A database for publications published by researchers and students at SimulaMet.
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- Journal articles (217)
- Books (8)
- Edited books (2)
- Proceedings, refereed (241)
- Book chapters (12)
- Talks, keynote (17)
- PhD theses (4)
- Proceedings, non-refereed (19)
- Posters (9)
- Technical reports (10)
- Manuals (1)
- Talks, invited (159)
- Talks, contributed (20)
- Public outreach (49)
- Miscellaneous (17)
Books
Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences
Chichester, UK: John Wiley & Sons, 2022.Status: Published
Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Book |
| Year of Publication | 2022 |
| Publisher | John Wiley & Sons |
| Place Published | Chichester, UK |
Book chapters
5G-sikkerhet: Norge mellom stormaktene
In Digitalisering og internasjonal politikk. Universitetsforlaget, 2022.Status: Published
5G-sikkerhet: Norge mellom stormaktene
| Afilliation | Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Digitalisering og internasjonal politikk |
| Chapter | 7 |
| Date Published | 01/2022 |
| Publisher | Universitetsforlaget |
| ISBN Number | 9788215052557 |
Scenario Design for Healthcare Collaboration Training under Suboptimal Conditions
In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design, 197-214. Vol. 19. Springer International Publishing, 2022.Status: Published
Scenario Design for Healthcare Collaboration Training under Suboptimal Conditions
Health care today usually consists of various services covering various parts of the total health care of a region or country. These services are required to coordinate and collaborate, often using procedures and IT collaboration tools that may not be designed for interoperating across the evolving wider landscape of health care services. We posit that it is necessary to train personnel in collaboration skills using whatever infrastructure is in place. To this end, we present design principles for simulation-based collaboration training scenarios that emphasizes the inclusion of suboptimal infrastructure elements. We applied the principles in a co-creational workshop with healthcare stakeholders from a hospital and surrounding municipalities in Norway where we discussed cases where collaboration training is perceived as critical. We elicited five training vignettes concerning the general case of detecting, and following up on, clinical deterioration in a patient at home or in a nursing home. We found that the design principles spurred highly relevant discussions among participants and that novel ideas for collaboration training were brought forth on the basis of these principles. We conclude that there is a potential in using these principles for eliciting training vignettes that address the actual situation more accurately.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design |
| Volume | 19 |
| Series Volume | LNCS 13320 |
| Pagination | 197-214 |
| Publisher | Springer International Publishing |
| Keywords | Healthcare Collaboration, IT Services, Procedures, Scenario Design, Simulation-based training, Stakeholder Journey Analysis |
Stakeholder Perceptions on Requirements for Accessible Technical Condition Information in Residential Real Estate Transactions
In Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies , 242-259. Vol. 7. Springer International Publishing, 2022.Status: Published
Stakeholder Perceptions on Requirements for Accessible Technical Condition Information in Residential Real Estate Transactions
Buyers of residential real estate frequently experience dissatisfaction with the property they have purchased. Recent findings suggest that insufficient knowledge about the property is a key trigger to ensuing disappointment and claims for compensation. Further, a good technical condition report reduces the probability of dissatisfaction and insurance claims. For the purpose of designing services for improving technical condition information and its flow, we elicited stakeholder perceptions on the suitability of residential real estate technical condition reports. Specifically, we conducted multiple surveys which we content analyzed and used as the basis for a conceptual model of information products and dependencies needed to deliver better information to stakeholders in a real estate transaction process. The conceptual model, in turn, forms the basis for specific service design in future work.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies |
| Volume | 7 |
| Chapter | 16 |
| Series Volume | LNCS 13308 |
| Pagination | 242-259 |
| Publisher | Springer International Publishing |
| ISBN Number | 978-3-031-05027-5 |
| Keywords | Conflict Reduction, Information Services, Residential Real Estate Transactions, Technical Condition Information |
Smittestopp Backend
In Smittestopp − A Case Study on Digital Contact Tracing, 29-62. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp Backend
An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 29 - 62 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2.pdfhttps... |
| DOI | 10.1007/978-3-031-05466-210.1007/978-3-031-05466-2_3 |
Smittestopp analytics: Analysis of position data
In Smittestopp − A Case Study on Digital Contact Tracing, 63-79. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp analytics: Analysis of position data
Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 63 - 79 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2_4 |
| DOI | 10.1007/978-3-031-05466-210.1007/978-3-031-05466-2_4 |
Journal articles
Artificial intelligence in dry eye disease
The Ocular Surface 23 (2022): 74-86.Status: Published
Artificial intelligence in dry eye disease
Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | The Ocular Surface |
| Volume | 23 |
| Pagination | 74 - 86 |
| Date Published | Jan-01-2022 |
| Publisher | Elsevier |
| ISSN | 15420124 |
| Keywords | artificial intelligence, Dry eye disease, Machine learning |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324 |
| DOI | 10.1016/j.jtos.2021.11.004 |
Estimating tukey depth using incremental quantile estimators
Pattern Recognition 122 (2022): 108339.Status: Published
Estimating tukey depth using incremental quantile estimators
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Pattern Recognition |
| Volume | 122 |
| Pagination | 108339 |
| Publisher | {Elsevier |
A new symmetric linearly implicit exponential integrator preserving polynomial invariants or Lyapunov functions for conservative or dissipative systems
Journal of Computational Physics 449 (2022): 110800.Status: Published
A new symmetric linearly implicit exponential integrator preserving polynomial invariants or Lyapunov functions for conservative or dissipative systems
A new symmetric linearly implicit exponential integrator that preserves the polynomial first integrals or the Lyapunov functions for the conservative and dissipative stiff equations, respectively, is proposed in this work. The method is tested by both oscillated ordinary differential equations and partial differential equations, e.g., an averaged system in wind-induced oscillation, the Fermi–Pasta–Ulam systems, and the polynomial pendulum oscillators. The numerical simulations confirm the conservative properties of the proposed method and demonstrate its good behavior in superior running speed when compared with fully implicit schemes for long-time simulations.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Journal of Computational Physics |
| Volume | 449 |
| Pagination | 110800 |
| Date Published | Jan-15-2022 |
| Publisher | Journal of Computational Physics |
| ISSN | 00219991 |
| URL | https://arxiv.org/abs/2104.12118 |
| DOI | 10.1016/j.jcp.2021.110800 |
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
BMC Bioinformatics 23 (2022).Status: Published
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
Background: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data.
Results: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.
Conclusion: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | BMC Bioinformatics |
| Volume | 23 |
| Number | Article 31 |
| Date Published | 2022 |
| Publisher | Springer |
| DOI | 10.1186/s12859-021-04550-5 |
