Reviewerly/exHarmony huggingface dataset:
PEDAGOGICAL CONCEPT “SCIENCE EDUCATION”

In the article, we have been considered some approaches to understanding the term "science education", both foreign and domestic authors. Have been substantiated own understanding of the term, based on the experience of the Junior Academy of Sciences of Ukraine also. Have been described the most commonly used terms of science education and delimited their semantic field.
Image denoising using deep convolutional autoencoder with feature pyramids

Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted natural images are used during training to improve robustness against various noise levels. Experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state of the art methods. Extensive experiments showed the proposed method gives promising performance in a wide range of noise levels with a single network.
Influence of Sentiment in Question Answering Systems

The task of a question answering (QA) system is to automatically answer questions asked by humans, expressed in a natural language. In recent years, platforms, where QA systems are applicable, have ...
Innovative orientation of the university lecturer activity

The article deals with significant changes in the educational process of a modern university, focuses on the change in motivation and value orientations of teachers of an innovative university in connection with globalization. Attention is drawn to the characteristics of modern education as a new social industry, due to the digital economy and the new generation of students. The article notes that digital learning (such a concept was introduced in the thesaurus of professional pedagogy) sets new lines in pedagogical activity, meeting resistance of some teachers who are not ready to use electronic tools and perform new professional functions, for example, monitoring learning outcomes. An increase in the teaching load causes teachers to distrust the reforms carried out in education, and sometimes professional deformation.It is proved that the quality of education is determined not only by digital technologies, but also by the quality of educational conditions, professional and pedagogical competence of the teacher. At the same time, methodological support of experienced teachers is required to adapt to innovations and beginners in order to compensate for the lack of knowledge, skills and abilities when performing innovative tasks. The article provides a link to the experience of organizing advanced training for teachers at Samara University, the essence of which is that the results of research on relevant pedagogical problems by its teachers become the basis for determining the content of programs in the system of further education.
Benchmarking Database Cloud Services

Meanwhile, many database cloud services are available. The well-known providers are AWS (Amazon Web Service), Google Cloud, and Azure (Microsoft). Oracle and IBM offer cloud services for their in-house database products. In the past, the TPC organization has focused on performance measurement of database systems. Often, however, a database system is predefined, and the question arises as to the most efficient infrastructure and the best price/performance ratio - whether on-premise or as a cloud service. On the Internet, you can hardly find comparable and traceable information about the performance of database cloud services. Therefore, it is challenging to make corresponding price/performance comparisons [1]. The company Peakmarks was founded in 2011 to provide a robust and comprehensive benchmarking framework to identify representative performance indicators of database services. Peakmarks does not sell any hardware but runs benchmarks on behalf of users and manufacturers and thus guarantees absolute independence. Users can license Peakmarks benchmark software to perform their own performance tests. This presentation gives a rough overview of the Peakmarks benchmark software, its architecture, and workloads. Examples are used to show how understandable key performance metrics for database cloud services can be determined quickly and practically.
Privacy-Preserving Blockchain-Based Data Sharing Platform for Decentralized Storage Systems

Cloud-based storage services have been the dominating outsourcing solution for both individuals and organizations to share data digitally. Despite the advantages, users must rely on storage services for data confidentiality, data access control, user privacy, and data availability. Whereas data confidentiality can be protected by advanced encryption algorithms, the rest remain challenging. First, in existing centralized storage services, even though data access controls are mainly defined by data owners, they are maintained and enforced by the services, which can deny data retrieval requests of authorized users or allow requests of illegitimate users. Second, the identity of a user is often known to the services to verify its eligibility to access requested data according to the access control, thus making the user traceable in the system. More importantly, the lack of anonymity may make users reluctant to use such services in sensitive contexts. Third, a huge amount of data is daily generated and stored on a centralized party, simultaneously serving requests from many users, which may cause a collapse of the system during peak periods. To address all these concerns, we propose a privacy-preserving blockchain-based data sharing platform for the InterPlanetary File System (IPFS), a content-addressable peer-to-peer storage system. The platform allows protecting both user anonymity, data confidentiality, and provides high data availability due to being deployed upon the IPFS network.
Reactive Navigation in Cluttered Indoor Environment for Autonomous MAVs

In recent years, Micro Aerial Vehicles (MAVs) have been a desired solution for indoor applications. However, most of today's approaches are restricted to piloted applications for lack of autonomy in unknown scenario. There, the MAV must independently handle both infrastructure and arbitrary obstacles. We present a navigation system for autonomous flight in cluttered indoor environments. First, we transfer the Elliptic Limit Cycle (ELC) approach for target-oriented obstacle avoidance to the dynamics of MAV flights. In terms of automated perceptive functionality, laser data clustering and parameter estimation on the fly are presented. Second, we develop refinements to simultaneously handle both isolated obstacles and connected infrastructure, which is a necessary condition for any indoor application. We evaluate the automated obstacle perception by means of real experimental data acquired in interior space. The guidance system's proof of concept is given based on realistic indoor environment software in the loop tests.
Characterising correlations under informational restrictions

The strength of correlations observed between two separated events hinges on the amount of information transmitted between them. We characterise the correlations that can be created in classical and quantum experiments which feature a given amount of communicated information. For classical models, we present a complete characterisation of informationally restricted correlations in terms of linear programming. For quantum models, we develop a hierarchy of increasingly precise semidefinite relaxations to bound the set of informationally restricted quantum correlations. We leverage these techniques to i) derive device-independent witnesses of the information content of quantum communication, ii) the derivation of strict inequalities for different quantum information resources and iii) a new avenue for semi-device-independent random number generation based on the information assumption.
DIGITAL TRANSFORMATION CHALLENGES AND OPPORTUNITIES IN UNIVERSITY EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 8048-8055ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.2016Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Obtaining and Maintaining the Distribution of Natural Reference Materials for U-Pb Geochronology by LA-ICP-MS: Protocols and Future Perspectives

Journal Article Obtaining and Maintaining the Distribution of Natural Reference Materials for U-Pb Geochronology by LA-ICP-MS: Protocols and Future Perspectives Get access Guilherme Gonçalves, Guilherme Gonçalves Universidade de Brasília - UnB, Brasília, Distrito Federal, Brazil Search for other works by this author on: Oxford Academic Google Scholar Cristiano Lana, Cristiano Lana Universidade Federal de Ouro Preto - UFOP, Ouro Preto, Minas Gerais, Brazil Search for other works by this author on: Oxford Academic Google Scholar Ricardo Scholz, Ricardo Scholz Universidade Federal de Ouro Preto - UFOP, Ouro Preto, Minas Gerais, Brazil Search for other works by this author on: Oxford Academic Google Scholar Ian Buick, Ian Buick Stellenbosch University, Stellenbosch, Western Cape, South Africa Search for other works by this author on: Oxford Academic Google Scholar Maristella Santos Maristella Santos Instituto Federal de Minas Gerais - IFMG, Congonhas, Minas Gerais, Brazil Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 26, Issue S2, 1 August 2020, Pages 3054–3055, https://doi.org/10.1017/S1431927620023661 Published: 01 August 2020
USING WEB-BASED MUSIC COMPOSITION APPLICATIONS TO ENHANCE NON-SPECIALIST PRIMARY TEACHERS’ SELF-EFFICACY IN MAKING AND TEACHING MUSIC

USING WEB-BASED MUSIC COMPOSITION APPLICATIONS TO ENHANCE NON-SPECIALIST PRIMARY TEACHERS’ SELF-EFFICACY IN MAKING AND TEACHING MUSIC
An analysis framework of portable and measurable higher education for future cybersecurity workforce development

An educated workforce is essential to government and industry, hence the need to provide a high-quality workforce has been crucial in higher education academic program development. In the cybersecurity field, the situation is not quite satisfactory, the reason comes down to the fact that this new industry is lacking a portable and measurable framework to evaluate the efficacy of the academic programs, thus, to provide the industry with the unified high-quality workforce. In this paper, we aim to come up with a design of an analytical framework for portable and measurable academic programs for future workforce development.The ultimate purpose for our research is to develop cybersecurity workforce through the increase of the number of cybersecurity professionals with a 4-year degree, in this project we will develop a seamless pathway for students transferring from 2-year programs such as Ivy Tech Community College of Indiana(Ivy Tech) Cybersecurity AAS program to a 4-year program such as Purdue University Northwest(PNW) CIT program.
Exploring Structured Information Systems: A Keyword Search Approach

Google search, and other search engines (e.g., Bing), provide a convenient way to find web pages that contain different keywords. Each web page is composed of loosely-structured paragraphs of text. On the other hand, a vast amount of information systems data are stored as structured data (e.g., in relational databases). In a structured database, the information about a particular entity, or the relation between different entities might be scattered across many rows and columns in different tables. These pieces of information must be stitched together to form the final answer to a user's query. For a large database, there might be millions of answers to a search query. Thus, finding the right answer is a challenging task. In this work, we present an intuitive search system over structured information systems. This search system empower non-technical users to explore structured information systems and turn hidden information into useful insights.
A Comparative Study of Text Mining Algorithms for Anomaly Detection in Online Social Networks

Text mining is a process by which information and patterns are extracted from textual data. Online Social Networks, which have attracted immense attention in recent years, produces enormous text data related to the human behaviours based on their interactions with each other. This data is intrinsically unstructured and ambiguous in nature. The data involves incorrect spellings and inaccurate grammars leading to lexical, syntactic and semantic ambiguities. This causes wrong analysis and inappropriate pattern identification. Various Text Mining approaches are being used by researchers which can help in Anomaly Detection through Topic Modeling, identification of Trending Topics, Hate Speeches and evolution of the communities in Online Social Networks. In this paper, a comparative analysis of the performance of four classification algorithms, Support Vector Machine (SVM), Rocchio, Decision Trees and K-Nearest Neighbour (KNN) for a Twitter data set is presented. The experimental study revealed that SVM outperforms better than other classifiers, and also classifies the dataset into anomalous and non-anomalous user’s opinions.
Domain Adaptive Fusion for Adaptive Image Classification

Recent works in the development of deep adaptation networks have yielded progressive improvement on unsupervised domain adaptive classification tasks by reducing the distribution discrepancy between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated the unprecedented potential for utilizing unlabeled data to train a classification model in defiance of a discouragingly meager labeled dataset. In this paper, we propose Domain Adaptive Fusion (DAF), a novel domain adaptation algorithm that encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of our hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
An Intelligent 2D Secret Share Construction using Visual Cryptography for Secure Transmission

Data Security is the most challenging area in Internet communication, where most of the secret sharing schemes are proposed for binary images.But still it lacks in providing security for data communication, especially in image transmission.Traditional visual cryptography scheme generate meaningless diwies and the reconstruction phase leads to quality degradation over the secret image.In this work, an intelligent two dimensional secret share construction scheme is proposed.A secret image is expanded into n diwies with the choice of scheme selection.By Stacking all the qualified diwies to revert the secret image without content loss and less than s* -1 shares could not reveal any information about the secret image.The experimental results emphasize that the proposed secret share scheme is highly secured for image transmission.
SecAOnto

Security assessment is crucial to the implementation and use of secure web portals. Literature reports studies about knowledge representation models for systems assessment and information security areas; however, there is a lack of conceptual formalization for the security assessment area. The security assessment ontology (SecAOnto) objective is to formalize knowledge on security assessment. It is based on ontologies, taxonomies, vocabularies, glossaries, and market guidelines. This paper presents an application of SecAOnto with the objective of identifying concepts in descriptions of security assessment items; the coverage of security characteristics is determined by using a coverage calculus algorithm. The application of SecAOnto and of the coverage calculus algorithms to the well-known standard ISO/IEC 27001 highlights its expressiveness. The proposal is useful for security experts and researchers in the context of security assessment, as well as to support web-based conceptual architectures.
A generalized Nadaraya–Watson estimator for observations obtained from a mixture

A generalization of the NadarayaâWatson estimator is considered for observations obtained from a mixture with varying concentrations. The consistency and asymptotic normality of the estimators are established.
A general-purpose toolbox for efficient Kronecker-based learning

Pairwise learning is a machine learning paradigm where the goal is to predict properties of pairs of objects.Applications include recommender systems, molecular network inference, and ecological interaction prediction.Kronecker-based learning systems provide a simple yet elegant method to learn from such pairs.Using tricks from linear algebra, these models can be trained, tuned, and validated on large datasets.Our Julia package Kronecker.jlaggregates these shortcuts and efficient algorithms using a lazilyevaluated Kronecker product '⊗', such that it is easy to experiment with learning algorithms using the Kronecker product.
Students Awareness on Educational Tour Policies: Commission on Higher Education Memorandum Order (CMO) No. 17 Series of 2012

400 students became respondents in Higher Educational Institutions in S.Y. 2016 - 2017 with the study students’ level of awareness on the Educational Tour. The study used Descriptive – correlational design utilizing. Pearson r, Point Biserial and Eta Correlation as Statistical tools.  The findings state that most respondents answered a much aware level of awareness on the Educational Tour before, during and after the conduct of the study. Lack of money, time, safety and management deficiencies were the moderately felt problems. It concluded that the level of awareness in Educational Tour before, during then after educational tour is much aware. The Study recommends, sustainable awareness programs, inclusion of the discussion of educational tour in student, parent’s orientation and discussed in the student handbook. Constraints with educational tour be addressed before the activity, and a conduct of relevant studies regarding to educational tour be further addressed.
Do Corporate Social Responsibility Activities Influence Employees’ Job Performance?

The study investigated the influence of corporate social responsibility activities on employee job performance of listed food and beverages companies in Nigeria. The study, anchored on the Social Identity Theory, adopted the content analysis approach and obtained panel data from financial statements of sampled companies for the period 2014 to 2018. The study deployed descriptive, correlation and regression analyses as data analytical techniques. The findings indicated that employment of physically challenged, expenditure on staff training and development, and community based projects have a positive and significant association with employees’ job performance. The study further revealed that monetary donations and gifts to charity organisations and NGOs have a significant negative relationship with employees’ job performance. The study concludes that corporate social responsibility practices enhances job performance of employees and recommends amongst others the need for corporate managers to properly recognize the type of CSR activities that their employees respond favorably to since not all CSR activities motivate employee job performance.
The Role of Big Data Analytics in Internet of Things : Comparative Analytical Study

The study Aimed to Identify in a Relation Ship big data analytics in IOTapplications benefits big data in internet of things , and Show the most popularplatforms to big data and IOT such as : Kaa , Hadoop , MAPR , and study therequirements of big data analytics in IOT finally show challenges facing big datain IOT the study was approved on the comparative method and reached severalresults the most important of them , the main problems that you face big data inIOT security and privacy 97%The most important results of the study : Big data and the Internet of things are complementary to each other, andthis integration is achieved through data analysis platforms. One of the most common security problems in IoT applications with bigdata is data center security, access control, redundant data filtering. One of the standards of open source Internet of Things platforms isstandards of integration, security and confidentiality, protocols,visualization support, databases. One of the most used big data analytics platforms is Hadoop, with apercentage of 90%, followed by Map R with 72.3%. Kaa is also one of themost used Internet of Things platforms and uses its huge ability tomanage devices. One of the most used terms in big data studies is terms: big data, datamining, data delivery, digital archiving, decision making, artificialintelligence, and cloud computing.
Toward presizing and pretransitioning strategies for GraalPython

Presizing and pretransitioning are run-time optimizations that reduce reallocations of lists. These two optimizations have previously been implemented (together with pretenuring) using Mementos in the V8 Javascript engine. The design of Mementos, however, relies on the support of the garbage collector (GC) of the V8 runtime system.
Swarm intelligence-based optimisation algorithms: an overview and future research issues

Swarm intelligence-based optimisation algorithms, inspired by the collective intelligent behaviours of biology groups, have been widely recognised as efficient optimisers for many complex problems, e.g., dynamic optimisation problems, large-scale optimisation problems and many-objective optimisation problems. Swarm intelligence-based algorithms are the generic concepts to represent a range of metaheuristics with population-based iterative process, guided random search and parallel processing. This paper conducts an in-depth analysis of universality and difference of existing swarm intelligence-based algorithms. It also provides a systematical survey of some well-known algorithms. In addition, the expected research issues such as theoretical analysis, hybridisation strategy and complex problems optimisation are discussed thoroughly to inspire future study and more extensive applications.
Effective Speech Features for Distinguishing Mild Dementia Patients from Healthy Person

The questionnaire method is generally used for present dementia screening. However, this method requires time for 10 to 15 min with a doctor and a clinical psychologist, which puts a burden on hospitals and test subjects. The purpose of this study is to reduce the burden of users by constructing a system to distinguish patients with mild dementia and healthy persons from speech data. Before that this paper examines the effectiveness of speech features. MFCC has been confirmed to be effective in previous research, this paper extracted six kinds of other speech features that are likely to be correlated with symptoms of dementia. This paper got about 90% accuracy rate for a sentence of conversational speech in SVM and Random Forest. Moreover, this paper has calculated the importance of the features by using the SVM-RFE method. As a result, this showed that log-mel spectrum was more important than MFCC.