An Adaptive System for Home Monitoring Using a Multiagent Classification of Patterns

This research takes place in the S(MA)2D project which proposes software architecture to monitor elderly people in their own homes. We want to build patterns dynamically from data about activity, movements, and physiological information of the monitored people. To achieve that, we propose a multiagent method of classification: every agent has a simple know-how of classification. Data generated at this local level are communicated and adjusted between agents to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate global risks (epidemic, heat wave). These data are dynamic; the system has to maintain the built patterns and has to create new patterns. So, the system is adaptive and can be spread on a large scale

Continual Learning System with Sentence Embeddings

Recent improvements in computing power and ambient technologies have opened up new perspectives in ambient intelligence and proactive software systems. The need emerges for an ambient and supportive system that deeply understands the user\u27s needs and preferences under the current context. In this work, we propose a recommendation system that aggregates and understands the current situation and continuously learns from the user’s decision history. To leverage a rich pool of contextual data, the system dynamically changes the strategy and scope for contextualization and encodes multimodal data into unified embeddings. A contextual similarity database consisting of these embeddings is leveraged for finding, ranking and comparing scenarios. The proposed recommender is intended to fit into a decentralized service system and addresses challenges in application interaction, user engagement, user privacy and scalability

Annals of the Congregation of the Mission, Vol. 19, No. 1, part 3

Pages 240-36

L'adéquation fonctionnelle comme limite des systèmes auto-organisateur

Rencontres interdisciplinaires sur les systèmes complexes naturels et artificielsInternational audienc