scholarly journals Expert group information formalization based on Z-numbers

2020 ◽  
Vol 1703 ◽  
pp. 012010
Author(s):  
O M Poleshchuk
2013 ◽  
Vol 44 (6) ◽  
pp. 361-372 ◽  
Author(s):  
Natascha de Hoog

The underlying process of reactions to social identity threat was examined from a defense motivation perspective. Two studies measured respondents’ social identification, after which they read threatening group information. Study 1 compared positive and negative group information, attributed to an ingroup or outgroup source. Study 2 compared negative and neutral group information to general negative information. It was expected that negative group information would induce defense motivation, which reveals itself in biased information processing and in turn affects the evaluation of the information. High identifiers should pay more attention to, have higher threat perceptions of, more defensive thoughts of, and more negative evaluations of negative group information than positive or neutral group information. Findings generally supported these predictions.


2005 ◽  
Vol 38 (01) ◽  
Author(s):  
I Gaertner ◽  
P Baumann ◽  
C Hiemke ◽  
S Ulrich ◽  
G Eckermann ◽  
...  

2014 ◽  
Vol 13 (8) ◽  
pp. 4729-4737
Author(s):  
Nikhil Sakhare ◽  
Pawan Khade ◽  
Purshottam J. Assudani

Many users like to watch video by a mobile phone, but the media player has many limitations. With a rapid development of communication and network, multimedia based technology is adopted in media player. Different approaches shows in this paper are plug-in extension technology, multimedia based on hierarchy, media player based on file browser, media player based on FFmpeg (Fast Forward Moving Picture Expert Group), media player based on file server.


2020 ◽  
pp. 8-15
Author(s):  
Olga Abaeva

The article is a review of the history of formation of the modern system of certification commissions in healthcare, as bodies carrying out a business assessment of industry personnel, their working principles, issues and problems of the regulatory framework.


This paper focuses upon the magnitude of income-based poverty among non-farm households in rural Punjab. Based on the primary survey, a sample of 440 rural non-farm households were taken from 44 sampled villages located in all 22 districts of Punjab.The poverty was estimated on the basis of income level. For measuring poverty, various methods/criteria (Expert Group Criteria, World Bank Method and State Per Capita Income Criterion) were used. On the basis of Expert Group Income criterion, overall, less than one-third of the persons of rural non-farm household categories are observed to be poor. On the basis, 40 percent State Per Capita Income Criteria, around three-fourth of the persons of all rural non-farm household categories are falling underneath poverty line. Similarly, the occurrence of the poverty, on the basis of 50 percent State Per Capita Income Criteria, showed that nearly four-fifths of the persons are considered to be poor. As per World Bank’s $ 1.90 per day, overall, less than one-fifth of rural non-farm household persons are poor. Slightly, less than one-fourth of the persons are belonging to self-employment category, while, slightly, less than one-tenth falling in-service category. On the basis of $ 3.10 per day criteria, overall, less than two-fifth persons of all rural non-farm household categories were living below the poverty line.


Author(s):  
Andrea Renda

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.


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