Preference purification and the inner rational agent:A critique of the conventional wisdom of behavioural welfare economics

Neoclassical economics assumes that individuals have stable and context-independent preferences, and uses preference-satisfaction as a normative criterion. By calling this assumption into question, behavioural findings cause fundamental problems for normative economics. A common response to these problems is to treat deviations from conventional rational-choice theory as mistakes, and to try to reconstruct the preferences that individuals would have acted on, had they reasoned correctly. We argue that this preference purification approach implicitly uses a dualistic model of the human being, in which an inner rational agent is trapped in an outer psychological shell. This model is psychologically and philosophically problematic

Providing semantic knowledge to a set of pictograms for people with disabilities: a set of links between WordNet and Arasaac: Arasaac-WN

International audienceThis article presents a resource that links WordNet, the widely known lexical and semantic database, and Arasaac, the largest freely available database of pictograms. Pictograms are a tool that is more and more used by people with cognitive or communication disabilities. However, they are mainly used manually via workbooks, whereas caregivers and families would like to use more automated tools (use speech to generate pictograms, for example). In order to make it possible to use pictograms automatically in NLP applications, we propose a database that links them to semantic knowledge. This resource is particularly interesting for the creation of applications that help people with cognitive disabilities, such as text-to-picto, speech-to-picto, picto-to-speech...In this article, we explain the needs for this database and the problems that have been identified. Currently, this resource combines approximately 800 pictograms with their corresponding WordNet synsets and it is accessible both through a digital collection and via an SQL database. Finally, we propose a method with associated tools to make our resource language-independent: this method was applied to create a first text-to-picto prototype for the French language. Our resource is distributed freely under a Creative Commons license at the following URL: https://github.com/getalp/Arasaac-WN

Maladie de Parkinson et produits phytosanitaires : apport de la consultation de pathologies professionnelles et environnementales de Poitiers dans la caractérisation des matières actives d’intérêt

33. Congrès National de Santé au Travail, 2014/06/03-06, Lille (France)International audienc

Distant speech processing for smart home: comparison of ASR approaches in scattered microphone network for voice command

International audienceVoice command in multi-room smart homes for assisting people in loss of autonomy in their daily activities faces several challenges, one of them being the distant condition which impacts ASR performance. This paper presents an overview of multiple techniques for fusion of multi-source audio (pre, middle, post fusion) for automatic speech recognition for in-home voice command. The robustness of the models of speech is obtained by adaptation to the environment and to the task. Experiments are based on several publicly available realistic datasets with participants enacting activities of daily life. The corpora were recorded in natural condition, meaning background noise is sporadic, so there is no extensive background noise in the data. The smart home is equipped with one or two microphones in each room, the distance between them being larger than 1 meter. An evaluation of the most suited techniques improves voice command recognition at the decoding level, by using multiple sources and model adaptation. Although Word Error Rate (WER) is between 26% and 40%, Domotic Error Rate (identical to the WER, but at the level of the voice command) is less than 5.8% for deep neural network models , the method using Feature space Maximum Likelihood Linear Regression (fMLLR) with speaker adaptation training and Subspace Gaussian Mixture Model (SGMM) exhibits comparable results