Hierarchical and multiple hand action representation using temporal postural synergies.

The notion of synergy enables one to provide simplified descriptions of hand actions. It has been used in a number of different meanings ranging from kinematic and dynamic synergies to postural and temporal postural synergies. However, relatively little is known about how representing an action by synergies might take into account the possibility to have a hierarchical and multiple action representation. This is a key aspect for action representation as it has been characterized by action theorists and cognitive neuroscientists. Thus, the aim of the present paper is to investigate whether and to what extent a hierarchical and multiple action representation can be obtained by a synergy approach. To this purpose, we took advantage of representing hand action as a linear combination of temporal postural synergies (TPSs), but on the assumption that TPSs have a tree-structured organization. In a tree-structured organization, a hand action representation can involve a TPS only if the ancestors of the synergy in the tree are themselves involved in the action representation. The results showed that this organization is enough to force a multiple representation of hand actions in terms of synergies which are hierarchically organized.

Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods.

Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.