In this work, the authors design and prototype PassItOn, a fully distributed opportunistic messaging system. Our goal is to build up a proof-of-concept platform on real mobile devices, and thus show the feasibility and potentials of utilizing human movements for dissemination applications. Meanwhile, we seek to shed lights on the design, implementation and deployment issues in building such systems, and thus stimulate new ideas and perspectives on addressing these issues. Moreover, we aim to offer a real testbed on which new mechanisms, protocols and use cases can be tested and evaluated.

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, for example weather, news or TV program information. Widgets are typically rather static - they provide the information regardless of whether the information is relevant to the user's current information needs. In this paper we introduce Capricorn, which is an intelligent interface for mobile widgets. The interface uses various adaptive web techniques for facilitating navigation. For example, we use collaborative filtering to recommend suitable widgets and we dim infrequently used widgets. The demonstration presents the Capricorn interface focusing on the adaptive parts of the interface. The user interface is web-based, and as such platform independent. However, our target environment is mobile phones, and thus the interface has been optimized for mobile phones.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, e.g., weather, news or TV program information. Interactions with widgets take place through a so-called widget engine, which is a specialized client-side runtime component that also provides functionalities for managing widgets. As the number of supported widgets increases, managing widgets becomes increasingly complex. For example, finding relevant or interesting widgets becomes difficult and the user interface easily gets cluttered with irrelevant widgets. In addition, interacting with information sources can be cumbersome, especially on mobile platforms. In order to facilitate widget management and interactions, we have developed Capricorn, an intelligent user interface that integrates adaptive navigation techniques into a widget engine. This paper describes the main functionalities of Capricorn and presents the results of a usability evaluation that measured user satisfaction and compared how user satisfaction varies between desktop and mobile platforms.

Multimodal Integration addresses the problem of combining various user inputs into a single semantic representation that can be used in deciding the next step of system action(s). The method presented in this paper uses a statistical framework to implement the integration mechanism and includes contextual information additionally to the actual user input. The underlying assumption is that the more information sources are taken into account, the better picture can be drawn about the actual intention of the user in the given context of the interaction. The paper presents the latest results with a Maximum Entropy classifier, with special emphasis on the use of contextual information (type of gesture movements and type of objects selected). Instead of explaining the design and implementation process in details (a longer paper to be published later will do that), only a short description is provided here about the demonstration implementation that produces above 91% accuracy for the 1st best and higher than 96% for the accumulated five N-bests results.

This paper introduces a generic architecture that enables the development and execution of mobile multimodal applications proposed within the EU IST-511607 project MobiLife. Mobi Life aims at exploiting the synergetic use of multimodal user interface technology and contextual information processing, with the ultimate goal that the two together can provide a beyond-the-state-of-the-art user experience. And this led to an integrated concept, components of the underlying architecture are described in detail and the interfaces towards the application back-end as well as towards context aware resources are discussed. The paper also positions the current work against existing standardisation efforts and it pinpoints technologies required to support the implementation of a device and modality function within the MobiLife architecture

Integration of various user input channels for a multimodal interface is not just an engineering problem. To fully understand users in the context of an application and the current session, solutions are sought that process information from different intentional, i.e. user-originated, as well as from passively available sources in a uniform manner. As a first step towards this goal, the work demonstrated here investigates how intentional user input (e.g. speech, gesture) can be seamlessly combined to provide a single semantic interpretation of the user input. For this classical Multimodal Integration problem the Maximum Entropy approach is demonstrated with 76.52% integration accuracy for the 1st and 86.77% accuracy for the top 3-best candidates. The paper also exhibits the process that generates multimodal data for training the statistical integrator, using transcribed speech from MIT's Voyager application. The quality of the generated data is assessed by comparing to real inputs to the multimodal version of Voyager.

It has been hypothesized that the Finnish language is well suited to speech-to-text conversion for the communication aids of the hearing impaired. In a related study it was shown that, depending on context, 10 to 20 % of phoneme errors can be tolerated with good comprehension when reading text converted from raw phonemic recognition. Two sets of phoneme recognition experiments were carried out in this study in order to evaluate the performance of existing speech recognition systems in this application. For telephone bandwidth speech both systems showed speaker dependent error scores of about 10 % or below, thus supporting the feasibility of the application. For speaker independent cases the error rate was typically more than 20 % which is too high for effortless and fluent communication. 1.