From e-voting to smart-voting : e-Tools in and for elections and direct democracy in Switzerland

To what extent do and could e-tools contribute to a democracy like Switzerland? This paper puts forward experiences and visions concerning the application of e-tools for the most traditional democratic processes- elections and, of special importance in Switzerland, direct-democratic votes.Having the particular voting behaviour of the Swiss electorate in mind (low voter turnout - especially among the youngest age group, low political knowledge, etc.) we believe that e-tools which provide information in the forefront of elections or direct-democratic votes offer an enormous service to the voter. As soon as e-voting will be possible in Switzerland (as planned by the government), those e-tools for gathering information online will become indispensable and will gain power enormously. Therefore political scientists should not only focus on potential effects of e-voting itself but rather on the combination of (connected)e-tools of the pre-voting and the voting sphere. In the case of Switzerland, we argue in this paper, the offer of VAAs such as smartvote for elections and direct-democratic votes can provide the voter with more balanced and qualitatively higher information and thereby make a valuable contribution to the Swiss democracy.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.