Social Mpower: An Educational Game for Energy Efficiency

A number of serious games have been developed for energy systems that act as an educational tool and help energy consumers to better understand concepts such as resource allocation, electricity prices and grid sustainability. In such gamified environments, players use technology to solve environmental problems including greener environment, optimised energy and water infrastructure, sustainable resources and reduced energy use. Social Mpower game is a representation of an autonomous energy community for local power generation and distribution in which the participants have to avoid a collective blackout by individually reducing their energy consumption by synchronising and coordinating their actions. Our experimental hypothesis is that collective awareness can be enhanced by appropriate features of the game interface, and therefore to increase the opportunities and prospects for successful collective action (e.g to avoid a blackout)

In search of ‘lost’ knowledge and outsourced expertise in flood risk management

This paper examines the parallel discourses of ‘lost’ local flood expertise and the growing use of commercial consultancies to outsource aspects of flood risk work. We critically examine the various claims and counter-claims about lost, local and external expertise in flood management, focusing on the aftermath of the 2007 floods in East Yorkshire, England. Drawing on interviews with consultants, drainage engineers and others, we caution against claims that privilege ‘local’ floods knowledge as ‘good’ and expert knowledge as somehow suspect. This paper urges carefulness in interpreting claims about local knowledge, arguing that it is important always to think instead of hybrid knowledge formations. We conclude by arguing that experiments in the co-production of flood risk knowledge need to be seen as part of a spectrum of ways for producing shared knowledge

Recommending audio mixing workflows

This paper describes our work on Audio Advisor, a workflow recommender for audio mixing. We examine the process of eliciting, formalising and modelling the domain knowledge and expert’s experience. We are also describing the effects and problems associated with the knowledge formalisation processes. We decided to employ structured case-based reasoning using the myCBR 3 to capture the vagueness encountered in the audio domain. We detail on how we used extensive similarity measure modelling to counter the vagueness associated with the attempt to formalise knowledge about and descriptors of emotions. To improve usability we added GATE to process natural language queries within Audio Advisor. We demonstrate the use of the Audio Advisor software prototype and provide a first evaluation of the performance and quality of recommendations of Audio Advisor