LODifying personal content sharing

The advent of contemporary mobile devices and their increasing computing power and location capabilities combined with the most innovative Web technologies has provided mobile users with new possibilities to share experiences on-the-go. The growing quantity of multimedia content present on the Web makes it difficult for mobile users to retrieve suitable content. Typically, users looking for interesting content related to their current position or POI (point of interest), access Web search engines relying on keywords to describe their ideas. Unfortunately such descriptions are often subjective and thus retrieval can be ineffective. To address these issues, our platform provides users with an application targeted for modern mobile devices that allows content acquisition and publication. Published content is automatically analyzed and stored on our server with semantic annotations based on the user's context and content, for further semantic search. We describe how and why we migrated from a triple-tags technology to semantics, hoping for related Linked Dat

Social Consensus: Contribution to Design Methods for AI Agents That Employ Personal Data

The emerging complex IoT ecosystems, embodied through Artificially Intelligent (AI) Agents on the front-end interaction with the user, rise many new considerations to be taken into account during the design process, among which the use of sensitive personal data. This paper introduces a case study, a concluded project of a system supported by AI algorithms for delivering tailored services to the drivers, including insurance offerings and supporting drivers in practicing safer driving style. We report on a segment of user studies done within this project that relates to the use of personal data, and we discuss the notion of emerged user values within. Accordingly, we observe and propose inclusion of social consensus considerations within the design process and evaluation of the same

LODifying personal content sharing

The advent of contemporary mobile devices and their increasing computing power and location capabilities combined with the most innovative Web technologies has provided mobile users with new possibilities to share experiences on-the-go. The growing quantity of multimedia content present on the Web makes it difficult for mobile users to retrieve suitable content. Typically, users looking for interesting content related to their current position or POI (point of interest), access Web search engines relying on keywords to describe their ideas. Unfortunately such descriptions are often subjective and thus retrieval can be ineffective. To address these issues, our platform provides users with an application targeted for modern mobile devices that allows content acquisition and publication. Published content is automatically analyzed and stored on our server with semantic annotations based on the user's context and content, for further semantic search. We describe how and why we migrated from a triple-tags technology to semantics, hoping for related Linked Data

LODifying personal content sharing Oscar Rodríguez Rocha

carmen.criminis

Individual mobility deep insight using mobile phones data

Abstract The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows