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Shareable interfaces, those that can be interacted simultaneously by several users, are a common tool used both in CSCW research and in real world applications. They tend however to lack a capability that has been traditionally relevant to the usefulness of computing systems: multi-tasking. In this paper we explain why a combination of the multi-user features of shareable interfaces and the multi-tasking capabilities of general-purpose computing, could be relevant for building useful systems, and why these features are not present today in most of the current prototypes and systems. We also discuss possible approaches for solving the problems that prevent shareable interfaces to fully support multi-tasking, and we present a novel approach based on a distributed, application-centered, content-based gesture disambiguation. We describe how an already existing framework, GestureAgents, implements this new approach, focusing on expanding the description of the relevant elements related to this problem, and conclude with some example applications and a discussion. ; The research leading to these results has received funding from the European Union Seventh Framework Programme FP7 / 2007–2013 through PHENICX project under grant agreement n° 601166.

In this article, we evaluate b-Reactable, a digital music instrument that combines implicit physiology-based interaction through EEG and ECG, and explicit gestural interaction for sound generation and control. This multimodality is embodied in tangible objects named physiopucks, which are driven by biosignals. We hypothesize that multimodality increases users' motivation in a musical task, compared to the use of a gesture-only tabletop system (the Reactable). We compared motivational aspects in dyads collaborating in three experimental groups (N = 56): the Physio group (one physiology- and one gesture-based user), the Sham group (one prerecorded physiology- and one gesture-based user), and the Control group (two gesture users). Between-group comparisons showed that motivation dimensions of Confidence and Satisfaction were higher in b-Reactable than in the gesture-only tangible interface, and that fake physiology-based feedback significantly reduced these effects. Our study also shows the potential of combined implicit and explicit interaction modes in multiuser HCI scenarios. ; The results leading to this publication have been partially funded by the European Union's Horizon 2020 research and innovation programme through the RAPID-MIX Project (nr 644862) and by TEC2010-11599- E. A. Valjamäe received funding from Marie Curie Actions of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA GA-303172.

Drum tracks of electronic dance music pieces are a central and style-defining element. Yet,creating them can be a cumbersome task, mostly due to lack of appropriate tools and inputdevices. In this work we use a UI prototype that aims at supporting musicians to compose therhythmic patterns for drum tracks, to compare different algorithms for drum pattern variation.Starting with a basic pattern (seed pattern), which is provided by the user, a list of variationswith varying degrees of similarity to the seed pattern is generated. The variations are createdusing one of the three algorithms compared: (i) a similarity-based lookup method using arhythm pattern database, (ii) a generative approach based on a stochastic neural network, and(iii) a genetic algorithm using similarity measures as a target function. The interface visualizesthe patterns and provides an intuitive way to browse through them. User test sessions withexperts in electronic music production were conducted to evaluate aspects of the prototype andalgorithms. Additionally a web-based survey was performed to assess perceptual properties ofthe variations in comparison to baseline patterns created by a human expert. The web surveyshows that the algorithms produce musical and interesting variations and that the differentalgorithms have their strengths in different areas. These findings are further supported by theresults of the expert interviews. ; This work is supported by the European Union's sev-enth Framework Programme FP7 / 2007-2013 for research,technological development, and demonstration under grantagreement no. 610591 (GiantSteps).

We present the GiantSteps project, an EU-funded project/ninvolving institutions from academia, practitioners, and industrial/npartners with the goal of developing new concepts for/nintelligent and collaborative interfaces for music production/nand performance. At the core of the project is an iterative,/nuser-centric research approach to music information retrieval/n(MIR) and human computer interaction (HCI) that is designed/nto allow us to accomplish three main targets, namely (1) the/ndevelopment of intelligent musical expert agents to support/nand inspire music makers, (2) more intuitive and collaborative/ninterfaces, and (3) low-complexity methods addressing lowcost/ndevices to enable affordable and accessible production/ntools and apps. In this paper, we report on the main findings/nand achievements of the project's first 18 months. ; This work is supported by the European Union Seventh/nFramework Programme FP7 / 2007-2013 through the GiantSteps/nproject (grant agreement no. 610591).