Usually, human-computer dialogue systems rely on ad-hoc solutions for the component performing speech turn generation, in natural language. However, integration of taskspecific and general world knowledge in order to provide a more reliable and natural interaction with humans also through more sophisticated language generation techniques becomes needed. In this paper we present performance improvements of a module simulating in first-order logic Segmented Discourse Representation Theory for language generation in dialogue. These improvements concern reductions in computational costs and enhancements in rhetorical coherence for the discourse structures obtained, and are obtained using speech-act related information for driving rhetorical relations computations.

After having outlined the uses of new technologies such as smartphones, touch-screen tablets, and laptops, this article presents the TangiSense interactive tabletop, equipped with RFID technology tagged on tangible objects, as a new paradigm of interaction for ambient intelligence. Within its framework, this article aims to distribute surfaces (tables) interacting mainly with tangible objects. Leads for interactive surface distribution such as interactive tables are given. The article proposes to describe some tangible objects, which are the interaction supports; these are called Tangigets. They are defined according to an augmented Presentation-Abstraction-Control structure to take the tangibility element into account. Six categories of Tangigets are also proposed, which are tangible objects, and the supports of distributed collaboration. To validate the Tangiget concept and its use on the TangiSense tabletop, illustrations in centralized and distributed configurations are proposed. A first evaluation is also presented. To conclude, the article presents the directions under consideration for our future research.

International audienceWe propose a design for collaborative support system in distant tangible environments, in the framework of activity theory. We model collaboration as driven by individual-centered and group-centered rules. Context sharing is core to this process, but reveals difficult in the case of distant tangible communication. We propose to model collaboration as a trace-based process in which tangible object traces are stored, analyzed, enriched and shared. We draw on a normative multi-agent approach in which explicit norms are meant to operate at various levels, from the physical to the social level. These norms do not act as a prerequisite, or as a way to place a priori constraints on action. Rather, they result in a set of signs situating the activity. Such design offers novel ways for embedding activity theory in the current trend of socio-physical computing

Abstract-an intelligent building is an environment that contains a number of sensor and camera, which aims to provide information that give the various actions taken by individuals, and their status to be processed by a system of detection and classification of behaviors . This system of detection and classification uses this information as input to provide maximum comfort to people who are in this building with optimal energy consumption, for example if I workout in the room then the system will lower the heating . My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use RGB color histograms INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO. 4, SEPTEMBER 2013 1317 and textures for LBP represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for the detection and classification of the behavior of people in this intelligent building in order to give maximum comfort with optimized energy consumption. The classification is performed using the classification of k 1 and k = 11 in our case, we built 11 models in the learning phase using different nucleus in order to choose the best models that give the highest classification rate and finally for, the classification phase, to classify the behavior, we compare it to the 11 behaviors, that is to say, we make 11 classification and take the behavior that has the highest classification rate. This work has been carried out within the University Joseph Fourier in Grenoble precisely LIG (Grenoble computer lab) in the team MULTI COM and the University of Oran Algeria USTO. Our contribution in this field is the design and implementation of a robust, and accurate system that make detection and classification of 11 behaviors cameras in an intelligent building, varying illumination it means, whatever lighting is our system must be capable of detecting and classifying behaviors.