents in a searchable manner by classification. The German Waterways and Shipping Administration (WSV) is one of the organizations facing this problem. Manually classifying is due to the considerable expense nearly impossible. In parallel, text classification with machine learning increasingly draws attention. Classification belongs to supervised machine learning, where large labeled data samples are needed. In the filing system used in WSV, only a small amount of data with ground-truth labels are available. It is tedious and expensive to annotate manually. To solve the shortage of training data, we propose applying weakly supervised learning, where noisy and inexact labels can be used in the training process. In this study, we inject the domain knowledge in the training process with weak supervision framework Snorkel to construct a labeling model that programmatically annotates data. We then trained classifiers on the original dataset together with the dataset annoted by the labeling model. The results show that even though the programmatically annoted dataset is noisy, it can still train a generalized classifier and improve the classifiers' performance.
A Proposal for a Method of Determining Contextual Semantic Frames by Understanding the Mutual Objectives and Situations Between Speech Recognition and Interlocutors

In this study, we will examine how the speech sounds generated by one-to-one or one-to-n human communication are not only simple exchanges of intentions and opinions, but are also clearly divided into linguistic expression and linguistic understanding. In this course, we will discuss the problems of interaction between interlocutors and the complex interplay of phenomena that occur in individual interlocutors. We propose a method to determine the semantic frame of a dialogue contextually by integrating and calculating the features of speech and the features of the meaning of words.