Reviewerly/exHarmony huggingface dataset:
The artistic-creative communication in the music class

The article addresses the issue of preparing the music pedagogues for artistic-creative communication with the artistic imagery of music and with the students in the music classes. The integral factor for mastering the parameters of the artistic pedagogy by the students is the mutual conditionality of the artistic musical thinking, the complex overall knowledge of music and its perception through the prism of the musical activities in the music classes. A key moment in the development of the ability for artistic-creative communication is the direct connection between the processes of internalization of knowledge (its transformation into personal knowledge) and its implementation in a real environment through student pedagogical practice.
CoviChain: A Blockchain Based COVID-19 Vaccination Passport

Vaccination passports are being issued by governments around the world in order to open up their travel and hospitality sectors. Civil liberty campaigners on the other hand argue that such mandatory instruments encroach upon our fundamental right to anonymity, freedom of movement, and are a backdoor to issuing “identity documents” to citizens by their governments. We present a privacy-preserving framework that uses two-factor authentication to create a unique identifier that can be used to locate a person’s vaccination record on a blockchain, but does not store any personal information about them. Our main contribution is the employment of a locality sensitive hashing algorithm over an iris extraction technique, that can be used to authenticate users and anonymously locate vaccination records on the blockchain, without leaking any personally identifiable information to the blockchain. Our proposed system allows for the safe reopening of society, while maintaining the privacy of citizens.
Research on The Application of Blockchain Technology in Supply Chain Financial Business

Although supply chain finance is in a stage of continuous development, the current application of supply chain finance mainly uses traditional models, which will increase transaction costs and make operations more complicated. This article will analyze the possibility of blockchain application in supply chain finance, and finally proposes the future development trend of blockchain technology in financial services.
Current trends in Corpus Linguistics and textual variation

Corpus Linguistics has proved of great value as a methodological tool in shedding light on how discourse is constructed in different text types. This opening contribution to the special issue “Corpus-linguistic perspectives on textual variation” provides an account of some of the most common applications of Corpus Linguistics, describes some of the most widely used corpora, and pins down some of the most influential corpus-based research works. In so doing, we contextualise the contributions to this collection of articles. The main aim of this special issue is to showcase cutting-edge research on textual variation based on linguistic corpora, thus illustrating how Corpus Linguistics draws from but also feeds a multiplicity of linguistic branches, such as (Critical) Discourse Analysis, Register Studies, Historical Linguistics, and Dialectology.
Blockchain Application to the SMEs in the Food Industry

The agri-food sector is the highest application of blockchain technology to track and trace components and raw material along the supply chain. Blockchain presents many potential advantages; however, small and medium enterprises often lack the means and knowledge to exploit this technology fully. This chapter presents a case study of blockchain application, namely Trusty, a platform for the notarization of fully traceable information on blockchain in the agri-food sector. Thanks to its limited up-front implementation costs and its modularity, it seems particularly suitable for use by SMEs. The authors' goal is to identify some useful issues for practitioners and inspiration for future research for academics. The most relevant topics that will be placed are (1) needs and expectations of SMEs in the agri-food sector towards the blockchain, (2) difficulties of introducing blockchain into SMEs, (3) the agnostic approach to blockchain, (4) the different value of notarized information for B2B and B2C companies, and (5) limits of some blockchains compared to others.
Comparison of Chinese 50 ETF put option pricing based on four algorithms

The article starts with the traditional Black-Scholes(B-S) option pricing models. Three more models: Long-term and short-term memory networks(LSTM), support vector machine (SVM) and random forest(RF) are introduced to be compared to the B-S model and to each other on 50 ETF put option pricing. It is showed that each model has its advantages when used in different position. The neural network pricing result is better than that of the B-S model From the four evaluation indicators of MD, MSD, MAD and MPD, the absolute values of the four errors of the prediction results of the neural network are all smaller than the absolute values of the corresponding errors of the prediction results of the B-S model.
How I Learned to Stop Worrying and Love the Cloth:

Most research libraries house substantial quantities of publishers’ bindings, but it is not clear how well they accommodate them from a collection management or descriptive cataloging perspective. To gain some insight on this issue, a survey was carried out across four research libraries in the United Kingdom: the Wellcome Collection, the Victoria and Albert Museum, University College London, and Senate House Library (University of London). This article presents and discusses the survey’s findings and makes several recommendations designed to improve professional practice in this area.
Abstractive Text Summarizer: A Comparative Study on Dot Product Attention and Cosine Similarity

Text summarization is the process of extracting a subset of the document in such a way that the idea conveyed by the passage is understood while omitting peripheral details which do not have any impact on the passage. The aim of this work is to design an abstractive text summarizer using natural language processing that takes as input a newspaper article and provide a summary on that article in about 100 words. The model is designed using a Sequence to Sequence architecture coupled with an attention mechanism so that the model learns to pay attention to important words rather than trying to remember all of them. The model is trained using a dataset containing newspaper articles and their summaries provided by Kaggle. Pre-trained models such as BERT and T5 are also used to generate summaries and evaluate the performance of the proposed model against the pre-trained models. The three models such as Seq-Seq, BERT and T5 are evaluated on four datasets such as BBC-News-Dataset, Amazon food reviews, News-summary and NewsRoom datasets. Their rouge scores are analysed to select the ideal algorithm for summarization. The attention mechanism is customised to use cosine similarity instead of dot product. Cosine similarity is found to work better in the case of short summaries while dot product is found to work better for long summaries.
Software localization: from the Past to the Present

The article is devoted to the origin and development of computer software localization. The internationalization aspect is highlighted. Comparisons are made of the differences in localization in different languages. The history of Windows localized versions is considered. The features of specialized software localization are analyzed.
Important Features Identification for Prostate Cancer Patients Stratification Using Isolation Forest and Interactive Clustering Method

Prostate-specific Antigen (PSA) levels are commonly used to screen prostate cancer patients. However, because of the wide range of PSA levels in men, the classification results pertain to extensive false positives and false negatives that may impact the patient treatment. This paper presents a method to cluster prostate cancer patient clinical and demographics data into homogenous groups to support prostate cancer patients' classification with high accuracy. The proposed method is based on the isolation forest and interactive (two-step) clustering algorithm. We further analyze each group for commonalities and differences. The dataset used in this paper is collected from participants enrolled in the Alberta Prostate Cancer Research Initiative (APCaRI) study, which includes (after pre-processing) 2,878 patients with 20 clinical and demographics variables. The APCaRI study enrolled the population of men undergoing prostate cancer diagnosis in Calgary and Edmonton, Canada. These patients are referred for a diagnostic biopsy based on conventional clinical guidelines (e.g., elevated PSA or abnormal digital rectal examination). The data contains three different PSA levels measured at three follow-up times and the initial screening PSA level. The analysis results show that the PSA levels are a significant factor within each group, and there is a significant overlap between PSA levels between groups, and it may not be the best factor to classify prostate cancer patients. The data's majority group has PSA levels (10.83%, 10.44%, and 10.14%) smaller than the remaining groups. This paper concludes that it is maybe better to design an independent classifier per group to identify prostate cancer patients from clinical and demographics data.
Baikal–Vilyui Paleoproterozoic Belt of The Siberian Platform: Regional Gold-Controlling Structure

Abstract The article demonstrates that the placer gold content of the Vilyui Syneclise is governed by the regional structure of the crystalline basement of the Siberian Platform—the Baikal–Vilyui Paleoproterozoic belt, the boundaries and tectonics of which are substantiated by analysis of the gravitational field structure. The belt includes a system of basement blocks with a common northeastern strike, which form horsts (of the Suntar type) and grabens (of the Kempendyai type). The gold-bearing placers of the Vilyui Syneclise are mainly confined to the Suntar, Tyukyan, and Chybyda blocks of the belt, the metamorphic and igneous rocks of which were the primary gold sources in the sedimentary cover. The suture zone of the Baikal–Vilyui belt was very permeable to deep fluids responsible for gold migration. The types of possible primary gold sources (gold–platinoid, low-sulfidation gold-quartz, and gold–silver) reflect the peculiarities in the evolution of Early Precambrian gold during sedimentation in the Vilyui Syneclise. The areas controlled by the Suntar, Kempendyai, Tyukyan, Chybyda, and Khapchagai blocks of Early Precambrian rocks, which may contain gold objects, should be considered promising for buried gold placers in the Vilyui Syneclise.
Modeling the dynamics of a changing range genetic algorithm in noisy environments

The paper discussed the dynamic model of a genetic algorithm (GA) with an adjustment of a search space size in a noisy environment. A linear pseudo-Boolean function with positive coefficients was used for modeling the GA with a search space size adjustment. The impact of an additive noise on the fitness value of the GA is assessed, and analytical expressions are derived that described how GA’s selection and size adjustment operators alter the macroscopic statistical properties of population. Calculations based on the theory are compared with experiments demonstrating the accurate predictions for the macroscopic statistical properties of population.
Domain-oriented News Recommendation in Security Applications

The unprecedented growth of information on the Internet has brought about the problem of information overload. To alleviate this problem, news recommendation aims to select news articles for users according to their personal interests. In security applications such as intelligence collection and public opinion monitoring, it is of great importance to obtain valuable information quickly from massive news resources. Different from other application settings, users in security-related scenarios tend to browse news with a domain-oriented purpose. In contrast to the existing news recommendation methods which focus on general-purpose solutions, news recommendation in security applications needs domain-oriented solutions to incorporate users’ interests in a specific domain. To this end, in this paper, we propose the problem of domain-oriented news recommendation and develop a specific news recommendation model for security applications. Specifically, our proposed Domain-oriented News Recommendation (DNR) model extracts both general and specific preferences of the user, and performs matching between the user and the candidate news from the above two aspects to combine into the final result. We construct three security-related datasets using a large-scale real-world dataset and validate the effectiveness of our method.
Workflow and concept study to design mixed reality assisted safety training in the wind energy sector

Abstract Mixed reality Technology creates new environments and visualizations to teach, train, and facilitate work, either offline or remote online. The aim is to provide effective learning and training sources and reduce human errors and cognitive workload. However, the concept study to design mixed reality (MR)-assisted training lacks the workflow process. Therefore, the purpose of this paper is to develop a workflow to design MR-assisted training together with a worksheet that can be used to prioritize training tasks that should be virtualized and augmented. The workflow is demonstrated with the help of safety training for wind turbine towers. The results show that the proposed workflow has identified 4 out of 8 tasks to be critical for virtualization and augmentation. The proposed workflow is helpful to screen and identify critical training and work scenarios and tasks, besides the ability to determine the technical specifications of the required virtualization and augmentation.
Analysis for the reconstruction of attractors by reconstruction of phase spaces using delay times

Abstract Being able to estimate the behavior of a system from observable data is one of the great difficulties that any system presents. This problem presents a challenge for researchers who perform scenario estimation and forecasting. In most problems it is proposed to perform data analysis, but in this article, we propose to perform synthesis in such a way that a dipheomorphic attractor is constructed. that models the system. In the treatment of the analysis, we start from the inputs and assume some equations that describe the system, in the case of synthesis the most important thing is the data produced by the system, since these are real with some associated noise, so from those data and using Takens’ theorem, we can build an attractor that models the system we model in a more real way.
On-line modification of T-S fuzzy models based on gradient descent algorithm

The gradient descent algorithm is applied to the model parameter correction algorithm in view of the shortcoming that T-S fuzzy model cannot be used for real-time adaptive parameter correction. In this paper, an online model correction method combining T-S fuzzy model with gradient descent algorithm is proposed, and the operation data of a subway air-conditioning system in Wuhan is used for verification. The experimental results show that the T-S fuzzy model modified by gradient descent algorithm has better accuracy and higher operational efficiency.
Website, AR, VR: Comparison for Learning Motivation

There are media which can help people to learn such as website, augmented reality (AR), and virtual reality (VR). A previous study explained that AR can increase learning motivation for the student. Up until now, there is a limited study to compare learning motivation on website, AR, and VR. The purpose of this research was to compare learning motivation on the website, AR, and VR. This research using the ARCS (attention, relevance confidence, and satisfaction) model to compare learning motivation in website, AR, and VR. A total of 34 participant's data will be analyzed using one-way ANOVA within subjects. Participant will try three media and answer online questionnaire. The result of this study explained that VR is the higher media for learning motivation on attention, relevance, and confidence than AR and website. But AR has higher satisfaction mean values than VR. AR has 4.50 VR has 4.30 for the maen value of satisfaction.
Cloud Pattern Classification for Rainfall Prediction using Convolutional Neural Network

Rainfall prediction is an important research topic because of its wide range of applications in disaster and agricultural communities. It depends on several features of earth's atmosphere such as cloud information, speed and direction of wind, temperature, dew point, atmospheric pressure, etc. Most of the existing rainfall prediction models are based on time series dataset. Considering the computational complexity, and cost factors of time series dataset, in this paper we extensively explored the performance of different Convolutional Neural Networks (CNNs) architectures for rainfall prediction using cloud images in different scenarios. In our work, we have used two different stages for effective prediction of rainfalls from cloud images. Experiment results on SWIMCAT dataset reveals that usefulness and effectiveness of CNNs for rainfall prediction. This study can be a useful contribution for the research community of weather forecasting with broad range of applications i.e., flight navigation to agriculture and tourism.
A Secured Framework for E-Commerce in Cloud Computing Environment : A Literature Review

Our understanding of the importance of cloud computing-based electronic commerce in the digital world involves researching and looking for the most important issues faced by electronic commerce and security threats, and user information protection, and as a result, an e-commerce application framework can be proposed which may be based on the concepts, the origins, and development trend of cloud computing which copes with the problem of e-commerce and the storage of resources. As it is on record that cloud computing is widely known for its robust and strong support for data storage and mining. It also offers a most reliable platform for safe, secure, and speedy data transactions at a very manageable cost and a high level of privacy. Therefore, this paper has examined, analyzed, and discussed the current state of e-commerce and also, we provide much literature that has researched the security problems e-commerce faces in a cloud computing environment and some of them suggested some e-commerce security solutions.
Research on School-Enterprise Online Education Cooperation in China's Higher Vocational Colleges: —Based on the perspective of Internet distance teaching

School-enterprise cooperation is one of the important talent training modes of higher vocational education. This article analyzes the current situation of school-enterprise cooperation in higher vocational colleges in China. In response to the existing problems, this article studies the school-enterprise cooperation mechanism of higher vocational colleges from the perspective of the integration of Internet informatization and vocational education. This paper proposes a new mode of promoting school-enterprise cooperation based on a remote teaching system based on a network remote conference platform and a teaching resource sharing network database, and finally proposes corresponding solutions.
SoK: Cryptographic Confidentiality of Data on Mobile Devices

Abstract Mobile devices have become an indispensable component of modern life. Their high storage capacity gives these devices the capability to store vast amounts of sensitive personal data, which makes them a high-value target: these devices are routinely stolen by criminals for data theft, and are increasingly viewed by law enforcement agencies as a valuable source of forensic data. Over the past several years, providers have deployed a number of advanced cryptographic features intended to protect data on mobile devices, even in the strong setting where an attacker has physical access to a device. Many of these techniques draw from the research literature, but have been adapted to this entirely new problem setting. This involves a number of novel challenges, which are incompletely addressed in the literature. In this work, we outline those challenges, and systematize the known approaches to securing user data against extraction attacks. Our work proposes a methodology that researchers can use to analyze cryptographic data confidentiality for mobile devices. We evaluate the existing literature for securing devices against data extraction adversaries with powerful capabilities including access to devices and to the cloud services they rely on. We then analyze existing mobile device confidentiality measures to identify research areas that have not received proper attention from the community and represent opportunities for future research.
Neural Text Question Generation for Russian Language Using Hybrid Intelligent Information Systems Approach

The paper considers the task of natural language text generation, particularly the task of question generation based on the given text. Recent research in the area of text generation shows that better quality is achieved using the autoregressive generative models trained on language modeling tasks. However, such models do not use the metainformation of the text structure. In the paper, this approach is applied to Russian language texts. The architecture of the intelligent question generation system is described in detail.
Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.
A Case Study Integrating Knowledge Graphs and Intuitionistic Logic

In this work, we present a way to model and reason over Knowledge Graphs via an Intuitionistic Description Logic called iALC. We also introduce a Natural Deduction System for iALC to reason over our modelling of the information of the Knowledge Graphs. Furthermore, we apply this modelling to a case study in a context that aims to support the definition of concepts of Trust, Privacy, and Transparency, and the solution of apparent conflicts between them without the need for additional strategies, using only terms of the logic itself.