Skill-Aware Task Assignment in Crowdsourcing Applications

International audienceBesides simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills. In such a setting we need to model skills that are required to execute a particular job. At the same time in order to match tasks to the crowd, we have to model the expertise of the participants. We present such a skill model that relies on a taxonomy. We also introduce task assignment algorithms to optimize the result quality. We illustrate the effectiveness of our algorithms and models through preliminary experiments with synthetic datasets

Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications

International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201

Lightweight Privacy-Preserving Task Assignment in Skill-Aware Crowdsourcing: [Full Version]

Crowdsourcing platforms dedicated to work, be it paid or voluntary, essentially consist in intermediating between tasks—sent by requesters—and workers. They are used by a growing number of individuals and organizations, for tasks that are more and more diverse, complex , and that require specific skills, availabilities, experiences, or even devices. On the one hand, these highly detailed task specifications and worker profiles enable high-quality task assignments. On the other hand, detailed worker profiles may disclose a large amount of personal information to the central platform (e.g., personal preferences, availabilities, wealth, occupations), jeopardizing the privacy of workers. In this paper, we propose a lightweight approach to protect workers privacy against the platform along the current crowdsourcing task assignment process. Our approach (1) satisfies differential privacy by building on the well-known randomized response technique, applied by each worker to perturb locally her profile before sending it to the platform, and (2) copes with the resulting perturbation by benefiting from a taxonomy defined on workers profiles. We describe the lightweight upgrades to be brought to the workers, to the platform, and to the requesters. We show formally that our approach satisfies differential privacy, and empirically, through experiments performed on various synthetic datasets, that it is a promising research track for coping with realistic cost and quality requirements

Découverte et analyse des communautés implicites par une approche sémantique en ligne (l'outil WebTribe)

Avec l essor du Web 2.0 et des technologies collaboratives qui y sont rattachées,le Web est aujourd hui devenu une vaste plate-forme d échanges entre internautes.La majeure partie des sites Web sont actuellement soit dédiés aux interactionssociales de leurs utilisateurs, soit proposent des outils pour développer ces interactions.Nos travaux portent sur la compréhension de ces échanges, ainsi que desstructures communautaires qui en découlent, au moyen d une approche sémantique.Pour répondre aux besoins de compréhension propres aux analystes de siteWeb et autres gestionnaires de communautés, nous analysons ces structures communautairespour en extraire des caractéristiques essentielles comme leurs centresthématiques et contributeurs centraux. Notre analyse sémantique s appuie notammentsur des ontologies légères de référence pour définir plusieurs nouvelles métriques,comme la centralité sémantique temporelle et la probabilité de propagationsémantique. Nous employons une approche en ligne afin de suivre l activitéutilisateur en temps réel, au sein de notre outil d analyse communautaire Web-Tribe. Nous avons implémenté et testé nos méthodes sur des données extraites desystèmes réels de communication sociale sur le WebWith the rise of Web 2.0 and collaborative technologies that are attached to,the Web has now become a broad platform of exchanges between users. The majorityof websites is now dedicated to social interactions of their users, or offerstools to develop these interactions. Our work focuses on the understanding of theseexchanges, as well as emerging community structures arising, through a semanticapproach. To meet the needs of web analysts, we analyze these community structuresto identify their essential characteristics as their thematic centers and centralcontributors. Our semantic analysis is mainly based on reference light ontologiesto define several new metrics such as the temporal semantic centrality and thesemantic propagation probability. We employ an online approach to monitor useractivity in real time in our community analysis tool WebTribe. We have implementedand tested our methods on real data from social communication systemson the WebDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Extraction de chroniques discriminantes

International audienceL'extraction de motifs séquentiels vise à extraire des comportements récurrents dans un ensemble de séquences. Lorsque ces séquences sont étiquetées, l'extraction de motifs discriminants engendre des motifs caractéristiques de chaque classe de séquences. Cet article s'intéresse à l'extraction des chroniques discriminantes où une chronique est un type de motif temporel représentant des durées inter-évènements quantitatives. L'article présente l'algorithme DCM dont l'originalité réside dans l'utilisation de méthodes d'apprentissage automatique pour extraire les intervalles temporels. Les performances computationnelles et le pouvoir discriminant des chroniques extraites sont évalués sur des données synthétiques et réelles

DataTime: a Framework to smoothly Integrate Past, Present and Future into Models

International audienceModels at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of fullfledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (ie. values of properties of interest). DataTime provides a unifying interface to query the past, present and future (predicted) states of the system. This unifying interface provides i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, ii) the ability to get the last available value provided by the system’s sensors, and iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can deprecate the use of heterogeneous tools for managing data from the past, the present and the future, and facilitate the development of digital twins

Platform Design for Crowdsourcing and Future of Work

International audienceOnline job platforms have proliferated in the last few years. We anticipate a future where there exists thousands of such platforms covering wide swathes of tasks. These include crowdsourcing platforms such as Amazon Mechanical Turk (AMT), CrowdWorks, Figure Eight; specialized services such as ridehailing; matching markets such as TaskRabbit that matches workers with local demand and so on. It is widely anticipated that a vast majority of human workforce will be employed in these platforms. In this article, we initiate discussions about the under studied aspect of platform design-how to design platforms that maximize the satisfaction of various stakeholders. We also contribute a novel taxonomy for platform ecosystems that categorizes existing and emerging platforms. Finally, we discuss the need for interoperability between these platforms so that workers and requesters are not tied to a single platform

Reasoning over Time into Models with DataTime

International audienceModels at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (ie. values of properties of interest). DataTime offers a unifying interface to query the past, present and future (predicted) states of the system. This unifying interface provides i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, ii) the ability to get the last available value provided by the system's sensors, and iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can be used for managing data from the past, the presentand the future, and facilitate the development of digital twins

Query-preserving watermarking of relational databases and Xml documents

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Databases Watermarking

Database watermarking techniques allow for hiding information in a database, like a copyright mark. While watermarking methods are numerous in the multimedia setting, databases present various specificities. This work addresses some of them: how to watermark a numerical database while preserving the result of interesting aggregate queries, how to watermark a structured stream like a typed XML stream or a symbolic music score, how to watermark geographical data sets.Les techniques de tatouage de bases de données permettent la dissimulation d'information pertinente dans les n-uplets, comme par exemple l'identité du propriétaire des données. Les techniques de tatouage sont nombreuses dans le domaine multimédia, mais le tatouage des bases de données présente de nombreuses spécificités. Certaines d'entre elles sont traitées dans ce document : comment tatouer une base de données numérique tout en préservant le résultat de requêtes d'agrégat importantes, comment tatouer un flux structuré, comme un flux XML typé ou une partition musicale symbolique, comment tatouer une base de données géographiques