Biblio

We present data from detailed observations of a “controlled in-the-wild” study of Brain-Computer Interface (BCI) system. During 10 days of demonstration at seven nonspecialized public events, 1563 people learned about the system in various social configurations. Observations of audience behavior revealed recurring behavioral patterns. From these observations a framework of interaction with BCI systems was deduced. It describes the phases of passing by an installation, viewing and reacting, passive and active interaction, group interactions, and follow-up actions. We also conducted semi-structured interviews with the people who interacted with the system. The interviews revealed the barriers and several directions for further research on BCIs. Our findings can be useful for designing the BCIs foxr everyday adoption by a wide range of people.

In this paper we use car games as a simulator for real automobiles, and generate driving logs that contain the vehicle data. This includes values for parameters like gear used, speed, left turns taken, right turns taken, accelerator, braking and so on. From these parameters we have derived some more additional parameters and analyzed them. As the input from automobile driver is only routine driving, no explicit feedback is required; hence there are more chances of being able to accurately profile the driver. Experimentation and analysis from this logged data shows possibility that driver profiling can be done from vehicle data. Since the profiles are unique, these can be further used for a wide range of applications and can successfully exhibit typical driving characteristics of each user.

Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.