Advances in web technologies have opened the way to real-time collaborative music applications, enabling musicians to co-create and perform music from anywhere in the world. This paper focuses on the use of Web Audio Modules, an audio plugin standard for the web (WAMs, aka “VSTs for the Web”) in such applications, with a particular emphasis on sequencer.party, a web-based platform for collaborative music creation. We explore how WAMs can form the basis for modular, real-time music composition and performance, enabling users to manipulate audio, MIDI and generative processes in a collaborative way. The presented platform integrates real-time collaborative music installation creation, state synchronization using conflict-free replicated data types (CRDT) and a flexible, track-oriented workflow for live music performance. Through this case study we demonstrate how WAM-based tools can support both educational and performative contexts, offering a new approach to collaborative digital music creation that bridges the gap between traditional environments and web accessibility.

We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, as their structure segmentation, their topic, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The creation of the resource is still ongoing: so far, the corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. Such corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation recommendation of songs.

In this paper, we propose a multi-agent recommender system based on the Belief-Desire-Intention (BDI) model applied to multi-context systems. First, we extend the BDI model with additional contexts to deal with sociality and information uncertainty. Second, we propose an ontological representation of planning and intention contexts in order to reason about plans and intentions. Moreover, we show a simple real-world scenario in healthcare in order to illustrate the overall reasoning process of our model.

Building on top of our results on semantic social network analysis, we present a community detection algorithm, SemTagP, that takes benefits of the semantic data that were captured while structuring the RDF graphs of social networks. SemTagP not only offers to detect but also to label communities by exploiting (in addition to the structure of the social graph) the tags used by people during the social tagging process as well as the semantic relations inferred between tags. Doing so, we are able to refine the partitioning of the social graph with semantic processing and to label the activity of detected communities. We tested and evaluated this algorithm on the social network built from Ph.D. theses funded by ADEME, the French Environment and Energy Management Agency. We showed how this approach allows us to detect and label communities of interest and control the precision of the labels.

The advent of tagging and folksonomies for organizing shared resources on the social Web brought promising opportunities to help communities of users capture their knowledge. However, the lack of semantics, or the spelling variations between tags lowers the potentials for browsing and exploring these data. To overcome these limitations, we propose exploiting the interactions between the users and the systems to validate or correct semantic analysis automatically applied to the tags. This process is based upon our model of the assistance of folksonomies enrichment which supports conflictual points of view. Several strategies can then be applied to propose novel browsing facilities to users.

In the emerging field of e-commerce, we show the great potential interest to create 3D virtual shopping centres. The study of a specific application (the creation of a household electrical appliances warehouse) enables us to highlight the possibilities of the VRML 2.0 language to model, visualize and perform interactive walkthroughs inside the warehouse. The key features of VRML 2.0 are underlined and new extensions are proposed in order to achieve a better, more realistic and "human-like" behaviour inside the virtual warehouse.

Presents a vision system for autonomous navigation based on stereo perception without 3D reconstruction. This approach uses weakly calibrated stereo images, i.e. images for which only the epipolar geometry is known. The vision system first rectifies the images, matches selected points between the two images, and then computes the relative elevation of the points relative to a reference plane as well as the images of their projections on this plane. We have integrated this vision module into a complete navigation system. In this system, the relative elevation is used as a shape indicator in order to compute appropriate steering directions everytime a new stereo pair is processed. We have conducted initial experiments in unstructured, outdoor environments with an wheeled rover.>

This paper describes the development and implementation of a reactive visual module utilized on an autonomous mobile robot to automatically correct in trajectory. The authors use a multisensorial mechanism based on inertial and visual cues. The authors report only on the implementation and the experimentation of this module, whereas the main theoretical aspects have been developed elsewhere.