Socialising around media. Improving the second screen experience through semantic analysis, context awareness and dynamic communities

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole

D4.3.2 Report on IOT Living Labs Continuous Exploration and Evaluation (interim version)

no abstrac

Online S3 D2.3 - Online S3 mechanism for knowledge-based policy advice

No abstract available

Online S3 D2.3 - Online S3 mechanism for knowledge-based policy advice

No abstract available

Report on IOT Living Labs Continuous Exploration and Evaluation (final)

Elliot Delivrable D4.3.3no abstrac

Pelvic trauma : WSES classification and guidelines

Complex pelvic injuries are among the most dangerous and deadly trauma related lesions. Different classification systems exist, some are based on the mechanism of injury, some on anatomic patterns and some are focusing on the resulting instability requiring operative fixation. The optimal treatment strategy, however, should keep into consideration the hemodynamic status, the anatomic impairment of pelvic ring function and the associated injuries. The management of pelvic trauma patients aims definitively to restore the homeostasis and the normal physiopathology associated to the mechanical stability of the pelvic ring. Thus the management of pelvic trauma must be multidisciplinary and should be ultimately based on the physiology of the patient and the anatomy of the injury. This paper presents the World Society of Emergency Surgery (WSES) classification of pelvic trauma and the management Guidelines.Peer reviewe

Für | For Manfred from his Students

Dieses Buch enthält Beiträge von Personen, die ihre Magister- oder Doktorarbeit unter der Betreuung von Manfred Krifka geschrieben haben. Es ist als kleines Abschiedsgeschenk für Manfred Krifka zum Ende seiner Amtszeit als Direktor des Leibniz-Zentrums für Allgemeine Sprachwissenschaft gedacht. Die Herausgeberin und der Herausgeber haben Beiträge zu sprachwissenschaftlichen und nicht-sprachwissenschaftlichen Themen in einer Vielzahl von Genres gesammelt. Diese Vielfalt spiegelt die Interessen und Forschungsthemen von Manfred Krifka wider. Sie spiegelt auch die Vielfalt der Menschen wider, denen Manfred Krifka geholfen hat

Complex-Network theoretic clustering for identifying groups of similar listeners in p2p systems.

ABSTRACT This article presents an approach to automatically create virtual communities of users with similar music preferences in a distributed system. Our goal is to create personalized music channels for these communities using the content shared by its members in peer-to-peer networks for each community. To extract these communities a complex network theoretic approach is chosen. A fully connected graph of users is created using epidemic protocols. We show that the created graph sufficiently converges to a graph created with a centralized algorithm after a small number of protocol iterations. To find suitable techniques for creating user communities, we analyze graphs created from real-world recommender datasets and identify specific properties of these datasets. Based on these properties, different graph-based community-extraction techniques are chosen and evaluated. We select a technique that exploits identified properties to create clusters of music listeners. The suitability of this technique is validated using a music dataset and two large movie datasets. On a graph of 6,040 peers, the selected technique assigns at least 85% of the peers to optimal communities, and obtains a mean classification error of less than 0.05% over the remaining peers that are not assigned to the best community