Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

Method and user device for providing context awareness service using speech recognition

Abstract: A method for providing a context awareness service is provided. The method includes defining a control command for the context awareness service depending on a user input, triggering a playback mode and the context awareness service in response to a user selection, receiving external audio through a microphone in the playback mode, determining whether the received audio corresponds to the control command, and executing a particular action assigned to the control command when the received audio corresponds to the control command.

Enabling Learning on Demand in Semantic Work Environments: The Learning in Process Approach

Abstract: The new flexibility of workers and work environments makes traditional conceptions of training in advance, in rather large units and separate from work activities, more and more obsolete. It is not only the problem of inert knowledge (i.e., knowledge that can be reproduced, but not applied; Bereiter & Scardamalia, 1985), but also the degree of individualization of learning paths these traditional methods cannot cope with. What we actually need is learning on demand, embedded into work processes, responding to both requirements from the work situation and from employee interests, a form of learning crossing boundaries of e-learning, knowledge management, and performance support (Schmidt, 2005). Many see self-steered learning as the salvation for that new paradigm (in contrast to course-steered learning activities), but this ignores the fact that guidance is essential—both for the learner (reducing the cognitive load) and for the company (enabling the manageability of learning processes). As a consequence, we have elaborated a concept in between: context-steered learning in which learners get contextualized recommendations of learning opportunities. Implementing such a method requires a semantic work environment infrastructure that allows computer systems for getting hold of work situations and the learning needs arising out of them. Especially crucial is a semantic model of human resource development in such a setting just at the right level of complexity (not simplifying too much, but still manageable), a set of services and a user context management component for capturing and maintaining the information about what the user is currently doing and what’s her state.

Exploiting Context-Awareness to Enhance Source-Location Privacy in Wireless Sensor Networks

Abstract: The source-location privacy problem in Wireless Sensor Networks has been traditionally tackled by the creation of random routes for every packet transmitted from the source nodes to the base station. These schemes provide a considerable protection level at a high cost in terms of message delivery time and energy consumption. This overhead is due to the fact that the data routing process is done in a blind way, without knowledge about the location of the attacker. In this work, we propose the Context-Aware Location Privacy (CALP) approach, which takes advantage of the ability of sensor nodes to perceive the presence of a mobile adversary in their vicinity in order to transmit data packets in a more energy-efficient and privacy-preserving manner. In particular, we apply the concepts of CALP to the development of a shortest-path CALP routing algorithm. A permissive and a strict version of the protocol are studied for different adversarial models and the proposed schemes are evaluated through simulation experiments in terms of privacy protection and energy consumption. Finally, we present the conclusions of the paper as well as possible extensions of this work.

Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

Abstract: Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status. Findings This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit. Research limitations/implications To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season. Practical implications Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated. Originality/value This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.