A database for publications published by researchers and students at SimulaMet.
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- All (370)
- Journal articles (136) Remove Journal articles <span class="counter">(136)</span> filter
- Books (2)
- Edited books (1)
- Proceedings, refereed (165)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (8)
- Talks, invited (18)
- Talks, contributed (15)
- Public outreach (3)
- Miscellaneous (8)
Journal articles
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Trauma, Violence, & Abuse (2023).Status: Accepted
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Livestreaming of child sexual abuse is an established form of online child sexual exploitation
and abuse. However, only a limited body of research has examined this issue. The Covid-19
pandemic has accelerated internet use and user knowledge of livestreaming services
emphasising the importance of understanding this crime. In this scoping review, existing
literature was brought together through an iterative search of eight databases containing peer-
reviewed journal articles, as well as grey literature. Records were eligible for inclusion if the
primary focus was on livestream technology and online child sexual exploitation and abuse,
the child being defined as eighteen years or younger. Fourteen of the 2,218 records were
selected. The data were charted and divided into four categories: victims, offenders,
legislation, and technology. Limited research, differences in terminology, study design, and
population inclusion criteria present a challenge to drawing general conclusions on the
current state of livestreaming of child sexual abuse. The records show that victims are
predominantly female. The average livestream offender was found to be older than the
average online child sexual abuse offender. Therefore, it is unclear whether the findings are
representative of the global population of livestream offenders. Furthermore, there appears to
be a gap in what the records show on platforms and payment services used and current digital
trends. The lack of a legal definition and privacy considerations pose a challenge to
investigation, detection, and prosecution. The available data allow some insights into a
potentially much larger issue.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Trauma, Violence, & Abuse |
| Publisher | SAGE Publications |
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
Algorithms, no. 1 (2023): 30.Status: Published
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Algorithms |
| Issue | 1 |
| Pagination | 30 |
| Date Published | Jan-01-2023 |
| Publisher | MDPI |
| URL | https://www.mdpi.com/1999-4893/16/1/30 |
| DOI | 10.3390/a16010030 |
MatCoupLy: Learning coupled matrix factorizations with Python
SoftwareX 21, no. 101294 (2023).Status: Published
MatCoupLy: Learning coupled matrix factorizations with Python
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | SoftwareX |
| Volume | 21 |
| Issue | 101294 |
| Date Published | Feb-01-2023 |
| Publisher | Elsevier |
| ISSN | 2352-7110 |
| URL | https://linkinghub.elsevier.com/retrieve/pii/Shttps://www.sciencedirect.... |
| DOI | 10.1016/j.softx.2022.101292 |
A multi-center polyp detection and segmentation dataset for generalisability assessment
Nature Scientific Data 10 (2023).Status: Published
A multi-center polyp detection and segmentation dataset for generalisability assessment
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Nature Scientific Data |
| Volume | 10 |
| Publisher | Nature |
| URL | https://doi.org/10.1038/s41597-023-01981-y |
| DOI | 10.1038/s41597-023-01981-y |
Approximate Bayesian Inference Based on Expected Evaluation
Bayesian Analysis 1, no. 1 (2023).Status: Published
Approximate Bayesian Inference Based on Expected Evaluation
Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL.
Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Bayesian Analysis |
| Volume | 1 |
| Issue | 1 |
| Date Published | Jan-01-2023 |
| Publisher | Project euclid |
| URL | https://projecteuclid.org/journals/bayesian-analysis/volume--1/issue--1/... |
| DOI | 10.1214/23-BA1368 |
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
WIREs Data Mining and Knowledge Discovery (2023).Status: Accepted
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , DeCipher |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | WIREs Data Mining and Knowledge Discovery |
| Publisher | Wiley |
| URL | https://arxiv.org/abs/2209.00322 |
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
IEEE Internet of Things Journal (2023).Status: Submitted
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Internet of Things Journal |
| Publisher | IEEE Internet of Things Journal |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
Distributed Linear Network Operators via Successive Graph Shift Matrices
IEEE Transactions on Signal and Information Processing over Networks 9 (2023): 315-328.Status: Published
Distributed Linear Network Operators via Successive Graph Shift Matrices
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Volume | 9 |
| Pagination | 315-328 |
| Date Published | 04/2023 |
| Publisher | IEEE Transactions on Signal and Information Processing over Networks |
| ISSN | 2373-776X |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the PETROMAKS Smart-Rig grant 244205 and the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
| DOI | 10.1109/TSIPN.2023.3271148 |
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
IEEE Transactions on Signal Processing (2023).Status: Published
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE |
| ISSN | 1941-0476 |
| Other Numbers | Print ISSN: 1053-587X |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
| URL | https://ieeexplore.ieee.org/document/10141675 |
| DOI | 10.1109/TSP.2023.3282068 |
Journal articles
DPER: Direct Parameter Estimation for Randomly missing data
Knowledge-Based Systems 240 (2022): 108082.Status: Published
DPER: Direct Parameter Estimation for Randomly missing data
{Parameter estimation is an important problem with applications in discriminant analysis, hypothesis testing, etc. Yet, when there are missing values in the data sets, commonly used imputation-based techniques are usually needed before further parameter estimation since works in direct parameter estimation exists in only limited settings. Unfortunately, such two-step procedures (imputation-parameter estimation) can be computationally expensive. Therefore, it motivates us to propose novel algorithms that directly find the maximum likelihood estimates (MLEs) for an arbitrary one-class/multiple-class randomly missing data set under some mild assumptions. Furthermore, due to the direct computation, our algorithms do not require multiple iterations through the data, thus promising to be less time-consuming while maintaining superior estimation performance than state-of-the-art methods under comparisons. We validate these claims by empirical results on various data sets of different sizes.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Knowledge-Based Systems |
| Volume | 240 |
| Pagination | 108082 |
| Publisher | Elsevier |
| ISSN | 0950-7051 |
| Keywords | MLEs, parameter estimation, Randomly missing data |
| URL | https://www.sciencedirect.com/science/article/pii/S0950705121011540 |
| DOI | 10.1016/j.knosys.2021.108082 |