Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

Developing an Efficient DMCIS with Next-Generation Wireless Networks

The impact of extreme events across the globe is extraordinary which continues to handicap the advancement of the struggling developing societies and threatens most of the industrialized countries in the globe. Various fields of Information and Communication Technology have widely been used for efficient disaster management; but only to a limited extent though, there is a tremendous potential for increasing efficiency and effectiveness in coping with disasters with the utilization of emerging wireless network technologies. Early warning, response to the particular situation and proper recovery are among the main focuses of an efficient disaster management system today. Considering these aspects, in this paper we propose a framework for developing an efficient Disaster Management Communications and Information System (DMCIS) which is basically benefited by the exploitation of the emerging wireless network technologies combined with other networking and data processing technologies.Comment: 6 page

A Secure Lightweight Approach of Node Membership Verification in Dense HDSN

In this paper, we consider a particular type of deployment scenario of a distributed sensor network (DSN), where sensors of different types and categories are densely deployed in the same target area. In this network, the sensors are associated with different groups, based on their functional types and after deployment they collaborate with one another in the same group for doing any assigned task for that particular group. We term this sort of DSN as a heterogeneous distributed sensor network (HDSN). Considering this scenario, we propose a secure membership verification mechanism using one-way accumulator (OWA) which ensures that, before collaborating for a particular task, any pair of nodes in the same deployment group can verify each other-s legitimacy of membership. Our scheme also supports addition and deletion of members (nodes) in a particular group in the HDSN. Our analysis shows that, the proposed scheme could work well in conjunction with other security mechanisms for sensor networks and is very effective to resist any adversary-s attempt to be included in a legitimate group in the network.Comment: 6 page

Patterns of Participation and Motivation in Folding@home: The Contribution of Hardware Enthusiasts and Overclockers

Folding@home is a distributed computing project in which participants run protein folding simulations on their computers. Participants complete work units and are awarded points for their contribution. An investigation into motivations to participate and patterns of participation revealed the significant contribution of a sub-community composed of individuals who custom-build computers to maximise their processing power. These individuals, known as “overclockers” or “hardware enthusiasts,” use distributed computing projects such as Folding@home to benchmark their modified computers and to compete with one another to see who can process the greatest number of project work units. Many are initially drawn to the project to learn about computer hardware from other overclockers and to compete for points. However, once they learn more about the scientific outputs of Folding@home, some participants become more motivated by the desire to contribute to scientific research. Overclockers form numerous online communities where members collaborate and help each other maximise their computing output. They invest heavily in their computers and process the majority of Folding@home’s simulations, thus providing an invaluable (and free) resource

A COMPUTATIONAL PIPELINE FOR MCI DETECTION FROM HETEROGENEOUS BRAIN IMAGES

The aging population has increased the importance of identifying and understanding mild cognitive impairment (MCI), particularly given that 6 - 15 % of MCI cases convert to Alzheimer\u27s disease (AD) each year. The early identification of MCI has the potential for timely therapeutic interventions that would limit the advancement of MCI to AD. However, it is difficult to identify MCI-related pathology based on visual inspection because these changes in brain morphology are subtle and spatially distributed. Therefore, reliable and automated methods to identify subtle changes in morphological characteristics of MCI would aid in the identification and understanding of MCI. Meanwhile, usability becomes a major limitation in the development of clinically applicable classifiers. Furthermore, subject privacy is an additional issue in the usage of human brain images. To address the critical need, a complete computer aided diagnosis (CAD) system for automated detection of MCI from heterogeneous brain images is developed. This system provides functions for image processing, classification of MCI subjects from control, visualization of affected regions of interest (ROIs), data sharing among different research sites, and knowledge sharing through image annotation