Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal Periodic Sets

In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical models behind the notion of granularity and the algorithms to manage temporal data based on those models. A second stream of research investigated symbolic formalisms providing a set of algebraic operators to define granularities in a compact and compositional way. However, only very limited manipulation algorithms have been proposed to operate directly on the algebraic representation making it unsuitable to use the symbolic formalisms in applications that need manipulation of granularities. This paper aims at filling the gap between the results from these two streams of research, by providing an efficient conversion from the algebraic representation to the equivalent low-level representation based on the mathematical models. In addition, the conversion returns a minimal representation in terms of period length. Our results have a major practical impact: users can more easily define arbitrary granularities in terms of algebraic operators, and then access granularity reasoning and other services operating efficiently on the equivalent, minimal low-level representation. As an example, we illustrate the application to temporal constraint reasoning with multiple granularities. From a technical point of view, we propose an hybrid algorithm that interleaves the conversion of calendar subexpressions into periodical sets with the minimization of the period length. The algorithm returns set-based granularity representations having minimal period length, which is the most relevant parameter for the performance of the considered reasoning services. Extensive experimental work supports the techniques used in the algorithm, and shows the efficiency and effectiveness of the algorithm

μGraph: Haptic Exploration and Editing of 3D Chemical Diagrams

People with visual impairments or blindness (VIB) encounter diffculties in exploring graphical representations that are widely used for the study of STEM subjects. In particular, graphs are used to represent many different scientifc notations: fowcharts, automata, cognitive maps, and more. Among these, structural chemical formulae are characterized by a complex, often 3-dimensional structure, which makes them hard to access and author with traditional assistive tools. We propose \ub5Graph, a multimodal system that combines haptic and speech feedback to enable people with VIB to explore and edit structural chemical formulae. Two main contributions are presented: (i) a novel, non-visual interaction paradigm for exploring graphs and its implementation in the \ub5Graph system, and (ii) an extensive evaluation of the proposed system with 10 participants with VIB showing that \ub5Graph is thoroughly accessible and that the haptic feedback enhances understanding of the geometric properties of a graph

WordMelodies: supporting children with visual impairment in learning literacy

We present WordMelodies, an inclusive, cross platform, mobile app that supports children with visual impairments in the acquisition of basic literacy skills through 8 different exercises. WordMelodies has been designed and evaluated by three domain experts in assistive technologies and education for children with visual impairments. After three design and evaluation iterations the app is fully accessible, except for one limitation of the cross platform development toolkit used

Privacy in geo-social networks: proximity notification with untrusted service providers and curious buddies

A major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service

SmartWheels: Detecting urban features for wheelchair users’ navigation

People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed. In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and in-the-wild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier

AudioFunctions.web: Multimodal Exploration of Mathematical Function Graphs

We present AudioFunctions.web, a web app that uses sonifcation, earcons and speech synthesis to enable blind people to explore mathematical function graphs. The system is designed for personalized access through different interfaces (touchscreen, keyboard, touchpad and mouse) on both mobile and traditional devices, in order to better adapt to different user abilities and preferences. It is also publicly available as a web service and can be directly accessed from the teaching material through a hypertext link. An experimental evaluation with 13 visually impaired participants highlights that, while the usability of all the presented interaction modalities is high, users with different abilities prefer different interfaces to interact with the system. It is also shown that users with higher level of mathematical education are capable of better adapting to interaction modalities considered more diffcult by others

ASSETS 2020 UX Panel Report: “Lockdown Experiences”

This contribution describes the organization and the outcomes of the User Experience panel at the ASSETS 2020 virtual conference. The panel was the first one in the history of ASSETS to be conducted as a virtual event, which required a number of changes, both in terms of panellist and attendee involvement. Adapting to this new situation required new solutions that we believe can be useful for other future events - be they virtual, physical or hybrid. We believe the experiences reported also provide helpful insights for reflection on future work in the domain of accessible computing

Towards Large Scale Evaluation of Novel Sonification Techniques for Non Visual Shape Exploration

© 2015 ACM.There are several situations in which a person with visual impairment or blindness needs to extract information from an image. Examples include everyday activities, like reading a map, as well as educational activities, like exercises to develop visuospatial skills. In this contribution we propose a set of 6 sonification techniques to recognize simple shapes on touchscreen devices. The effectiveness of these sonification techniques is evaluated though Invisible Puzzle, a mobile application that makes it possible to conduct non-supervised evaluation sessions. Invisible Puzzle adopts a gamification approach and is a preliminary step in the development of a complete game that will make it possible to conduct a large scale evaluation with hundreds or thousands of blind users. With Invisible Puzzle we conducted 131 tests with sighted subjects and 18 tests with subjects with blindness. All subjects involved in the process successfully completed the evaluation session, with high engagement, hence showing the effectiveness of the evaluation procedure. Results give interesting insights on the differences among the sonification techniques and, most importantly, show that, after a short training, subjects are able to identify many different shapes

SafeBox : adaptable spatio-temporal generalization for location privacy protection

Spatial and temporal generalization emerged in the literature as a common approach to preserve location privacy. However, existing solutions have two main shortcomings. First, spatiotemporal generalization can be used with different objectives: for example, to guarantee anonymity or to decrease the sensitivity of the location information. Hence, the strategy used to compute the generalization can follow different semantics often depending on the privacy threat, while most of the existing solutions are specifically designed for a single semantics. Second, existing techniques prevent the so-called inversion attack by adopting a top-down strategy that needs to acquire a large amount of information. This may not be feasible when this information is dynamic (e.g., position or properties of objects) and needs to be acquired from external services (e.g., Google Maps). In this contribution we present a formal model of the problem that is compatible with most of the semantics proposed so far in the literature, and that supports new semantics as well. Our BottomUp algorithm for spatio-temporal generalization is compatible with the use of online services, it supports generalizations based on arbitrary semantics, and it is safe with respect to the inversion attack. By considering two datasets and two examples of semantics, we experimentally compare BottomUp with a more classical top-down algorithm, showing that BottomUp is efficient and guarantees better performance in terms of the average size (space and time) of the generalized regions

Accessible Mathematics on Touchscreen Devices: New Opportunities for People with Visual Impairments

In recent years educational applications for touchscreen devices (e.g., tablets) become widespread all over the world. While these devices are accessible to people with visual impairments, educational applications to support learning of STEM subjects are often not accessible to visually impaired people due to inaccessible graphics. This contribution addresses the problem of conveying graphics to visual impaired users. Two approaches are taken into account: audio icons and image sonification. In order to evaluate the applicability of these approaches, we report our experience in the development of two didactic applications for touchscreen devices, specifically designed to support people with visual impairments or blindness while studying STEM subjects: Math Melodies and Audio Functions. The former is a commercial application to support children in primary school in an inclusive class. It adopts an interaction paradigm based on audio icons. The latter is a prototype application aimed at enabling visually impaired students to explore function diagrams and adopts an image sonification approach