Inférence de la grammaire structurelle d’une émission TV récurrente à partir du contenu

TV program structuring raises as a major theme in last decade for the task of high quality indexing. In this thesis, we address the problem of unsupervised TV program structuring from the point of view of grammatical inference, i.e., discovering a common structural model shared by a collection of episodes of a recurrent program. Using grammatical inference makes it possible to rely on only minimal domain knowledge. In particular, we assume no prior knowledge on the structural elements that might be present in a recurrent program and very limited knowledge on the program type, e.g., to name structural elements, apart from the recurrence. With this assumption, we propose an unsupervised framework operating in two stages. The first stage aims at determining the structural elements that are relevant to the structure of a program. We address this issue making use of the property of element repetitiveness in recurrent programs, leveraging temporal density analysis to filter out irrelevant events and determine valid elements. Having discovered structural elements, the second stage is to infer a grammar of the program. We explore two inference techniques based either on multiple sequence alignment or on uniform resampling. A model of the structure is derived from the grammars and used to predict the structure of new episodes. Evaluations are performed on a selection of four different types of recurrent programs. Focusing on structural element determination, we analyze the effect on the number of determined structural elements, fixing the threshold applied on the density function as well as the size of collection of episodes. For structural grammar inference, we discuss the quality of the grammars obtained and show that they accurately reflect the structure of the program. We also demonstrate that the models obtained by grammatical inference can accurately predict the structure of unseen episodes, conducting a quantitative and comparative evaluation of the two methods by segmenting the new episodes into their structural components. Finally, considering the limitations of our work, we discuss a number of open issues in structure discovery and propose three new research directions to address in future work.Dans cette thèse, on aborde le problème de structuration des programmes télévisés de manière non supervisée à partir du point de vue de l'inférence grammaticale, focalisant sur la découverte de la structure des programmes récurrents à partir une collection homogène. On vise à découvrir les éléments structuraux qui sont pertinents à la structure du programme, et à l’inférence grammaticale de la structure des programmes. Des expérimentations montrent que l'inférence grammaticale permet de utiliser minimum des connaissances de domaine a priori pour atteindre la découverte de la structure des programmes

PrivCheck: Privacy-Preserving Check-in Data Publishing for Personalized Location Based Services

International audienceWith the widespread adoption of smartphones, we have observed an increasing popularity of Location-Based Services (LBSs) in the past decade. To improve user experience, LBSs often provide personalized recommendations to users by mining their activity (i.e., check-in) data from location-based social networks. However, releasing user check-in data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users'check-in data. In this paper, we propose PrivCheck, a customizable and continuous privacy-preserving check-in data publishing framework providing users with continuous privacy protection against inference attacks. The key idea of PrivCheck is to obfuscate user check-in data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which ensures the utility of the obfuscated data to empower personalized LBSs. Since users often give LBS providers access to both their historical check-in data and future check-in streams, we develop two data obfuscation methods for historical and online check-in publishing, respectively. An empirical evaluation on two real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized LBS

Privacy-preserving social media data publishing for personalized ranking-based recommendation

Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users' activity data. In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key idea is to continuously obfuscate user activity data such that the privacy leakage of user- specified private data is minimized under a given data distortion budget, which bounds the ranking loss incurred from the data obfuscation process in order to preserve the utility of the data for enabling recommendations. An empirical evaluation on both synthetic and real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized ranking-based recommendation. Compared to state-of-the-art approaches, PrivRank achieves both a better privacy protection and a higher utility in all the ranking-based recommendation use cases we tested

Using grammar induction to discover the structure of recurrent TV programs

International audienceVideo structuring, in particular applied to TV programs which have strong editing structures, mostly relies on supervised approaches either to retrieve a known structure for which a model has been obtained or to detect key elements from which a known structure is inferred. In this paper, we propose an unsupervised approach to recurrent TV program structuring, exploiting the repetitiveness of key structural elements across episodes of the same show. We cast the problem of structure discovery as a grammatical inference problem and show that a suited symbolic representation can be obtained by filtering generic events based on their reoccurring property. The method follows three steps: i) generic event detection, ii) selection of events relevant to the structure and iii) grammatical inference from a symbolic representation. Experimental evaluation is performed on three types of shows, viz., game shows, news and magazines, demonstrating that grammatical inference can be used to discover the structure of recurrent programs with very limited supervision

Content-based discovery of multiple structures from episodes of recurrent TV programs based on grammatical inference

International audienceTV program structuring is essential for program indexing and retrieval. Practically, various types of programs lead to a diversity of program structures. In addition, several episodes of a recurrent program might exhibit different structures. Previous work mostly relies on supervised approaches by adopting prior knowledge about program structures. In this paper, we address the problem of unsupervised program structuring with minimal prior knowledge about the programs. We propose an approach to identify multiple structures and infer structural grammars for recurrent TV programs of different types. It involves three sub-problems: i) we determine the structural elements contained in programs with minimal knowledge about which type of elements may be present; ii) we identify multiple structures for the programs if any and model the structures of programs; iii) we generate the structural grammar for each corresponding structure. Finally, we conduct use cases on real recurrent programs of three different types to demonstrate the effectiveness of proposed approach

MODIS: an audio motif discovery software

International audienceMODIS is a free speech and audio motif discovery software developed at IRISA Rennes. Motif discovery is the task of discovering and collecting occurrences of repeating patterns in the absence of prior knowledge, or training material. MODIS is based on a generic approach to mine repeating audio sequences, with tolerance to motif variability. The algorithm implementation allows to process large audio streams at a reasonable speed where motif discovery often requires huge amount of time

Content-based inference of structural grammar for recurrent TV programs from a collection of episodes

Dans cette thèse, on aborde le problème de structuration des programmes télévisés de manière non supervisée à partir du point de vue de l'inférence grammaticale, focalisant sur la découverte de la structure des programmes récurrents à partir une collection homogène. On vise à découvrir les éléments structuraux qui sont pertinents à la structure du programme, et à l’inférence grammaticale de la structure des programmes. Des expérimentations montrent que l'inférence grammaticale permet de utiliser minimum des connaissances de domaine a priori pour atteindre la découverte de la structure des programmes.TV program structuring raises as a major theme in last decade for the task of high quality indexing. In this thesis, we address the problem of unsupervised TV program structuring from the point of view of grammatical inference, i.e., discovering a common structural model shared by a collection of episodes of a recurrent program. Using grammatical inference makes it possible to rely on only minimal domain knowledge. In particular, we assume no prior knowledge on the structural elements that might be present in a recurrent program and very limited knowledge on the program type, e.g., to name structural elements, apart from the recurrence. With this assumption, we propose an unsupervised framework operating in two stages. The first stage aims at determining the structural elements that are relevant to the structure of a program. We address this issue making use of the property of element repetitiveness in recurrent programs, leveraging temporal density analysis to filter out irrelevant events and determine valid elements. Having discovered structural elements, the second stage is to infer a grammar of the program. We explore two inference techniques based either on multiple sequence alignment or on uniform resampling. A model of the structure is derived from the grammars and used to predict the structure of new episodes. Evaluations are performed on a selection of four different types of recurrent programs. Focusing on structural element determination, we analyze the effect on the number of determined structural elements, fixing the threshold applied on the density function as well as the size of collection of episodes. For structural grammar inference, we discuss the quality of the grammars obtained and show that they accurately reflect the structure of the program. We also demonstrate that the models obtained by grammatical inference can accurately predict the structure of unseen episodes, conducting a quantitative and comparative evaluation of the two methods by segmenting the new episodes into their structural components. Finally, considering the limitations of our work, we discuss a number of open issues in structure discovery and propose three new research directions to address in future work

Economic optimization on two time scales for a hybrid energy system based on virtual storage

Abstract This paper proposes an economic optimization method with two time scales for a hybrid energy system based on the virtual storage characteristic of a thermostatically controlled load (TCL). The optimization process includes two time scales in order to ensure accuracy and efficiency. Based on the forecast load and energy supply of the system, the first time scale is day-ahead economic operating optimization, carried out to determine the minimum operating cost for the whole day, and to find the period of greatest cost to which the second time scale optimization is applied. Using the virtual storage characteristic, the second time scale is short term detailed optimization carried out for these particular hours. By dispatching thermal load in this period and adjusting energy supply accordingly, we can find the optimal economic performance, and customer requests are taken into account to ensure satisfaction. A case study in Tianjin illustrates the effectiveness of this method and proves that a TCL can make a great contribution to improving the economic performance of a hybrid energy system

Participatory cultural mapping based on collective behavior data in location-based social networks

International audienceCulture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive "home" location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey dat