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

Performance Evaluation

Performance Evaluation functions as a leading journal in the area of modeling, measurement, and evaluation of performance aspects of computing and communication systems

Making paper labels smart for augmented wine recognition

An invisible layer of knowledge is progressively growing with the emergence of situated visualizations and reality-based information retrieval systems. In essence, digital content will overlap with real-world entities, eventually providing insights into the surrounding environment and useful information for the user. The implementation of such a vision may appear close, but many subtle details separate us from its fulfillment. This kind of implementation, as the overlap between rendered virtual annotations and the camera’s real-world view, requires different computer vision paradigms for object recognition and tracking which often require high computing power and large-scale datasets of images. Nevertheless, these resources are not always available, and in some specific domains, the lack of an appropriate reference dataset could be disruptive for a considered task. In this particular scenario, we here consider the problem of wine recognition to support an augmented reading of their labels. In fact, images of wine bottle labels may not be available as wineries periodically change their designs, product information regulations may vary, and specific bottles may be rare, making the label recognition process hard or even impossible. In this work, we present augmented wine recognition, an augmented reality system that exploits optical character recognition paradigms to interpret and exploit the text within a wine label, without requiring any reference image. Our experiments show that such a framework can overcome the limitations posed by image retrieval-based systems while exhibiting a comparable performance

Modeling and shaping the lifetime of target detection sensor networks

In this work we model the lifetime of a clustered sensor network deployed for target detection. We assume sensors are randomly dropped on a bidimensional field in order to detect target traversals occuring stochastically. Once a target enters the sensing area of a sensor, the sensor transmits such information to a cluster head, in charge of receiving and retransmitting the messages received from the sensors deployed on the field. The contribution of this work is threefold. We first identify the sensing nodes whose behavior is key to model the duration of sensing operations. We then proceed, providing a heuristic estimation of the traffic received by the cluster head to quantify its energy requirements, resorting to specific lifetime definitions. We finally evaluate the relationship between our probabilistic and heuristic models and the time until when the network fulfills its purpose, i.e., it remains capable of detecting and reporting the traversal of any target to a sink, as obtained by simulation

Models and performance evaluation of event goodput in sensor platforms

Despite the introduction of novel energy harvesting technologies, the lifetime of a sensor platform remains one of its most important performance metrics. Performance, however, may also be assessed in terms of the fraction of events which may successfully/unsuccessfully be detected and reported within a time interval of interest, i.e., mission time. Such a performance metric, here termed event goodput, is key for all random event-driven networks, ranging from surveillance and intrusion detection applications operating in time critical scenarios, to mobile and wearable crowd-sensed ecosystems, where mobile sensors are utilized by a number of different applications. When reporting the appearance of a series of possible, but unknown, phenomena, predicting which event goodput may be obtained during the planned mission time is challenging. In this paper, we address this issue by reducing the network-wide problem to the analysis of the performance of individual nodes, in terms of their capability of handling given fractions of event arrivals under fixed probabilistic guarantees. This is obtained as a function of mission time, event arrival and energy consumption models of different, detection and communication schemes, all realistic

Modeling the Energy Consumption of Upload Patterns on Smartphones and IoT Devices

In this letter, we provide the probabilistic analysis of the energy consumed by installed mobile apps when uploading data to the cloud based on the occurrence of random events during a given time interval. A polynomial algorithm to calculate the probability distribution of the energy they consume is obtained here. The validity of our approach is corroborated with a bound and simulation analysis, resorting to the use of real-world data