Reviewerly/exHarmony huggingface dataset:
Pupils’ attitudes toward distance education during the COVID-19 pandemic

The educational context has been disrupted by the ongoing COVID-19 pandemic. The aim of the study was to explore attitudes of pupils in Serbia toward distance education during the pandemic. Specifically, we aimed to explore the cognitive component of pupils? attitudes through their perception of positive and negative aspects of distance education, the motivational component based on the expressions of how distance education affects pupils? motivation to learn, and emotional component through verbal expressions of emotions toward distance education. The sample for the study included 110 pupils attending primary (5th to 8th grade) and secondary schools in Serbia. A semi-structured interview was employed. Data were analysed using qualitative content analysis. The findings suggest that majority of pupils have negative attitudes toward distance education and that they find insufficient interaction between teachers and pupils to be the major shortcoming of distance education. Positive aspects of distance education which could be preserved after the pandemic according to the participants? views are: communication between teachers and pupils using information technologies (IT), technical possibilities for a better presentation of learning topics and permanent availability of learning materials on online platforms. The study revealed the lack of intrinsic learning motivation and deficiencies in pupils? self-regulation of learning which are important to be overcome as they impede pupils? academic development both in distance and in classroom education.
Computational Bibliometric Analysis on Publication of Techno-Economic Education

Techno-economics involves the systematic evaluation of economic aspects to provide solutions to technical problems encountered. The study of techno-economic analysis can provide a basis for decision-making related to economic factors. This study was to analyze the scope of research on techno-economic education using bibliometric evaluation and data mapping approach (i.e., VOSviewer software). Material research data was gathered from application reference manager databases. The study material titles, keywords, and abstracts are used to guide the search process. In the period 2017-2022, the analysis was conducted using the number of publications collected, which totaled 288 related papers. Based on the number of publications related to techno-economic educations, the total publications from 2017-2022 are unstable. However, in 2021 publications related to techno-economic education have increased. This study highlights the value of bibliometric analysis in providing information on how phenomena occur. This study is meant to assist and serve as a reference for researchers undertaking and deciding on research topics.
A Blockchain-Assisted Electronic Medical Records by Using Proxy Reencryption and Multisignature

Electronic medical records (EMR) have been commonly used in medical institutions in recent years. In particular, the combination of EMR and the cloud server has significantly improved the work efficiency and therapeutic level of the hospital. It also raises some security concerns, e.g., the information leaks. Blockchain has features including decentralization, traceability, openness, and tamper resistance. Therefore, the technology may be used to overcome the above flaws. In this paper, we introduce a new blockchain-assisted EMR in the cloud environment by using proxy reencryption and sequential multisignature. Firstly, blockchain makes the scheme have high-security performance without a trusty center. Secondly, we use proxy reencryption to protect personal medical data while helping doctors to access patients’ historical medical records. Moreover, the doctors have used a sequential multisignature, which is practical and can effectively improve security performance. The analysis results show that the proposed scheme can satisfy various security features of EMR and has an ideal computational and communication cost. Finally, the scheme is implemented to show its performance.
Demystifying Attention Mechanisms for Deepfake Detection

Manipulated images and videos, i.e., deepfakes have become increasingly realistic due to the tremendous progress of deep learning methods. However, such manipulation has triggered social concerns, necessitating the introduction of robust and reliable methods for deepfake detection. In this work, we explore a set of attention mechanisms and adapt them for the task of deepfake detection. Generally, attention mechanisms in videos modulate the representation learned by a convolutional neural network (CNN) by focusing on the salient regions across space-time. In our scenario, we aim at learning discriminative features to take into account the temporal evolution of faces to spot manipulations. To this end, we address the two research questions ‘How to use attention mechanisms?’, and ‘What type of attention is effective for the task of deepfake detection?’ Towards answering these questions, we provide a detailed study and experiments on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.
Design and Implementation of Deep Depth Decision Algorithm for Complexity Reduction in High Efficiency Video Coding (HEVC)

High efficiency video (HEVC) coding made its mark as a codec which compress with low bit rate than its preceding codec that is H.264, but the factor that stop HEVC from many applications is its complex encoding procedure.The rate distortion optimisation (RDO) cost calculation in HEVC consume complex calculations.In this paper, we propose a method to cross out the issue of complex calculations by replacing the traditional inter-prediction procedure of brute force search for RDO by a deep convolutional neural network to predict and perform this process.In the first step, the modelling of the deep depth decision algorithm is done with optimum specifications using convolutional neural network (CNN).In the next step, the model is designed and trained with dataset and validated.The trained model is tested by pipelining it to the original HEVC encoder to check its performance.We also evaluate the efficiency of the model by comparing the average time of encoding for various resolution video input.The testing is done with mutually independent input to maintain the accuracy of the system.The system shows a substantial saving in encoding time that proves the complexity reduction in HEVC.
PROFESSIONAL SEMINAR "BASIS OF PROFESSIONALISM OF TEACHER OF GENERAL SECONDARY EDUCATION INSTITUTION" IN DUAL EDUCATION SYSTEM OF MASTERS "SECONDARY EDUCATION (MATHEMATICS)" SPECIALTY

Formulation of the problem. In modern economic conditions, the vast majority of master students of specialty "Secondary Education (Mathematics)" in NPU named after M.P. Dragomanov is in a situation of forced "duality" of their education. As a result, there are difficulties with the organization of the learning process of such students, as well as the quality of their assimilation of the material of the educational program. It seems natural to transfer such students to the dual form of education. This will solve a significant part of the mentioned organizational and methodological problems. Dual education requires fundamentally new forms of learning organization. An important element in its structure is the professional seminar "Basis of professionalism of teacher of general secondary education institution." This seminar is designed to quickly respond to current problems of young teachers that arise in the process of adapting to working conditions at school. Materials and methods. To achieve the aims of the article, we use empirical methods: our own survey using an electronic tool (Google forms), monitoring the learning process of students, as well as analysis of the results of their achievements. The article used a set of methods of scientific cognition: comparative analysis to clarify different views on the problem; systematization and generalization for the formulation of conclusions and methodological advices; generalization of pedagogical experience and observations Results. The content of the professional seminar we can divide into three main content modules - "Methods of organizing the educational process in modern school", "Modern methods of assessing the educational activities of students" and "Organization of extracurricular and educational work in modern school". Each of them is designed to form the professional competencies of the undergraduate in accordance with his (her) educational program. The content and procedure for studying of these modules is not strictly defined, this content should be tailored to the needs of a particular student. The head of the seminar also has the opportunity to involve external experts who are specialists in pedagogy, psychology, law, as well as practicing teachers and school administrators. Conclusions. According to the survey of masters and observation of their work during the professional seminar lessons, most students are positive about its introduction to the educational program and are satisfied with the quality of classes. All the material of the professional seminar curriculum is considered by master students as relevant and necessary for their further work that shows the correctness of the chosen direction of their training. Thus, the pedagogically balanced use of the dual form of education with the wide involvement of distance and mixed forms of classes based on modern technologies will ensure the proper quality of preparation of future teachers of mathematics.
The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information

The scholarly knowledge ecosystem presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale. This article draws upon significant reports on aspects of the ecosystem to characterize the most important research challenges and promising potential approaches. The focus of this review article is the fundamental scientific research challenges related to developing a better understanding of the scholarly knowledge ecosystem. Across a range of disciplines, we identify reports that are conceived broadly, published recently, and written collectively. We extract the critical research questions, summarize these using quantitative text analysis, and use this quantitative analysis to inform a qualitative synthesis. Three broad themes emerge from this analysis: the need for multi-sectoral cooperation and coordination, for mixed methods analysis at multiple levels, and interdisciplinary collaboration. Further, we draw attention to an emerging consensus that scientific research in this area should by a set of core human values.
An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain’s linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger’s construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.
State-of-the-Art Approach to e-Learning with Cutting Edge NLP Transformers: Implementing Text Summarization, Question and Distractor Generation, Question Answering

Amid the worldwide wave of pandemic lockdowns, there has been a remarkable growth in E-learning.Online learning has become a challenge for students.It has become difficult for students to find the content they need.The mounting accessibility of textual content has necessitated comprehensive study in the areas of automatic text summarization and question generation.Multiple Choice Questions is very smooth for evaluations, and its assessment is implemented through computerized applications in order that results may be declared within some hours, and the evaluation system is 100% pure.The system proposes an interactive reading platform where the user can upload an E-Book and get textual summary and generates questions like MCQs, fill in the blanks and one word.The user can also evaluate the questions answered.The proposed system is an all-in-one interactive reading platform.
Extractive Research on Summarization Framework for Extracted Features

In the information age, the growth of e-commerce has brought the products’ sale and purchase online and many of the customers prefer to buy it online. To support this preference the users’ reviews of the products plays an important role. So, online merchants wish to take the reviews; experiences of the user, to enhance their business and revenue. Popular and trending products may attract large number of reviews. Further, many of which could be elongated. Extracting useful information with efficiency and accuracy from these so many reviews, of which there are some very long, is a challenging task. This work is an attempt to summarize the customer reviews on products into more useful and shorter version that can help another users’ decision. Reviews available online are crawled for product, each time after extraction, first identification of features of the product will be done and hence polarity will be detected i.e. either a review is positive review or a negative review. After the calculations, summarization of all the features of the product will be generated.
The Impact of Cloud Computing-Based Big Data Platform on IE Education

“Mass Entrepreneurship, Mass Innovation” is for the benign, rapid, and better development of our economy and society. In the 21st century, innovation and entrepreneurship (IE) under the background of technological development must take the masses as the main body to stimulate the vitality of mass IE. Compared with traditional IE education, the impact of big data platforms on education cannot be underestimated. Therefore, this article aims to research the influence of cloud computing-based big data platforms on IE education. This article proposes to use the cloud computing-based big data platform as a carrier to carry out IE education to change the drawbacks of traditional IE education. This research uses the literature analysis method and questionnaire survey method. It takes college students as the survey subjects of IE education, who are also the main object, and fully collects data. The research shows that cloud computing-based big data platforms have multiple impacts on IE education. During the epidemic, noncontact education is closely related to network technology.
Practical Active Learning with Model Selection for Small Data

Active learning is of great interest for many practical applications, especially in industry and the physical sciences, where there is a strong need to minimize the number of costly experiments necessary to train predictive models. However, there remain significant challenges for the adoption of active learning methods in many practical applications. One important challenge is that many methods assume a fixed model, where model hyperparameters are chosen a priori. In practice, it is rarely true that a good model will be known in advance. Existing methods for active learning with model selection typically depend on a medium-sized labeling budget. In this work, we focus on the case of having a very small labeling budget, on the order of a few dozen data points, and develop a simple and fast method for practical active learning with model selection. Our method is based on an underlying pool-based active learner for binary classification using support vector classification with a radial basis function kernel. First we show empirically that our method is able to find hyperparameters that lead to the best performance compared to an oracle model on less separable, difficult to classify datasets, and reasonable performance on datasets that are more separable and easier to classify. Then, we demonstrate that it is possible to refine our model selection method using a weighted approach to trade-off between achieving optimal performance on datasets that are easy to classify, versus datasets that are difficult to classify, which can be tuned based on prior domain knowledge about the dataset.
Increased learning in a college physics course with timely use of short multimedia summaries

Carefully designed one-minute videos and short text summaries following a lecture can significantly increase students' understanding of the material and their ability to organize the material into useful mental frameworks.
Anwaha's Education Digitalization Mission

Based on the results of the study, it was concluded that Anwaha's mission of digitizing education was realized by visualizing, futuristic thinking, showing foresight, proactive planning, creative thinking, taking risks, process alignment, coalition building, continuous learning, and embracing change so as to be able to adapt to digitalization of administration and teaching. Develop a model for digitizing the publication of school activities.
No-go theorems for deterministic purification and probabilistic enhancement of coherence

Abstract The manipulation of quantum coherence is one of the principal issues in the resource theory of coherence, with two critical topics being the purification and enhancement of coherence. Here, we present two no-go theorems for the deterministic purification of coherence and the probabilistic enhancement of coherence, respectively. Specifically, we prove that a quantum state cannot be deterministically purified if it can be expressed as a convex combination of an incoherent state and a coherent state. Besides, we give an easy-to-verified sufficient and necessary condition to determine whether a state can be probabilistically enhanced via a stochastic strictly incoherent operation. Our findings provide two feasibility criteria for the deterministic purification and the probabilistic enhancement of coherence, respectively. These results have repercussions on the understanding of quantum coherence in real quantum systems.
Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.
An Exploratory Study of Indonesian Teachers’ Digital Literacy Competences

This study aims to reveal how high school teachers adjust to the demand of utilizing digital literacy skills in two selected areas of the Lombok Island, Indonesia. The study sought to get deep understanding of how high school teachers developed their digital literacy competences and kept up with the demand of online teaching of English during the pandemic time. The study also sought to identify problems pertaining to the utilization of digital literacy in the pandemic time. The study employed a qualitative method with multiple-case design. A purposive Sampling technique was used to determine the key informants for the study. The data were obtained from various sources, which included interviews, observations and document studies. The process of data analysis encompassed data reduction, data presentation, and conclusion drawing. This was done through triangulation of data iteratively. The data were then analyzed thematically. The study yields that teachers in the two selected research areas acquired varying degrees of digital literacy competences due to different goal-setting, interests, knowledge and skills. A number of problems pertaining to teachers’ differing competences in digital literacy were identified, i.e. (1) lack of internet access (2) the absence of professional development, (3) and institutional support system. All of these problems were intertwined in nature. The study concludes that a professional development program needs to be devised for the participants to improve their performance and competence in utilizing digital literacy.
On the Development and Practice of AI Technology for Contemporary Popular Music Production

Although the use of AI technology for music production is still in its infancy, it has the potential to make a lasting impact on the way we produce music. In this paper we focus on the design and use of AI music tools for the production of contemporary Popular Music, in particular genres involving studio technology as part of the creative process. First we discuss how music production practices associated with those genres can differ significantly from traditional views of how a musical work is created, and how this affects AI music technology. We argue that—given the role of symbolic representations in this context, as well as the integration of composition activities with editing and mixing—audio-based AI tools are better suited to support the artist’s creative workflow than purely piano-roll/MIDI-based tools. Then we give a report of collaborations with professional artists, in which we look at how various AI tools are used in practice to produce music. We identify usage patterns as well as issues and challenges that arise in practical use of the tools. Based on this we formulate some recommendations and validation criteria for the development of AI technology for contemporary Popular Music.
Densely Populated Regions Face Masks Localization and Classification Using Deep Learning Models

Over the last year, the correct wearing of facial masks in public is still a relevant matter in the fight against the COVID-19 pandemic.A popular approach that helps regulate the situation by global researchers is building smart systems for face mask detection.Following such spirit, this paper will contribute to the literature in two main aspects:(1) We first propose a new face mask detector model using the state-of-the-art RetinaFace for face localization in populous regions and the ResNet50V1 classifier to group the faces under 3 categories: correctly-worn, incorrectly-worn and no-masks-worn.(2) In order to select the ResNet50V1 as the backbone for the final model, we also analyzed its performance in accordance with another 3 classifiers on a face mask dataset beforehand.Performance metrics from the test phase have shown that our detector achieved the best accuracy among all the works compared, with 94, 59% on one test dataset and a less satisfactory 69.6% on another due to certain characteristics of the set.The code is available at: https://github.com/barbatoz0220/Densely-populated-FMD.git