A Platform for the Analysis of Qualitative and Quantitative Data about the Built Environment and its Users

There are many scenarios in which it is necessary to collect data from multiple sources in order to evaluate a system, including the collection of both quantitative data - from sensors and smart devices - and qualitative data - such as observations and interview results. However, there are currently very few systems that enable both of these data types to be combined in such a way that they can be analysed side-by-side. This paper describes an end-to-end system for the collection, analysis, storage and visualisation of qualitative and quantitative data, developed using the e-Science Central cloud analytics platform. We describe the experience of developing the system, based on a case study that involved collecting data about the built environment and its users. In this case study, data is collected from older adults living in residential care. Sensors were placed throughout the care home and smart devices were issued to the residents. This sensor data is uploaded to the analytics platform and the processed results are stored in a data warehouse, where it is integrated with qualitative data collected by healthcare and architecture researchers. Visualisations are also presented which were intended to allow the data to be explored and for potential correlations between the quantitative and qualitative data to be investigated

Interactive semantic mapping: Experimental evaluation

Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping

Research and Development Workstation Environment: the new class of Current Research Information Systems

Against the backdrop of the development of modern technologies in the field of scientific research the new class of Current Research Information Systems (CRIS) and related intelligent information technologies has arisen. It was called - Research and Development Workstation Environment (RDWE) - the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The RDWE class system's generalized information model is represented in the article as a three-tuple composite web service that include: a set of atomic web services, each of them can be designed and developed as a microservice or a desktop application, that allows them to be used as an independent software separately; a set of functions, the functional filling-up of the Research and Development Workstation Environment; a subset of atomic web services that are required to implement function of composite web service. In accordance with the fundamental information model of the RDWE class the system for supporting research in the field of ontology engineering - the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity - the automated preparation of application documents for patenting inventions in Ukraine was developed. It was called - Personal Research Information System. A distinctive feature of such systems is the possibility of their problematic orientation to various types of scientific activities by combining on a variety of functional services and adding new ones within the cloud integrated environment. The main results of our work are focused on enhancing the effectiveness of the scientist's research and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian. Published. Prepared for special issue (UkrPROG 2018 conference) of the scientific journal "Problems of programming" (Founder: National Academy of Sciences of Ukraine, Institute of Software Systems of NAS Ukraine

Enhancing knowledge management in online collaborative learning

This study aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support as regards group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains

The urban screen as a socialising platform: exploring the role of place within the urban space

In this paper we explore shared encounters mediated by technologies in the urban space. We investigate aspects that influence the interactions between people and people and people and their surroundings when technology is introduced in the urban space. We highlight the importance of space and the role of place in providing temporal and spatial mechanisms facilitating different types of social interactions and shared encounters. An emperical experiment was condeucted with a prototype that was implemented in the form of a digital screen, embeded in the physical surrounding in selected locations with low, medium and high pedestrian flows in the heritage City of Bath, UK. The aim is to create a novel urban experience that triggers shared encounters among friends, observers or strangers. Using the body as an interaface, the screen acted as a non-traditional interface and a facilitator between people and people and people and their surrounding environment. Here we outline early findings from deploying the digital screen as a socialiasing platform in a city context. We describe the user experience and demonstrate how people move, congregate and socialize around the digital surface. We illustrate the impact of the spatial and syntactical properties on the type of shared interactions in and highlight related issues. The initial findings indicated that introducing a digital platform as a public interactive installation in the urban space may provide a stage for emergent social interactions among various people and motivate users to actively and collaboratively play with the media. However, situating the digital platform in various locations, and depending on the context, might generate diverse and unpredicted social behaviours designers might be unaware of. In this respect we believe that the final experience is shaped by interconnection of structural, social, cultural, temporal and perhaps personal elements. We conclude by mentioning briefly our on going work

Data literacy in the smart university approach

Equipping classrooms with inexpensive sensors for data collection can provide students and teachers with the opportunity to interact with the classroom in a smart way. In this paper two approaches to acquiring contextual data from a classroom environment are presented. We further present our approach to analysing the collected room usage data on site, using low cost single board computer, such as a Raspberry Pi and Arduino units, performing a significant part of the data analysis on-site. We demonstrate how the usage data was used to model specifcic room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was then integrated in a room recommender system, reasoning on the formalised usage data, allowing for a convenient and intuitive end user experience based on the collected raw sensor data. Having implemented and tested our approaches we are currently investigating the possibility of using (XML)Schema-informed compression to enhance the security and efficiency of the transmission of a large number of sensor reports generated by interpreting the raw data on-site, to our central data sink. We are investigating this new approach to usage data transmission as we are aiming to integrate our on-going work into our vision of the Smart University to ensure and enhance the Smart University's data literacy

Approaches to the use of sensor data to improve classroom experience

quipping classrooms with inexpensive sensors can enable students and teachers with the opportunity to interact with the classroom in a smart way. In this paper an approach to acquiring contextual data from a classroom environment, using inexpensive sensors, is presented. We present our approach to formalising the usage data. Further we demonstrate how the data was used to model specific room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was than integrated in a room recommendations system, reasoning on the formalised usage data. We also detail on our on-going work to integrating the systems presented in this paper into our Smart University vision

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives; and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multi-objective optimization, genetic algorithms and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics