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A trend towards drier conditions during the April to October ‘cool’ season across southern Australia has been observed in the past few decades. Frequent and prolonged droughts have a significant impact on the financial stability of affected farming communities. Forecast-based Financing (FbF) is a novel proactive aid approach that provides support measures to increase resilience during the window between drought early warnings, and the actual onset and intensification of drought. Using the Northern Murray-Darling Basin as a case study, we investigated whether FbF combined with a user-centred Integrated Early Warning System (I-EWS) for drought has the potential to increase the drought resilience of Australian farming communities. This study shows that farming businesses most impacted by drought have three common factors: (i) lower levels of business management skills, (ii) lower levels of pre-drought preparedness during non-drought periods, and (iii) slower responses when the intensity of drought increases. The results suggest that FbF in its current form is not recommended for a market economy such as Australia, as forms of direct assistance may have adverse long-term effects through disrupting the market itself and may not encourage farm operators to regularly assess and adapt their drought management strategies. Results also suggest that providing farmers, service providers, and all levels of government with tools that incorporate a user-centred I-EWS for drought can improve overall decision-making before, during, and even after drought. This change from a reactive to a proactive approach to managing drought impacts can be a highly effective form of increasing the drought resilience of farming communities.

International audience; Many users struggle when they have to use complex interfaces to complete everyday computing tasks. Offering intelligent, proactive assistance is becoming commonplace yet determining the right time to provide help is still difficult. We conducted an empirical study that aimed to uncover what user factors influenced following advice. Our results describe a user's background and expectations that appear to play a role in heeding assistance. Our work is a step towards understanding how to provide the right assistance at the right time and build proactive assistance systems that are personalized for individual users.

International audience; The main characteristic of intelligent devices that compose our environment is their capability to perceive and collect relevant information (context awareness) in order to assist users in their daily tasks. However, these tasks evolve frequently and require dynamic and evolutionary systems (context-aware systems) to improve intelligent devices skills according to user's context. Some context-aware systems are described in the literature, but most of them have extremely tight coupling between the semantic used in the application and sensors used to obtained the data for this semantic interpretation. The objective of our research is to study and implement a proactive approach able to use existing sensors and to create dynamically human-machine conversational situations when needed. The system presented in this paper is named X-CAMPUS (eXtensible Conversational Agent for Multichannel Proactive Ubiquitous Services). It aims to assist user in his/her daily tasks thanks to its ability to perceive the state of the environment and interact effectively according to the user's needs. In this paper we describe our approach for proactive intelligent assistance and we illustrate it through some scenarios showing that according to a given multi-parameters context, our X-CAMPUS agent notifies the user via personalized messages (e.g., suggestion of restaurants according to menus and users' preferences) across the most appropriate channel (instant messaging, e-mail or SMS) and the most appropriate modality (text, gesture or voice). Then, we discuss our quantitative results, based on four principal hypotheses in order to evaluate our system's capability to manage many users simultaneously with different contextual information. We argue and we show that the proactive assistance is very relevant in complex situations with various criteria to take into account (user's profile, location, task, etc.).

International audience; User needs are expanding and becoming more and more complex with the emergence of newly adopted technologies. As a result, the convergence of smart devices, having the capability to communicate as well as sharing information and ensuring user need satisfaction, leads to profoundly change the way we interact with our environment. They should provide an adaptive assistance in both reactive and proactive mode and new communication methods focused on multimodal and multichannel interfaces. However, most of existing context-aware systems have extremely tight coupling between applications' semantic and sensor's details. So, the objective of our research is to implement an approach which can support the ability to reuse sensors and to evolve existing applications to use new context types. In this paper, we illustrate our approach for proactive intelligent assistance and we describe our architecture based on three principal layers. These layers are designed in order to build applications which can increase the welfare of the user situated in intelligent environment.

The contribution of new technologies in the development of the computing gave a rise to news domains of researches such as the Ambient Intelligent (AmI), where the new technologies are used as tools to assist users in their daily tasks. Our study mainly the proactive assistance and the interaction human-machine; we study the contextual proactive assistance in order to design an adaptive system able to determine the needs of user wishing have a virtual assistant. We are interested in the contextual proactive interaction between a human and a conversational agent, that we have named X-CAMPUS. X-CAMPUS stands for eXtensible Conversational Agent for Mul- tichannel Proactive Ubiquitous Services. It aims to assist user in his daily tasks thanks to its ability to perceive the state of the environment and interact effectively according to the user's needs. We show that according to the context, X-CAMPUS notifies the user via personalized messages (e.g., suggestion of restaurants according to their menus and the users' preferences) and also according to the most appropriate channel (instant messaging, e-mail and SMS)., La contribution des nouvelles technologies au développement de l'informatique a donné naissance à de nouveaux domaines de recherches tel que celui de l'Intelligence Ambiante (IAm), où les nouvelles technologies sont utilisées comme des outils pour assister les utilisateurs dans leurs tâches quotidiennes. Notre étude porte principalement sur l'assistance proactive et l'interaction homme-machine ; nous étudions l'assistance proactive contextuelle dont le but est de concevoir un système adaptatif capable de déterminer les besoins de l'utilisateur souhaitant disposer d'un assistant virtuel. Nous nous sommes intéressés à l'interaction proactive contextuelle, dans le cadre d'un dialogue entre un humain et un agent conversationnel en ligne nommé X-CAMPUS. X-CAMPUS est l'acronyme de eXtensible Conversational Agent for Multichannel Proactive Ubiquitous Services. Il a pour vocation d'assister l'utilisateur dans ses tâches quotidiennes grâce à sa capacité de percevoir l'état de l'environnement et d'interagir pertinemment selon les besoins de l'utilisateur. Nous montrons que selon le contexte, l'agent notifie l'utilisateur avec des messages personnalisés (ex : suggestion de restaurants selon leurs menus, en accord avec les préférences de l'utilisateur) via le canal le plus approprié (messagerie instantané, e-mail et SMS).