Learning to compare with few data for personalised human activity recognition.

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related (but different) new tasks. Unlike task-specific model training, a meta-learner’s training instance - referred to as a meta-instance - is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN), which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model-agnostic machine-learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners

Combining data-driven and domain knowledge components in an intelligent assistant to build personalized menus

In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the original menu plan in sub-plans. Each sub-plan is in charge to optimize one of the meals of one day in the personal menu of the user. The main latent reasoning mechanism used is case-based reasoning, which reuses previous menu configurations according to the nutritional plan and the corresponding hard constraints and the user preferences to meet a personalized recommendation menu for a given user. It uses the cognitive analogical reasoning technique in addition to ontologies, nutritional databases and expert knowledge. The preliminary results with some examples of application to test the new contextual components have been very satisfactory according to the evaluation of the experts.This work has been partially supported by the project Diet4You (TIN2014-60557-R), the Spanish Thematic Network MAPAS [TIN2017-90567-REDT (MINECO/FEDER EU)], and the Consolidated Research Group Grant from AGAUR (Generalitat de Catalunya) IDEAI-UPC (AGAUR SGR2017-574).Peer ReviewedPostprint (author's final draft

The type and frequency of interactions that occur between staff and children outside in Early Years Foundation Stage settings during a fixed playtime period when there are tricycles available

This study reports on an investigation into adult and child interactions observed in the outdoor play environment in four Local Authority early years foundation stage settings in England. In this instance the common two features across the settings were the presence of tricycles and a timetabled outdoor play period. In total, across the four schools, there were 204 children. The study aimed to gain an understanding of the nature of the dialogues between staff and children, that is, the types of exchange that occurred when either the child approached an adult or the adult approached a child. The most frequent type of utterance was also analysed. The study concludes that adults in these settings spoke more than children and the greatest type of utterance was that of the adult about domestic matters. When the child initiated the conversation there were more extended child utterances than domestic utterances. This may suggest that children wish to be involved in conversations of depth and meaning and that staff need to become aware of how to develop this conversational language with children

Designing nutritional menus using case-based and rule-based reasoning

Case-based reasoning (CBR) and rule-based reasoning (RBR) are two paradigms for building knowledge-based systems. They represent both distinct approaches to knowledge-based systems development and distinct cognitive models of human problem solving. They are usually viewed as competing, rather than complementary, paradigms. However, our investigation shows that in combination, they can provide both a stronger approach to knowledge-based systems development and a broader cognitive model. The domain of our investigation is the design of nutritious, yet appetizing, menus. Both logic and experience play roles in this domain. Our approach is to construct two expert systems, one case-based and one rule-based, to perform the same task. We compare and contrast our two systems, to identify the strengths and weaknesses of each