Philip Moore

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Context represents a fundamental issue in research into pervasive and cooperative computing applications. This paper presents a survey on context modelling issues, together with a discussion on approaches to context modelling in intelligent context-aware pervasive systems. There is a parametric evaluation of the approaches considered, the conclusion drawn(More)
To enhance and improve the interoperability of meteorological Web Services, we are currently developing an Integrated Web Services Brokering System (IWB). IWB uses a case-based classifier to automatically discover Web Services. In this paper, we explore the use of rough set techniques for selecting features prior to classification. We demonstrate the(More)
We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users’ contexts, including user profiles, EEG data, the situation and(More)
Identification of individuals is ubiquitous with increasing reliance by financial and governmental organizations on reliable and robust personal recognition systems to determine and confirm the identity and policy constraints for specific individuals in 'real-time' when reacting to service requests. The traditional identification approaches to user(More)
Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typically employ discrete approximations of these models. This limits the set of actions that can be modeled, and may lead to non-optimal solutions. We introduce the Continuous Action(More)
Rapid expansion of wireless technologies has provided a platform to support intelligent systems in the domain of mobile marketing. Utilizing Location Based Services (LBS) and Global Navigational Satellite Systems (GNSS) infrastructure provides the transport of real-time, scheduled, location-based advertising to individuals and business. In this paper a(More)
Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter(More)
Developments in higher education have driven interest in personalised education. Concomitant with these developments are the evolving capabilities of mobile technologies. Context has been shown to facilitate personalisation in mobile systems, the issue is how to effectively create and implement an individual's profile (termed context). ‘Intelligent(More)
Technical advances in the neuroelectric recordings and in the computational tools for the analysis of the brain activity and connectivity make it now possible to follow and to quantify, in real time, the interactive brain activity in a group of subjects engaged in social interactions. The degree of interaction between persons can then be assessed by(More)