- 5 publikasjoner funnet
Dahl, Fredrik Andreas. (2026).
Narratives of Possible AI Futures: The Good, the Bad and the Ugly. SFI Visual Intelligence
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People have different views on the current level of intelligence in AI systems, the likely speed of further progress, and the societal impact AI may have in the future. This has contributed to a polarized debate, with narratives ranging from “AI will give us some useful tools”, via “Many jobs will disappear” and “We will all be rich”, to “AI will take over and kill us all”. In this presentation, I will map the main narratives about AI risk and benefit using a railway metaphor in which human extinction is the final destination. The different stops along the way represent arguments for more favourable outcomes. The goal is not to argue for the likelihood of any particular scenario, but to structure the discussion of these narratives in a systematic way.
Das, Bhagwan; Ali, Nawaz; Pirbhulal, Sandeep; Aloi, Gianluca; Pace, Pasquale og Sodhro, Ali Hassan. (2026).
Adaptive Federated Learning for 6G: A Multi-Agent Architecture for 6G Edge Intelligence.
Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Mylius-Kroken, Johan; Giese, Martin; Kampffmeyer, Michael; Ehsani, Rezvan; Vitelli, Valeria; Milosevic, Vladan og Wetzer, Elisabeth. (2026).
Extracting Knowledge from Spatial Biology: Evaluating Cell Type Hierarchies in Breast Cancer Imaging Data. UiT - The Arctic University fof Norway
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Vitenskapelig foredrag
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Hyperbolic representation learning has shown compelling advantages over conventional Eu- clidean representation learning in modelling hierarchical relationships in data. In this work, we evaluate its potential to capture biological relations between cell types in highly multiplexed imaging data, where capturing subtle, hierarchical relationships between cell types is crucial to understand tissue composi- tion and functionality. Using a recent and thoroughly validated 42-marker Imaging Mass Cytometry (IMC) dataset of breast cancer tissue, we embed cells into both Euclidean and Lorentzian latent spaces via a fully hyperbolic variational autoencoder. We then introduce an information-theoretic framework based on k-nearest neighbour estimators to rigorously quantify the clustering performance in each geom- etry using mutual information and conditional mutual information. Our results reveal that hyperbolic embeddings retain significantly more biologically relevant information than their Euclidean counter- parts. We further provide open-source tools to extend Kraskov-Stögbauer-Grassberger based mutual information estimation to Lorentzian geodesic spaces, and to enable UMAP visualizations with hyper- bolic distance metrics. This work contributes a principled evaluation method for geometry-aware learning and supports the growing evidence of hyperbolic geometry’s benefits in spatial biology. Code is
available at: https://github.com/youssefwally/FlatlandandBeyond
Chen, Siyan; Wickstrøm, Kristoffer og Jenssen, Robert. (2026).
Evaluating AI-based Weather Forecasting Models for Local Wind Speed Prediction in Northern Norway. Robert Jenssen, Tian Tian, Tommy Sonne Alstrøm
Halbach, Till og Moe, Marius. (2026).
Hvordan jobbe med universell utforming i produktutvikling. Norway Health Tech og Norsk Regnesentral
Fuglerud, Kristin Skeide og Halbach, Till. (2026).
Universell utforming som innovasjons- og konkurransefortrinn. Norway Health Tech og Norsk Regnsentral
Chockalingam, Sabarathinam; Pirbhulal, Sandeep og Abie, Habtamu. (2026).
Improving Security and Privacy of Cognitive Digital Twins Through Dynamic Consent for Healthcare and Resilient Societies.
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Cognitive Digital Twins (CDTs) are virtual models of physical systems that integrate cognitive functions with real-time Internet of Things (IoT) data to support simulation, decision-making, and cyber resilience. In domains such as healthcare and smart cities, CDTs enable advanced capabilities but also introduce significant privacy and security risks, especially due to continuous, real-time data exchange and automated decision-making. This paper presents DC-TWIN, a dynamic consent-enabled CDT framework that addresses the limitations of static, one-time consent models in real-time, data-intensive environments. DC-TWIN introduces a multi-layered architecture consisting of: (i) a multi-layered architectural stack, including user control, policy management, dynamic trust, and data governance layers, that supports compliance monitoring, risk-aware user interfaces, consent history tracking, federated identity and role binding, and adaptive trust modeling, and (ii) a dynamic consent reasoning engine that uses contextual signals (e.g., user role, device status, network activity) and human factors (e.g., cognitive load, trust calibration, fatigue) to assess data access requests in real time, issuing granular decisions (grant, deny, prompt) or escalating for clarification. We highlight key use cases in healthcare, welfare technologies, and smart cities. The framework empowers users with real-time, contextual control over how their data is accessed, shared, and reused through adaptive interfaces and personalized consent mechanisms. By integrating dynamic consent reasoning, trust calibration, and continuous feedback loops, DC-TWIN supports transparent, compliant, and user-aligned data governance. It contributes to the secure and ethical deployment of CDTs by reinforcing user autonomy, enhancing risk communication, and enabling responsive consent management in critical domains such as healthcare.
Selstad, Knut; Pirbhulal, Sandeep; Abie, Habtamu; Lehkonen, Riku og Ari, Ismail. (2026).
SecureIoT: Robust AI-Driven Cyber Threat Detection for IoT Applications.
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The cyberattack surface in critical sectors is expanding due to the rapid proliferation of Internet of Things (IoT) devices. Artificial Intelligence (AI) models, such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), offer promising capabilities for detecting and classifying cyber threats. However, these models often struggle to generalize to previously unseen attacks after deployment. This study investigates how well different AI techniques can generalize to such novel threats in the presence of class imbalance. We evaluate three data balancing strategies: Generative Adversarial Networks (GAN), Synthetic Minority Over-sampling Technique (SMOTE), and class weighting. Experimental results indicate that DNNs outperform CNNs when provided with identical input data. While each balancing method has distinct advantages and trade-offs, the highest multiclass accuracy of 81.16 % was achieved by a DNN using GAN-augmented data for the previously seen attack types. The best performance on unseen attacks was achieved by a DNN trained with SMOTE, yielding a multiclass accuracy of 51 % among eight classes. The binary classification (benign vs. malicious) results were satisfactory, with DNN using GAN-augmented data achieving an accuracy of 99.20 %. These findings highlight the importance of not only separating data into training and test splits, but also incorporating a “seen vs. unseen” evaluation strategy.
Tvete, Ingunn Fride og Klemp, Marianne. (2026).
Temporal dynamics of prognostic factors in breast cancer survival.
Palomares, Alfonso Diz-Lois og Storvik, Geir Olve. (2026).
Parameter estimation in Conditional Sequential Monte Carlo algorithms through Particle Learning. Department of Mathematics and Statistics of the University of Helsinki
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Vitenskapelig foredrag
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In this work, we explore particle learning strategies for the joint estimation of static parameters and latent states within conditional sequential Monte Carlo (CSMC) algorithms. Building on this idea, we propose the p(parameter)-CSMC algorithm, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to Gibbs sampling in settings where strong internal correlations may challenge effective exploration. We include an application to the estimation of weights in a branching process model against synthetic data and show that, in this setting, performance is dramatically enhanced, with substantially faster mixing and markedly reduced autocorrelation compared with standard particle Gibbs implementations.
Wu, Zhiyuan; Choi, Changkyu; Yu, Shujian; Jenssen, Robert og Ramezani-Kebrya, Ali. (2026).
Mitigating Embedding Leakage via Latent Disruption with Controlled Reconstruction.
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Pre-trained encoders produce semantically rich latent embeddings, which, however, may expose unintended information through malicious inference or exploitation. We propose SEAL, a framework that mitigates embedding leakage by disrupting latent representations based on information-theoretic principles. It reduces the risk of potential misuse while enabling controlled reconstruction for trusted users. SEAL learns to encode controlled perturbations by minimizing the Matrix Norm-based Quadratic Mutual Information (MQMI) functional between original and perturbed embeddings within a hyperspherical latent space. Meanwhile, a private decoder, jointly trained with the SEAL encoder, is trained to reconstruct the original data that is accessible only to authorized users under an access-controlled setting. Extensive experiments on vision and text datasets demonstrate that SEAL reduces latent leakage, weakens the effectiveness of evaluated inference attacks, and preserves reconstruction under the considered setting.
Cepeda, Santiago; Esteban-Sinovas, Olga; Luppino, Luigi Tommaso; Kuttner, Samuel; Wodzinski, Marek; Romero-Oraá, Roberto; Escudero, Trinidad; Garzón, Jesús; Arrese, Ignacio; Hornero, Roberto og Sarabia, Rosario. (2026).
Radiomics-based mapping of glioblastoma infiltration beyond contrast enhancement: diffusion–perfusion correlations and survival analysis in large public cohorts.
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Background
Radiomic models from multiparametric MRI can characterize tumor infiltration within the non-enhancing peritumoral region but remain insufficiently compared with diffusion and perfusion. This study assessed concordance between voxelwise radiomic predictions and these physiological modalities and evaluated prognostic value of infiltration metrics in two external cohorts.
Methods
UPenn-GBM and UCSF-PDGM datasets were analyzed. Voxelwise radiomic classification generated peritumoral infiltration-probability maps from standard MRI. Fractional anisotropy (FA), dynamic susceptibility contrast–derived relative cerebral blood volume (DSC-rCBV; UPenn), and arterial spin labeling–derived relative cerebral blood flow (ASL-CBF; UCSF) were compared between peritumoral regions classified as high- versus low-infiltration. Radiomic metrics included infiltration burden (voxel fraction exceeding predefined probability thresholds) and radial extent (normalized maximum distance from enhancing margin sustaining high infiltration probability) were quantified, and survival assessed using univariable Cox models.
Results
Across 872 subjects, high-infiltration regions showed significantly lower FA (median difference: UPenn −0.232; UCSF −0.226; both p < 0.001) and higher perfusion (median difference: UPenn DSC-rCBV + 0.282; UCSF ASL-CBF + 0.565; both p < 0.001) compared with low-infiltration regions. Infiltration burden at the 0.50 threshold demonstrated prognostic value (UPenn hazard ratio (HR) 2.758, 95% confidence interval (CI) 1.189–6.396, p = 0.018; UCSF HR 21.277, 95% CI 6.024–71.429, p < 0.001). Radial extent was also associated with survival (UPenn HR 2.371, 95% CI 1.215–4.625, p = 0.011; UCSF HR 4.405, 95% CI 1.695–11.494, p = 0.002).
Conclusions
Voxelwise radiomic infiltration mapping from standard MRI aligns with diffusion and perfusion abnormalities and yields prognostic value. These metrics highlight the role of structural radiomics for characterizing non-enhancing infiltrative spread in glioblastoma.
Østmo, Eirik Agnalt; Radiya, Keyur; Wickstrøm, Kristoffer; Kampffmeyer, Michael; Mikalsen, Karl Øyvind og Jenssen, Robert. (2026).
Liver, vessel, and tumor segmentation from partially labeled CT and multi-label masked learning.
Wetzer, Elisabeth; Handegard, Nils Olav; Kampffmeyer, Michael og Jenssen, Robert. (2026).
Problem-Driven AI Methodology for Fisheries Innovation. University of the Faroe Islands, Ministry of Foreign Affairs and Culture, Faroe Marine Research Institute
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The Norwegian Centre for Research-based Innovation, Visual Intelligence, advances deep learning in marine science. The work focuses on analyzing multifrequency echosounder data to support ecosystem and fisheries management. To address limited labeled data for identifying sand eels in the North Sea, a semi-supervised learning method was developed that combines labeled and unlabeled data, significantly improving accuracy (Choi et al., ICES JMS 2021). Building on this, the method was extended to semantic segmentation (Choi et al., IEEE J. Ocean. Eng. 2023), enabling detailed classification of acoustic signals while reducing the need for costly annotations. More recently, foundation models were explored to tackle challenges like changing conditions in marine environments. By aligning these models with echosounder data and using semantic tokenization, they achieved strong performance with minimal labeled data (Choi et al., NAIS 2025). These innovations highlight the transformative role of AI in exploring and understanding the underwater world.
Wetzer, Elisabeth; Choi, Changkyu; Jenssen, Robert; Handegard, Nils Olav og Ebbesson, Lars O.E.. (2026).
Artificial Intelligence for Sustainable Fisheries: Methods, Monitoring, and Practice. University of the Faroe Islands, Ministry of Foreign Affairs and Culture, Faroe Marine Research Institute
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Arctic and Subarctic fisheries face increasing pressure as traditional practices struggle to keep pace with shifting ocean conditions. These challenges call for intelligent, adaptive systems to support sustainable monitoring and management. This session explores how Artificial Intelligence (AI) can contribute to the future of fisheries science by bridging algorithmic innovation with practical application and fostering interdisciplinary exchange.
We invite papers that demonstrate how AI, ranging from machine learning and computer vision to emerging LLM-based agentic systems, can support fisheries research and regulation. Relevant applications include acoustic data interpretation for species identification, improved stock assessments, vessel activity monitoring, and adaptive regulatory strategies. Submissions addressing challenges such as limited computational resources, sparse data availability, or operational constraints in remote polar environments are particularly encouraged.
The session also highlights the importance of engaging Indigenous knowledge holders and coastal communities in AI development. We especially welcome co-designed frameworks that promote fairness, transparency, and local agency in contexts where environmental data and governance intersect.
Through this session, we aim to bring together diverse perspectives that advance responsible and locally grounded uses of AI for sustainable marine stewardship.
Haugen, Marion og Aldrin, Magne Tommy. (2026).
Estimated effects of a lice treatment from experimental data – second update: Appendix.
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Rapport
Haugen, Marion og Aldrin, Magne Tommy. (2026).
Estimated effects of a lice treatment from experimental data – second update.
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Rapport
Aastveit, Marthe Elisabeth; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2026).
Predicting partially observed survival curves via factor analysis with application to demand forecasting in short-term rental markets. STOR-i, Lancaster University
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Faglig foredrag
Aas, Kjersti. (2026).
Hvordan benytte AI til å forbedre kredittrisikomodeller? BI
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Faglig foredrag
Trier, Øivind Due og Lund, Carl William. (2026).
Utvikling og validering av maskinlæringsmodeller i innovasjonsprosjektet LAVDAS. Geoforum
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Vitenskapelig foredrag
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Utviklingen i innovasjonsprosjektet «landsdekkende myrdatasett» har vært preget av både teknologiske fremskritt og utfordringer knyttet til å etablere en landsdekkende KI-modell for kartlegging av myr og våtmark. Presnetasjonen tar for seg utviklingen og testingen av ulike modellutgaver, og hvordan de kan kombinere dem for å lage et mer robust og presist resultat, samt innsikt i testresultatene som foreligger.
Manzanares-Salor, Benet; Sánchez, David og Lison, Pierre. (2026).
Unsupervised utility evaluation of text anonymization methods via neural language models.
Schulz, Trenton og Badescu, Claudia-Andreea. (2026).
A Custom Web Application to Control NAO using Hypertext Transfer Protocol Secure.
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We present a web application for controlling NAO V5 and NAO V6 robots using Hypertext Transfer Protocol Secure (HT TPS). The application was designed for a study in a special school. The school staff have found the application easy-to-use and versatile, and it may be useful to other researchers or people interested in controlling NAO. The HT TPS constraint and the locked-down nature of NAO introduced additional development challenges, and the solutions to these challenges are worth sharing with the HRI community. We present characteristics of the web application, implementation details, how it has been set up, and how to use it. Although the application is usable in its current form, there are still things that can be improved to make the application more useful in other contexts. We therefore document how the remote control can be extended and potential starting points for improvement.
Holthaus, Patrick; Schulz, Trenton; Riches, Lewis; Badescu, Claudia-Andreea og Amirabdollahian, Farshid. (2026).
ZTL: Lightweight Communication Patterns for HRI.
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Human-robot interaction (HRI) programmers often struggle with operating older robot hardware due to the short support period provided by manufacturers and difficulties integrating modern software solutions. This paper introduces the ZTL Task Library (ZTL), a lightweight communication framework and protocol designed to decouple robot hardware from the operating platform via socket communication, thereby increasing robot lifetime. We present a task-based communication protocol facilitating the co-design of robot behaviours with non-programming experts. Our approach has been shown across different platforms to effectively mitigate incompatibilities between middlewares, simplifying control and usability, allowing for simultaneous addressing of multiple devices.
Erceylan, Gizem; Abraham, Doney; Akbarzadeh, Aida; Gkioulos, Vasileios og Pirbhulal, Sandeep. (2026).
A Digital Twin-Assisted Threat Modeling Framework for Predicting APT Attack Flows in Industrial Control Systems.
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Industrial Control Systems (ICSs), which are essential components of critical infrastructures, are inherently complex and vulnerable to cyberattacks. Advanced Persistent Threats (APTs) that target these systems are multi-stage, coordinated attacks that can lead not only to information loss but also to physical damage and loss of life. Traditional threat modeling approaches fall short in adapting to the dynamic nature of ICSs, necessitating new methodologies to predict and prevent such complex attacks. This work presents a digital twin-assisted dynamic threat modeling framework for ICS environments. The framework leverages a knowledge graph that integrates system data and cyber threat intelligence to predict potential attacks. In addition, the digital twin environment enables the validation of mitigation strategies before deployment in the physical system, while also supporting adaptive response and real-time mitigation. To predict the attacker’s next move, we propose a Relational Graph Convolutional Network (RGCN)-based model that utilizes enriched relational data such as tactics, campaigns, groups, techniques, and assets. The proposed RGCN model achieves a recall of 0.887, an F1-score of 0.893, and an AUC of 0.957 in predicting potential attack sequences. These results demonstrate that the model provides reliable and well-balanced predictive performance.
Lison, Pierre; Ruenes, David Sánchez og Stalla-Bourdillon, Sophie. (2026).
Search Data, Privacy, and the Limits of Heuristics: A Critical Reading of the EC's Preliminary Findings against Alphabet.
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Nettsider (opplysningsmateriale)
Lison, Pierre og Schwemer, Sebastian Felix. (2026).
Ideen om en innholdsavgift for KI brer seg.
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Kronikk
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Å kompensere mennesker som bidrar med originalt innhold, handler ikke bare om rettferdighet. Det er også en investering i et bærekraftig, digitalt økosystem.
Gavriluk, Oxana; Snapkow, Igor; Thalabard, Jean-Christophe; Holden, Lars; Holden, Marit; Bøvelstad, Hege Marie og Lund, Eiliv. (2026).
Gene Expression Profiling of Peripheral Blood and Endometrial Cancer Risk Factors: Systems Epidemiology Approach in the NOWAC Postgenome Cohort Study.
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ntroduction: The increasing incidence of endometrial cancer (EC) requires an extensive search for novel preventive tools and early intervention approaches. However, the development of reliable predictive models is impossible without knowledge of genetic alterations prior to diagnosis. In this work, we aimed to establish whether known EC risk factors are associated with peripheral blood gene expression changes in a prospective design and whether such associations differ between women who later developed EC and matched controls. Methods: First, we selected variables (parity status, lifetime number of years of menstruation, coffee consumption, body mass index (BMI), age at menopause, use of oral contraceptives) that were shown to have an impact on EC risk in a large prospective cohort (165,000 women). Next, using BeadChip microarray technology, we tested the association between these variables and gene expression profiles in RNA extracted from mixed circulating immune cells in a nested case-control study (79 case-control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis (GSEA). Results: At overall gene expression level, we found no difference between the EC cases and controls. The introduction of parity status into the statistical model revealed changes in the expression of 1,379 genes in the controls, while we did not observe any expression changes in the cases. Twenty-seven genes were associated with BMI increase in the controls, whereas there was no association observed between changes in BMI and gene expression in women with EC. In GSEA, 2,407 significantly enriched gene sets were attributed to a parity increase among cancer-free women. Conclusion: In this study, we found that an increased number of parities has a life-long effect on the gene expression profile in the peripheral blood of women who never developed cancer. In contrast, in women who were diagnosed with EC later in life, neither multiparity nor elevated BMI showed a significant association with gene expression patterns. However, given the modest sample size and exploratory nature of the study, these findings should be verified in larger cohorts.
Martiniussen, Marit Almenning; Bergan, Marie Burns; KRISTIANSEN, MERETE UNDRUM; Moshina, Nataliia; Larsen, Anne Sofie Frøyshov; Larsen, Marthe; Dahl, Fredrik Andreas og Hofvind, Solveig Sand-Hanssen. (2026).
High risk score of breast cancer by artificial intelligence (AI) on screening mammograms: a review of negative and cancer cases.
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Abstract Objectives To investigate mammographic features associated with high artificial intelligence (AI) risk scores as provided by two AI models applied to screening mammograms. Materials and methods This retrospective study included 130,031 screening mammograms from 42,371 women attending BreastScreen Norway, 2008–2018. Two AI models (A and B) developed for cancer detection on screening mammograms were applied. An informed radiological review was conducted for mammograms within the highest 5% of AI risk scores by both models in two study samples: (1) High AI risk score, but no breast cancer detected within 6 years ( n = 120), and (2) High AI risk score in mammograms with screen-detected cancers ( n = 120). Mammographic density (BI-RADS a–d), features (mass, spiculated mass, asymmetry, architectural distortion, calcification alone, and density with calcification), and radiologists’ interpretation scores (1–5) were analyzed descriptively. Results Mammographic density was higher in sample 1 compared to sample 2 (BI-RADS d: 11% vs 3%, respectively). In sample 1, calcifications alone were the most frequent AI-marked feature (model A: 72%; model B: 68%), predominantly with amorphous morphology and a cluster distribution, and 76% were interpreted as benign by the radiologists (interpretation score 1). In sample 2, a spiculated mass was the most frequent mammographic feature among the screen-detected cancers (29%). Conclusion Mammograms assigned high AI risk scores exhibit distinct features depending on screening outcome. Systematic characterization of these features may help refine AI thresholds, improve specificity, reduce AI false-positive findings, and decrease the recall rate in breast cancer screening. Key Points Question Knowledge about mammographic features associated with high AI risk scores is essential for distinguishing cancer from non-cancer cases. Findings Calcifications were the dominant feature in non-cancers in screening mammograms with high AI risk score, whereas spiculated mass was the most frequent feature among cancers. Clinical relevance Calcifications in non-cancer screening mammograms with a high AI risk score were frequently interpreted as benign or probably benign by radiologists. This knowledge may help refine AI thresholds and thereby improve specificity and reduce false-positive results in mammographic screening. Graphical Abstract
Tvete, Ingunn Fride; Deilkås, Ellen Catharina Tveter; Neef, Linda Reiersølmoen; Patrono, Wenche; Narbuvold, Hanne og Haugen, Marion. (2026).
Assessing Inter-rater Agreement Across Five Teams Applying the Global Trigger Tool to Review 200 Inpatient Medical Records.
Fuglerud, Kristin Skeide. (2026).
Digitalt utenforskap, digital sårbarhet og universell utforming. Akershus fylkeskommune
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Faglig foredrag
Olsen, Lars Henry Berge. (2026).
Methods for Estimating Conditional Shapley Values in Model Explanation. International Monetary Fund (IMF)
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Annen presentasjon
Reithe, Haakon; Patrascu, Monica; Torrado, Juan Carlos; Førsund, Elise; Husebø, Bettina Elisabeth Franziska; Kverneng, Simon Ulvenes; Sheard, Erika; Tzoulis, Charalampos og Marty, Brice Sylvain Daniel. (2026).
Wavelet-Based Tremor Quantification From Wrist-Worn Sensor Data in Home-Dwelling People With Parkinson’s Disease.
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Objective: Tremor symptoms in Parkinson’s disease (PD) are challenging to assess due to low resolution and subjectivity from standard clinical scales. To address this, wearable devices have been used, but algorithms have been relying on controlled or limited activity conditions. Our objective is to create a context-independent metric quantifying tremor in free-living conditions to bridge the gap between biomedical engineering and the PD field. Methods and Procedures: We designed an algorithm which computes a tremor index (TI) from accelerometer data, collected via the Empatica E4 worn on the wrist by home dwelling people with PD. For validation, we use a within-participant design, comparing the TIs of the most and least tremor-affected hand. We included seven participants with unilateral tremor, monitored for two weeks each. The algorithm is able to compute TIs for a set of frequencies identified in literature as associated with different tremor types (3–12 Hz), over adjustable sampling time windows. Results: We show that the most tremor-affected hand yields a higher TI than the other hand for frequency sets that are individual to each person, in particular around 5-6 Hz where rest tremor typically occurs. We find that we can disambiguate tremor across 3-12 Hz from general movement and resting states. The number of frequencies with inter-hand separation correlate with the MDS-UPDRS part III tremor items. Conclusion: The designed tremor quantification algorithm can quantify tremor symptoms over time for people with PD and can be used to identify the individualized frequency ranges where these movements happen, in free-living conditions.
Aas, Kjersti. (2026).
MCCE: Monte Carlo sampling of realistic counterfactual explanations. University of Oslo
NVA
Faglig foredrag
Eikvil, Line og Løland, Anders. (2026).
Industrielle problemer trenger fortsatt prediktiv kunstig intelligens.
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Generativ kunstig intelligens er imponerende, men ikke alltid så nyttig til å løse industrielle problemer.
Selig, Elizabeth; Achi, Nahla Gedeon; Sundnes, Frode; Wabnitz, Colette C.C.; Nakayama, Shinnosuke; Hjermann, Dag Øystein; Palacios-Abrantes, Juliano; Spijkers, Jessica; Hara, Mafaniso; Isaacs, Moenieba; McClanahan, Timothy R.; McKown, Ethan; Mensah, Adelina; Overå, Ragnhild; Rustad, Siri Camilla Aas; Thorarinsdottir, Thordis Linda og Tollefsen, Andreas Forø. (2026).
Patterns of marine resource conflicts across Africa highlight need for fair access and benefit sharing for a blue economy.
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An increased focus on the blue economy across coastal African countries requires effective strategies for reducing marine resource conflicts to achieve goals of sustainable, equitable ocean development. We created a spatial database documenting marine resource conflicts (2008–2018) and conducted an expert survey to analyze patterns in conflict types and how they relate to actors, drivers, and resolution. Our findings indicate that 73% of conflicts were associated with access disputes and 28% were between non-fisheries sectors. National governments, small-scale or industrial fishers, and state enforcement agents were the most frequent actors. Illegal fishing, inequitable benefit distribution, and inadequate regulations were commonly reported conflict drivers. Less than one third of conflicts were resolved, but increased governance was cited as important for resolution. These results suggest policymakers may need to focus on access and benefit sharing issues and increase engagement of key actors in governance processes to realize blue economy ambitions.
Jenssen, Robert; Eikvil, Line; Solberg, Anne H Schistad; Solheim, Inger og Bjørklund, Petter. (2026).
Visual Intelligence Annual Report 2025.
Ross, Theodor Anton; Pöntinen, Anna Kaarina; Holsbø, Einar; Samuelsen, Ørjan; Hegstad, Kristin; Kampffmeyer, Michael; Corander, Jukka og Gladstone, Rebecca Ashley. (2026).
Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes for Escherichia coli sequence type 131 clade C surveillance across infection types.
Løland, Anders; Engebretsen, Solveig og Rognebakke, Hanne. (2026).
Method for estimation of DRS and total collection rate by unit – 2026 update.
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Rapport
Løland, Anders; Engebretsen, Solveig og Rognebakke, Hanne. (2026).
Estimation of DRS collection rate by unit and total collection rate by unit for 2025.
NVA
Rapport
Løland, Anders; Engebretsen, Solveig og Rognebakke, Hanne. (2026).
Beregning av pantegrad og innsamlingsgrad for 2025.
NVA
Rapport
Papadopoulou, Anthi; Lison, Pierre; Anderson, Mark David; Øvrelid, Lilja og Pilán, Ildikó. (2026).
Neural text sanitization with privacy risk indicators: an empirical analysis.
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Text sanitization is the task of redacting a document to mask all occurrences of (direct or indirect) personal identifiers, with the goal of concealing the identity of the individual(s) referred in it. In this paper, we consider a two-step approach to text sanitization and provide a detailed analysis of its empirical performance on two recently published datasets: the Text Anonymization Benchmark (Pilán et al., 2022) and a collection of Wikipedia biographies (Papadopoulou et al., 2022a). The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information. This privacy-oriented entity recognizer is trained by combining a standard named entity recognition model with a gazetteer populated by person-related terms extracted from Wikidata. The second step of the text sanitization process consists in assessing the privacy risk associated with each detected text span, either isolated or in combination with other text spans. We present five distinct indicators of the re-identification risk, respectively based on language model probabilities, text span classification, sequence labelling, perturbations, and web search. We provide a contrastive analysis of each privacy indicator and highlight their benefits and limitations, notably in relation to the available labeled data.
Guldberg, Karen; Eide, Tom; Eide, Hilde; Flatås, Bjørn Aksel; Jensen, Renate; Larsen, Kenneth; Torrado-Vidal, Juan-Carlos; Schulz, Trenton; Thygesen, Hilde; Søfting, Bente og Fuglerud, Kristin Skeide. (2026).
Methodological principles to guide innovation in robot-mediated education for autistic pupils.
Gutiérrez, Eladio; Rummelhoff, Ivar; Romero, Sergio; Kristoffersen, Thor; Tirado-Domínguez, José A.; López, Maria Del Carmen og Plata, Oscar. (2026).
Preserving Long-Term Access to Decommissioned Database Systems With Immortal Database Access (iDA).
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When a database system is decommissioned, retaining its data, structure, and query capabilities is often crucial for future restoration and access. The Immortal Database Access (iDA) solution focuses on preserving decommissioned databases along with their stored information and retrieval functionalities. Building upon the Immortal Virtual Machine (iVM) technology, iDA provides tools that ensure long-term preservation of databases on physical storage media. This includes not only safeguarding the stored content but also enabling its regeneration with functional search capabilities. For this purpose, two innovative elements are introduced: DbSpec, a new language for managing the decommissioning process, and a Read-Only Access Engine (ROAE) which serves as an interface to future users who wish to retrieve the decommissioned information stored on the long-term substrate. ROAE complements the SIARD (Software Independent Archiving of Relational Databases) standard. Although SIARD is effective at preserving database data and metadata, it lacks the ability to capture the essential search and query functions necessary for meaningful information retrieval. iDA addresses this limitation, ensuring that decommissioned systems remain accessible and functional for future users.
Schulz, Trenton; Fuglerud, Kristin Skeide og Stølen, Vibeke. (2026).
Rapport fra workshops, personaer brukerreise og spørreundersøkelse.
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Dette notatet oppsummerer arbeidet gjort i ReDUGUSON-prosjektet. Prosjektet ble gjennomført i samarbeid med VoiceOn AS og finansiert av FORREGION VIKEN. Prosjektet hadde til hensikt å kartlegge hvem som kunne dra nytte av innholdsopplesningstjenester.
Vi holdt workshopper med personer med nedsatt syn og personer som hadde hatt slag og hadde afasi. I workshopene diskuterte man temaer knyttet til informasjon på offentlige sider, hjelpemidler og lovgivning. Deltakerne fikk mulighet til å undersøke tjenester som tilbød innholdsopplesning og ga tilbakemeldinger på dem. Noen ideer til forbedringer rundt etikk er presentert her. I tillegg skapte deltakerne sammen utkast til personaer og brukerreiser. De endelige utgavene av disse er presentert i rapporten.
Deltakerne hadde også mulighet til å fylle ut et spørreskjema. Spørreskjemaet viste stor nytte av innholdsopplesning for deltakerne.
Til slutt presenterer vi noen mulige steder for et hovedprosjekt.
Stolpe, Audun; Kristoffersen, Thor O. og Østvold, Bjarte M.. (2026).
Regelverksforenkling med generativ KI: Å kappe hodet av en hydra?
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Offentlige virksomheter er i ferd med å ta i bruk generativ kunstig intelligens (KI) for mange ulike oppgaver, blant annet tekstredigering og svar på spørsmål om tekster. For offentlig sektor er regelverk en særlig viktig type tekster, og to sentrale oppgaver knyttet til regelverk, som lover og forskrifter, er utforming og anvendelse av regelverket. Lover og forskrifter følger en bestemt logikk, der teksten er bygget opp av nøstede strukturer av forbud, tillatelser og unntak. For å arbeide med en slik tekst er det nødvendig å forstå hvordan denne strukturen gir den enkelte bestemmelse en semantisk kontekst. Vi studerer i hvilken grad og med hvilken kvalitet generativ KI kan håndtere oppgaver knyttet til forenkling og anvendelser av slikt regelverk. Våre undersøkelser viser at generativ KI per i dag er langt fra å være moden for slike oppgaver.
Salomonsen, Christian; Luppino, Luigi T.; Aspheim, Fredrik Emil; Wickstrøm, Kristoffer; Wetzer, Elisabeth; Kampffmeyer, Michael; Berzaghi, Rodrigo; Sundset, Rune; Jenssen, Robert og Kuttner, Samuel. (2026).
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [18F] FDG PET imaging.
Tvete, Ingunn Fride; Narbuvold, Hanne; Deilkås, Ellen Catharina Tveter; Neef, Linda Reiersølmoen; Patrono, Wenche og Haugen, Marion. (2026).
A Comprehensive Review of the Global Trigger Tool for Identifying Adverse Events in Hospitals: Methodological Insights and Opportunities for Improvement. Institute for Healthcare Improvement (IHI) og BMJ Group.
NVA
poster
Roksvåg, Thea Julie Thømt; Vandeskog, Silius Mortensønn; Wulff, C. Ole og Wergeland, Kamilla Klock. (2026).
An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants.
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We propose a Long Short-Term Memory (LSTM) network to estimate historical daily streamflow and hydropower generation in Norway, with particular focus on run-of-river (ROR) plants. Historical records from such plants are often limited, and typically only contain hydropower generation data, which are truncated at the plants’ capacity limits and therefore do not capture high-flow conditions. The proposed LSTM model improves predictions in data-sparse and ungauged catchments, and for high-flow conditions, by learning from both hydropower generation data from ROR plants and streamflow data from other Norwegian catchments. Our model builds upon the neuralhydrology package, by adding a component that transforms streamflow into hydropower generation before loss calculations. The model is trained using streamflow and hydropower generation data from 190 Norwegian catchments and 136 ROR plants, with precipitation, temperature and catchment attributes as input variables. The LSTM model outperforms more traditional hydrological models for predictions in both gauged and ungauged catchments. Furthermore, the combined LSTM model yields hydropower generation estimates that are comparable to or better than those from a model trained only on hydropower generation data, while producing considerably better streamflow estimates. Our approach highlights the added value of additional data sources for hydrological modeling for both local calibration and the task of regionalization, and demonstrates that data-driven methods are suitable for leveraging their potential.
Aarnes, Ingrid og Tanilkan, Sinan. (2026).
Datadrevet felles situasjonsforståelse for ressursdeling i brannvesenet ved kriser. Kartverket
NVA
Annen presentasjon
Vis sammendrag
BRACE - et praktisk, datadrevet rammeverk for samhandling om deling av ressurser ved store og samtidige hendelser.
Dahl, Fredrik Andreas; Trier, Øivind Due og Solberg, Rune. (2026).
Analyse av avvikskarakteristikk for snødekningsgrad.
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I denne rapporten beskrives arbeidet og resultater fra en utvidelse av validering og evaluering av produkter for snødekningsgrad (FSC) fra 2024. Hensikten har vært å undersøke hvordan avvikene (feil) i FSC-verdier, relativt til «fasiter» fra bilder med høyere oppløsning, varierer i tid og rom med aggregeringsnivå, arealdekke og terreng. Analysene er gjort for Østlandet og tilsigsområder valgt ut av NVE med satellittdata over flere år. Referansedata («fasiter») er basert på Sentinel-2 MSIprodukter i 10 m oppløsning, mens FSC-produktene som ble analysert, er basert på 0,5 km data fra Sentinel-3 SLSTR. Resultatene viser at avvikene har tydelig romlig og tidsmessig struktur. Romlig aggregering reduserer
MAE og RMSE, mens bias i hovedsak bevares. En betydelig del av feilen midles likevel ikke effektivt ut ved aggregering opp til de største testede skalaene, noe som tyder på systematiske avvik over større områder. I høydesoneanalysene fremkommer et robust mønster med økte avvik rundt 400-500 m. Når FSC aggregeres til sone-middelverdier per dato reduseres avvikene sammenliknet med pikselbasert stratifisering, men mønsteret i høyde avtar ikke fullt ut. Avvikene er generelt større i skog enn i områder med bart fjell og sparsom vegetasjon, med en negativ bias i skog, som er konsistent med utfordringer knyttet til snø under trekroner. Samlet viser analysen hvordan avvik endrer karakter
ved aggregering til modelleringsrelevante enheter, og peker på forhold som bør tas hensyn til ved bruk av FSC som areal- og høydesoneaggregert modellinput
Utseth, Ingrid; Vedal, Amund Hansen; Thomas, Sarina og Eikvil, Line. (2026).
Comparing Foundation Models for Medical Images: A Study on Limited Data and Generalization.
Vis sammendrag
In this study we have investigated how vision foundation models, pretrained on different domains, compete with a specialized model for classification as a function of the size of the labeled training set of medical images. Furthermore, we have looked into the different models' ability to generalize to difficult cases. Our experiments are conducted for cardiac ultrasound images and the downstream task of view recognition. Still, this classification task is meant to serve as a demonstrative example, where we think that the findings should be transferable to other classification tasks and other domains. Through these experiments we found that the foundation models were able to beat the performance of our task-specific supervised model when labelled training data were limited. This was true even for models trained on natural images and when using the simple linear probing method to create a classifier. We observed that more domain-specific foundation models achieved an even higher performance with limited data. On the other hand, the more general models showed a greater ability to generalize and perform well on difficult, out-of-distribution cases. Still, for typical in-domain cases with sufficient labeled data, a task-specific ResNet model was competitive with the foundation models, while also being both smaller and faster.
Abie, Habtamu. (2026).
SFI NORCICS Norwegian Ecosystem for Secure IT-OT Integration (NESIOT) at the ResCri Kickoff Meeting. Norsk Regnesentral
NVA
Annen presentasjon
Abie, Habtamu. (2026).
NESIOT - Norwegian Ecosystem for Secure IT-OT Integration at ResCri Webinar. IFE
NVA
Faglig foredrag
Rognebakke, Hanne. (2026).
January 2025 - December 2025 Validation of property value estimates: Second home market.
NVA
Rapport
Rognebakke, Hanne. (2026).
January 2025 - December 2025 Validation of property value estimates.
NVA
Rapport
Løland, Anders; Forgaard, Theodor Johannes Line og Salberg, Arnt Børre. (2026).
THOR: Den nye, norske KI-modellen som kan endre hvordan vi overvåker jorda.
NVA
MediaPodcast
Jullum, Martin og Aas, Kjersti. (2026).
Seminar: Datadrevet antihvitvasking og svindeldeteksjon. Norsk Regnesentral
NVA
Annen presentasjon
Jullum, Martin. (2026).
shapr – Conditional Shapley Value Explanation in R and Python. Epidemiology and Data Science department, Amsterdam University Medical Centers
NVA
Vitenskapelig foredrag
Jensen, Are Charles; Ziksari, Mahsa Sotoodeh; Austeng, Andreas og Näsholm, Sven Peter. (2026).
A Coherence-Restoring Subspace Projection for Adaptive Array Spectral Estimation.
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Wide-beam or single-transmit acquisitions often reduce local spatial coherence, breaking the narrowband model assumed by high-resolution array spectral estimators such as IAA and Capon and thereby degrading performance. We propose a discrete prolate spheroidal sequence (DPSS) subspace projection of delay-focused aperture data. This projection suppresses incoherent off-angle energy and restores local spatial coherence, enabling scanline-wise adaptive spectral estimation under severe model mismatch. Each delay-focused aperture vector is projected onto a DPSS subspace spanned by the first K eigenvectors corresponding to a small angular bandwidth. The approach is lightweight, with precomputation and a per-point complexity of O(MK), and integrates naturally into standard delay-focused processing pipelines. Frequency–angle plots reveal how the projection reconstructs coherent ridge structures that are otherwise obscured by wide‑beam incoherence. Simulations in both plane-wave and diverging-wave ultrasound scenarios demonstrate improved resolution and contrast in single-transmit wide-beam imaging. Qualitative results on recorded channel data from the public PICMUS dataset provide an experimental sanity check and validation, indicating that the same coherence-restoration behavior is observed in real recordings. All experimental validation in this work is confined to ultrasound imaging; assessment of other array-processing applications is left for future work.
Halbach, Till og Simon-Liedtke, Joschua Thomas. (2026).
Empati-workshop. Norsk Regnesentral
NVA
Faglig foredrag
Høst, Anders Mølmen; Lison, Pierre og Moonen, Leon. (2026).
A Systematic Approach to Predict the Impact of Cybersecurity Vulnerabilities Using LLMs.
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Vulnerability databases, such as the National Vulnerability Database (NVD), offer detailed descriptions of Common Vulnerabilities and Exposures (CVEs), but often lack information on their real-world impact, such as the tactics, techniques, and procedures (TTPs) that adversaries may use to exploit the vulnerability. However, manually linking CVEs to their corresponding TTPs is a challenging and time-consuming task, and the high volume of new vulnerabilities published annually makes automated support desirable.This paper introduces Triage, a two-pronged automated approach that uses Large Language Models (LLMs) to map CVEs to relevant techniques from the Att&ck knowledge base. We first prompt an LLM with instructions based on MITRE’s CVE Mapping Methodology to predict an initial list of techniques. This list is then combined with the results from a second LLM-based module that uses in-context learning to map a CVE to relevant techniques. This hybrid approach strategically combines rule-based reasoning with data-driven inference. Our evaluation reveals that in-context learning outperforms the individual mapping methods, and the hybrid approach improves recall of exploitation techniques. We also find that GPT-4o-mini performs better than Llama3.3-70B on this task. Overall, our results show that LLMs can be used to automatically predict the impact of cybersecurity vulnerabilities and Triage makes the process of mapping CVEs to Att&ck more efficient.
Jemterud, Torkild; Engebretsen, Solveig; Kvellestad, Anders og Swang, Ole. (2026).
Hvem av oss har seg med flest?
Heimstad-Bergseng, Camilla; Torrado, Juan Carlos og Salinas, Veronica. (2026).
Demokratisk tilgang til ASK-symboler i Norge: Sluttrapport.
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Noen mennesker kan ikke kommunisere ved hjelp av talespråk, og vil ha behov for alternativ eller supplerende kommunikasjon (ASK). En av de vanligste formene for ASK er grafiske symboler som står for et ord eller begrep, og brukes av både barn og voksne som deres språklige uttrykksform.
Det finnes hverken en gratis symbolbank eller en offisiell standard for grafiske symboler i Norge. Symbolbankene som brukes per i dag krever betaling av lisenser og programmer. Det finnes land som har en åpen, tilgjengelig symbolbank, som Spania (ARASAAC) og Sverige (Bildstöd). Det er lite kunnskap om hva som må løses for å integrere eller utvikle en tilsvarende løsning i Norge.
Prosjektet «Demokratisk tilgang til ASK-symboler i Norge» har undersøkt hvordan tilgang til symbolbank oppleves, og om det er behov for en gratis, åpen og universell symbolbank tilgjengelig for alle i Norge.
Spørreskjema på nett og to samskapingsverksted med et strategisk utvalg bestående av fagpersoner, ASK-språklige og deres nærpersoner har vært benyttet som metode. På det første samskapingsverkstedet fikk informantene i utvalget beskrive sine erfaringer med tilgang til symbolspråk. Videre ble også et spørreskjema utarbeidet i samarbeid med utvalget. Spørreskjemaet ble sendt ut via nettverk og digitale portaler til hele landet.
Forskerne har, sammen med deltakerne i det andre samskapingsverkstedet, systematisert, analysert og dokumentert kunnskapen og erfaringene fra spørreskjemaet. Resultatene fra både spørreskjema og samskapingsverkstedene, viser at tilgang til grafiske symboler oppleves tungvindt og lite tilgjengelig. Det etterspørres en åpen og gratis tilgang for å endre tilgjengelighet, kunnskap om og bruk av et grafisk symbolspråk i Norge. Denne rapporten dokumenterer dette arbeidet. Avslutningsvis er det foreslått et videre arbeid for å nå ambisjonen om en åpen, nasjonal symbolbank i Norge med bakgrunn i studiens funn.
Boudko, Svetlana og Tjøstheim, Ingvar. (2026).
Evaluating Industry - Academia Collaboration.
NVA
Rapport
Fuglerud, Kristin Skeide. (2026).
Inclusive AI for Health: Designing Technology That Works for Everyone. University of Oslo
NVA
Vitenskapelig foredrag
Fuglerud, Kristin Skeide. (2026).
Digitale barrierer i arbeidslivet. Næringslivets Hovedorganisasjon (NHO)
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Det ble gitt en oversikt over digitale barrierer som påvirket arbeidsdeltakelsen for personer med funksjonsnedsettelser, og statistikk som viser at mange opplever utfordringer med bruk av IKT‑løsninger, og at digital eksklusjon berører en betydelig del av befolkningen. Videre ble forskjellen mellom universell utforming av IKT og individuelle hjelpemiddelløsninger gjennomgått, samt utfordringene som oppstår når arbeidslivet hovedsakelig bygget på individuell tilrettelegging. Det ble vist til en studie i IDA-prosjektet som tyder på at universelt utformede systemer gir gevinster for både arbeidstakere og virksomheter. Avslutningsvis ble det pekt på at for for å oppnå bedre inkludering i det digitale arbeidslivet er det behov for bedre tilgang til hjelpemidler, tilpasset opplæring og tydeligere krav til universell utforming.
Boudko, Svetlana. (2026).
Towards Safer AI: Challenges and Opportunities in Privacy-Preserving Federated Learning. Technische Hochschule Ingolstadt – University of Applied Sciences
NVA
Faglig foredrag
Sandvik, Lise Vikan; Steinsbekk, Aslak Irgens; Schofield, Daniel; Olsen, Alexander; Håberg, Asta; Østerlie, Thomas og Strumke, Inga. (2026).
KI-assistenter i undervisning og vurdering: Brett opp ermene!
NVA
Populærvitenskapelig artikkel
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- Vi må frambringe den forskningsbaserte kunnskapen som kan gi oss et bedre grunnlag for å forstå hvordan KI faktisk påvirker læring og vurdering i høyere utdanning, skriver syv ansatte ved en rad NTNU-institutter.
Kaiser, Daniel; Frigessi, Arnoldo; Ramezani-Kebrya, Ali og Ricaud, Benjamin. (2026).
CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Density.
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Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor interference, and task length. To enable more precise failure analysis, we introduce CogniLoad, a novel synthetic benchmark grounded in Cognitive Load Theory (CLT). CogniLoad generates natural-language logic puzzles with independently tunable parameters that reflect CLT's core dimensions: intrinsic difficulty ($d$) controls intrinsic load; distractor-to-signal ratio ($\rho$) regulates extraneous load; and task length ($N$) serves as an operational proxy for conditions demanding germane load. Evaluating 22 SotA reasoning LLMs, CogniLoad reveals distinct performance sensitivities, identifying task length as a dominant constraint and uncovering varied tolerances to intrinsic complexity and U-shaped responses to distractor ratios. By offering systematic, factorial control over these cognitive load dimensions, CogniLoad provides a reproducible, scalable, and diagnostically rich tool for dissecting LLM reasoning limitations and guiding future model development.
Spremic, Mina og Barker, Daniel Martin L. (2026).
Refining posterior Markov chain.
NVA
Rapport
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We describe different approaches that were used to test and investigate potential improvements in various aspects of the posterior Markov chain. This includes different ways of combining Up and Down chains, and inclusion of all available transitions within a window. Additionally, an alternative algorithm for computing the posterior Markov chain was tested, relying on forward-backward algorithm, using a higher order chain to obtain the posterior Lfcs. Proposed approaches were tested on both PCube paper example and Volund dataset, and results from the latter are presented. First approaches yielded some changes, but not significant improvements implying that current approach is a robust and reasonable choice. On the other hand, the proposed alternative algorithm produced different results. However, it is not straightforward to conclude, whether the results are to be preferred over the ones produced by the existing approach.
Aas, Kjersti. (2026).
MCCE: Monte Carlo sampling of realistic counterfactual explanations. Amsterdam University Medical Centers
NVA
Vitenskapelig foredrag
Wally, Youssef; Mylius-Kroken, Johan; Kampffmeyer, Michael; Ehsani, Rezvan; Milosevic, Vladan og Wetzer, Elisabeth. (2026).
Hyperbolic Representation Learning for Spatial Biology: Evaluating Cell Type Hierarchies in Breast Cancer Imaging Data. UiT - The Arctic University of Norway
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We demonstrate that hyperbolic representation learning effectively captures hierarchical cellular relationships in breast cancer. Using information-theoretic metrics, Lorentzian embeddings are shown to preserve significantly more biologically meaningful structure than Euclidean ones. Code: https://github.com/youssefwally/FlatlandandBeyond.
Tvete, Ingunn Fride; Neef, Linda Reiersølmoen og Haugen, Marion. (2026).
Pasientskader må avdekkes systematisk – ikke bare rapporteres.
NVA
Kronikk
Fuglerud, Kristin Skeide; Østvold, Bjarte M.; Robertson, Nicholas og Moen, Martin Styrmoe. (2026).
Smart trygghet for eldre: Proof of Concept for proaktiv sensorteknologi i private hjem. Resultater fra et forprosjekt.
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Norge står overfor en demografisk endring med en raskt voksende eldre befolkning. Dette øker presset på helse- og omsorgstjenestene. Samtidig ønsker de fleste eldre å bo trygt og selvstendig i eget hjem så lenge som mulig.
Forprosjektet "Smart trygghet for eldre" har tatt utgangspunkt i dette behovet og gjennomført en Proof of Concept (PoC) for en innovativ sensorløsning utviklet av Eldurai AS. Hovedmålet har vært å validere et teknisk konsept som, ved hjelp av sensorer og kunstig intelligens (KI), kan monitorere bevegelsesmønstre og proaktivt avdekke unormale hendelser eller tidlige tegn på forverret helsetilstand. Løsningen er installert og testet i 7 leiligheter hos enslige eldre over 65 år. I samarbeid med Norsk Regnesentral (NR) har prosjektet innhentet brukerinnsikt fra både beboere og pårørende gjennom intervjuer og observasjon. Sentrale forskningsområder er brukeropplevelse, personvern, nytteverdi og balansen mellom trygghet og personlig integritet. Resultatene fra PoC-en, inkludert brukererfaringer, brukskvalitet, nytteopplevelse, aksept, teknisk validering, analyse av bevegelsesmønstre, bidrar til å danne grunnlag for beslutning om videreføring til et hovedprosjekt og en kommersiell løsning
Vollestad, John Enok; Holden, Lars og Løland, Anders. (2026).
Personopplysninger i forskningsprosjekter ved Norsk Regnesentral, 2025.
NVA
Rapport
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Dette er en oppdatering av notat ADMIN/1/19 der Erik Vasaasen var medforfatter. Hensikten med dette dokumentet er å dokumentere rutinene for behandling av sensitive personopplysninger i NRs forskningsprosjekter i tråd med NRs rutiner for internkontroll. Det sentrale formålet er å fastlegge hvem som er ansvarlig for hva når personopplysninger skal håndteres.
Dokumentet inneholder det som kreves etter Personopplysningsloven, med siste oppdatering i juni 2021 som inkorporerer EUs regelverk GDPR.
Utseth, Ingrid; Vedal, Amund Hansen; Thomas, Sarina og Eikvil, Line. (2026).
Comparing Foundation Models for Medical Images: A Study on Limited Data and Generalization. Universitetet i Tromsø
NVA
poster
Vis sammendrag
In this study we have investigated how vision foundation models, pretrained on different domains, compete with a specialized model for classification as a function of the size of the labeled training set of medical images. Furthermore, we have looked into the different models' ability to generalize to difficult cases. Our experiments are conducted for cardiac ultrasound images and the downstream task of view recognition. Still, this classification task is meant to serve as a demonstrative example, where we think that the findings should be transferable to other classification tasks and other domains. Through these experiments we found that the foundation models were able to beat the performance of our task-specific supervised model when labelled training data were limited. This was true even for models trained on natural images and when using the simple linear probing method to create a classifier. We observed that more domain-specific foundation models achieved an even higher performance with limited data. On the other hand, the more general models showed a greater ability to generalize and perform well on difficult, out-of-distribution cases. Still, for typical in-domain cases with sufficient labeled data, a task-specific ResNet model was competitive with the foundation models, while also being both smaller and faster.
Rummelhoff, Ivar og Østvold, Bjarte M.. (2026).
Regelverk, digitalisering og KI.
NVA
Rapport
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Oppsummering av arbeidet i prosjektet «Regelverk,digitalisering og KI» i 2025.
Løland, Anders. (2026).
Understanding and using statistics when covering/writing about scientific research. OsloMet
NVA
Faglig foredrag
Holden, Lars. (2026).
Norwegian Historical Population Register. University of Oslo Library
NVA
Vitenskapelig foredrag
Aasen, Nora Røhnebæk; Engebretsen, Solveig; Aldrin, Magne Tommy og Jansen, Peder A. (2026).
Estimating the effect of wrasses (Labridae) and lumpfish (Cyclopterus lumpus) as control measures against salmon lice in Norwegian fish farms.
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Cleaner fish are commonly used as a biological control measure against salmon lice infestations in salmonid farming. However, there have been few attempts at quantifying the effect of cleaner fish on the louse abundance at commercial farms. Our study estimates a delousing effect for wrasses (Labridae) and lumpfish (Cyclopterus lumpus) by fitting a partially stage-structured lice development model to historical production data on all salmonid farms in Norway. We investigate different models, and evaluate them according to a statistical model selection criterion (BIC). The final model includes temperature dependence for the delousing effect of cleaner fish, as a quadratic function. The estimated optimal temperature for lice grazing was found to be 6.7 °C for lumpfish and 15.3 °C for wrasses. The final model also included separate delousing effects for adult female and other motile salmon lice. In general, the estimated delousing effect was larger for wrasses than for lumpfish. However, for temperatures below 8.3 °C, the estimated delousing effect of other motile lice was larger when using lumpfish compared to wrasses. The estimated delousing effect was larger for lower abundances of salmon lice. This implies that cleaner fish should be used at low louse abundances, and not as a delousing method during outbreaks. Our study is an important contribution to quantifying the temperature-dependent delousing effect of cleaner fish, which can be used to guide the farmers in their decision-making when planning cleaner fish strategies.
Barker, Daniel Martin L. (2026).
Alignment of PP and PS seismic using PCube+ likelihood calculations.
NVA
Rapport
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We describe the work done to estimate residual time-shifts (after initial correction) using PCube+ likelihood calculations. The method shows some promise, but low-frequency variations of likelihoods currently cause too much alignment discrepancy to give a satisfactory solution.
Arnberg, Mie Prik; Jensen, Are Charles; Sample, James Edward; Salberg, Arnt-Børre; Hancke, Kasper; Gundersen, Hege og Molværsmyr, Sindre. (2026).
From pictures to numbers: Multi-species seabird surveys using drone imagery and neural networks.
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Seabirds are among the most threatened avian taxa globally, with over half of all species in decline. Accurate population estimates are essential for tracking trends and informing conservation, yet traditional survey methods are limited by logistical challenges, high costs, and the potential for wildlife disturbance, particularly in remote coastal areas. Unoccupied aerial vehicles (UAVs or drones) offer an efficient and low-disturbance alternative, but the vast volumes of imagery they produce are often labour-intensive to analyse.
In this study, we combined drone imagery with deep learning techniques to estimate colony size and abundance of surface-nesting seabirds based on counts of visible individuals. High-resolution aerial imagery was collected from 163 colonies along the southern and central Norwegian coastline over three breeding seasons (2022–2024), covering a total of 7.67 km2. A convolutional neural network (Faster R-CNN with ResNet-101 backbone) was trained on 131 annotated orthomosaics and evaluated on 32 additional annotated orthomosaics from geographically distinct colonies.
Across all data, 23,062 individual seabirds were annotated. Colonies hosted an average of 141.5 ± 193.9 individuals and 4.1 ± 2.3 focal species per site. At a confidence threshold of 0.7, the model achieved a detection rate of 87.5 % and a macro F1-score of 0.88. It performed well across multiple focal species, including terns, gulls, and cormorants, and remained robust in mixed-species colonies. Most errors involved false negatives or confusion among visually similar species.
Our results demonstrate the potential for deep learning models to support efficient, scalable, and low-disturbance seabird monitoring across diverse habitats, reducing manual annotation effort and informing conservation practice.
Stige, Leif Christian; Aldrin, Magne Tommy; Engebretsen, Solveig; Rafoss, Trond og Jansen, Peder A. (2026).
The efficacy of lumpfish in controlling salmon lice in fish farms.
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Lumpfish (Cyclopterus lumpus) are frequently used as “cleaner fish” to control parasitic sea lice in salmonid fish farms, but it has been questioned whether the benefits in terms of sea louse control outweigh the economic and lumpfish-welfare costs involved. Here we estimated the efficacy of lumpfish in controlling salmon lice (Lepeophtheirus salmonis) in salmonid farms by combining analyses of occurrence of salmon lice in stomach contents of lumpfish and experimental results on digestion time. We then conducted scenario simulations of salmon louse dynamics in salmonid fish farms, by combining the lumpfish feeding model with a lumpfish growth model and a salmon louse population model. Results showed that at a mean lumpfish weight of 50 g and typical conditions for other factors (9 °C, 2 kg salmonids, and 0.8 pre-adult and adult salmon lice per salmonid), the estimated feeding rate was 0.17 salmon lice per lumpfish per day (95 % confidence interval: 0.12–0.22). This rate increased with salmon louse concentration, temperature and salmonid weight and decreased with lumpfish weight. Scenario simulations of salmon louse dynamics under conditions representative for salmonid farms in Norway suggested that stocking lumpfish from the start of the production cycle with a 1:10 lumpfish per salmon ratio on average postponed the first salmon louse treatment by 43 days. The longest postponement was at intermediate external infestation pressure. Scenario simulations for a network of farms suggested that coordinated and strategic use of lumpfish after a spring treatment may succeed in supressing salmon louse outbreaks through spring and summer.
Scheuerer, Michael og Anderson, Mark David. (2026).
Climate-Aware Analysis of Alternative Portfolios.
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This report describes the research related to use case 3 within pilot ♯6 of the FAME project: Embedding Climatic Predictions in Property Insurance Products.
The changing climate poses risks to the global economy in various ways. In addition to the immediate impacts on the real estate sector, e.g., due to changing living conditions, and impacts on the insurance sector as a result of more frequent and intense weather-related disasters, it calls for the transition to a greener and more sustainable economy, which comes with a significant price tag. While the latter risk cannot be directly related to specific weather events, an increasing body of research suggests that climate risk indices constructed through textual analysis of newspaper articles are able to represent the different types of risks and can thus help build climate risk hedge portfolios.
In this report, we document our own efforts to construct such a news-based climate risk index and analyze whether climate-focused funds perform differently than a benchmark representing the general market. Our risk index is constructed from an analysis of climate change-related newspaper articles in the New York Times which were further filtered by utilizing large language model (LLM) with zero-shot classification. Both full and subcategory-based indices are defined at a daily resolution and were aggregated to both weekly and monthly resolution in order to analyze impacts on stock market returns at different time scales.
Our analysis, however, does not show a clear and robust link between the climate risk index and the stock market returns of climate-focused funds compared to the general market. A more refined selection of ’green’ vs. ’brown’ stocks may be required to see significant, climate risk index-dependent differences in performance that can be exploited for the purpose of portfolio optimization.
Cunen, Celine Marie Løken; Roksvåg, Thea Julie Thømt; Heinrich-Mertsching, Claudio Constantin og Lenkoski, Frank Alexander. (2026).
Combining predictive distributions for time-to-event outcomes in meteorology.
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Combining forecasts from multiple numerical weather prediction (NWP) models has shown substantial benefit over the use of individual forecast products. Although combination, in a broad sense, is widely used in meteorological forecasting, systematic studies of combination methodology in meteorology are scarce. In this article, we study several combination methods, both state-of-the-art and of our own making, with a particular emphasis on situations where one seeks to predict when a particular event of interest will occur. Such time-to-event forecasts require particular methodology and care. We conduct a careful comparison of the different combination methods through an extensive simulation study, where we investigate the conditions under which the combined forecast will outperform the individual forecasting products. Further, we investigate the performance of the methods in a case study modelling the time to the first hard freeze in Norway and parts of Fennoscandia.
Kapar, Jan; Koenen, Niklas og Jullum, Martin. (2026).
What’s Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models.
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Abstract Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from statistical distances to predictive performance, often providing conflicting results. Moreover, they fail to explain or pinpoint the specific weaknesses in the synthetic data. To address this, we apply explainable AI (XAI) techniques to a binary detection classifier trained to distinguish real from synthetic data. While the classifier identifies distributional differences, XAI concepts such as feature importance and feature effects, analyzed through methods like permutation feature importance, partial dependence plots, Shapley values and counterfactual explanations, reveal why synthetic data are distinguishable, highlighting inconsistencies, unrealistic dependencies, or missing patterns. This interpretability increases transparency in synthetic data evaluation and provides deeper insights beyond conventional metrics, helping diagnose and improve synthetic data quality. We apply our approach to two tabular datasets and generative models, showing that it uncovers issues overlooked by standard evaluation techniques.
Kolstø, Johannes Voll; Vandeskog, Silius Mortensønn og Haug, Ola. (2026).
Framtidige skadebeløp etter overvannsflom for bygninger i Norge.
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Norsk Regnesentral har etablert en statistisk risikomodell for vannskader etter overvannsflom på bygninger i Norge. Modellen kobler forsikringsdata fra Gjensidige sammen med nedbørdata fra seNorge og annen lokal eksponeringsinformasjon. Vi finner at risikoen for vannskader lar seg beskrive gjennom sesongvise mål på mengde kraftig nedbør og avvik fra typisk kraftig nedbør. Kombinert med klimaframskrivninger levert av Norsk Klimaservicesenter simulerer modellen forventede endringer i skadebeløp fra referanseperioden 1991–2020 til to framtidige scenarioperioder under et lavt, middels og høyt utslippsscenario for CO2. På nasjonalt nivå antyder simuleringene en økning på opptil 33 % fram mot slutten av århundret. Skadeframskrivningene er følsomme for variabiliteten i klimaframskrivningene, og vi anbefaler å utvise forsiktighet med bruk av lave og høye kvantiler av endringene i skadebeløp på kommune- og fylkesnivå.
Mylius-Kroken, Johan; Wetzer, Elisabeth; Ramezani-Kebrya, Ali; Jenssen, Robert og Wickstrøm, Kristoffer. (2025).
geobin: Geometric Binning Estimator. Integreat
NVA
poster
Møller, Bjørn Leth; Amiri, Sepideh; Igel, Christian; Wickstrøm, Kristoffer; Jenssen, Robert; Keicher, Matthias; Azampour, Mohammad Farid; Navab, Nassir og Ibragimov, Bulat. (2025).
NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks.
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A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is the first XAI method to explain learned representations with high accuracy at real-time speed. NEM transforms a given differentiable model into a self-explaining system by augmenting it with a neural network-based explanation module. This module is trained in an unsupervised manner to output occlusion-based explanations for the original model. However, the current framework does not consider labels associated with the inputs. This makes it unsuitable for many important tasks in the medical domain that require explanations specific to particular output dimensions, such as pathology discovery, disease severity regression, and multi-label data classification. In this work, we address this issue by introducing a loss function for training explanation modules incorporating labels. It steers explanations toward target labels alongside an integrated smoothing operator, which reduces artifacts in the explanation masks. We validate the resulting Neural Explanation Masks with target labels (NEMt) framework on public databases of lung radiographs and skin images. The obtained results are superior to the state-of-the-art XAI methods in terms of explanation relevancy mass, complexity, and sparseness. Moreover, the explanation generation is several hundred times faster, allowing for real-time clinical applications. The code is publicly available at https://github.com/baerminator/NEM_T
Knutsen, Leif; Hannay, Jo og Tanilkan, Sinan. (2025).
Exploring agile practice adoption: A survey in the Norwegian public sector.
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The uptake of practices labeled as "agile" is a topic of widespread discussion in research and practitioner communities. Within the broad topic of agility, there are discussions about the separation between agility in what one might call traditional software development, agility in the form of product orientation, and agility as expressed in continuous delivery. Although particular cases have been studied, the magnitude and manner of adoption and use of agile practices under these three themes remain unclear. We therefore sought to test hypotheses about their growth, prevalence, and implementation patterns in the Norwegian public sector. Aiming to form a comprehensive picture, we distributed surveys three years apart to IT executives at all Norwegian public institutions likely to build digital solutions. The results supported the view that agile practices are prevalent, but gave mixed support for their increase in use. We found no support for the view that agility in, respectively, software development, product orientation, and continuous delivery are treated as distinct disciplines in practice. We were also unable to identify other patterns in implementing these practices. The adoption of agile appears to be enabled primarily by commitment at the team and individual levels and inhibited by factors specific to the public sector. These findings should be compared with other sectors and countries. We propose issues for (a) further research on the scope of agile practices, (b) better indicators for adoption, (c) interaction among agile practices, and (d) factors that enable or inhibit the adoption of agile practices.
Jenssen, Robert; Eikvil, Line; Solberg, Anne H Schistad; Solheim, Inger og Bjørklund, Petter. (2025).
Visual Intelligence Annual Report 2024.
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The Visual Intelligence Annual Report 2024 highlights the centre's progress, activities and achieved innovations for 2022. It describes new deep learning methods which address pressing societal needs in the fields of medicine and health, marine science, the energy sector, and earth observation.
Boudko, Svetlana og Grønvold, Kristian Teig. (2025).
A Privacy-Preserving Federated Learning Framework with Multiparty Threshold Homomorphic Encryption.
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Federated learning enables collaborative computation across multiple decentralized devices, minimizing data transfer overhead while enhancing privacy by keeping data local. However, it remains susceptible to inference attacks and potential data leakage. To strengthen privacy guarantees, especially for sensitive domains, advanced privacy-preserving techniques such as homomorphic encryption are recommended. This work proposes a privacy-preserving federated learning framework that integrates threshold homomorphic encryption into the federated learning pipeline to enable secure aggregation and protect intermediate computations. We employ threshold homomorphic encryption, a cryptographic technique well-suited for multiuser environments such as federated learning. We utilize the Cheon-Kim-Kim-Song (CKKS) scheme, as implemented in the OpenFHE library. Our approach extends the standard Federated Averaging (FedAvg) algorithm by homomorphically encrypting model updates and performing aggregation directly on encrypted data. To assess the trade-offs between efficiency and security, we evaluate the performance of the proposed method against a baseline. The design prioritizes practical constraints, including computational efficiency, making it suitable for deployment in privacy-sensitive domains such as healthcare and finance. To ensure compatibility with continuous integration and deployment (CI/CD) pipelines, all components of the solution are containerized using Docker.
Narwani, Kamlesh; Lin, Hongzhi; Pirbhulal, Sandeep og Hassan, Mir. (2025).
Toward AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension.
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Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a large-scale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.
Handegard, Nils Olav; Smith-Johnsen, Silje; Holmin, Arne Johannes; Mas, Cristian Muñoz; Utseth, Ingrid og Dondorp, Daniel. (2025).
Operationalizing and Testing Machine Learning Models for Acoustic Target Classification. IARIA
Trier, Øivind Due. (2025).
LAVDAS kildekode.
NVA
Rapport
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Dette er dokumentasjon av programvaren i LAVDAS slik den foreligger per mars 2025
Dahl, Fredrik Andreas og Brautaset, Olav. (2025).
Analysing the effect of change in mammography screening sequences.
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In the AIforScreening project we have tested different ways of utilizing time sequence information in mammography screening, including published methods and home-grown ones. The simplest ones include regression modelling, where we apply a single-image breast cancer risk model at a sequence of images of a given breast and use linear regression to onstruct a modified score. This method givs a very modest improvement of the order 0.001 on the AUC scale, which was statistically significant only for the inferior holistic model. The more advanced methods try include co-registration of the current and previous images and various ways of merging the model’s features to produce improved risk scores, utilizing various so-called Siamese net models. Over-all, the results were negative, as none of the advanced methods gave improvements above the linear model. This is contrary to published results, and we speculate that this may be due to the fact that our model has a high performance to begin with, leaving less room for improvement. The linear model places positive weight on the previous risk scores, which go against the intuition that an increase in risk score over time should increase the likelihood of cancer. Apparently, the ’direct effect’ that an elevated risk score is associated with future cancer is stronger.
Rocha-Gomes, João; Pirbhulal, Sandeep og Abie, Habtamu. (2025).
Adaptive digital twin analysis in healthcare: An opportunity for prescription digital therapeutics.
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Health systems in Norway and across Europe are under increasing strain from chronic conditions, long waiting times, and limited clinical capacity. At the same time, evidence-based digital therapeutics (DTx) are emerging as regulated tools to prevent, manage, and treat disease through clinically validated software interventions. Scandinavia has played a pioneering role in evaluating internet-delivered cognitive behavioural therapy for conditions such as insomnia and perinatal depression, demonstrating that well-designed, integrated digital solutions can complement existing healthcare services. However, despite promising trial evidence, large-scale adoption of DTx remains inconsistent due to regulatory, reimbursement, organisational, and cultural barriers. In parallel, simulation-based methods such as discrete-event modelling and digital twins are increasingly applied to optimise healthcare delivery and test alternative service configurations. Building on these developments, this project proposes the creation of an adaptive digital twin of a chronic care pathway to analyse how different deployment strategies for prescription digital therapeutics could impact system access, resilience, and resource utilisation.