SCRAT Group - Semantic and Cognitive Research Adaptivity Technologies

Projects

PersonaWeb (ΤΠΕ/ΠΛΗΡΟ/0311(ΒΙΕ)/10)
The aim of this project is multi-fold: (i) to evaluate the significance of human factors in the generic web structures and more specifically in the area of eServices' field, (ii) to propose a Web browser enhancement that will extend the well known html model with a new set of xml tags based on cognitive semantics, (iii) to propose a new methodology for the dynamic extraction of users' cognitive perceptual preferences based on their navigation behaviour, and (iv) to propose a set of design guidelines for the automatic reconstruction of Web content based on users' individual characteristics.
smartag
The aim of smartag is multi-fold: (i) to evaluate the significance of human factors in the generic web structures and more specifically in the area of eServices’ field, (ii) to propose a Web browser enhancement that will extend the well known html model with a new set of xml tags based on cognitive semantics, (iii) to propose a new methodology for the dynamic extraction of users’ cognitive perceptual preferences based on their navigation behaviour, and (iv) to propose a set of design guidelines for the automatic reconstruction of Web content based on users’ individual characteristics.
EKPAIDEION (eLearning Adaptation and Personalization System)
EKPAIDEION aims at the research and analysis of parameters and contexts such as users intellectuality, mental capabilities, socio-psychological factors, emotional states and attention grabbing strategies, that could affect the apt collection of students' customization requirements offering in return the best adaptive environments to their preferences and demands.
AdaptiveWeb
AdaptiveWeb (within the framework of the Greek Programme Pythagoras) aims at establishing a more concrete definition of the Web Personalization notion introducing, and technologically evaluating methodologies and models derived from the research areas of visual processing, attention economy, social-cognitive psychology, affective computing.
CONET   (INFSO-ICT-224053)
The CONET Consortium is working on building a strong Community in the area of Cooperating Objects including research, public sector and industry partners from the areas of embedded systems, pervasive computing and wireless sensor networks.

The vision of Cooperating Objects is relatively new and needs to be understood in more detail and extended with inputs from the relevant individual communities that compose it. This will enable us to better understand the impact on the research landscape and to steer the available resources in a meaningful way.

CONET Project's Objectives are the following:
  • Create a visible and integrated community of researchers on the topics related to Cooperating Objects capable of driving the domain in the coming years.
  • Identify, arise awareness and steer academic research efforts towards industry-relevant issues without forgetting fundamental scientific issues; make the community more reactive to novel issues and approaches, and to coordinate its efforts; establish tight relationships with the European industry, leveraging interactions with leading US institutions in the field.
  • Stimulate cooperation between researchers in order to achieve a lasting and sustainable architecture that is able to cope with the vision of Cooperating Objects.
Recognizing Emotions using Wireless Sensor Networks (BSN) research cluster's main goal is is to investigate the application of cooperative techniques between multiple BSNs and between BSNs and environmental WSNs for recognition of human emotions and other mental conditions. The research will focus both on the required communication and signal processing algorithms, as well as on the integration of the algorithms and their implementations in an integrated software framework.

Expected results are:
  • Methods for cooperative data fusion between multiple BSNs and WSNs.
  • Methods for inferring socially relevant interactions between the users.
  • Methods for personalized content adaptation based of the users emotional state.
  • Methods for adaptation of human-machine control interfaces based of the users emotional state.
  • Demonstrators illustrating the developed techniques.