This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user's needs based on both the user's interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a contextaware application platform. We describe how this integration has been accomplished and how users feel about such an adaptive tourist application.

Abstract. Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the user's query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the user's query, of the services and of the contextual information that is considered relevant in the matching process.

This paper examines the accuracy of trip and mode choice detection of the last wave of the Dutch Mobile Mobility Panel, a large-scale three-year, smartphone-based travel survey. Departure and arrival times, origins, destinations, modes, and travel purposes were recorded during a four week period in 2015, using the MoveSmarter app for a representative sample of 615 respondents, yielding over 60 thousand trips. During the monitoring period, respondents also participated in a web-based prompted recall survey and answered additional questions. This enables a comparison between automatic detected and reported trips. Most trips were detected with no clear biases in trip length or duration, and transport modes were classified correctly for over 80 percent of these trips. There is strong evidence that smartphone-based trip detection helps to reduce underreporting of trips, which is a common phenomenon in travel surveys. In the Dutch Mobile Mobility Panel, trip rates are substantially higher than trip-diary based travel surveys in the Netherlands, in particular for business and leisure trips which are often irregular. The rate of reporting also hardly decreased during the four-week period, which is a promising result for the use of smartphones in long duration travel surveys.

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In this paper we present a web services-based platform that facilitates and speeds up the development and deployment of context-aware, integrated mobile speech and data applications. The platform is capable of handling different types of context and offers sophisticated personalization mechanisms. To illustrate the usefulness of the platform and to validate the claim that cross-platform application development, in particular mobile, context-aware applications, is easier and faster with web services technologies, we present a demonstration application. It serves tourists with interesting information and services in their specific context, and contributes to the achievement of their current goals. Finally, we present a number of problems that we experienced in the implementation process as well as the feedback that we received from real users who tested our application.

This paper addresses differences between operational speaker verification (SV) systems and laboratory experiments in terms of performance and methods for measuring performance. It is concluded that operational SV systems need an indication of the quality of newly enrolled speaker models, to decide whether t o re-enrol or request more enrolment material. We have investigated the impact of ASR errors on model quality. While attempting to design measures for the quality of speaker models we have developed a novel method for assigning weights to the contribution of models in accordance with their discriminative ability.

We present IYOUIT, a prototype service to pioneer a context-aware mobile digital lifestyle and its reflection on the Web. The application is based on a distributed infrastructure that incorporates Semantic Web technologies in several places to derive qualitative interpretations of a user's digital traces in the real world. Networked components map quantitative sensor data to qualitative abstractions represented in formal ontologies. Subsequent classification processes combine these with formalized domain knowledge to derive meaningful interpretations and to recognize exceptional events in context histories. The application is made available on Nokia Series-60 phones and designed to seamlessly run 24/7.