Reviewerly/exHarmony huggingface dataset:
Análise bibliométrica dos artigos da base de dados da Scopus sobre a Produção Científica Brasileira da Covid-19 / Bibliometric analysis of articles from the Scopus Database on the Brazilian Scientific Production of Covid-19

This work aimed to analyze the main characteristics of the articles in the Scopus database on the Brazilian scientific production of COVID-19.Bibliometrics being used as a statistical and quantitative technique for measuring the production and dissemination of scientific knowledge indices, topics of interest such as title, abstract and body of text were exported from the Scopus database and processed by the bibliometric analysis software VOSviewer (R).It is verified the great value of the subject dealt with due to its worldwide
The AVMSD as a harmonized approach to moderating hate speech?

While the EU Commission has set its sights on much broader online content regulation through the Digital Services Act (DSA), and the ongoing reforms to the eCommerce Directive (eCD), the AudioVisual Media Services Directive (AVMSD) is set to play a role in online hate regulation for content on video-sharing platforms (VSPs). The challenges though are greater than fitting within the DSA agenda and include particular barriers from national content moderation laws across various member-states, together with a fragmented umbrella of pan-European content regulation mechanisms. The convergence in timing of the AVMSD transposition, the draft DSA and the reform of the eCD (and its liability shield) offers a unique opportunity to assess the implications of harmonized content moderation responsibilities for hate speech from 2020 and beyond. This article explores the AVMSD, DSA and eCD as a ‘trend’, before discussing the impact of the reformulated AVMSD on VSPs and their efforts to tackle online hate. It argues that the AVMSD is an underappreciated tool in the increasingly harmonized approach to moderating online hate speech, but also a trigger-point for the new EU trend towards tackling platform power.
(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

Due to the advancement of software and hardware technologies such as geographic information systems, for example, optical fiber storage, mobile network, digital camera, and high-definition network, it has promoted the establishment of a data digital management system. Network sharing, to a certain extent, makes it the smart city video sensor of today’s era. It is a key component of the city’s smart management system. In the context of the rapid development of the information age, I explore the role of video sensors in the visual design of smart cities. I propose three methods to analyze the theory of big data video sensors: theoretical analysis of big data video sensors, information visualization, and visual signal output, and then model the video signal output to visualize the complex multilayer network decision. Starting from the RBF neural network, I simulate a video perception prediction model system, import the data into Matlab for experimental analysis and simulation, and compare RBF and BP are based on both data predictions. RBF obviously has stronger predictive video perception. Experimental data show that the error between video perception and actual data is within 3%. It can be seen that video sensors play a key role in the construction of smart cities.
Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.
Deep Kernelized Network for Fine-Grained Recognition

Convolutional Neural Networks (CNNs) are based on linear kernel at different levels of the network. Linear kernels are not efficient, particularly, when the original data is not linearly separable. In this paper, we focus on this issue by investigating the impact of using higher order kernels. For this purpose, we replace convolution layers with Kervolution layers proposed in []. Similarly, we replace fully connected layers alternatively with Kernelized Dense Layers (KDL) proposed in [] and Kernel Support vector Machines (SVM) []. These kernel-based methods are more discriminative in the way that they can learn more complex patterns compared to the linear one. Those methods first maps input data to a higher space. After that, they learn a linear classifier in that space which is similar to a powerful non-linear classifier in the first space. We have used Fine-Grained datasets namely FGVC-Aircraft, StanfordCars and CVPRIndoor as well as Facial Expression Recognition (FER) datasets namely, RAF-DB, ExpW and FER2013 to evaluate the performance of these methods. The experimental results demonstrate that these methods outperform the ordinary linear layers when used in a deep network fashion.
Efficient Access History for Race Detection

Previous chapter Next chapter Full AccessProceedings 2022 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX)Efficient Access History for Race DetectionYifan Xu, Anchengcheng Zhou, Grace Q. Yin, Kunal Agrawal, I-Ting Angelina Lee, and Tao B. SchardlYifan Xu, Anchengcheng Zhou, Grace Q. Yin, Kunal Agrawal, I-Ting Angelina Lee, and Tao B. Schardlpp.117 - 130Chapter DOI:https://doi.org/10.1137/1.9781611977042.10PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract While there has been extensive research on race-detection algorithms for task parallel programs, most of this research has focused on optimizing a particular component — namely reachability analysis, which checks whether two instructions are logically in parallel. Little attention has been paid to the other important component, namely the access history, which stores all memory locations previous instructions have accessed. In theory, the access history component adds no asymptotic overhead; however, in practice, it is often the most expensive component of race detection since it is queried and (possibly) updated at each memory access. We optimize this component based on the observation that, typically, strands within parallel programs access contiguous blocks of memory. Therefore, instead of maintaining the access history at the granularity of individual memory locations, we maintain it at the granularity of these (varying size) intervals. To enable this access history, we propose (1) compiler and runtime mechanisms that allow us to efficiently collect these intervals and (2) a tree-based access history data structure that allows us to update and query it at this interval granularity. The resulting tool can race detect fork-join code with amortized constant overhead, assuming the number of intervals is small compared to the total work of the computation. Our evaluations indicate that this technique improves the performance of race detection on several benchmarks. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-704-2 https://doi.org/10.1137/1.9781611977042Book Series Name:ProceedingsBook Code:PRAL22Book Pages:iii-220
Habitat Mapping for Ecosystem-Based Management of Deep-Sea Mining

Abstract Spatial considerations are important at multiple stages in the development of a deep-sea mining (DSM) project, from resource definition, to identification of preservation and management zones within a contract area, to planning of suitable ecological strata for baseline studies and impact assessment, to mine planning and adaptive management. Large investments are made to collect remote sensing data early in exploration to support geological resource studies, but environmental considerations are often instigated at later stages of exploration and can become disconnected from spatial frameworks. We outline a process of harmonizing the environmental and geological aspects of DSM project development by incorporating a habitat approach early in the development cycle. This habitat approach supports ecosystem-based management, which is a central requirement of environmental assessments. Geostatistical techniques are described that are used alongside a hierarchical classification scheme to describe and map geoforms and substrates. This foundational habitat model can form the basis of spatially explicit ecosystem models and can inform sampling design and spatial planning at critical junctures of a project development, ensuring that sampling campaigns are connected by an ecosystem logic early in the cycle. We provide an example application from the NORI-D polymetallic nodule exploration contract area in the Clarion-Clipperton Zone.
Bitdefender: Mid-Year Threat Landscape Report 2020

There are no prizes for guessing the major theme in cyber criminal activity in the first half of this year. It's the same topic that seems to infiltrate every part of our lives right now – the Covid-19 pandemic. But Bitdefender's report, based on telemetry from users of its solutions, indicates that the pandemic is being exploited in more ways than you might think.
Digital Twins with Application of AR and VR in Livestock Instructions

The paper considers the opportunities offered by the development of virtual and augmented reality as a modern approach to be included in education in the field of animal husbandry.The potential of different augmented reality (AR) and virtual reality (VR) tools and environments for use in different learning modes is well known and often explored.There are many examples of the application of VR/AR in medical education and human health.Therefore, there is no reason why this should not work to improve veterinary science education or to improve the professional experience of breeders -the technology is available, and it is all a matter of appropriate applications.The application of technologies for 3D visualization and modelling with haptic and sensory capabilities for feedback -AR/VR -in the field of veterinary science and animal husbandry is discussed and analysed.Using augmented reality and virtual reality tools to enhance the professional experience of breeders is considered.Improving animal welfare efforts through AR and VR is examined.The framework of this study lies in the proposed in the paper integration of the Five-Domain Model with the concept of Precision Livestock Farming.Methodology of digitalization of education and creation of new digital AR/VR educational resources in the field of animal husbandry is suggested.
Dutch SQuAD and Ensemble Learning for Question Answering from Labour Agreements

The Dutch Ministry of Social Affairs and Employment has to regularly explore the content of labour agreements. Studies on topics such as diversity and work flexibility are conducted on the regular basis by means of specialised questionnaires. We show that a relatively small domain-specific dataset allows to train the state-of-the-art extractive question answering (QA) system to answer these questions automatically. This paper introduces the new dataset, Dutch SQuAD, obtained by machine translating the original SQuAD v2.0 dataset from English to Dutch (made publicly available on https://gitlab.com/niels.rouws/dutch-squad-v2.0 ). Our results demonstrate that it allows us to improve domain adaptation for QA models by pre-training these models first on this general domain machine-translated dataset. In our experiments, we compare fine-tuning the pre-trained Dutch versus multilingual language models: BERTje, RobBERT, and mBERT. Our results demonstrate that domain adaptation of the QA models that were first trained on a general-domain machine-translated QA dataset to the Dutch labour agreement dataset outperforms the models that were directly fine-tuned on the in-domain documents. We also compare several ensemble learning techniques and show how they allow to achieve additional performance gain on this task. A new approach of string-based voting is introduced and we showed that it performs on par with a previously proposed approach.
Adaptation of Students from Far Abroad and Neighboring Countries at South-Russian Universities

The problem of successful adaptation of foreign students is considered from the perspective of strengthening the international status of not only Russian education, but also the government as a whole.Two sides of the adaptation -psychological (generalized) and socio-cultural (expressed)were studied on 397 foreign students from southern Russian universities using diagnostic tools designed on A.L. Sventsitsky "Self-assessment of psychological adaptability", and the scales "Cultural commitment to the country" and "Social commitment to the country" from S.V. Frolova' questionnaire.Spearman's rank correlation coefficient was used for statistical data processing.Psychological and socio-cultural adaptation were considered in the relation "isolationidentification" (V.I.Slobodchikov) and found a correlation ρ<0.01.According to the results, students from far abroad showed impoverished psychological adaptability with high demands for the success of socio-cultural adaptation.Assistance in its achievement from university teachers will occur when they will combine academic work with solving problems of educational and professional as well as social and communicative content.Students from the neighboring countries demonstrated an interconnected set of claims for successful socio-cultural adaptation, focusing on social ties and moving away from cultural ones.In this regard, the work of teachers is required to ensure the positive dynamics of intercultural relations with the involvement of the traditions of interethnic good-neighborliness in the territory of the southern region of Russia.Professional development programs for teaching staff should take into account the difference in cultural distance, manifested through the difficulties of a linguistic, cooperative, national, confessional nature.
A Conceptual review on different data clustering algorithms and a proposed insight into their applicability in the context of Covid-19

AI has paved the way which has enabled us to produce machines resembling human intelligence.Because of AI it is now possible that machines learn from experience and perform real thinking and tasks.For which this has been possible is named Machine Learning which has various sections under it.There are four different types of this area -Supervised Learning, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning.Machine Learning basically focuses on the learning of computers and performing tasks by themselves.Unsupervised learning is the one where no labelled input is there so, machine only identiies patterns in data and separate them into different clusters.Data clustering algorithm is an unsupervised type of machine learning where clusters get created from scattered data of any shape from unlabelled input.In this paper, some renowned data clustering algorithms to date and their applications will be analysed and discussed comparatively.And also, will provide an insight into helping them to be used in applications on the research ield of Covid-19.Studying and analysing the Data Clustering Algorithms and their applications and utilising that to help in research ield of Contagious diseases like, Covid-19 has been discussed and proposed in our research.Literature survey has been conducted to carry out this paper work.Google web search engine and Google Scholar search engine have been used to conduct research.Comparatively studying the Data Clustering Algorithms and their applications gave us insight that these can be employed into the research ield of Covid-19 analysis.As have been discussed in the proposed hypothesises, this paper can widely be operated in the ield of Biotech or Medicine, in genome research, or also, getting statistical data like infection regions, infection patterns, infected population etc. can be covered which will inally help in mitigating the impact of this disease, Covid-19.This is our expectation that with this review of data clustering algorithms as a future work, the hypothesises proposed here shall be researched more into experiments which will help measure the impact and effectiveness of Covid-19 like contagious diseases.
Uso del Software Educativo ISA’MATCH en el aprendizaje de Matemática en 8avo de Básica

The main objective of this article is to integrate the learning of mathematics with the educational software ISA'MATCH developed in Haeduc.For this purpose, a session is designed for the knowledge of the educational software and six sessions for the learning of the activity, included in the algebra and functions unit of the eighth year of general basic education, which include contents, resources, instruments and evaluations necessary to achieve optimal learning results.To complement the use of educational software, the learning of active methodologies and the flipped classroom model are promoted, which facilitates the interactivity of students with the teaching-learning activity and its improvement.For this proposal it was necessary to review the theoretical foundation based on educational software and mathematical competence, in order to adequately employ the instructional techniques and methodologies that we want to combine.Finally, the proposal offers a teaching tool that promotes learning and motivation, allowing the student to participate and be responsible for his educational process and the teacher to guide him.
Professional Network Community for Training Youth Workers: Conceptual Framework and Prerequisites for the Creation

Currently, in the context of the transition to hybrid education, new network forms of communication and interaction of teachers, students as well as specialists are in demand in the framework of personnel training, research and professional activities. The article reveals the main aspects of the integrated network community for training professionals working with youth in the field of educational and youth social policy. Network community can be viewed as an important resource for the development of both the participants themselves and the specific professional sphere. The purpose of the article is to describe the conceptual framework of a professional network community for training personnel for working with youth in the context of social, educational and youth policy and identify the prerequisites for its creation.In the course of the work, we used such methods as structural and functional analysis, system synthesis, modeling and an online survey. The sample comprised university students, faculty members, and youth workers (n = 147) from six federal districts of Russia.The conceptual framework of the professional network community presented on the basis of systemic-synergetic and constructive methodological approaches enables 1) to formulate its idea as a collaboration for personnel training, scientific research, joint projects implementation and self-development of participants; 2) to identify systemic contradictions, the solution of which is directed by the interaction of community members; 3) to determine the axiological basis, purpose, objectives and stages of deployment; 4) to identify the degree of its relevance, the presence of common interests among all its participants on the basis of the results of empirical research that may be useful in the professional network communities design.
Semantic enrichment of web services using linked open data

With the rise of the internet, a lot of web services (WSs) are offered. However, most of these WSs lack explicit and sufficient semantic information. As a result, relevant WSs are not delivered to consumers during service discovery and WS composition and selection are so challenging. A possible solution to this is to provide the existing syntactic WSs with explicit information of their meaning through an annotation. Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. In this paper, we first conduct a literature review and then we present an approach for semantically annotating WSs based on linked open data (LOD) knowledge. The proposal uses similarity measure algorithms for matching the appropriate LOD concepts to the corresponding WS' parameters. A performance evaluation on real-world WSs was performed to validate the proposed approach.
The Impact of Cryptocurrency Adoption as a Legal Tender in El Salvador

Cryptocurrency is taking the world by storm. It is certainly not a new technology anymore to the world. Countries all over the world are deciding on how they should treat the cryptocurrency as it directly impacts their existing ecosystem of fiat currency and how people use it. Different countries have adopted different approaches like considering it as a financial assets, securities, outright illegal or even legal tender in some countries. There are still a good number of countries which are yet to declare whether trading, using or mining it is legal or illegal. In such chaos, El Salvador is one such country which has adopted Bitcoin as a legal tender in 2021. It has been found that bitcoin adoption is expected to help their economy as GDP will probably increase after large scale adoption of Bitcoin. The electricity consumption for bitcoin mining has reached an all-time high. The environmental concerns are rising. However, El Salvador is keen on using 100% clean energy sources. El Salvador has been producing the largest amount of geothermal energy in all of Central America.
Deep Text Matching in Medical Question Answering System

The retrieval question-answering(Q&A) system based on Q&A library is a system that can retrieve the most similar question from Q&A library to get the correct answer. Classic approaches only use TF-IDF, BM25 and other algorithms to calculate the shallow correlation between the sentences in the input question and the sentences in the Q&A library, without fully considering the semantic information of the sentences. Recently, pre-trained language models have made remarkable achievements in many fields of natural language processing(NLP). In this work we apply the pre-trained language model in the medicine field to the text matching stage of medical question answering system. We also improve and propose a deep text matching model based on BERT, the Potential Topic extraction Medical Bert model(PT-McBERT). We conduct several experiments on the medical text matching dataset CHIP-STS, the results show that our model achieves improvements over the classic methods. Finally, we design a real-world Chinese medical question answering system and apply the optimal model to the question matching stage, which can greatly improve the matching effect of the system.