In this paper we propose a method for video recommendation in Social Networks based on crowdsourced and automatic video annotations of salient frames. We show how two human factors, users' self-expression in user profiles and perception of visual saliency in videos, can be exploited in order to stimulate annotations and to obtain an efficient representation of video content features. Results are assessed through experiments conducted on a prototype of social network for video sharing. Several baseline approaches are evaluated and we show how the proposed method improves over them.

In this demo we present smArt, a low-cost framework to quickly set up indoor exhibits featuring a smart navigation system for museums. The framework is web-based and allows the design on a digital map of a sensorized museum environment and the dynamic and assisted definition of the multimedia materials and sensors associated to the artworks. The knowledge-base uses semantic technologies and it is exploited by museum visitors to get directions and to have multimedia insights in a natural way. Indoor localisation and routing is provided taking advantage of active and passive sensors advertisements and user interactions. In this way we overcome the Global Positioning System (GPS) unavailability issue in indoor environments.

In this demo we present PITAGORA\footnote{Demo video available at http://bit.ly/1GgtUrN}: a mobile web contextual social network designed for the check-in area of an airport. The app provides recommendation of potential friends, local experts and targeted services. Recommendation is hybrid and combines social media analysis and collaborative filtering techniques. Users' recommendation has been evaluated through a user study with good results.

In this paper we present a system for content-based video recommendation that exploits visual saliency to better represent video features and content\footnote{Demo video available at http://bit.ly/1FYloeQ}. Visual saliency is used to select relevant frames to be presented in a web-based interface to tag and annotate video frames in a social network; it is also employed to summarize video content to create a more effective video representation used in the recommender system. The system exploits automatic annotations from CNN-based classifiers on salient frames and user generated annotations. We evaluate several baseline approaches and show how the proposed method improves over them.

In this paper we present Loki+Lire, a framework for the creation of web-based interfaces for search, annotation and presentation of multimedia data. The framework provides tools to ingest, transcode, present, annotate and index different types of media such as images, videos, audio files and textual documents. The front-end is compliant with the latest HTML5 standards, while the back-end allows system administrators to create processing pipelines that can be adapted for different tasks and purposes.

Two important challenges for social networks are the creation of targeted and personalized content for their users, selecting the most interesting material from the huge amount of user-generated content, and keeping user engagement , e.g. through creation and curation of users' profiles. In this demo we show a system for video commenting, sharing and interest discovery that combines recommendation algorithms, clustering techniques, tools for video tagging and evaluation of semantic resources relatedness. Combining these tools and techniques it becomes possible to provide personalized multimedia services and to improve and propagate interests and inter-personal connections through the network.

In this demo we present Flarty, a mobile location-based social network for the dynamic construction and recommendation of art routes in the city of Florence, Italy, via item based similarity algorithms, places topic extraction and user interest modeling. To achieve this goal Flarty derives knowledge from users check-ins and combines clustering techniques and recommendation algorithms, as well as features such as geo-location, to define groups of similar artworks or POIs (Points Of Interest) and to compute the most efficient routes likely to meet user's interests. Model analysis takes into account ratings, topics extracted from textual features associated with the POIs, and users preferences computed exploiting collaborative filtering techniques on their past behavior.

This demo presents a system based on social relationships, social knowledge and automatic video and textual content analysis for the discovery of videos in social networks. The system, developed as a web application, allows users to annotate, manually and automatically, and comment video frames and scenes enriching their content with tags, references to Facebook users and pages and Wikipedia resources. These annotations are used to semantically model the profile of each user extracting and expanding his interests and folksonomy, as well as resources of interest in his social graph. The automatically generated profile page is used to suggest to users new resources, Facebook friends and videos whose content is related to their interests and allows profile curation. A screencast showing an example of these functionalities is publicly available at: http://vimeo.com/miccunifi/facetube

We present a work-in-progress interactive exhibit for the mu- seum of Onna (L'Aquila, Italy). The Onna Onlus and the citizens of Onna have the ambitious project to rebuild the town affected by the earthquake of April 2009 and to create a museum in memory of Onna. In addition to the tradi- tional fruition tools for museums, we have been asked for an interactive system capable to communicate the past events and the efforts invested. We are working on a multi-modal system composed of an indoor environment in which visitors can interact with a natural interface system and an outdoor module based on a cross-platform mobile application. A pro- filing method is also exploited in order to extract a profile of interest of each visitor and then use it to suggest in-depth personalized and geo-located information about the disaster and the local history via multimedia contents.

In this paper we present a complete system for semantic and syn tactic annotation, browsing and search of multimedia data, that is based on a service oriented architecture, with web-based interfaces developed following the Rich Internet Application paradigm. The system has been designed to be: i) flexible and extendable, allowing users to select only the services they need or to add their own tools to the multimedia processing pipelines; ii) distributed, with services that can be executed in a cloud computing infrastructure and accessed through web applications; Hi) user-friendly, with interfaces that have a uniform interface on every platform and that have an interaction level similar to that of desktop applications. Extensive user trials in real-world setup, performed by archive and broadcaster professionals, have shown the efficacy and usability of the proposed solution.

In this paper we present a work-in-progress interactive exhibit for the museum of Onna, a little town near to L'Aquila (Italy), almost completely destroyed by the earthquake of April 2009. The installation will be developed as an environment in which visitors of the museum can interact with a natural interaction system and then discover the history of the disaster via rich multimedia contents. Visitors are detected through the adoption of an RFID-based technology, which allows to store their interaction history and build an interest profile used to enrich the experience. Different scenarios have been implemented and tested in order to evaluate the effectiveness of the proposed solution.

This paper addresses the design and development of MediaPick [1], an interactive multi-touch system for semantic search of multimedia contents. Our solution provides an intuitive, easy-to-use way to select concepts organized according to an ontological structure and retrieve the related contents. Users are then able to examine and organize such results through semantic or subjective criteria. As use case we considered professionals who work with huge multimedia archives (journalists, archivists, editors etc.).