This paper introduces a conceptual and architectural frame- work for addressing the design, execution and verification of tasks by a crowd of performers. The proposed framework is substantiated by an ongoing application to a problem of trademark logo detection in video collections. Preliminary results show that the contribution of crowds can improve the recall of state-of-the-art traditional algorithms, with no loss in terms of precision. However, task-to-executor matching, as expected, has an important influence on the task performance.@en

The Cubrik Project is an Integrated Project of the 7th Frame- work Programme that aims at contributing to the multimedia search domain by opening the architecture of multimedia search engines to the integration of open source and third party content annotation and query processing components, and by exploiting the contribution of humans and communities in all the phases of multimedia search, from content processing to query processing and relevance feedback processing. The CUBRIK presentation will showcase the architectural concept and scientific background of the project and demonstrate an initial scenario of human-enhanced content and query processing pipeline.@en

Multi-domain search answers to queries spanning multiple entities, like "Find a hotel in Milan close to a concert venue, a museum and a good restaurant", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., five stars hotels) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.

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Multi-domain search answers to queries spanning multiple entities, like "Find an affordable house in a city with low criminality index, good schools and medical services", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., inexpensive houses) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance (i.e., high values of the ranking function) with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.@en

Search computing focuses on building answers to complex search queries (for example, "Where can I attend an interesting conference in my field near a sunny beach?") by interacting with a constellation of cooperating search services, and using result ranking and joining as the dominant factors for service composition. The service computing paradigm has so far been neutral to the specific features of search applications and services. To address this weakness, search computing advocates a new approach in which search, join, and ranking are the central aspects for service composition.

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In this paper we propose a novel approach for contentbased music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail representations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation technique, to be more meaningful to the user from a semantic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algorithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter assigns a similarity and recommendation score to a pair of songs, by computing the distance between the representations of their segments. To assign the global similarity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction algorithm on weighted edges that connect segments of different songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.@en