International audienceBesides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets

Watermarking allows robust and unobtrusive insertion of information in a digital document. Very recently, techniques have been proposed for watermarking relational databases or XML documents, where information insertion must preserve a specific measure on data (e.g. mean and variance of numerical attributes.)In this paper we investigate the problem of watermarking databases or XML while preserving a set of parametric queries in a specified language, up to an acceptable distortion.We first observe that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or tree-like structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.

International audienceThis paper presents a walermarking/fingerprinting system for relational databases. It features a built-in declarative language to specify usability constraints that watermarked data sets must comply with. For a subset of these constraints, namely, weight-independent constraints, we propose a novel watermarking strategy that consists of translating them into an integer linear program. We show two watermarking strategies: an exhaustive one based on integer linear programming constraint solving and a scalable pairing heuristic. Fingerprinting applications, for which several distinct watermarks need to be computed, benefit from the reduced computation time of our method that precomputes the watermarks only once. Moreover, we show that our method enables practical collusion-secure fingerprinting since the precomputed watermarks are based on binary alterations located at exactly the same positions. The paper includes an in-depth analysis of false-hit and false-miss occurrence probabilities for the detection algorithm. Experiments performed on our open source software WATERMILL assess the watermark robustness against common attacks and show that our method outperforms the existing ones concerning the watermark embedding speed

Watermarking allows robust and unobtrusive insertion of information in a digital document. During the last few years, techniques have been proposed for watermarking relational databases or XML documents, where information insertion must preserve a specific measure on data (for example the mean and variance of numerical attributes).In this article we investigate the problem of watermarking databases or XML while preserving a set of parametric queries in a specified language, up to an acceptable distortion. We first show that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or treelike structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.