Hasil untuk "Norwegian literature"

Julia Beck, Siska Humlesjö, Kristin Åkerlund et al.

This paper introduces the Dawit Isaak Database of Censorship (DIDOC), a pilot initiative designed to advance research on censorship practices through the systematic documentation of banned and suppressed literature. Developed in collaboration among the Dawit Isaak Library, the Gothenburg Research Infrastructure for Digital Humanities (GRIDH), Swedish PEN, and Lund University, the project combines cultural heritage resources, scholarly expertise, and digital infrastructure. DIDOC builds on a curated selection of 160 titles from the Dawit Isaak Library’s larger collection of approximately 1,200 censored works. Each entry links bibliographic information to documented censorship events, categorized by type, reason, and geographical location. Implemented on the Omeka-S platform and structured according to Linked Open Data principles, the database supports semantic annotation, interoperability, and future integration with external resources such as the Norwegian Beacon for Freedom of Expression dataset. The project contributes methodologically by demonstrating how digital infrastructures and metadata standards can be applied to the study of censorship, while also addressing ethical questions concerning data selection, representation, and public accessibility. Beyond its role as a public resource for educators, journalists, and in libraries, DIDOC establishes a foundation for international comparative research on censorship across different historical and geographical contexts.

Shreyansh Padarha, Ryan Othniel Kearns, Tristan Naidoo et al.

Systematic literature reviews are essential for synthesizing scientific evidence but are costly, difficult to scale and time-intensive, creating bottlenecks for evidence-based policy. We study whether large language models can automate the complete systematic review workflow, from article retrieval, article screening, data extraction to report synthesis. Applied to epidemiological reviews of nine WHO-designated priority pathogens and validated against expert-curated ground truth, our open-source agentic pipeline (AgentSLR) achieves performance comparable to human researchers while reducing review time from approximately 7 weeks to 20 hours (a 58x speed-up). Our comparison of five frontier models reveals that performance on SLR is driven less by model size or inference cost than by each model's distinctive capabilities. Through human-in-the-loop validation, we identify key failure modes. Our results demonstrate that agentic AI can substantially accelerate scientific evidence synthesis in specialised domains.

David Mercier

Engineering and materials software is increasingly difficult to track in the scholarly and technical literature because publication volume is growing rapidly and software citation practices remain inconsistent. This is particularly true for the Ansys Granta product family, which is used for materials education, materials and process selection, sustainability-driven design, and enterprise materials information management. We present a structured and reproducible framework to consolidate evidence of \emph{operational} Granta usage and to support quantitative monitoring of adoption patterns, application domains, and technical impact. The framework is implemented as a curated reference database in \textit{Ansys Granta MI Enterprise}: bibliographic metadata are ingested semi-automatically (e.g., via DOI and citation-file parsing) and complemented by expert curation of usage descriptors (product, context, application domain, and technical depth), with relational links to authors and institutions. Downstream analytics are performed with Python, dashboards, and bibliometric/network visualization tools to enable reproducible querying and reporting. As of September~2025, the database contains more than 1{,}100 curated records spanning journals, conferences, theses, books, patents, standards, and reports, and supports rapid retrieval of validated case studies, reproducible literature reviews, and technology scouting. Example analyses highlight dominant domains, key institutions, and recurring integrations with CAD/CAE/FEM environments. Overall, the approach converts heterogeneous software-usage evidence into structured, analyzable knowledge to improve visibility of engineering software impact and to support evidence-based assessment and strategic decision-making.

Fride Flobakk-Sitter, Jannike Gottschalk Ballo

Social participation through practice placements may be considered a fundamental form of learning, where students are given the opportunity to gradually become part of their community of professionals. Most professional education programs have compulsory practice placement as part of the course. However, practice placement is differently organized and of different scope across professional educations. Research literature evaluating practice placement quality often relates to professional educations individually, without comparing quality criteria across educational fields. The objective of this review is to examine conditions for high quality in professional educations’ practice placement. A rapid systematic review was performed to identify and analyze aspects of quality in practice placement using Norwegian data published during the last 15 years on 19 different educational programs. The current study adds to extant literature by comparing quality in practice placement across educational programs and between relevant actors, summarizing differences and similarities, and pointing out knowledge gaps for further research. 

Tianhang Zhang, Shengnan Fu, David M. Schultz et al.

Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.

Cynthia Zastudil, David H. Smith, Yusef Tohamy et al.

Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda

Humour has long been recognized as a key factor in enhancing creativity, group effectiveness, and employee well-being across various domains. However, its occurrence and impact within software engineering (SE) teams remains under-explored. This paper introduces a comprehensive, literature review-based taxonomy exploring the characterisation and use of humour in SE teams, with the goal of boosting productivity, improving communication, and fostering a positive work environment while emphasising the responsible use of humour to mitigate its potential negative impacts. Drawing from a wide array of studies in psychology, sociology, and organizational behaviour, our proposed framework categorizes humour into distinct theories, styles, models, and scales, offering SE professionals and researchers a structured approach to understanding humour in their work. This study also addresses the unique challenges of applying humour in SE, highlighting its potential benefits while acknowledging the need for further empirical validation in this context. Ultimately, our study aims to pave the way for more cohesive, creative, and psychologically supportive SE environments through the strategic use of humour.

Krishno Dey, Diogo Barradas, Saqib Hakak

The Metaverse utilizes emerging technologies such as Extended Reality (XR), Artificial Intelligence (AI), blockchain, and digital twins to provide an immersive and interactive virtual experience. As Metaverse continues to evolve, it bring a range of security and privacy threats, such as identity management, data governance, and user interactions. This survey aims to provide a comprehensive review of the enabling technologies for the Metaverse. It also aims to provide a thorough analysis of key vulnerabilities and threats that may compromise its sustainability and user safety. We perform a systematic literature review (SLR) to identify key vulnerabilities and their countermeasures in Metaverse platforms. Metaverse offers a much larger attack surface compared to conventional digital platforms. Immersive, decentralized, and permanent characteristics of the Metaverse generate new vulnerabilities. Although there are many countermeasures to these vulnerabilities, most of them are theoretical or have not been tested in real-world environments. Our review highlights current advancements, identifies research gaps, and outlines future directions to ensure a secure, resilient, and ethically governed Metaverse.

Ali Asgari, Milan de Koning, Pouria Derakhshanfar et al.

Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices. Despite these capabilities, robustness remains a critical quality attribute for deep-code models as they may produce different results under varied and adversarial conditions (e.g., variable renaming). Metamorphic testing has become a widely used approach to evaluate models' robustness by applying semantic-preserving transformations to input programs and analyzing the stability of model outputs. While prior research has explored testing deep learning models, this systematic literature review focuses specifically on metamorphic testing for deep code models. By studying 45 primary papers, we analyze the transformations, techniques, and evaluation methods used to assess robustness. Our review summarizes the current landscape, identifying frequently evaluated models, programming tasks, datasets, target languages, and evaluation metrics, and highlights key challenges and future directions for advancing the field.

Torkild Bakken, Toril Loennechen Moen

Records of cirriped species in the family Scalpellidae from the Nordic Seas are scarce. New records of the four species Amigdoscalpellum hispidum (G. O. Sars, 1890), Ornatoscalpellum stroemi (M. Sars, 1859) Tarasovium cornutum (G.O. Sars, 1879) and Weltnerium nymphocola (Hoek, 1883) from the eastern part of the Norwegian Sea are reported. The record of W. nymphocola is the first from the Norwegian coastline, found on the shelf slope, and the rarely found T. cornutum was found in a depth representing the lower depth range for this species. Based on new data from the Norwegian Sea and from the literature the species’ connection to specific oceanographical regimes is discussed.

Karoline Nysted Nilsen, Frank‐David Øhrn, Asbjørn Årøen et al.

ABSTRACT Purpose Meniscal injuries in the knee are usually treated surgically with arthroscopic partial resection (APR) or arthroscopic repair (AR). APR has been shown to increase the risk of osteoarthritis and the focus has shifted to repairing the meniscus with AR. The extent of this shift is yet to be established and an analysis of incidence rates (IR) of APR and AR for meniscal injuries could highlight this. Methods Data from the Norwegian Patient Registry (NPR) and Statistics Norway (SN) from 2010 to 2020 were collected. The number of procedures, demographics and facilities providing meniscal surgery were obtained from NPR, while population size and catchment area were collected from SN. IR of APR and AR and APR/AR rate ratios were estimated and compared. Results A total of 119,528 knee arthroscopies were performed, 89.6% of which were APR. The number of APR performed nationally decreased by 72%, while AR procedures increased by 178%. The national IR of APR decreased from 298 to 82/100,000 inhabitants (p < 0.001). For AR, the national IR increased annually from 13/100,000 inhabitants to a peak in 2019 of 32/100,000 inhabitants (p < 0.001). The APR/AR rate ratio decreased from 22 to below five and the APR/AR trend curves showed a statistically significant decrease (p < 0.001). Conclusion Surgical treatment of meniscal injuries has changed, with a substantial reduction in APR and a strong increase in AR. The reduction in APR, especially in older patients, suggests that meniscal surgery in Norway has undergone a paradigmatic shift, in line with recent literature. Level of Evidence Level IV.

Håvard Lorås, Monika Haga, Ruben Vist Hagen et al.

The objective of this study was to examine the psychometric properties of the Adolescent Motor Competence Questionnaire (AMCQ) for Norwegian adolescents. To this end, a sample of 349 Norwegian-speaking adolescents (13–16 years old) were recruited and completed the AMCQ. Initial results showed that confirmatory factor analysis (CFA) did not indicate statistical support for previous statistical models reported in the literature. Further analysis indicated factorial validity for a novel three-factor model identified through exploratory factor analysis, encompassing measures of fine motor skill (α = 0.65), gross motor skill (α = 0.74), and activities of daily living (ADL; α = 0.79) with acceptable internal consistency coefficients. Subsequent analysis indicated indices of measurement invariance in the study sample, as males rated their competence higher compared to females in 19 of the 27 items, and better model fit was obtained for the female adolescents. Strong invariance was tenable, and no factor mean differences were found across older and younger adolescents or across BMI scores. Overall results thus suggested that the AMCQ has acceptable psychometric properties and can be confidently used in further work with perceived motor competence in Norwegian 13–16 years-old adolescents.

Yangyang Liu, Shoubin Li

In the rapidly evolving field of scientific research, efficiently extracting key information from the burgeoning volume of scientific papers remains a formidable challenge. This paper introduces an innovative framework designed to automate the extraction of vital data from scientific PDF documents, enabling researchers to discern future research trajectories more readily. AutoIE uniquely integrates four novel components: (1) A multi-semantic feature fusion-based approach for PDF document layout analysis; (2) Advanced functional block recognition in scientific texts; (3) A synergistic technique for extracting and correlating information on molecular sieve synthesis; (4) An online learning paradigm tailored for molecular sieve literature. Our SBERT model achieves high Marco F1 scores of 87.19 and 89.65 on CoNLL04 and ADE datasets. In addition, a practical application of AutoIE in the petrochemical molecular sieve synthesis domain demonstrates its efficacy, evidenced by an impressive 78\% accuracy rate. This research paves the way for enhanced data management and interpretation in molecular sieve synthesis. It is a valuable asset for seasoned experts and newcomers in this specialized field.

Xuetao Li, Yuxia Zhang, Cailean Osborne et al.

Open source software (OSS) has been playing a fundamental role in not only information technology but also our social lives. Attracted by various advantages of OSS, increasing commercial companies take extensive participation in open source development and have had a broad impact. This paper provides a comprehensive systematic literature review (SLR) of existing research on company participation in OSS. We collected 92 papers and organized them based on their research topics, which cover three main directions, i.e., participation motivation, contribution model, and impact on OSS development. We found the explored motivations of companies are mainly from economic, technological, and social aspects. Existing studies categorize companies' contribution models in OSS projects mainly through their objectives and how they shape OSS communities. Researchers also explored how commercial participation affects OSS development. We conclude with research challenges and promising research directions on commercial participation in OSS. This study contributes to a comprehensive understanding of commercial participation in OSS development.

Nyaga Fred, I. O. Temkin

Software engineering (SE) is a dynamic field that involves multiple phases all of which are necessary to develop sustainable software systems. Machine learning (ML), a branch of artificial intelligence (AI), has drawn a lot of attention in recent years thanks to its ability to analyze massive volumes of data and extract useful patterns from data. Several studies have focused on examining, categorising, and assessing the application of ML in SE processes. We conducted a literature review on primary studies to address this gap. The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes. The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation. It also highlights the specific ML techniques that have been leveraged in these domains, such as supervised learning, unsupervised learning, and deep learning. Keywords: machine learning, deep learning, software engineering, natural language processing, source code

Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor et al.

With generative artificial intelligence (AI), particularly large language models (LLMs), continuing to make inroads in healthcare, it is critical to supplement traditional automated evaluations with human evaluations. Understanding and evaluating the output of LLMs is essential to assuring safety, reliability, and effectiveness. However, human evaluation's cumbersome, time-consuming, and non-standardized nature presents significant obstacles to comprehensive evaluation and widespread adoption of LLMs in practice. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare. We highlight a notable need for a standardized and consistent human evaluation approach. Our extensive literature search, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, includes publications from January 2018 to February 2024. The review examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Drawing on the diverse evaluation strategies employed in these studies, we propose a comprehensive and practical framework for human evaluation of LLMs: QUEST: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. This framework aims to improve the reliability, generalizability, and applicability of human evaluation of LLMs in different healthcare applications by defining clear evaluation dimensions and offering detailed guidelines.

Raveen Kanishka Jayalath, Hussain Ahmad, Diksha Goel et al.

Microservice architectures are revolutionizing both small businesses and large corporations, igniting a new era of innovation with their exceptional advantages in maintainability, reusability, and scalability. However, these benefits come with significant security challenges, as the increased complexity of service interactions, expanded attack surfaces, and intricate dependency management introduce a new array of cybersecurity vulnerabilities. While security concerns are mounting, there is a lack of comprehensive research that integrates a review of existing knowledge with empirical analysis of microservice vulnerabilities. This study aims to fill this gap by gathering, analyzing, and synthesizing existing literature on security vulnerabilities associated with microservice architectures. Through a thorough examination of 62 studies, we identify, analyze, and report 126 security vulnerabilities inherent in microservice architectures. This comprehensive analysis enables us to (i) propose a taxonomy that categorizes microservice vulnerabilities based on the distinctive features of microservice architectures; (ii) conduct an empirical analysis by performing vulnerability scans on four diverse microservice benchmark applications using three different scanning tools to validate our taxonomy; and (iii) map our taxonomy vulnerabilities with empirically identified vulnerabilities, providing an in-depth vulnerability analysis at microservice, application, and scanning tool levels. Our study offers crucial guidelines for practitioners and researchers to advance both the state-of-the-practice and the state-of-the-art in securing microservice architectures.

Cecilie Takle

Denne artikkelen undersøker humor i tre ulike versjoner av fortellinga om Buster Oregon Mortensen fra 1979, 1984 og 2021. Jeg gjør en komparativ analyse i form av en tematisk nærlesning av de tre verkene med et spesielt blikk på humor i relasjon til maktbalanser og til temaer som oppleves som vanskelige eller tabubelagte.  

Hadle Oftedal Andersen

I denne artikkelen undersøker eg skjønnlitterære tekstar av tre sentrale forfattarar knytte til tidsskriftet Profil på 1960-talet: Dag Solstad, Einar Økland og Jan Erik Vold. Dei tekstane eg fokuserer på, er alle kjenneteikna ved at dei er humoristiske, og at dei på ulike måtar nyttar denne humoren til å kritisera andre lyriske skrivemåtar enn den som kjenneteiknar Profil-gruppa sin eigen poetikk. Nærmare bestemt handlar det om kritikk av den idealistiske tradisjonen; kritikk av den moraliserande litteraturen; kritikk av nasjonalromantikken; kritikk av særlege, poetiske motivkrinsar; kritikk av symbolbruk; og kritikk av høgstemd, poetisk særspråk. Saman med analysane av Profil-forfattarane sine tekstar, går eg inn på døme på desse kritiserte skrivemåtane, av Paal Brekke, Arnulf Øverland, Erling Christie, Tore Ørjasæter og A.O. Vinje. Og eg peikar på to internasjonale inspirasjonskjelder, Werner Aspenström og Lewis Carroll. Det overgripande poenget er at Profil-forfattarane ikkje fokuserer på éin hovudmotstandar, men femner særs vidt i sin humoristisk-kritiske omgang med tradisjonen.

Tanmay Kulkarni, Yuvraj Pardeshi, Yash Shah et al.

Through the advancement in natural language processing (NLP), specifically in speech recognition, fully automated complex systems functioning on voice input have started proliferating in areas such as home automation. These systems have been termed Automatic Speech Recognition Systems (ASR). In this review paper, we explore the feasibility of an end-to-end system providing speech and text based natural language processing for job interview preparation as well as recommendation of relevant job postings. We also explore existing recommender-based systems and note their limitations. This literature review would help us identify the approaches and limitations of the various similar use-cases of NLP technology for our upcoming project.