Augmented Bodies: Functional and Rhetorical Uses of Augmented Reality in Fashion

Augmented Reality (AR) is increasingly changing our perception of the world. The spreading of Quick Response (QR), Radio Frequency (RFID) and AR tags has provided ways to enrich physical items with digital information. By a process of alignment the codes can be read by the cameras contained in handheld devices or special equipment and add computer-generated contents – including 3-D imagery – to real objects in real time. As a result, we feel we belong to a multi-layered dimension, to a mixed environment where the real and the virtual partly overlap. Fashion has been among the most responsive domains to this new technology. Applications of AR in the field have already been numerous and diverse: from Magic Mirrors in department stores to 3-D features in fashion magazines; from augmented fashion shows, where models are covered with tags or transformed into walking holograms, to advertisements consisting exclusively of more or less magnified QR codes. Bodies are thus at the same time augmented and encrypted, offered to the eye of the digital camera to be transfigured and turned into a secret language which, among other functions, can also have that of becoming a powerful tool to bypass censorship

A Hybrid Representational Proposal for Narrative Concepts: A Case Study on Character Roles

In this paper we propose the adoption of a hybrid approach to the computational representation of narrative concepts, combining prototype-based and ontology-based representations. In particular we focus on the notion of narrative roles. Inspired by the characterization provided by the TvTropes wiki, where narrative devices are discussed across old and new media, we provide a representation of roles based on the integration of a set of typicality-based semantic dimensions (represented by using the Conceptual Spaces framework) with their corresponding classical characterization in terms of necessary and sufficient conditions (represented in terms of Formal Ontologies)

Sensing the Cultural Significance with AI for Social Inclusion

Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes