Who am I talking with? A face memory for social robots

In order to provide personalized services and to develop human-like interaction capabilities robots need to rec- ognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide user- aware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot’s system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

The First EU Social Partner Agreement in Practice: Parental Leave in the 15 Member States. IHS Political Science Series: 2004, No. 96

In this paper, we analyze the impact of one specific EU social policy measure, the Parental Leave Directive. This Directive is based on the first Euro-collective agreement, concluded in November 1995 by the ETUC, UNICE and CEEP. Contrary to the rather sceptical assessments presented by many observers at the time of its adoption, our in-depth analysis of the Directive's implementation in all 15 member states reveals rather far-reaching effects. The Directive induced significant policy reforms in the majority of member states and thus facilitated the reconciliation of work and family life for many working parents. These effects were not only brought about by compliance with the compulsory minimum standards of the Directive, but also by a considerable number of voluntary reforms. We argue that domestic party politics and processes of policy learning may explain the occurrence of these "unforced" changes, which have hitherto received little attention by Europeanisation scholars