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The average work experience of each interviewee in their respective areas of expertize was seven years. Benefits: Greatly increased the ease of reuse between different projects and the possibility to benefit from other participants’ experience. MVPCs enable feedback provision from industry to academia in the early stage of the solution development, which increases the probability of creating the outcomes of mutual benefit. The magnetization calculated for LDA and GGA, shown in Fig. 4(b), decreases drastically as the copper content increases. Benefits: Mutual learning based on active exchange of knowledge, which creates more meaningful collaborations, increases enthusiasm, creates synergies, and helps the project progress faster. Benefits: Bidirectional feedback provided often helps validate results often and ensure that potential misinterpretations and missed requirements are identified and fixed when the time is appropriate. The time boundedness and the concreteness of the activity greatly helps establish reciprocity. Such MVPCs help get started with co-creation and create a sense of concreteness in a joint activity. In such a managed co-creation environment, individuals with different background, expertise and perspectives are brought together to share knowledge and work on a joint problem whose solving brings benefits to all participating parties. For tangible assets, impact can be determined by measuring return of investment (RoI) or other quantitative metrics, for example, economic benefits

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Users may also be unaware of how the system operates on a technical level in terms of data processing and storage (Luger and Sellen, 2016). For these reasons, the unique context of co-creative AI that can communicate to users concerning user privacy should be considered while devising the ethical guidelines for co-creative AI. For most systems, direct manipulation provides a way for minimal communication and does not provide users with a way to communicate more broadly. Very few systems in the dataset use other communication channels other than direct manipulation for human to AI intentional communication. Construction of SQS dataset. We perform two separate tasks to assess the effects that EUVB variations have on the metallicity of our absorbers. Here we describe the methods by which we determine the metallicities of our absorption systems and how we assess the effects of a varying EUVB slope. We selected a sample of absorbers most likely to have some constraining power over the EUVB slope, at least over the redshift and column density range explored here

The motivation for the RCN to support such an IA collaborative project was to foster economic and social impact in Norway, by improving the software quality and thus competitiveness of the Norwegian software industry and public sector services. For instance, after being initially developed with the Norwegian Toll Customs, Depict was also improved due to tests with the database systems at the Cancer Registry of Norway. The tool Depict (Sen et al., 2017), for instance, could be deployed by Toll Customs in a matter of minutes on their large test and production databases. This allowed Toll Customs to easily transition from Sybase to Oracle during their migration. Our goal for data collection was to capture both positive and negative experience of IA collaboration, and to find out what worked and what did not, and why, in different collaboration settings. However these metrics do not capture a multifaceted impact of IA collaborations. Context: Practitioners engage in research collaborations with the objective of applying in their context the knowledge created in the collaboration and relevant to their problems, for solving practical problems

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