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Spam is serious problem that affects email users (e.g. phishing attacks, viruses and time spent reading unwanted messages). We propose a novel spam email filtering approach based on network-level attributes (e.g. the IP sender geographic coordinates) that are more persistent in time when compared to message content. This approach was tested using two classifiers, Naive Bayes (NB) and Support Vector Machines (SVM), and compared against bag-of-words models and eight blacklists. Several experiments were held with recent collected legitimate (ham) and non legitimate (spam) messages, in order to simulate distinct user profiles from two countries (USA and Portugal). Overall, the network-level based SVM model achieved the best discriminatory performance. Moreover, preliminary results suggests that such method is more robust to phishing attacks.

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Rock mass characterization is normally carried out through the application of empirical classification systems which use a set of geomechanical data and provide an overall description of the rock. Moreover, they allow obtaining other important information like support needs, stand-up time, geomechanical parameters among others. The different classification systems have some well known drawbacks and limitations due mainly to their empirical base (Miranda, 2003). However, they are still very useful in practice therefore there is a need to improve their efficiency. Two of the most used classification systems are the RMR-Rock Mass Rating (Bieniawski, 1989) and the Q-system (Barton et al, 1974). The RMR system is based on the consideration of six geological/geotechnical parameters. To each parameter is assigned a relative weight related to the rock mass characteristics and the final RMR value is the sum of these weights and can vary from 0 to 100. The parameters considered by this system are the following: P 1-uniaxial compressive strength; P 2-Rock Quality Designation (RQD); P 3-Discontinuities spacing; P 4-Discontinuities conditions; P 5-Underground water conditions; and P 6-Discontinuities orientation. The Q-system also uses six parameters to which values have to be assigned depending on the rock mass characteristics. The final Q value is then obtained through the following expression:

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