Exploiting fashion x-commerce through the empowerment of voice in the fashion virtual reality arena. Integrating voice assistant and virtual reality technologies for fashion communication

The ongoing development of eXtended Reality (XR) technologies is supporting a rapid increase of their performances along with a progressive decrease of their costs, making them more and more attractive for a large class of consumers. As a result, their widespread use is expected within the next few years. This may foster new opportunities for e-commerce strategies, giving birth to an XR-based commerce (x-commerce) ecosystem. With respect to web and mobile-based shopping experiences, x-commerce could more easily support brick-and-mortar store-like experiences. One interesting and consolidated one amounts to the interactions among customers and shop assistants inside fashion stores. In this work, we concentrate on such aspects with the design and implementation of an XR-based shopping experience, where vocal dialogues with an Amazon Alexa virtual assistant are supported, to experiment with a more natural and familiar contact with the store environment. To verify the validity of such an approach, we asked a group of fashion experts to try two different XR store experiences: with and without the voice assistant integration. The users are then asked to answer a questionnaire to rate their experiences. The results support the hypothesis that vocal interactions may contribute to increasing the acceptance and comfortable perception of XR-based fashion shopping

Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification

IMAGO: A family photo album dataset for a socio-historical analysis of the twentieth century

Although one of the most popular practices in photography since the end of the 19th century, an increase in scholarly interest in family photo albums dates back to the early 1980s. Such collections of photos may reveal sociological and historical insights regarding specific cultures and times. They are, however, in most cases scattered among private homes and only available on paper or photographic film, thus making their analysis by academics such as historians, social-cultural anthropologists and cultural theorists very cumbersome. In this paper, we analyze the IMAGO dataset including photos belonging to family albums assembled at the University of Bologna's Rimini campus since 2004. Following a deep learning-based approach, the IMAGO dataset has offered the opportunity of experimenting with photos taken between year 1845 and year 2009, with the goals of assessing the dates and the socio-historical contexts of the images, without use of any other sources of information. Exceeding our initial expectations, such analysis has revealed its merit not only in terms of the performance of the approach adopted in this work, but also in terms of the foreseeable implications and use for the benefit of socio-historical research. To the best of our knowledge, this is the first work that moves along this path in literature