DISA

When? Friday January 12th 14-16
Where? Onsite: B1006 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/YsyLzBd7K6tL1hmX6 by January 10th (especially important if you plan on attending onsite so we have fika for everyone)

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Senadin Alisic, PhD student Combitech
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Improving access to digital archives pertaining to the Sámi with HTR and automatic subject indexing, Johannes Widegren, PhD in Computer and Information Science at the Department of Cultural Sciences
15.50 -16.00 Sum up and plan for our next seminar on February 2nd

Abstracts

Senadin Alisic, PhD student Combitech

As we approach a significant shift in global population demographics, with an
expected 70% of people residing in urban areas by 2050, governments and urban
planners face considerable challenges. These challenges span from energy and
waste management to resource scarcity, air pollution, health concerns, traffic
congestion, outdated business models, and aging infrastructure. The urgency to
transform cities into smart cities has never been more apparent in light of these
issues.

Digital transformation propels cities to become smarter, more sustainable,
and more efficient, and smart cities and neighborhoods enhance urban life
and services through data-driven decision-making, streamline city services,
and promote citizen and other actors’ engagement through a digitally driven
ecosystem, platform, and open data principles.

Establishing and operating a digital ecosystem and platform for smart cities
or neighborhoods presents significant challenges. Most of the challenges are
related to the governance of innovation, business, and software ecosystem and
their components.

This thesis explores the architectural challenges experienced while establishing
a digital ecosystem for a sustainable smart neighborhood in southern Sweden.
We describe the concept of a smart city or neighborhood ecosystem, detailing
the roles of various actors and highlighting the barriers that must be addressed
when establishing a digital ecosystem for a smart city or neighborhood. We also
present a conceptual view of the ecosystem architecture, shedding light on the
significant gaps in understanding and managing digital transformation within
urban development processes.

This report contributes valuable insights to the ongoing dialogue on smart city
development and is helpful for researchers, urban planners, and policymakers.
Cities and other active participants in urban development could benefit from
adopting more systematic strategies to navigate the complexities and challenges
of integrating a digital ecosystem into urban development processes.

Keywords: Smart Cities, Digital Transformation, Digital Ecosystem, Ecosystem
Architecture, Platform Strategies

Improving access to digital archives pertaining to the Sámi with HTR and automatic subject indexing, Johannes Widegren, PhD in Computer and Information Science at the Department of Cultural Sciences

Ultimate purpose: The issue of access to archives has a long tradition, surpassing the notion of archives as ‘collective memory’. This is particularly pertinent in the case of archives of relevance to national minorities, such as the Sámi people of Sápmi, northern Fennoscandia. In this context, archives and their accessibility are crucial to the dual endeavor of uncovering historical wrongdoings against the Sámi by the Scandinavian states and preserving knowledge of Sámi culture for future generations.

While more and more collections become accessible online on platforms such as Nuohtti, access to archives is impeded by the scarcity of subject metadata. Where metadata does exist, it is typically a reflection of the colonial perspective of the record-generating institutions. Ameliorating metadata is time-consuming and costly; it requires attention to ethical considerations as well as archival principles.

Machine learning and related technologies offer promising opportunities to improve access to archival materials, firstly by automatic transcription with handwritten text recognition (HTR), secondly by automatically generating or improving metadata via e.g. automatic subject indexing, topic modelling and named entity recognition. The output can then be made accessible as Linked Data. The ultimate aim of this project is to test how and to what degree metadata from archives of Sámi relevance can be automatically generated and improved using machine learning approaches.