A database for publications published by researchers and students at SimulaMet.
Research area
Publication type
- All (362) Remove All <span class="counter">(362)</span> filter
- Journal articles (132)
- Books (2)
- Edited books (1)
- Proceedings, refereed (162)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (7)
- 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 |
Proceedings, refereed
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
In Nordic Artificial Intelligence Research and Development. Springer, 2023.Status: Published
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | Nordic Artificial Intelligence Research and Development |
Pagination | 81-93 |
Publisher | Springer |
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Accepted
Multimedia datasets: challenges and future possibilities
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
PARAFAC2-based coupled Matrix and Tensor Factorizations
In ICASSP , 2023.Status: Accepted
PARAFAC2-based coupled Matrix and Tensor Factorizations
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ICASSP |
Talks, invited
Extracting Insights from Complex Data: Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations
In IPAM Workshop on Explainable AI for the Sciences: Towards Novel Insights, 2023.Status: Published
Extracting Insights from Complex Data: Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
Publication Type | Talks, invited |
Year of Publication | 2023 |
Location of Talk | IPAM Workshop on Explainable AI for the Sciences: Towards Novel Insights |
URL | http://www.ipam.ucla.edu/abstract/?tid=18155&pcode=XAI2023 |