Parte il contest grafico/letterario organizzato dal progetto Europeo DOGANA
"Contraband Pixels & Texts, or... make stories, not phishing" is a literary-graphic competition on social engineering and phishing, organized by PRA Lab and CNIT (Consorzio nazionale interuniversitario per le telecomunicazioni), partner of the DOGANA project, funded under the HORIZON 2020 programme.
Participants: writers and cartoonists / illustrators.
Registration: registration is free and open to people residing in EU countries (Austria, Belgium, Bulgaria, Cyprus, Croatia, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg , Malta, Netherlands, Poland, Portugal, United Kingdom, Czech Republic, Romania, Slovakia, Slovenia, Spain, Sweden, Hungary), Israel and Switzerland.
Rules: writers must submit a short story (max. 5000 characters including spaces, excluding title) addressing the theme provided by the organization;illustrators must submit a table of up to 1024x768 pixels resolution, representing or summarizing the project theme in a drawing or in a comic strip. The same author may submit multiple tables and short stories. Artworks can be submitted in Italian, in English and / or in both languages (Italian and English). Artworks presented in two languages will receive an additional bonus.
Artworks must be shared on the Facebook page dedicated to the competition (https://www.facebook.com/pixelettere), starting from February, 13th, 2017. Last date to submit artworks is June, 10th, 2017. Dates for intermediate selections will be communicated time to time
The European project FORC, launched in January and lead by PRA Lab, goes live. Media coverage.
FORC (Pathway in Forensic Computing) is a project funded by the European Commission under the ERASMUS+ programme. The project, lead by University of Cagliari - PRA Lab, involves 8 partners from Europe (UK, Ireland), Palestine and Jordan, aims to improve the level of competences and skills of the partner universities in Palestine and Jordan by enabling them to develop sustainable integrated curricula in Forensic Computing, across the domains of Law and technology.
The specific objectives of the project are:
- To update the bachelor programs by defining the structure of the new Forensic Computing Pathway, which recognized by all consortium partners.
- To develop, validate and implement a set of courses 8 courses, on emerging areas of Forensic Computing that address the following: Digital Investigation, Issues in Criminal Justice, Digital Forensics, Ethical Hacking, Digital Evidence.
- To develop 4 case studies in Forensic Computing using student-centred adaptive elearning contemporary education methodology, which focused on computer crime and computer Investigation.
- To improve the level of competencies and skills of staff in partner country universities by (i) training visits for staff to EU partners to develop Forensic Computing expertise in curriculum development and innovative learning, and (ii) providing research collaboration opportunities with EU staff through joint- supervision of students' projects.
- Additionally, to create opportunities of collaboration between academia and industry in Forensic Computing field.
- To establish Forensic Computing laboratories at partner countries universities, which will be used for teaching and research.
Media coverage related to the project's kick-off.
Special Session on "Sparse Data Machine Learning for Domains in Multimedia" at CBMI 2017, June 19-21, Florence - Call for papers
This is a cordial invitation to submit a paper to the special session on "Sparse Data Machine Learning for Domains in Multimedia" at CBMI 2017- Content-Based Multimedia Indexing, to be held in Florence, Italy (19 - 21 June, 2017).
Sparse Data Machine Learning for Domains in Multimedia
Using multimedia in specific fields such as medicine or psychology is an emerging trend, which gives the multimedia community an opportunity to perform research that can have societal impact and help people. Nevertheless, these domains often come with some challenges. One of the biggest challenges is the availability of labeled data. In the area of medicine, there are some areas and diseases that are well covered, for example image and video data for polyp detection. Thus, even the well covered areas have, compared to other datasets, very little data. This fact makes it very challenging to apply machine learning methods and get meaningful results. Especially, the highly praised and used deep learning is very depending on a lot of good training data. Moreover, deep learning might not be the silver bullet for tackling every problem. With this special session, we want to emphasize that there are relevant and emerging topics that might not or are against the odds be solvable with deep learning and require a more open and broader point of view.
The scope of this session is machine learning in areas that come with a lack of data, such as medicine, addressing the challenges described in the motivation.
Examples for topic of interest are:
- Traditional machine learning vs Deep learning
- Machine Learning on small data
- Deep learning with small datasets
- Unsupervised machine learning
- Data creation
- Data annotation
- Knowledge transfer
- Areas with small multimedia datasets (for example medicine, psychology)
- Multimedia tools and applications in fields with lack of data (for example medicine, psychology)
More information about the conference can be found here.
For guidelines and submission procedure see Paper submission.
The CBMI proceedings are traditionally indexed and distributed by IEEE Xplore and ACM DL.
In addition, authors of the best papers of the conference will be invited to submit extended versions of their contributions to a special issue of Multimedia Tools and Applications journal (MTAP).
- Full/short paper submission deadline: February 28, 2017
- Notification of acceptance: April 10, 2017
- Camera-ready papers: April 21, 2017
- Konstantin Pogorelov - Simula Research Laboratory, Norway - konstantin(at)simula.no
- Duc-Tien Dang-Nguyen, Dublin City University, Ireland - (duc-tien.dang-nguyen(at)dcu.ie)
- Michael Riegler - Simula Research Laboratory, Norway - michael(at)simula.no
- Luca Piras, University of Cagliari, Italy (luca.piras(at)diee.unica.it)
- Paolo Rota - Vienna University of Technology, Austria - rota(at)caa.tuwien.ac.at
- Pål Halvorsen - Simula Research Laboratory, Norway - paalh(at)simula.no