A database for publications published by researchers and students at SimulaMet.
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- Journal articles (228)
- Books (8)
- Edited books (2)
- Proceedings, refereed (252)
- Book chapters (9)
- Talks, keynote (17)
- PhD theses (4)
- Proceedings, non-refereed (19)
- Posters (11)
- Technical reports (11)
- Manuals (1)
- Talks, invited (161)
- Talks, contributed (18)
- Public outreach (49)
- Miscellaneous (15)
Proceedings, refereed
A 4G/5G Packet Core as VNF with Open Source MANO and OpenAirInterface
In Proceedings of the 28th IEEE International Conference on Software, Telecommunications and Computer Networks (SoftCOM). Hvar, Dalmacija/Croatia: IEEE, 2020.Status: Published
A 4G/5G Packet Core as VNF with Open Source MANO and OpenAirInterface
5G, the fifth generation of mobile broadband networks, is going to make a large range of new applications possible. However, further research is necessary, and the basic step, i.e. setting up a 4G/5G testbed infrastructure, is a complicated and error-prone task. In this abstract and poster, we introduce our open source SimulaMet EPC Virtual Network Function (VNF), as an easy way to set up a 4G/5G testbed based on Open Source MANO and OpenAirInterface. We would like to showcase how a researcher can use our VNF as part of his own research testbed setup. Therefore, the focus is particularly on the user interface details and features of the SimulaMet EPC VNF.
| Afilliation | Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications, 5G-VINNI: 5G Verticals INNovation Infrastructure , NorNet |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Proceedings of the 28th IEEE International Conference on Software, Telecommunications and Computer Networks (SoftCOM) |
| Date Published | 09/2020 |
| Publisher | IEEE |
| Place Published | Hvar, Dalmacija/Croatia |
| Keywords | Evolved Packet Core (EPC), Network Function Virtualisation (NFV), Open Source MANO (OSM), OpenAirInterface, Testbed, Virtual Network Function (VNF) |
Journal articles
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Medical Image Analysis 70 (2021): 102007.Status: Published
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Gastrointestinal endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Medical Image Analysis |
| Volume | 70 |
| Pagination | 102007 |
| Publisher | Elsevier |
| Keywords | Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018 |
| DOI | 10.1016/j.media.2021.102007 |
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
IEEE Journal of Biomedical and Health Informatics 25, no. 6 (2021): 2029-2040.Status: Published
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib PolypDB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF andTTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model’s performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist,196sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Volume | 25 |
| Issue | 6 |
| Pagination | 2029 - 2040 |
| Publisher | IEEE |
| Keywords | colonoscopy, conditional random field, generalization, Polyp segmentation, ResUNet++, test-time augmentation |
| DOI | 10.1109/JBHI.2021.3049304 |
A field experiment on trialsourcing and the effect of contract types on outsourced software development
Information and Software Technology 134 (2021): 106559.Status: Published
A field experiment on trialsourcing and the effect of contract types on outsourced software development
Context: To ensure the success of software projects, it is essential to select skilled developers and to use suitable work contracts. Objective: This study tests two hypotheses: (i) the use of work-sample testing (trialsourcing) improves the selection of skilled software developers; and (ii) the use of contracts based on hourly payment leads to better software project outcomes than fixed-price contracts. Method: Fifty-seven software freelancers with relevant experience and good evaluation scores from previous clients were invited to complete a two-hour long trialsourcing task to qualify for a software development project. Thirty-six developers completed the trialsourcing task with acceptable performance, and, based on a stratified allocation process, were asked to give a proposal based on an hourly payment or a fixed-price contract. Eight hourly payment-based and eight fixed-priced proposals were accepted. The process and product characteristics of the completion of these 16 projects were collected and analysed. Results and Conclusion: Only partial support for our hypotheses was observed. While the use of trialsourcing may have prevented the selection of developers with insufficient skills, the performance on the trialsourcing task of the selected developers did not predict performance on the project. The use of hourly payments led to lower costs than fixed-price contracts, but not to improved processes or products. We plan to follow up these, to us unexpected, results with research on how to design more skill-predictive trialsourcing tasks, and when and why different project contexts give different contract consequences.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Information and Software Technology |
| Volume | 134 |
| Pagination | 106559 |
| Date Published | June 2021 |
| Publisher | Elsevier |
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
IEEE Journal of Selected Topics in Signal Processing 15, no. 3 (2021): 506-521.Status: Published
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Journal of Selected Topics in Signal Processing |
| Volume | 15 |
| Issue | 3 |
| Pagination | 506 - 521 |
| Publisher | IEEE |
| DOI | 10.1109/JSTSP.2020.3045848 |
Proceedings, refereed
A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents
In Proceedings of SAI Intelligent Systems Conference. Springer, 2021.Status: Published
A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of SAI Intelligent Systems Conference |
| Pagination | 549–558 |
| Publisher | Springer |
A Demo of Workload Offloading in Mobile Edge Computing Using the Reliable Server Pooling Framework
In Proceedings of the 46th IEEE Conference on Local Computer Networks (LCN). Edmonton, Alberta, Canada: IEEE Computer Society, 2021.Status: Published
A Demo of Workload Offloading in Mobile Edge Computing Using the Reliable Server Pooling Framework
Mobile Edge Computing (MEC) places cloud resources nearby the user, to provide support for latency-sensitive applications. Offloading workload from resource-constrained mobile devices (such as smartphones) into the cloud ecosystem is becoming increasingly popular. In this demonstration, we show how to deploy a mobile network (with OpenAirInterface and Open Source MANO), as well as to adapt the Reliable Server Pooling (RSerPool) framework to efficiently manage MEC as well as multi-cloud resources to run an interactive demo application.
| Afilliation | Communication Systems |
| Project(s) | SMIL: SimulaMet Interoperability Lab, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing, 5G-VINNI: 5G Verticals INNovation Infrastructure , The Center for Resilient Networks and Applications, NorNet |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 46th IEEE Conference on Local Computer Networks (LCN) |
| Date Published | 10/2021 |
| Publisher | IEEE Computer Society |
| Place Published | Edmonton, Alberta, Canada |
| Keywords | Demonstration, Evolved Packet Core (EPC), Mobile Edge Computing (MEC), Multi-Cloud Computing, Network Function Virtualisation (NFV), Reliable Server Pooling (RSerPool) |
| URL | https://www.ieeelcn.org/lcn46demos/Demo_4_1570754367.pdf |
Talks, invited
A Flexible Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorizations based on the Alternating Direction Method of Multipliers
In Europt21, 18th Workshop on Advances in Continuous Optimization, 2021.Status: Published
A Flexible Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorizations based on the Alternating Direction Method of Multipliers
| 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 | 2021 |
| Location of Talk | Europt21, 18th Workshop on Advances in Continuous Optimization |
Talks, contributed
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
In Asilomar Conference on Signals, Systems, and Computers, 2021.Status: Published
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
An effective way of jointly analyzing data from multiple sources is through coupled matrix and tensor factorizations (CMTF). Different characteristics of datasets from multiple sources require to employ various regularizations, constraints, loss functions and different types of coupling structures between datasets. While existing algorithmic approaches for CMTF can incorporate constraints, linear couplings and different loss functions, none of them has been shown to achieve the flexibility to incorporate all. We propose a flexible algorithmic framework for coupled matrix and tensor factorizations, which utilizes Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The framework facilitates the use of a variety of constraints, loss functions and couplings with linear transformations. Numerical experiments on simulated datasets and real data from chemometrics and hyperspectral super-resolution demonstrate that the proposed approach is accurate, flexible and computationally efficient with comparable or better performance than available CMTF algorithms.
While we focus on CANDECOMP/PARAFAC (CP) –based CMTF models, we will also briefly discuss the use of an AO-ADMM based algorithmic approach for fitting a PARAFAC2 model. We demonstrate that the proposed algorithmic approach enables imposing constraints in all modes, which has been a challenge using the traditional alternating least squares-based algorithm used for PARAFAC2.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | Asilomar Conference on Signals, Systems, and Computers |
| Type of Talk | Invited Session Talk |
Proceedings, refereed
A Distributed Infrastructure to Analyse SIP Attacks in the Internet
In Proceedings of the IFIP Networking Conference (Networking 2014). IFIP, 2014.Status: Published
A Distributed Infrastructure to Analyse SIP Attacks in the Internet
VoIP systems, based on the Session Initiation Protocol\~(SIP), are becoming more and more widespread in the Internet. However, this creates security issues and opens up new opportunities for misuse and fraud. The most widespread threat are multi-stage attacks to commit Toll Fraud. To devise effective countermeasures, it is crucial to know how attacks on these systems are performed in reality. In this paper, we introduce a novel distributed monitoring system with Sensor nodes located in Norway, Germany and China that allow to detect SIP-based attacks from the Internet. Based on experiences from experiments spanning several years, we propose a new setup which allows simple and straightforward addition of new remote observation points. We have deployed this setup in the NorNet testbed and highlight its advantages compared to a previous setup with physically distributed Sensors. We also present results from a 45 day field test with 13 observation points. These results confirm the advantages of a widely distributed monitoring setup and give some new insights into the behavior of the attackers.
| Afilliation | Communication Systems, , Communication Systems |
| Project(s) | The Center for Resilient Networks and Applications |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2014 |
| Conference Name | Proceedings of the IFIP Networking Conference (Networking 2014) |
| Date Published | June |
| Publisher | IFIP |
| Keywords | Conference |