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To fulfil the increasing need for food of the growing population and face climate change, modern technologies have been applied to improve different farming processes. One important application scenario is to detect and measure natural hazards using sensors and data analysis techniques. Crowdsensing is a sensing paradigm that empowers ordinary people to contribute with data their sensor-enhanced mobile devices gather or generate. In this paper, we propose to use Twitter as an open crowdsensing platform for acquiring farmers knowledge. We proved this concept by applying pre-trained language models to detect individual's observation from tweets for pest monitoring.

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Space is a matter of distance between social objects. The following article is based on the "agencement" concept seen as a framework to formalize new projects territories. The area of research is PARIS-SACLAY Campus, which views the sitting of a world science cluster. The agencements are modelized by means of mereology and simplicial complexes. Its objective is to offer new decision-making tools to urban actors.

Personal assistants are becoming more pervasive in our envi-ronments but still do not provide natural interactions. Their lack of realism in term of expressiveness and their lack of visual feedback can create frustrating experiences and make users lose patience. In this sense, we propose an end-to-end trainable neural architecture for text-driven 3D mouth animations. Previous works showed such architectures provide better realism and could open the door for integrated affective Human Computer Interface (HCI). Our study shows that such visual feedback improves users’ comfort for 78%of the candidates significantly while slightly improving their time perception.

Sustainable agriculture is crucial to society since it aims at supporting the world's current food needs without compromising future generations. Recent developments in Smart Agriculture and Internet of Things have made possible the collection of unprecedented amounts of agricultural data with the goal of making agricultural processes better and more efficient, and thus supporting sustainable agriculture. These data coming from different types of IoT devices can also be combined with relevant information published in online social networks and on the Web in the form of textual documents. Our objective is to integrate such heterogeneous data into knowledge bases that can support farmers in their activities, and to present global, real-time and comprehensive information to researchers. Semantic technologies and linked data provide a possibility for data integration and for automatic information extraction. This paper aims to give a brief review on the current semantic web technology applications for agricultural corpus, then to discuss the limits and potentials in construction and maintenance of existing ontologies in agricultural domain.

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