Abstract
Deriving from the scholarly debate surrounding design policies in the past 25 years, this paper proposes a novel triangulation of fundamental issues which remain unsolved, by highlighting them and analyzing how they relate to each other. The issues under analysis are innovation policies, the role of design in research, development and innovation and the multiplicity of design definitions. These are intrinsically linked with Design policies, but their interconnections are not well established or visible, leading to conflicting perspectives and tensions. Ultimately, this approach provides new insights on the rationale and construction of Design policies, towards prospective interpretations for their role and aspirations.

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Abstract

Abstract
Distributed power sources will become increasingly ubiquitous in the near future. In this power production paradigm, photovoltaic conversion systems will play a fundamental role due to the growing tendency of energy price, and an opposed trend for the photovoltaic panels. This will lead to increased pressure for the installation of this particular renewable energy source in home buildings. In particular, on-grid photovoltaic systems where the generated power can be injected directly to the main power grid. This strategy requires the use of DC-AC inverters whose output is synchronized, in phase, with the main grid voltage. In order to provide steady output in the presence of load disturbances, the inverter must work in closed-loop. This work presents a new way to design an inverter controller by resorting to the CDM design technique. The obtained results suggest that the controller achieved with this method, although simpler than other methods, leads to an acceptable and robust closed-loop response. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
We present a scoping review of biosensors appropriation as control structures in interactive music systems (IMSs). Technical and artistic dimensions promoted by transdisciplinary approaches, ranging from biomedicine to musical performance and interaction design fields, support a taxonomy for biosensor-driven IMSs. A broad catalog of 70 biosensor-driven IMSs, ranging in publication dates from 1965 to 2019, was compiled and categorized according to the proposed taxonomy. From the catalog data, we extrapolated representative historical trends, notably to critically verify our working hypothesis that biosensing technologies are expanding the array of control structures within IMSs. Observed data show that our hypothesis is consistent with the historical evolution of the biosensor-driven IMSs. From our findings, we advance future challenges for novel means of control across humans and machines that should ultimately transform the agents involved in interactive music creation to form new corporalities in extended performative settings.

Abstract
The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users' control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.