Design of an ontology for decision support in VR exposure therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare. Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy. However, adapting the VR environment to the needs of the patient is time-consuming. Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy. In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR. The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology. Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data. The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

A Generic Agent Organisation Framework For Autonomic Systems

Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems

The practitioner perspective on the modeling of pedagogy and practice

The promotion of e-learning in policies internationally has led to questions about how best to employ technology in support of learning. A range of models has since been developed that attempts to relate pedagogy to technology. However, research into the effectiveness of such models in changing teaching practice is sparse, and work that compares these models to practitioners’ own representations of their practice is absent. The study described here involved asking practitioners to model their own practice, and to compare these with a model developed by a government organisation. Practitioners were adept at using existing models and repurposing them to suit their own context. Our research provided evidence of broad acceptance of the existing model with practitioners, but indicated that practitioners would take this tool and remodel it for their own contexts of learning to make it meaningful, relevant and useful to them

The Design and Evaluation of a Clinical Process Mapping Methodology (CPMM) to Support Information Systems (IS) Innovation in a Healthcare Context

This paper discusses the development, and assesses the appropriateness, of a Clinical Process Mapping Methodology (CPMM) to support information systems (ISs) innovation in acute hospitals. It is based on an ongoing longitudinal study in acute academic teaching hospitals in Ireland. The key rationale underpinning the research was that any attempt to develop ISs to support or change clinical work, must be based on a sophisticated, holistic and granular understanding of existing practices. Drawing on the insights gleaned through this observational study, an initial CPMM was developed by adapting elements from existing modelling languages to fit the clinical context in question. Our observations highlight the complex, collaborative and contingent nature of clinical practice, and the important mediating role played by technical and non-technical artefacts. This complexity would caution against viewing modelling as a panacea, which can be used to map the world in an objective or unproblematic manner. While modelling can be very helpful for facilitating new perspectives on work, and for facilitating productive collective sensemaking processes, it should be borne in mind that all models are purposeful, and necessarily partial, representations of the \u27real\u27 world. This underlines the importance of using any modelling approach in a discriminating and reflective way

Adaptability Checking in Multi-Level Complex Systems

A hierarchical model for multi-level adaptive systems is built on two basic levels: a lower behavioural level B accounting for the actual behaviour of the system and an upper structural level S describing the adaptation dynamics of the system. The behavioural level is modelled as a state machine and the structural level as a higher-order system whose states have associated logical formulas (constraints) over observables of the behavioural level. S is used to capture the global and stable features of B, by a defining set of allowed behaviours. The adaptation semantics is such that the upper S level imposes constraints on the lower B level, which has to adapt whenever it no longer can satisfy them. In this context, we introduce weak and strong adaptabil- ity, i.e. the ability of a system to adapt for some evolution paths or for all possible evolutions, respectively. We provide a relational characterisation for these two notions and we show that adaptability checking, i.e. deciding if a system is weak or strong adaptable, can be reduced to a CTL model checking problem. We apply the model and the theoretical results to the case study of motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte

Climate change impacts on water for irrigated horticulture in the Vale of Evesham. Final Report

This project has undertaken a scoping review and assessment of the impacts of climate change on irrigated horticulture in the Vale of Evesham, an area of intense irrigated production located within the Environment Agency’s Warwickshire Avon CAMS Catchment. The research was based on a combination of methodologies including desk-based review of published and grey literature, computer agroclimatic and water balance modelling, GIS mapping, meetings with key informants and a stakeholder workshop. Future climate datasets were derived from the latest UK Climate Impacts Programme (UKICIP02) climatology, using selected emission scenarios for the 2020s, 2050s and 2080s. These scenarios were then used to model and map the future agroclimatic conditions under which agriculture might operate and the consequent impacts on irrigation need (depths of water applied) and volumetric demand. This was complimented by a postal survey to abstractors and a stakeholder workshop, to identify, review and assess farmer adaptation options and responses. The key findings arising from the research, implications for water resource management and recommendations for further work are summarised below. Using a geographical information system (GIS), a series of agroclimate maps have been produced, for the baseline and selected UKCIP02 scenario. The maps show major changes in agroclimate within the catchment over the next 50 years. The driest agroclimate zones are currently located around Worcester, Evesham, Tewkesbury and Gloucester, corresponding to areas where horticultural production and irrigation demand are most concentrated. By the 2020s, all agroclimate zones are predicted to increase in aridity. By the 2050s the entire catchment is predicted to have a drier agroclimate than is currently experienced anywhere in the driest parts of the catchment. This will have major impacts on the pattern of land use and irrigation water demand. Cont/d

Incremental Adversarial Domain Adaptation for Continually Changing Environments

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.Comment: International Conference on Robotics and Automation 201

User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience

A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step towards an iterative design that considers the user interaction a central point. The paper discusses how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the developments that followed that first experience: our findings seem still valid despite the passed time