13 research outputs found

    Integrated Urban Sensing: A Geo-sensor Network for Public Health Monitoring and Beyond

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    Pervasive environmental monitoring implies a wide range of technical, but also socio-political challenges, and this applies especially to the sensitive context of the city. In this paper, we elucidate issues for bringing out pervasive urban sensor networks and associated concerns relating to fine-grained information provision. We present the Common Scents project, which is based on the Live Geography approach, and show how it can overcome these challenges. As opposed to hitherto sensing networks, which are mostly built up in monolithic and closed systems, the Common Scents approach aims to establish an open, standards based and modular infrastructure. This ensures interoperability, portability and flexibility, which are crucial prerequisites for pervasive urban sensing. The implementation – a real-time data integration and analysis system for air quality assessment – has been realised on top of the CitySense sensor network in the City of Cambridge, MA US together with the city’s Public Health Department responding to concrete needs of the city and its inhabitants. The second pilot using mobile sensors mounted on bicycles has been deployed in Copenhagen, Denmark. Preliminary results show highly fine-grained variability of pollutant dispersion in urban environments.Singapore-MIT Alliance. Center for Environmental Sensing and MonitoringSingapore-MIT Alliance for Research and Technology CenterEuropean Commission (FP7 GENESIS project)Bundesministerium für Wissenschaft und ForschungResearch Studio iSPAC

    BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

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    Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

    Designing Conversational Interfaces With Multimodal Interaction

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    Our current research focuses on developing conversational interfaces to on-line applications through speech recognition technology. We have developed a prototype system that combines pen and speech input from the on-line user in a web-browser. VoiceLog is a voice-enabled connection to a web-server that allows one to obtain vehicle diagrams and to place orders for specific parts in these diagrams. VoiceLog features a novel client-server approach to speech recognition, modular reusable components and a simple Java-based interface. This paper briefly describes the system and its architecture including the handling of simultaneous input from pen and speech, the production of audio and visual feedback, and the management of multimodal dialogue. 1. INTRODUCTION Traditional keyboard and mouse interfaces are impractical on small portable devices. Spoken language systems, such as voice enabled browsing, offer an intuitive way of accessing the growing amount of on-line information. The next ge..
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