Vulcanus 2.0: A Recommender System for Accessibility

Even though the use of recommender systems is already widely spread in several application areas, there is still a lack of studies for accessibility research field. One of these attempts to use recommender system benefits for accessibility needs is Vulcanus. The Vulcanus recommender system uses similarity analysis to compare user’s trails. In this way, it is possible to take advantage of the user’s past behavior and distribute personalized content and services. The Vulcanus combined concepts from ubiquitous computing, such as user profiles, context awareness, trails management, and similarity analysis. It uses two different approaches for trails similarity analysis: resources patterns and categories patterns. In this work we performed an asymptotic analysis, identifying Vulcanus’ algorithm complexity. Furthermore we also propose improvements achieved by dynamic programming technique, so the ordinary case is improved by using a bottom-up approach. With that approach, many unnecessary comparisons can be skipped and now Vulcanus 2.0 is presented with improvements in its average case scenario.

The spatial character of sensor technology

By considering the spatial character of sensor-based interactive systems, this paper investigates how discussions of seams and seamlessness in ubiquitous computing neglect the complex spatial character that is constructed as a side-effect of deploying sensor technology within a space. Through a study of a torch (`flashlight') based interface, we develop a framework for analysing this spatial character generated by sensor technology. This framework is then used to analyse and compare a range of other systems in which sensor technology is used, in order to develop a design spectrum that contrasts the revealing and hiding of a system's structure to users. Finally, we discuss the implications for interfaces situated in public spaces and consider the benefits of hiding structure from users.

Arcabouço para construção de jogos ubíquos com foco em reabilitação

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.

How does the healthcare industry involve users in medical device development? Pointers for UbiHealth

This paper introduces the Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH) and outlines the problem of integrating a user-centred approach for development of medical devices together with the information and communication technology environments in which they are increasingly required to operate. We highlight some of the regulatory requirements that are relevant to user needs consideration in medical device development. Finally, we reveal a range of limitations in the current practice of the medical device industry in the area of user needs capture, based on responses from interviews with MATCH’s industry partners.

Cooperative learning and its influences on the diminishing of the digital divide

Digital divide is an issue that concerns our technology dominated society. The parents of ubiquitous computing dreamt of a total proliferation of information technology. But the reality we live in is not yet prepared for this future. There is current a need to develop programs in order to diminish this difference between the digitally included and the excluded one. PROEJA-Transiarte is a project ran by Universidade de Brasília in the city of Ceilândia, Federal District of Brazil. It proposes a different approach on the issue of digital divide, by introducing the cooperative creation of cyberart, based on the life stories of each participant, into the regular curriculum of EJA (Educação de Jovens e Adultos) classes, thus implementing the concept of solidary education. This research project investigated the role played by the cooperative learning the students put in practice during the workshops of the project in the diminishing of the digital exclusion a great part of the students feel. It looked into their activities, analyzing the development of their cooperation, putting it next in the context of the digital and social inclusion. After a multi-dimensional research on the theme, in the context of PROEJA-Transiarte, the conclusion shows the impact cooperative learning has in the reduction of the digital divide, analyzing the perception of the currently involved students, the researchers active in the project, or the former students that had their lives improved because of the workshops they participated in.

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.

An assisted navigation method for telepresence robots

Telepresence robots have emerged as a new means of interaction in remote environments. However, the use of such robots is still limited due to safety and usability issues when operating in human-like environments. This work addresses these issues by enhancing the robot navigation through a collaborative control method that assists the user to negotiate obstacles. The method has been implemented in a commercial telepresence robot and a user study has been conducted in order to test the suitability of our approach.

Enhancing smart environments with mobile robots

Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases are presented as evaluation of our proposal.

Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

Hierarchical Dirichlet processes (HDP) was originally designed and experimented for a single data channel. In this paper we enhanced its ability to model heterogeneous data using a richer structure for the base measure being a product-space. The enhanced model, called Product Space HDP (PS-HDP), can (1) simultaneously model heterogeneous data from multiple sources in a Bayesian nonparametric framework and (2) discover multilevel latent structures from data to result in different types of topics/latent structures that can be explained jointly. We experimented with the MDC dataset, a large and real-world data collected from mobile phones. Our goal was to discover identity–location– time (a.k.a who-where-when) patterns at different levels (globally for all groups and locally for each group). We provided analysis on the activities and patterns learned from our model, visualized, compared and contrasted with the ground-truth to demonstrate the merit of the proposed framework. We further quantitatively evaluated and reported its performance using standard metrics including F1-score, NMI, RI, and purity. We also compared the performance of the PS-HDP model with those of popular existing clustering methods (including K-Means, NNMF, GMM, DP-Means, and AP). Lastly, we demonstrate the ability of the model in learning activities with missing data, a common problem encountered in pervasive and ubiquitous computing applications.

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.

Ubiquitous green computing techniques for high demand applications in smart environments

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Cloud computing : what upstream oil and gas can learn from other industries

The topic of “the cloud” has attracted significant attention throughout the past few years (Cherry 2009; Sterling and Stark 2009) and, as a result, academics and trade journals have created several competing definitions of “cloud computing” (e.g., Motahari-Nezhad et al. 2009). Underpinning this article is the definition put forward by the US National Institute of Standards and Technology, which describes cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction” (Garfinkel 2011, p. 3). Despite the lack of consensus about definitions, however, there is broad agreement on the growing demand for cloud computing. Some estimates suggest that spending on cloudrelated technologies and services in the next few years may climb as high as USD 42 billion/year (Buyya et al. 2009).

An Effective Seamless Connectivity Scheme for Ubiquitous Applications

The idea of ubiquity and seamless connectivity in networks is gaining more importance in recent times because of the emergence of mobile devices with added capabilities like multiple interfaces and more processing abilities. The success of ubiquitous applications depends on how effectively the user is provided with seamless connectivity. In a ubiquitous application, seamless connectivity encompasses the smooth migration of a user between networks and providing him/her with context based information automatically at all times. In this work, we propose a seamless connectivity scheme in the true sense of ubiquitous networks by providing smooth migration to a user along with providing information based on his/her contexts automatically without re-registration with the foreign network. The scheme uses Ubi-SubSystems(USS) and Soft-Switches(SS) for maintaining the ubiquitous application resources and the users. The scheme has been tested by considering the ubiquitous touring system with several sets of tourist spots and users.

Activity Zones for Context-Aware Computing

Location is a primary cue in many context-aware computing systems, and is often represented as a global coordinate, room number, or Euclidean distance various landmarks. A user?s concept of location, however, is often defined in terms of regions in which common activities occur. We show how to partition a space into such regions based on patterns of observed user location and motion. These regions, which we call activity zones, represent regions of similar user activity, and can be used to trigger application actions, retrieve information based on previous context, and present information to users. We suggest that context-aware applications can benefit from a location representation learned from observing users. We describe an implementation of our system and present two example applications whose behavior is controlled by users? entry, exit, and presence in the zones.