Reviewerly/exHarmony huggingface dataset:
Digital Recognition of Handwritten Digits Using Convolutional Neural Networks

Convolutional neural networks are widely used in vast scopes, and the number of sectors in which profound learning may be used is growing all the time in fields due to their different scope of uses like example pattern reputation, sentence classification, speech popularity, face reputation, textual content categorization, file evaluation, visual image evaluation, and so on. In the current time of digitization, penmanship acknowledgment assumes a significant part in data handling by changing overwritten by hand characters into machine-decipherable organizations. The postal framework assumes a significant part in the improvement of mail transportation. The target of the task is to give an elective means to the customary arranging framework, which devours lesser time for handling and arranging the postcards in view of their individual regions. It likewise targets killing any conceivable human blunders which might happen during the manual arranging process.
Recognition of the Multioriented Text Based on Deep Learning

The development and use of systems for analyzing visuals, such as photos and videos, using benchmark datasets is a difficult but necessary undertaking. DNN and STN are employed in this study to solve the challenge at hand. The study's network design consists of a localization and recognition network. The localization network generates a sampling grid and locates and localizes text sections. In contrast, text areas will be entered into the recognition network, and this network will then learn to recognize text, including low resolution, curved, and multi-oriented text. Street View house numbers and the 2015 International Conference on Document Analysis and Recognition were used to gauge the system's performance for this study's findings (ICDAR). Using the STN-OCR model, we are able to outperform the literature.
Special Issue on New Frontiers in Virtual Reality: Methods, Devices and Applications

Virtual reality (VR) has the potential to dramatically change the way we create and consume content in our everyday life [...]
A Study of Subject Headings vs. User Generated Tags

User-generated tagging or folksonomy is one of the most popular Web 2.0 applications and has attracted the interest of libraries and information centres, which has developed services that facilitate user community collaboration. In the digital environment, social tagging has the potential to overcome certain limitations of traditional subject headings. The main objectives of this research paper are to identify the scope of user-generated tags as compared to Subject Heading Lists and to explore the extent to which these user-generated tags can be used to enhance subject access. In this research paper, the problem is comparison between the Subject heading List and user generated tags. This work is an exploratory research and has followed a case study method. Five searches were performed in Library Thing using its Tag mash feature (essentially a keyword search of the user tag field) using 5 tags. After searching articles and interpreting all data, a comparison between the subject heading list and User-generated tags have been made.
Performance Analysis of Data Security Mechanisms for Data Deduplication in Hybrid Cloud Computing Environment

One of the major concerns of users and organizations outsourcing their data to cloud providers is the issue of data integrity and privacy.Data deduplication is a technique to reduce storage space and eliminate duplicate copies of data by identifying redundant data using hash values to compare data chunks, storing only one copy, and creating logical pointers to other copies instead of storing other actual copies of the redundant data.Deduplication reduces data volume so disk space and network bandwidth can be reduced which reduces costs and energy consumption for running storage systems.Data deduplication can be applied at nearly every point in which data is stored or transmitted in cloud storage.Many cloud providers offer disaster recovery and deduplication can be used to make disaster recovery more effective by replicating data after deduplication for speeding up replication time and bandwidth cost savings.Backup and archival storage in clouds can also apply data deduplication in order to reduce physical capacity and network traffic.Moreover, in the live migration process, we need to transfer a large volume of duplicated memory image data.Duplication of data stored in the cloud occupies more space.However, during data updates, duplicate data must be changed in more than one place, which is more complex to rectify and would increase operational costs in the cloud.
Self-study Activities of Vietnamese High School Students via Mobile Devices

Mobile devices are popular classroom tools in the era of the Industrial Revolution 4.0. The availability of smartphones at affordable prices has increased and extended their use beyond that of communication to all facets of life including tourism, entertainment, productivity and study. Over the past decade, many studies have examined the potential of mobile phones, also known as M-learning, for educational purposes in a variety of contexts. Recent findings show that they can improvement student performance. This chapter presents the application of M-learning application for self-study activities of high school students in Vietnam. The chapter consists of two main parts: The first part presents an overview of M-learning, including studies on M-learning application in education in Vietnam, then analyzes and clarifies the implementation context and M-learning systems in Vietnam. The second part presents the M-learning system model in the Vietnamese context to support self-study high school students based in three perspectives: ensuring practical feasibility, promoting positive elements of M-learning model, and pedagogy.
Smart City Energy Technology in the Face of Emergency Situations: Electric Supply, Electric Transportation, and Communication

This article examines critical smart city infrastructure components, like electricity supply, transportation, and telecommunication, in the face of an emergency like COVID-19. The electricity infrastructure is a critical component of any smart city and significantly impacts other systems, like transportation, communication, and water delivery and treatment.
Analisis Metodologi dan Materi Buku Bahasa Indonesia bagi Penutur Asing

This study is to analyse the methode and material of Bahasa Indonesia Business Comunication for foreigner text book level 1 students published by UMN Press 2019. This study uses qualitative describtive approach. The data source used is the BIPA Comunication Business textbook for BIPA level 1 students. This research focuses on the feasibility of method and content aspect. The instrument used is in the form of an analysis sheet of the textbook feasibility assessment theory based on Hartono (2016) which analyse in each unit on. Based on the research results, it can be concluded that the BIPA Business textbook level 1 students is “quite feasible” to use, with an eligibility percentage of 90 %. Most of the shortcomings of the text book are caused by the lack of links and references as well as the incompatibility of the material and vocabulary with the learning targets and elements of BIPA Comunication Business level 1 competence. Keywords: Methode;Material;Bahasa Indonesia for foreigner.
Constructing an anti-Asian hate indicator for pandemic-related comments from mainstream media YouTube channels

Anti-Asian racism, linked to COVID-19, has become a serious social problem in the USA and all over the world and even led to hate crime and violence. Even though the current anti-Asian hate study focuses anti-Asian hate classification using machine learning and sentiment analysis toward tweets, this study provides a novel pandemic-news-related anti-Asian hate indicator to depict the anti-Asian hate shift of YouTube mainstream media commentary section. A new dataset for daily hate signal generation, which contains over one million YouTube comments, has been generated in this study. To train the classifier, 3,759 comments are sampled and manually labelled as hate and non-hate. In the model selection among machine learning and deep learning algorithms, a CNN model is selected as the best one with a 95% accuracy and a 0.99 AUC score, which can classify 1,433,246 comments.
SIGNORA: A Blockchain-Based Framework for Dataflow Integrity Provisioning in an Untrusted Data Pipeline

In a typical data pipeline, the dataflow starts from the first node, where the data is initiated and moves to the last node in the pipeline, where the processed data will be stored.Due to the sheer number of the involved participants, it is crucial to protect the dataflow integrity in the pipeline.While previous studies have outlined solutions to this matter, the solution for an untrusted data pipeline is still left unexplored, which motivates us to propose SIGNORA.Our proposal combines the concept of a chain of signatures with blockchain receipt to provide dataflow integrity in a data pipeline.The chain of signatures provides a non-repudiation guarantee from participants, while the hash of the data and signatures is anchored in the blockchain for a non-tampering guarantee through blockchain receipt.Aside from that, SIGNORA also satisfies essential requirements of running data pipeline processing in an open and untrusted environment, such as (i) providing reliable identity management, (ii) solving the trust and accountability issues through a reputation system, and (iii) supporting various devices through multiple cryptographic algorithms (i.e., ECDSA, EdDSA, RSA, and HMAC) and (iv) off-chain processing.Our experiment results show that SIGNORA can provide dataflow integrity provisioning in multiple scenarios of data payload size with reasonable overhead.Furthermore, the cost of smart contract methods has also been analyzed, and several off-chain solutions have been addressed to reduce transaction costs.Finally, the reputation system can adapt to the history of nodes' activities by increasing their scores when they actively perform honest behavior while reducing their scores when they become inactive.Therefore, SIGNORA can provide a high degree of accountability for participants collaborating in an untrusted environment.
Newton-Raphson method versus Lambert W function for photovoltaic parameter estimation

This paper compares the performances of the Lambert <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> function (LWF) and the Newton-Raphson method (NRM) to estimate the parameters of the photovoltaic (PV) models. Lambert <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> function explicitly represents the PV model under consideration and NRM allows to overcome the implicit nature of the PV model by determining the output current iteratively. LWF and NRM are classified as indirect approaches, and are used to calculate the output current, instead of the most used approach in the literature so far, which is a direct approach to solve the output current (DirectSolve), when metaheuristics are applied. Here, the single-diode model (SDM) was considered, and the estimation of its PV parameters was performed by the gaining-sharing knowledge (GSK) optimization algorithm. The analysis used standard datasets and experimentally measured datasets under different operating conditions. The results obtained from indirect and direct approaches were compared, showing identical accuracy and reliability when considering LWF or NRM. However, from a computational point of view, LWF was slightly more efficient than NRM (0.11% according to the mean number of evaluations). DirectSolve was on average about 35% less accurate than indirect approaches. The comparisons showed that LWF and NRM present a very competitive performance in the PV parameter estimation, differing only slightly in computational costs.
Detecting counterfeit banknotes with machine learning

&#x0D; &#x0D; &#x0D; The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in thefinancial market. During demonetization time it is seen that so much of fake currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this project six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication&#x0D; &#x0D; &#x0D;
Getting Started with AVS

Azure Cloud for VMware Solutions simplifies the process of harnessing the enormous potential of the cloud for an organization. You can migrate VMware workloads to the Azure Cloud using tools, technologies, and skills currently used in cloud consumer environments on-premises.
“NFTs: Future or Fad?” Excerpts From A Practical Discussion of NFT Use Cases and Copyright Concerns Raised by NFT Offerings

The current hype cycle surrounding non-fungible tokens, or NFTs, has been going on for close to two years. As we continue to move through this phase and into whatever is next, it is important to look at the current use cases out there and some of the intellectual property issues facing companies that avail themselves of this technology. What follows are excerpts from my contributions to a discussion panel that took place in November 2021, modified as necessary for clarity and for the purposes of this Article. &#x0D; The first question one might ask is, “Why are companies choosing to enter into the NFT space in the first place?”
REPRESENTATIONS SOCIALES DE LA PEDAGOGIE NUMERIQUE UNIVERSITAIRE EN COTE DIVOIRE : QUELLE STRATEGIE DE PERSUASION DES ETUDIANTS?

The social representations presented approach digital pedagogy as problematic. This is not to say that the technologies are problematic but rather to recognize that their integration into universities in CÃ´te dIvoire raises questions of primary importance. The study focuses on the reserved or unfavorable opinions of students regarding digital university pedagogy. The approach is to highlight the factors of possible reluctance and to propose remedial measures in order to obtain positive changes in students behaviors in the era of digital university pedagogy. This investigation is part of an empirical-theoretical approach and is based on the critical theories of digital education and on Maio and Olsons (2000) model of behavior change, because, persuasion results from exposure to a communication. The data of the survey is the result of a mixed approach (quantitative method through a questionnaire and qualitative method through focus group) on a total sample of one hundred (100) students from two public universities. The social polemics around the digital pedagogy are translated by reticences related to the digital divide, presuppositions on the university considered as an ideological and hegemonic company.
Persepsi dan Penggunaan Buku Teks dan Sumber Belajar Online Mahasiswa Universitas Sulawesi Barat pada Mata Kuliah Fisika Dasar

This research is done to (1) know the percentage of use of textbooks by students in the Basic Physics course; (2) to know the percentage of students using online learning resources in Basic Physics courses; (3) find out the tendency of students to use both learning sources while undergoing Basic Physics course activities in 2020/2021 even semester. This research is a quantitative descriptive study and applies incidental sampling. The population and sample are 259 and 50 students. The results showed that (1) the percentage of students using textbooks in the Basic Physics course was 54% stating often, 32% saying sometimes and 14% saying rarely; (2) the percentage of students using online learning resources in Basic Physics courses is 92% stating often, 6% stating sometimes, and 2% stating rarely; (3) the tendency of students to use both learning resources during their Even Semester 2020/2021 Basic Physics course shows that the use of online learning resources is greater than the use of textbooks.
Project management in the development of dual learning in the education system of Kazakhstan

Purpose of the research. This article is aimed to study the world experience in the application of project management and its approaches in the fi eld of education and in the system of dual education in the Republic of Kazakhstan too. The rapidly developing technologies of the labor market are tightening the requirements for young professionals, so the authors propose to consider new management approaches that will help educational organizations train highly qualifi ed personnel, so necessary for the labor market. The proposed approaches (predictive, adaptive and hybrid) include a set of project management techniques and methodologies that have the potential to solve all necessary organizational issues. Methodology. The research methodology was implemented through a literature review of scientifi c articles, according to the following keywords: «project management in education», «waterfall approach in education», «Agile in education» and «hybrid approach in education». Originality / value of the research. The relevance of the research topic lies in the potential for solving the social problem associated with youth unemployment in the Republic of Kazakhstan among the NEET (Not in Education, Employment, or Training) generation. The originality of the research is confi rmed and substantiated by a small number of scientifi c studies and articles that reveal the use of project management to organize the educational process, especially in the process of organizing a system of dual education in the Republic of Kazakhstan. Findings. The results of the article are the authors' recommendations on the application of the necessary and appropriate project management approaches at certain stages of the implementation of dual training for its effective development in the country.
Automated landform classification and mapping using a combined textural-morphometric approach: the Congo basin and surroundings

An automatic method of landform mapping applicable to large continental areas is presented, based on 30-meter SRTM (Shuttle Radar Topography Mission) data and combining texture analysis using Fourier 2D periodograms (FOTO method) with a set of morphometric variables.This integrated strategy was applied to the whole Congo Basin and adjacent regions in Central Africa, where landscapes and landforms mapping remains heterogeneous and partial with existing expert maps differing in aims and scales.Through the FOTO method, a principal component analysis (PCA) on obtained Fourier r-spectra yielded six textural features, which were further combined with seven morphometric criteria into a global PCA.A k-means classification from these output results provided an automatic mapping of 12 landform classes (at a final resolution of 900 m) which were successfully interpreted in terms of geomorphological meaning together with some hydrological and soil attributes.Finally, comparison of our landform map with existing, independent geomorphological sheets revealed a good spatial congruence.Overall, our method proved effective to depict landform assemblages at regional or continental scales based on complementary textural information and morphometric parameters.As such, it could serve as a sound basis for further predicting and mapping soil units at the landscape scale, given the close soil-landform imbrications and interactions at the catena level.It could serve as well as a predictive variable for biodiversity measures and biomass estimates, especially in the humid tropics where environmental data are lacking whilst ecological modelling is urgently needed to support land planning and forest management.
Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition

Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi-modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non-local multi-modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi-modality human action datasets, including NTU RGB+D 120 and PKU-MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non-local multi-modality compensation block can learn complementary features and enhance the recognition accuracy.
Art in the development of creativity and cognitive-logical capabilities through the prism of formal and non-formal education

Modeling the personality of students is any teachear’s major goal in his activity. The article addresses the role of the musical instrument teacher in the development of students’ creativity and cognitive-logical abilities. The indisputable role of creativity in the development of cognitive-logical abilities is highlighted, based on the theoretical investigation of bibliographic sources, theoretical generalization, as well as the analysis of work experience.
Statistical Analysis of Blockchain Models from a Cloud Deployment Standpoint

Blockchain is quickly becoming one the most useful data security standard for cloud computing. This is due to the fact that blockchain systems possess immutability, transparency, traceability and distributed computing capabilities, which makes them highly usable in cloud environments. Cloud deployments essentially consist of distributed virtual machines (VMs), thereby assisting in blockchain implementation. Blockchains can be coupled with data privacy models like t-closeness, m-privacy, l-diversity, etc. for enhancing their security performance. But due to a wide variety of algorithmic models available for both blockchains and privacy preservation, it is difficult for researchers and security experts to select the most optimum models and their combinations for efficient system security. Thus, this text initially reviews a wide variety of blockchain implementations, and discusses their advantages, limitations, and future prospects. This is accompanied with a detailed discussion about data privacy models, and their characteristics. Following these discussions, this text compares these models in terms of performance metrics including computational delay, security level, application, and scalability. This comparison will assist researchers and cloud security experts to identify the best models and their combinations suited for their deployments. It is observed that blockchain and privacy preservation models when combined with machine learning techniques like Genetic optimization, neural networks, fuzzy rules, etc. outperform their counterparts. Furthermore, this text also recommends various proven fusion combinations, which can be used in order to improve cloud security without compromising on quality of service (QoS) parameters.