Enactivism and Performance Art: Putting on Display Our Perception

"Seeing, according to the enactive approach, is not something that happens inside our brain, rather it is something we do, but, as I will argue thanks to the performance art, it is something we do together. The performing arts, with their characteristics – autopoietic feedback loop, spectator/performer exchange, oscillation of the dichotomous subject-object pair - constitute a model through which to in-vestigate the nature of our perception, which is constitutively relational, participative, and transformative. Keywords: Enactivis, performing arts, percep-tion, enactive loniness, transformation. "

Recognizing Unseen Attribute-Object Pair with Generative Model

In this paper, we are studying the problem of recognizing attribute-object pairs that do not appear in the training dataset, which is called unseen attribute-object pair recognition. Existing methods mainly learn a discriminative classifier or compose multiple classifiers to tackle this problem, which exhibit poor performance for unseen pairs. The key reasons for this failure are 1) they have not learned an intrinsic attributeobject representation, and 2) the attribute and object are processed either separately or equally so that the inner relation between the attribute and object has not been explored. To explore the inner relation of attribute and object as well as the intrinsic attribute-object representation, we propose a generative model with the encoder-decoder mechanism that bridges visual and linguistic information in a unified end-to-end network. The encoder-decoder mechanism presents the impressive potential to find an intrinsic attribute-object feature representation. In addition, combining visual and linguistic features in a unified model allows to mine the relation of attribute and object. We conducted extensive experiments to compare our method with several state-of-the-art methods on two challenging datasets. The results show that our method outperforms all other methods.

Two-Dimensional Fuzzy Spatial Relations: A New Way of Computing and Representation

Fuzziness is found everywhere, in modeling spatial relations, fuzziness is found at object level as well as in relation semantics. Commonly, fuzzy topological relations are computed between fuzzy objects. Fuzziness in relation semantics is represented by fuzzy topological relations between crisp objects and these types of fuzzy topological relations are much less developed. In this paper, we propose a method for combining fuzzy topological and directional relations. We also propose an algorithm for defuzzification of relations which provides us a binary topological and directional relation between a 2D object pair. These relations are represented in a neighborhood graph. For validation and assessment, a number of experiments have been performed on artificial data.

Using Action-Object Pairs as a Conceptual Framework for Transaction Log Analysis

In this chapter, we present the action-object pair approach as a conceptual framework for conducting transaction log analysis. We argue that there are two basic components in the interaction between the user and the system recorded in a transaction log, which are action and object. An action is a specific expression of the user. An object is a self-contained information object, the recipient of the action. These two components form one interaction set or an action-object pair. A series of action-object pairs represents the interaction session. The action-object pair approach provides a conceptual framework for the collection, analysis, and understanding of data from transaction logs. We believe that this approach can benefit system design by providing the organizing principle for implicit feedback and other interactions concerning the user and delivering, for example, personalized service to the user based on this feedback. Action-object pairs also provide a worthwhile approach to advance our theoretical and conceptual understanding of transaction log analysis as a research method.