Silvia Mirri

mPASS: Integrating people sensing and crowdsourcing to map urban accessibility

This paper presents mPASS (mobile Pervasive Accessibility Social Sensing), a system designed to collect data about urban and architectural accessibility and to provide users with personalized paths, computed on the basis of their preferences and needs. The system combines data obtained by sensing, crowdsourcing and mashing-up with main geo-referenced social systems, with the aim of offering services based on a detailed and valid data set.

On Combining Crowdsourcing, Sensing and Open Data for an Accessible Smart City

This work presents a novel geospatial mapping service, based on OpenStreetMap, which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowd sourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual accessibility feature and the real time of arrival at the stops of the buses. The resulting service supports citizens with reduced mobility (users with disabilities and/or elderly people) suggesting urban paths accessible to them and providing information related to travelling time, which are tailored to their abilities to move and to the bus arrival time. The manuscript demonstrates the effectiveness of the approach by means of a case study focusing on the differences between the solutions provided by our system and the ones computed by main stream geospatial mapping services.

Notes for a Collaboration: On the Design of a Wiki-type Educational Video Lecture Annotation System

We describe a collaborative annotation system for the production of rich media e-learning contents. Our system exploits a wiki-like interface that allows cooperative users to enrich didactical multimedia lectures with additional information, such as captions, annotations and comments. Before delivering them to users, these additional contents are automatically adapted and integrated with video lectures based on user profiles and requests. This way, the additional contents collaboratively provided by users not only represent an effective alternative that students may follow to better understand the lecture, but also allow for a fine-grained customization of the didactic material. This is an important aspect which represents a step forward towards the Web(2.0)-ification of e-learning technologies. An experimental assessment shows the viability of our approach.

A context-aware system for personalized and accessible pedestrian paths

This work presents mPASS (mobile Pervasive Accessibility Social Sensing), a social and ubiquitous context aware system to provide users with personalized and accessible pedestrian paths and maps. In order to collect a complete data set, our system gathers information from different sources: sensing, crowdsourcing and data produced by local authors and disability organizations. Gathered information are tailored to users needs and preferences on the basis of his/her context, defined by his/her location, his/her profile and quality of data about the personalized path. To support the effectiveness of our approach, we have developed a prototype, which is described in this paper, together with some results of the context-based adaptation.

Trustworthiness in crowd- sensed and sourced georeferenced data

This paper focuses on the trustworthiness of data gathered from different sources, including crowdsensing and crowdsourcing, in pervasive systems. The specific focus is on mPASS (mobile Pervasive Accessibility Social Sensing), a system devoted to support mobile users with accessibility needs in a smart city context. mPASS is in charge of collecting data about urban and architectural barriers and facilities, with the aim of providing mobile users with personalized paths, during their movement, computed on the basis of their preferences and accessibility needs. A trustworthiness model is presented that combines three sources of information, i.e., crowdsensed data, crowdsourced data and authoritative data. Simulations results witness the feasibility of our approach.

Augment browsing and standard profiling for enhancing web accessibility

The opportunity of effectively tailoring Web resources presentation - depending on each single user needs and preferences - represents a challenge and a necessity for accessibility and inclusion. On the Web, customizing means transcoding content according to some user and/or device (contextual) settings. Such a profiling refers to devices constraints, user habits, skills, different needs (or tastes) about interaction, in order to drive all the necessary procedures for content (re)shaping. The usual set up that users provide for assistive tools such as screen readers or speech-to-text applications, is a common practice (and a typical example) for a subjective, better enjoyment of resources. This work describes an augment browsing system, which allows users to set up their needs and preferences about Web pages presentation from the browser interface and is capable to automatically modify (transcode) content, according to such settings at client-side. The system is based on a widespread Web browser extension (GreaseMonkey) and well-known standards have been utilized to represent users settings. Finally a case-study of the system has been assessed on a widespread social network, also taking into account some evaluations about accessibility by a group of blind persons.

Monitoring accessibility: large scale evaluations at a Geo political level

Once we assumed that Web accessibility is a right, we implicitly state the necessity of a governance of it. Beyond any regulation, institutions must provide themselves with suitable tools to control and support accessibility on typically large scale scenarios of content and resources. No doubt, the economic impact and effectiveness of these tools affect accessibility level. In this paper, we propose an application to effectively monitor Web accessibility from a geo-political point of view, by referring resources to the specific (category of) institutions which are in charge of it and to the geographical places they are addressed to. Snapshots of such a macro level spatial-geo-political analysis can be used to effectively focus investments and skills where they are actually necessary.

A multimedia broker to support accessible and mobile learning through learning objects adaptation

The large diffusion of e-learning technologies represents a great opportunity for underserved segments of population. This is particularly true for people with disabilities for whom digital barriers should be overstepped with the aim of reengaging them back into society to education. In essence, before a mass of learners can be engaged in a collective educational process, each single member should be put in the position to enjoy accessible and customized educational experiences, regardless of the wide diversity of their personal characteristics and technological equipment. To respond to this demand, we developed LOT (Learning Object Transcoder), a distributed PHP-based service-oriented system designed to deliver flexible and customized educational services for a multitude of learners, each with his/her own diverse preferences and needs. The main novelty of LOT amounts to a broking service able to manage the transcoding activities needed to convert multimedia digital material into the form which better fits a given student profile. Transcoding activities are performed based on the use of Web service technologies. Experimental results gathered from several field trials with LOT (available online at http://137.204.74.83/∼lot/) have confirmed the viability of our approach.

CrowdSensing for smart mobility through a service-oriented architecture

Crowdsensing is a powerful approach to build representations of specific aspects of reality which are of interest for citizens in smart cities, and in particular for people with special needs. In this work, we present an application of the microservice paradigm to create a mobility services platform. By exposing each part of the process as a microservice, we achieve the ability of developing applications as orchestration of available components. Moreover, we leverage the possibility of sharing data between different applications in a controlled environment.

A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first “monolithic” prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies.