Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

Autor: Bucur, D., Iacca, G., Gaudesi, M., Squillero, G., Tonda, A.

Publikováno v: Applied Soft Computing, Volume 40, pp 416-426, 2016

In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users' mobility patterns and the message lo

Externí odkaz: http://arxiv.org/abs/1810.02713

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Test, Reliability and Functional Safety trends for Automotive System-on-Chip

Autor: Angione, F., Appello, D., Aribido, J., Bellarmino, N., Bernardi, P., Cantoro, R., de Sio, C., Foscale, T., Gavarini, G., Huch, M., Kilian, T, Mariani, R., Martone, R., Ruospo, A., Sanchez, E., Schlichtmann, U., Squillero, G., Sonza Reorda, M., Sterpone, L., Tancorre, V., Ugioli, R.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2153::c22fbd0c6fded605e92d672a9ba130d9
http://hdl.handle.net/11583/2955520

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Discovering potential clinical profiles of multiple sclerosis from clinical and pathological free text data with constrained non-negative matrix factorization

Autor: Acquarelli, J., Bianchini, M., Marchiori, E., Squillero, G., Burelli, P.

Publikováno v: Lecture Notes in Computer Science, 9597, 169-183. Springer Verlag GmbH
Squillero, G.; Burelli, P. (ed.), Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30--April 1, 2016, Proceedings, Part I, pp. 169-183
Squillero, G.; Burelli, P. (ed.), Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30--April 1, 2016, Proceedings, Part I, 169-183. Cham : Springer International Publishing
STARTPAGE=169;ENDPAGE=183;ISSN=0302-9743;TITLE=Squillero, G.; Burelli, P. (ed.), Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30--April 1, 2016, Proceedings, Part I

Constrained non-negative matrix factorization (CNMF) is an effective machine learning technique to cluster documents in the presence of class label constraints. In this work, we provide a novel application of this technique in research on neuro-degen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e4296ee3a234612f79f9c870c9feb1ce
https://pure.knaw.nl/portal/en/publications/104edf97-8212-4db5-878d-f52b4be6bf05