Targeted social mobilization in a global manhunt.

Autor: Alex Rutherford, Manuel Cebrian, Iyad Rahwan, Sohan Dsouza, James McInerney, Victor Naroditskiy, Matteo Venanzi, Nicholas R Jennings, J R deLara, Eero Wahlstedt, Steven U Miller

Publikováno v: PLoS ONE, Vol 8, Iss 9, p e74628 (2013)

Social mobilization, the ability to mobilize large numbers of people via social networks to achieve highly distributed tasks, has received significant attention in recent times. This growing capability, facilitated by modern communication technology,

Externí odkaz: https://doaj.org/article/c326cfd30fe945e9b5b72b3a39a4d428

From manifesta to krypta

Autor: Michele Piunti, Matteo Venanzi, Cristiano Castelfranchi, Rino Falcone

Publikováno v: ACM transactions on intelligent systems and technology
4 (2013). doi:10.1145/2438653.2438662
info:cnr-pdr/source/autori:Falcone R.; Piunti M.; Venanzi M.; Castelfranchi C./titolo:From manifesta to krypta: The relevance of categories for trusting others/doi:10.1145%2F2438653.2438662/rivista:ACM transactions on intelligent systems and technology (Print)/anno:2013/pagina_da:/pagina_a:/intervallo_pagine:/volume:4

In this article we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1c3b4713967478b221afe09380b63ad
https://doi.org/10.1145/2438653.2438662

Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems

Autor: Nicholas R. Jennings, Pushmeet Kohli, John Guiver, Matteo Venanzi

Publikováno v: Scopus-Elsevier

Crowdsourcing systems commonly face the problem of aggregating multiple judgments provided by potentially unreliable workers. In addition, several aspects of the design of efficient crowdsourcing processes, such as defining worker's bonuses, fair pri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ecca684f28133f5926f139c53f551fef
http://hdl.handle.net/10044/1/38350

Language Understanding in the Wild:Combining Crowdsourcing and Machine Learning

Autor: Steven Reece, Stephen J. Roberts, Nicholas R. Jennings, Pushmeet Kohli, Edwin Simpson, John Guiver, Matteo Venanzi

Publikováno v: Simpson, E 2015, Language Understanding in the Wild : Combining Crowdsourcing and Machine Learning . in WWW '15 : Proceedings of the 24th International Conference on World Wide Web . Association for Computing Machinery (ACM), pp. 992-1002, WWW 2015, Florence, Italy, 18/05/15 . https://doi.org/10.1145/2736277.2741689
24th International World Wide Web Conference (WWW 2015)
WWW

Social media has led to the democratisation of opinion shar-ing. A wealth of information about public opinions, cur-rent events, and authors insights into specific topics can be gained by understanding the text written by users. How-ever, there is a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0948449056f3f433e91fe3da0db4ae6
https://research-information.bris.ac.uk/en/publications/e6ed7b10-f0c0-4b9b-8588-81aee1329146

Community-based bayesian aggregation models for crowdsourcing

Autor: Matteo Venanzi, Gabriella Kazai, John Guiver, Milad Shokouhi, Pushmeet Kohli

Publikováno v: WWW

This paper addresses the problem of extracting accurate labels from crowdsourced datasets, a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of individual workers, for instance, by way of confusion matrices, and usi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed2a73909910685acff783940861ceca
https://doi.org/10.1145/2566486.2567989