As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

We present a system capable of recommending music playlists that take into account the temporal context of the user, i.e. they select user preferences as learned for the concrete time situation of the request.

Most current multi-player 3D games can only be played on dedicated platforms, requiring specifically designed content and communication over a prede-fined network. To overcome these limitations, the OLGA (On-Line GAming) consortium has devised a framework to develop distributive, multi-player 3D games. Scalability at the level of content, platforms and networks is exploited to achieve the best trade-offs between complexity and quality. Besides, stan-dardized content compression formats (MPEG-4, JPEG 2000) are used in OLGA's framework, enabling easy deployment over existing infrastructure, while keeping hooks to well-established practices in the game industry.

Interactive and seamless access to video content is facilitated by technology that is able to annotate and retrieve media data efficiently and automatically with the minimal human interventions. Media should be accessible by intended users quickly and independent of the database size, regardless of the homogeneous or heterogeneous nature of the delivery channels or the user computing platform. These requirements are driving research and development efforts in the European project BUSMAN; in which a system for efficient annotation, seamless delivery of and access to video components across heterogeneous channels is being developed. The BUSMAN system architecture is based on the needs and expectations of two types of users: content providers and end users. In this paper an overview of the BUSMAN system is presented and its main functionalities are described.

In order to complement subjective evaluation of the quality of segmentation masks, this paper introduces a procedure for automatically assessing this quality. Algorithmically computed figures of merit are proposed. Assuming the existence of a perfect reference mask (ground truth), generated manually or with a reliable procedure over a test set, these figures of merit take into account visually desirable properties of a segmentation mask in order to provide the user with metrics that best quantify the spatial and temporal accuracy of the segmentation masks. For the sake of easy interpretation, results are presented on a peaked signal-to-noise ratio-like logarithmic scale.