39,638 research outputs found

    Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

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    To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

    Fast design space exploration of vibration-based energy harvesting wireless sensors

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    An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25s and the average RSM estimation error is less than 16.5

    Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effect

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    We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability pcp_{c} per round. The agents and player choose one of three options: (1) Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among nIn_{I} randomly chosen bandits), and (3) Observe (social learning for a good bandit). Each agent has two parameters (c,pobs)(c,p_{obs}) to specify the decision: (i) cc, the threshold value for Exploit, and (ii) pobsp_{obs}, the probability for Observe in learning. The parameters (c,pobs)(c,p_{obs}) are uniformly distributed. We determine the optimal strategies for the player using complete knowledge about the rMAB. We show whether or not social or asocial learning is more optimal in the (pc,nI)(p_{c},n_{I}) space and define the swarm intelligence effect. We conduct a laboratory experiment (67 subjects) and observe the swarm intelligence effect only if (pc,nI)(p_{c},n_{I}) are chosen so that social learning is far more optimal than asocial learning.Comment: 18 pages, 4 figure

    Competing Claims on Natural Resources: What Role for Science?

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    Competing claims on natural resources become increasingly acute, with the poor being most vulnerable to adverse outcomes of such competition. A major challenge for science and policy is to progress from facilitating univocal use to guiding stakeholders in dealing with potentially conflicting uses of natural resources. The development of novel, more equitable, management options that reduce rural poverty is key to achieving sustainable use of natural resources and the resolution of conflicts over them. Here, we describe an interdisciplinary and interactive approach for: (i) the understanding of competing claims and stakeholder objectives; (ii) the identification of alternative resource use options, and (iii) the scientific support to negotiation processes between stakeholders. Central to the outlined approach is a shifted perspective on the role of scientific knowledge in society. Understanding scientific knowledge as entering societal arenas and as fundamentally negotiated, the role of the scientist becomes a more modest one, a contributor to ongoing negotiation processes among stakeholders. Scientists can, therefore, not merely describe and explain resource-use dynamics and competing claims, but in doing so, they should actively contribute to negotiation processes between stakeholders operating at different scales (local, national, regional, and global). Together with stakeholders, they explore alternatives that can contribute to more sustainable and equitable use of natural resources and, where possible, design new technical options and institutional arrangements

    Using GIS to Explore the Technical and Social Aspects of Site Selection for Radioactive Waste Disposal Facilities

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    This working paper reviews the current situation regarding radioactive waste disposal in the UK and questions the pursuance of a purely engineering approach to gaining public support. Past histories concerning the siting of nuclear industry facilities; power stations and latterly, waste repositories, are briefly discussed and used to demonstrate that more attention needs to be paid to the geographical and social science if current proposlas for a rock laboratory, and ultimately and operational repository, at Longlands Farm near Sellafield are to succeed. The usefulness of Geographical Information Systems (GIS) and associated spatial information technologies are highlighted. Suggestions are made as to how these may be made available for public use via the Internet in adopting a more open approach to public information, consultation and participation
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