André Péninou

Agent-oriented design of human-computer interface: application to supervision of an urban transport network

At the present time, the approaches found in the design of interactive systems use a modular structure, with the aim of achieving a better understanding of reactivity, flexibility, maintainability and reuse at the human-machine interface level. However, most architecture models are far more concerned with user-controlled applications and they do not consider the specificities of supervision applications in which the human operator acts as the controller and commander of an independent dynamic process. A multi-agent approach is a possible answer to this type of situation. The agent-oriented model put forward in this article is the subject of an application intended in the long term to supervise the user information system of an urban transport network.

Visualizing the Evolution of Users' Profiles from Online Social Networks

Nowadays, online social networks host more and more applications in order to provide their users with the possibility of finding everything they need on a single platform. The number and diversity of interactions that take place over time between users and applications within these platforms make these environments very good candidates for learning various types of information about users’ interests. We are particularly interested in the determination of users’ short-term and long-term interests which are essential for adaptative systems that take into account the evolution of user’s needs. While studies in adaptative systems focus on computing interests’ weight value and time periods to determine user’s short-term and long-term profile, we focus instead on temporal graphs’ visualization of users’ interests. From a case study on Facebook, we use dynamic graphs in order to view the influence of social ties on the user’s interests.

A Community Based Algorithm for Deriving Users' Profiles from Egocentrics Networks

Nowadays, social networks are more and more widely used as a solution for enriching users profiles in systems such as recommender systems or personalized systems. For an unknown users interest, the users social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the users social network, and strongly depend on each authors objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the users social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted users interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g. centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individuals based techniques, and the influence of structural measures (related to communities) on the quality of derived profiles. The results also raise the problem of users privacy in platforms such as online social networks. To enable users to better protect their privacy, these platforms should provide their users with a way to also make their friendlist private.

An Adaptation Approach: Query Enrichment by User Profile

In semi-structured information systems, generally, the adaptation of documents is essential to give the user the feeling that the query result is adapted to his preferences. The users needs can be defined in a user profile. But, in the literature, adaptation systems are designed for a particular domain and are oriented towards either navigation adaptation or content adaptation. Adaptation takes place after the users query has been evaluated. So, in this paper, we contribute to propose an adaptation algorithm which is domain independent and whose adaptation takes place before users query evaluation. This algorithm consists in enriching the user query on the basis of user profile in order to adapt the results to the user.

Dynamic enrichment of social users' interests

In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users interests dynamic. Indeed, users interests may change and become “outdated” through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isnt always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects users interests) and ii) the temporal information (that reflects the dynamic evolution of users interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same “important” resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

Video Spatio-Temporal Filtering Based on Cameras and Target Objects Trajectories -- Videosurveillance Forensic Framework

This paper presents our work about assisting video-surveillance agents in the search for particular video scenes of interest in transit network. This work has been developed based on requirements defined within different projects with the French National Police in a forensic goal. The video-surveillance agent inputs a query in the form of a hybrid trajectory (date, time, locations expressed with regards to different reference systems) and potentially some visual descriptions of the scene. The query processing starts with the interpretation of the hybrid trajectory and continues with a selection of a set of cameras likely to have filmed the spatial trajectory. The main contributions of this paper are: (1) a definition of the hybrid trajectory query concept, trajectory that is constituted of geometrical and symbolic segments represented with regards to different reference systems (e.g., Geodesic system, road network), (2) a spatio-temporal filtering framework based on a spatio-temporal modeling of the transit network and associated cameras.

Analyzing Tagged Resources for Social Interests Detection

Social networks provide an environment of information exchange. This environment reflects userâx80x99s opinions, tastes, preferences, interests, etc. We focus on analyzing userâx80x99s interests which are key elements for improving adaptation (recommendation, personalization, etc.). In this article, we are interested to overcome problems affected the adaptation quality in social networks, such as the accuracy of the userâx80x99s interests. The originality of our approach is based on the proposal of a new technique of interests detection by analyzing the accuracy of the tagging behaviour of the user in order to figure out the tags which really reflect the resources content. We focus on the semi-structured data (resources), since they provide more comprehensible information. Our approach has been tested and evaluated in the Delicious social database. A comparison between our approach and the tag-based approach shows that our approach performs better.

Self-Adaptation of a Learnt Behaviour by Detecting and by Managing User's Implicit Contradictions

This paper tackles the issue of ambient systems adaptation to users needs while the environment and users preferences evolve continuously. We propose the adaptive multi-agent system Amadeus whose goal is to learn from users actions and contexts how to perform actions on behalf of the users in similar contexts. However, considering the possible changes of users preferences, a previously learnt behaviour may become misfit. So, Amadeus must be able to observe if its actions on the system are contradicted by the users or not, without requiring any explicit feedback. The aim of this paper is to present the introspection capabilities of Amadeus in order to detect users contradictions and to self-adapt its behaviour at runtime. These mechanisms are then evaluated through a case study.

Deriving user's profile from sparse egocentric networks: Using snowball sampling and link prediction

Several studies demonstrate effectiveness and benefits of using users social network information to enrich users profile. In this context, one of our contributions [1] proposes an algorithm enabling to compute users interests using information from egocentric network extracted communities. Therefore, mining information from a small or a sparse network remains challenging because there is not enough information to enrich a relevant users profile. So, one of the main lock is to cope with the lack of information that is considered as an important issue to extract a relevant community and could lead to misinterpretations in the users profile modeling process. We aim to improve the performance of [1], regarding the lack of information problem, in the case of a small and/or a sparse network. We propose to add more information (i.e. relations) into users network before extracting the data and enriching his profile. To achieve this enrichment, we suggest using snowball sampling technique to identify and add users distance-2 neighbors (friends of a friend) into the users egocentric network. Our experimentation conducted in DBLP demonstrates the interest of node integration into small and sparse network. This leads to the study of link prediction that enables us to provide better performances and results compared to the existing work.

An Object-Oriented Approach to Produce Educational Hypermedia Software

First, we present the architecture developed for the COMPTA and SYSTEMDENT systems. These ITSs (Intelligent Tutoring Systems), named Hybrids, have been implemented using an authoring system connected to an expert system generator. They have allowed us to improve the production of ITSs from the authors point of view (prototyping and realization stages) thanks to the possibility of reusing both structures and knowledge from a hybrid system to another one.