Making sense of assets: Community asset mapping and related approaches for cultivating capacities

This working paper critically reviews some main aspects from asset based approaches highlights key strengths and weaknesses for future research/development. Drawing on a large body of reports and relevant literature we draw on different theoretical traditions and critiques, as well as practices and processes embedded within a broad range of approaches including, widely acknowledged frameworks such Asset Based Community Development (ABCD), Appreciative Inquiry (AI), Sustainable Livelihood Approaches (SLA) and Community Capitals Framework (CCF). Although these are presented as distinct approaches, there is a sense of evolution through them and many of them overlap (in terms of both theories and methodologies). We also include emerging frameworks, including geographical, socio-spatial, visual and creative approaches, stemming from a number of projects within AHRC’s Connected Communities programme and additional collaborations

The process of emergency, evolution, and sustainability of University-Firm relations in a context of open innovation

Existing studies on University-Firm (U-F) relations do not highlight, at least in an explicit way, the issue of open innovation. Such studies are still too centred on the advantages which the Firms are able to obtain from the relation with the Universities, failing taking into account the value that potentially goes to Universities from such links. The present paper intends to fill in this gap by empirically studying the process of emergency, evolution, and sustainability of the U-F relations in an open innovation context. Resorting to the case study methodology, we empirically demonstrate how the relations of a firm (Brisa) with the Universities (namely, ISEL) emerged, how they evolved and became sustained through time, giving special emphasis to the issue of mutual benefits derived from these relationships. Face-to-face interviews with the key-players at Brisa and ISEL, complemented with an extensive analysis of secondary sources, allowed us to conclude that the establishment of a connection between the two entities is a more complex and time consuming process (requiring a large relational and resources investment on both parts) than what the existing literature assumes. Besides the recognized gains for firms from adopting a more open-led perspective of innovation, namely based on U-F relations, our work (also) highlights the benefit deriving to the Universities from the link to companies. It is mainly due to the existence of mutual benefits that U-F relations are preserved in the long term; in other words, are sustainable.Open Innovation; University-Firm relations; Emergency; Sustainability; Benefits

Trust in social machines: the challenges

The World Wide Web has ushered in a new generation of applications constructively linking people and computers to create what have been called ‘social machines.’ The ‘components’ of these machines are people and technologies. It has long been recognised that for people to participate in social machines, they have to trust the processes. However, the notions of trust often used tend to be imported from agent-based computing, and may be too formal, objective and selective to describe human trust accurately. This paper applies a theory of human trust to social machines research, and sets out some of the challenges to system designers

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201