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What Is This Skítur : Viking Authenticity in Ys X: Nordics as a Japanese Role-Playing Game
(2026) Panaro, Luca Arruns; Bjarnason, Nökkvi Jarl; Faculty of Icelandic and Comparative Cultural Studies
This article explores how viking authenticity is filtered through the media ecology of Japan by employing Ys X: Nordics (Nihon Falcom 2023) as a case study. It examines how the game constructs and engages with historical imaginaries while simultaneously being shaped by conventions and sensibilities of Japanese game development and its broader media landscape. To address this, the discussion situates Ys X within the broader history of viking representation in video games, showing how the figure of the “digital viking” has been recontextualized to meet diverse cultural and commercial demands. Particular attention is then paid to how the national and production context of Ys X inform its representations, with the game blending viking motifs with the visual, narrative, and thematic conventions of anime, manga, and Japanese role-playing games. Through close analysis, the article identifies moments of what can be termed authentic recontextualization, while also being mindful of numerous decontextualized or ornamental uses of viking imagery, names, and mythological figures. These strategies produce a selectively authentic, viking-inflected world that resonates with audience expectations while eschewing historical authenticity. Moreover, the game’s tonal orientation diverges from the “gritty” medievalism common in contemporary Western media, favoring instead the colorful optimism and narrative ethos of mainstream manga and anime, with its emphasis on friendship, perseverance, and collective victory. Ultimately, the article argues that Ys X exemplifies how Japanese popular media reworks transnational historical motifs, embedding them within its own aesthetic and narrative logics. In doing so, it highlights the culturally contingent nature of authenticity in historical games, showing that viking imagery in Japanese contexts functions less as a claim to historical accuracy than as an adaptive, stylistic device within a global media landscape.
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World of ScoreCraft : Novel Multi-Scorer Experiment on the Impact of a Decision Support System in Sleep Staging
(2026-02) Holm, Benedikt; Óskarsson, Arnar; Þorleifsson, Björn Elvar; Hafsteinsson, Hörður Þór; Sigurðardóttir, Sigríður; Grétarsdóttir, Heiður; Hoelke, Kenan; Jouan, Gabriel Marc Marie; Penzel, Thomas; Arnardottir, Erna Sif; Óskarsdóttir, María; Department of Engineering; Department of Computer Science
Manual scoring of polysomnography (PSG) is a time-intensive task, prone to inter-scorer variability that can impact diagnostic reliability. This study investigates the integration of decision support systems (DSS) into PSG scoring workflows, focusing on their effects on accuracy, scoring time and potential biases toward recommendations from artificial intelligence (AI) compared to human-generated recommendations. Using a novel online scoring platform, we conducted a repeated-measures study with sleep technologists, who scored traditional and self-applied PSGs. Participants were occasionally presented with recommendations labelled as either human- or AI-generated. As the goal of this study was to isolate the effect of perceived recommendation sources on scorer behaviour, all recommendations were human-generated. We found that traditional PSGs tended to be scored slightly more accurately than self-applied PSGs, but this difference was not statistically significant. Correct recommendations significantly improved scoring accuracy for both PSG types, while incorrect recommendations reduced accuracy. No significant bias was observed toward or against AI-generated recommendations compared to human-generated recommendations. These findings highlight the potential of DSSs to enhance PSG scoring reliability. However, ensuring the accuracy of the suggestions is critical to maximising its benefits. Future research should explore the long-term impacts of DSS on scoring workflows and strategies for integrating AI in clinical practice.
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An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight
(2024) Holm, Benedikt; Jouan, Gabriel; Hardarson, Emil; Sigurðardottir, Sigríður; Hoelke, Kenan; Murphy, Conor; Arnardóttir, Erna Sif; Óskarsdóttir, María; Islind, Anna Sigríður; Department of Engineering; Department of Computer Science
Introduction: Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. Methods: A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. Results: We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ. Discussion: We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.
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Deep learning for sleep analysis on children with sleep-disordered breathing : Automatic detection of mouth breathing events
(2023) Sturludóttir, Jóna Elísabet; Sigurðardóttir, Sigríður; Serwatko, Marta; Arnardóttir, Erna S.; Hrubos-Strøm, Harald; Clausen, Michael Valur; Sigurðardóttir, Sigurveig; Óskarsdóttir, María; Islind, Anna Sigridur; Department of Engineering; Department of Computer Science
Introduction: Sleep-disordered breathing (SDB) can range from habitual snoring to severe obstructive sleep apnea (OSA). A common characteristic of SDB in children is mouth breathing, yet it is commonly overlooked and inconsistently diagnosed. The primary aim of this study is to construct a deep learning algorithm in order to automatically detect mouth breathing events in children from polysomnography (PSG) recordings. Methods: The PSG of 20 subjects aged 10–13 years were used, 15 of which had reported snoring or presented high snoring and/or high OSA values by scoring conducted by a sleep technologist, including mouth breathing events. The separately measured mouth and nasal pressure signals from the PSG were fed through convolutional neural networks to identify mouth breathing events. Results: The finalized model presented 93.5% accuracy, 97.8% precision, 89% true positive rate, and 2% false positive rate when applied to the validation data that was set aside from the training data. The model's performance decreased when applied to a second validation data set, indicating a need for a larger training set. Conclusion: The results show the potential of deep neural networks in the analysis and classification of biological signals, and illustrates the usefulness of machine learning in sleep analysis.
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Experiences of Dyslexic Software Engineers - A Qualitative Study
(Association for Computing Machinery, Inc, 2026-07-09) Cruz, Marcos Vinicius; Verma, Pragya; Liebel, Grischa; Department of Computer Science
Dyslexia is a common learning disorder that primarily impairs an individual’s reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring and keeping work. In Software Engineering (SE), reading and writing difficulties appear to pose substantial challenges for core tasks such as programming. However, initial studies indicate that these challenges may not significantly affect their performance compared to non-dyslexic colleagues. Conversely, strengths associated with dyslexia could be particularly valuable in areas like programming and design. However, there is currently no work that explores the experiences of dyslexic software engineers, and puts their strengths into relation with their difficulties. To address this, we present a qualitative study of the experiences of dyslexic individuals in SE. We followed the basic stage of the Socio-Technical Grounded Theory method and base our findings on data collected through 10 interviews with dyslexic software engineers, 3 blog posts and 153 posts on the social media platform Reddit. We find that dyslexic software engineers especially struggle at the programming learning stage, but can succeed and indeed excel at many SE tasks once they master this step. Common SE-specific support tools, such as code completion and linters are especially useful to these individuals and mitigate many of the experienced difficulties. Finally, dyslexic software engineers exhibit strengths in areas such as visual thinking and creativity. Our findings have implications to SE practice and motivate several areas of future research in SE, such as investigating what makes code less/more understandable to dyslexic individuals.

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