Mapping, sensing and visualising the digital co-presence in the public arena

This paper reports on work carried out within the Cityware project using mobile technologies to map, visualise and project the digital co-presence in the city. This paper focuses on two pilot studies exploring the Bluetooth landscape in the city of Bath. Here we apply adapted and ‘digitally augmented’ methods for spatial observation and analysis based on established methods used extensively in the space syntax approach to urban design. We map the physical and digital flows at a macro level and observe static space use at the micro level. In addition we look at social and mobile behaviour from an individual’s point of view. We apply a method based on intervention through ‘Sensing and projecting’ Bluetooth names and digital identity in the public arena. We present early findings in terms of patterns of Bluetooth flow and presence, and outline initial observations about how people’s reaction towards the projection of their Bluetooth names practices in public. In particular we note the importance of constructing socially meaningful relations between people mediated by these technologies. We discuss initial results and outline issues raised in detail before finally describing ongoing work

Motion in place: a case study of archaeological reconstruction using motion capture

Human movement constitutes a fundamental part of the archaeological process, and of any interpretationof a site’s usage; yet there has to date been little or no consideration of how movement observed (incontemporary situations) and inferred (in archaeological reconstruction) can be documented. This paper reports on the Motion in Place Platform project, which seeks to use motion capture hardware and data totest human responses to Virtual Reality (VR) environments and their real-world equivalents using round houses of the Southern British Iron Age which have been both modelled in 3D and reconstructed in the present day as a case study. This allows us to frame questions about the assumptions which are implicitlyhardwired into VR presentations of archaeology and cultural heritage in new ways. In the future, this will lead to new insights into how VR models can be constructed, used and transmitted

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065

Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings

This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations. The approach has been used in the co-simulation of building physics and human behaviour in order to assess the CO 2 concentration in an office.Comment: IBPC 2015 Turin , Jun 2015, Turin, Italy. 201

'Becoming experts': learning through mediation

Purpose – This study is largely founded on Vygotsky’s sociocultural theory, Feuerstein’s theory of Mediated Learning Experience and Lave and Wenger’s ‘community of practice’, which concerned building a community of learners that places mediation as central in learning and teaching. While the overall study involved Malaysian Year One English and Mathematics classrooms, this article focuses only on the latter. Two research questions were posed: 1) How does the teacher/peers mediate learning? 2) How does mediation influence the individual’s identity? Method – This qualitative study was conducted within a period of three months. Data collection included intense classroom observations, interviews, classroom discourse and dialogic discussions with teachers and pupils. Microgenetic analyses of transcripts were made to show moment-to moment changes observed.Findings – Four types of mediation emerged from the data : Environmental mediation, cognitive mediation, affective mediation and metacognitive mediation (i.e., an ECAM model for mediation).Findings suggest that mediation enabled the Mathematics teacher to change, to take ownership and to sustain her new pedagogical approaches within the classroom. This re-focusing benefited her pupils, and dramatically changed a particular less able pupil from one who was initially ‘lost in his world,’ into one who was able to engage in the learning process, take ownership of his own learning, as well as mediate other pupils’ learning. Value – Hence it is argued that the ECAM model for mediation provided opportunities for this teacher and her pupil to expand their capacity to learn and develop their identities as individuals capable of learning and becoming ‘experts’

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

Harnessing data flow and modelling potentials for sustainable development

Tackling some of the global challenges relating to health, poverty, business and the environment is known to be heavily dependent on the flow and utilisation of data. However, while enhancements in data generation, storage, modelling, dissemination and the related integration of global economies and societies are fast transforming the way we live and interact, the resulting dynamic, globalised and information society remains digitally divided. On the African continent, in particular, the division has resulted into a gap between knowledge generation and its transformation into tangible products and services which Kirsop and Chan (2005) attribute to a broken information flow. This paper proposes some fundamental approaches for a sustainable transformation of data into knowledge for the purpose of improving the peoples' quality of life. Its main strategy is based on a generic data sharing model providing access to data utilising and generating entities in a multi disciplinary environment. It highlights the great potentials in using unsupervised and supervised modelling in tackling the typically predictive-in-nature challenges we face. Using both simulated and real data, the paper demonstrates how some of the key parameters may be generated and embedded in models to enhance their predictive power and reliability. Its main outcomes include a proposed implementation framework setting the scene for the creation of decision support systems capable of addressing the key issues in society. It is expected that a sustainable data flow will forge synergies between the private sector, academic and research institutions within and between countries. It is also expected that the paper's findings will help in the design and development of knowledge extraction from data in the wake of cloud computing and, hence, contribute towards the improvement in the peoples' overall quality of life. To void running high implementation costs, selected open source tools are recommended for developing and sustaining the system. Key words: Cloud Computing, Data Mining, Digital Divide, Globalisation, Grid Computing, Information Society, KTP, Predictive Modelling and STI