Change search
Refine search result
1 - 5 of 5
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Maksimov, Yulian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    AI Competition: Textual Tree2019Other (Refereed)
    Download full text (pdf)
    AI Competition: Textual Tree
  • 2.
    Maksimov, Yulian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Consortium Project: Textual Tree2019Other (Refereed)
    Download full text (pdf)
    Consortium Project - Textual Tree-v3
  • 3.
    Maksimov, Yuliyan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Fricker, Samuel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Licensing in Artificial Intelligence Competitions and Consortium Project Collaborations2020In: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020 / [ed] Martini A.,Wimmer M.,Skavhaug A., Institute of Electrical and Electronics Engineers Inc. , 2020, p. 292-301, article id 9226354Conference paper (Refereed)
    Abstract [en]

    Platforms are emerging that allow data scientists, software and hardware engineers to collaborate through organisational boundaries to develop systems of Artificial Intelligence (AI). Such collaboration involves the exchange of assets representing Intellectual Property (IP) of the collaborators. The tension between permitting access and protecting IP is thus one of the critical challenges faced by organisations willing to innovate through collaboration. Licensing is a common way to address the issue, but the influence of the licensing rules on the intended form of collaboration is still unclear.In this paper, we identify and analyse the rules that are used to regulate IP exchanges in two common forms of collaboration: a) competitions where one customer benchmarks and selects among multiple suppliers and b) consortium projects where multiple parties collaborate to product joint results. Due to our interest in AI, we have chosen to analyse the terms and conditions of competitions hosted on KaggleTM a leading online platform for Competitions. For consortium projects, we have analysed the DESCA Consortium Agreement template. DESCA is often used for European projects, an increasing number of which are used to fund AI innovation projects. We have applied In Vivo Coding and Concept Coding coding techniques to highlight rules applicable to IP exchange. We structured the findings in the form of tree graphs consisting of interdependent textual phrases to extract, group and compare the terms and conditions of IP sharing in each collaboration form and how they relate to the characteristics of the studied collaborations.The results indicate that each form of collaboration has its own set of rules that address comparable concerns but have different content. Practitioners, both platform providers and collaborators, can utilise our results to implement licensing for IP exchange that fits the desired type of collaboration. For researchers, our results represent a step towards the automation of license generation and enforcement. © 2020 IEEE.

  • 4.
    Maksimov, Yuliyan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. FHNW University of Applied Sciences and Arts Northwestern Switzerland, CHE.
    Fricker, Samuel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Artifact Compatibility for Enabling Collaboration in the Artificial Intelligence Ecosystem2018In: Lecture Notes in Business Information Processing, Springer, 2018, Vol. 336, p. 56-71Conference paper (Refereed)
    Abstract [en]

    Different types of software components and data have to be combined to solve an artificial intelligence challenge. An emerging marketplace for these components will allow for their exchange and distribution. To facilitate and boost the collaboration on the marketplace a solution for finding compatible artifacts is needed. We propose a concept to define compatibility on such a marketplace and suggest appropriate scenarios on how users can interact with it to support the different types of required compatibility. We also propose an initial architecture that derives from and implements the compatibility principles and makes the scenarios feasible. We matured our concept in focus group workshops and interviews with potential marketplace users from industry and academia. The results demonstrate the applicability of the concept in a real-world scenario.

    Download full text (pdf)
    fulltext
  • 5.
    Maksimov, Yuliyan V.
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Fricker, Samuel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Framework for Analysis of Multi-Party Collaboration2019In: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019, IEEE Computer Society Digital Library, 2019, p. 44-53, article id 8933635Conference paper (Refereed)
    Abstract [en]

    In recent years, platforms have become important for allowing ecosystems to emerge that allow users to collaborate and create unprecedented forms of innovation. For the platform provider, the ecosystem represents a massive business opportunity if the platform succeeds to make the collaborations among the users value-creating and to facilitate trust. While the requirements flow for evolving existing ecosystems is understood, it is unclear how to analyse an ecosystem that is to be. In this paper, we draw on recent work on collaboration modelling in requirements engineering and propose an integrated framework for the analysis of multi-party collaboration that is to be supported by a platform. Drawing on a real-world case, we describe how the framework is applied and the results that have been obtained with it. The results indicate that the framework was useful to understand the ecosystem context for a planned platform in the domain of artificial intelligence, allowed identification of platform requirements and offered a basis to plan validation.

    Download full text (pdf)
    fulltext
1 - 5 of 5
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf