A database for publications published by researchers and students at SimulaMet.
Status
Research area
Journal articles
Measuring Roaming in Europe: Infrastructure and Implications on Users’ QoE
IEEE Transactions on Mobile Computing (2021).Status: Accepted
Measuring Roaming in Europe: Infrastructure and Implications on Users’ QoE
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Transactions on Mobile Computing |
| Publisher | IEEE |
IoT Traffic Offloading with MultiPath TCP
IEEE Communications Magazine, Special Issue on Transport Layer Innovations for Future Networks (2021).Status: Accepted
IoT Traffic Offloading with MultiPath TCP
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Communications Magazine, Special Issue on Transport Layer Innovations for Future Networks |
| Publisher | IEEE |
Multipath Scheduling for 5G Networks: Evaluation and Outlook
IEEE Communications Magazine, Special Issue on Transport Layer Innovations for Future Networks (2021).Status: Accepted
Multipath Scheduling for 5G Networks: Evaluation and Outlook
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Communications Magazine, Special Issue on Transport Layer Innovations for Future Networks |
| Publisher | IEEE |
Risks and risk mitigation in global software development: an update
Journal of Software: Evolution and Process (2021).Status: Accepted
Risks and risk mitigation in global software development: an update
Context: Interest in global software development (GSD) has led to the publication of numerous studies. Over time, these studies should be updated to verify if their findings and conclusions remain valid.
Objective: To update a tertiary study, published in 2014, focused on investigating risks and risk mitigation advice in the context of GSD.
Method: We conducted a systematic literature study based on forward snowballing, out of which we identified and selected 25 unique studies.
Results: We extracted: a) 122 risks (75 of which already identified in the original work), and b) 123 mitigation advice (44 of which were identified in the original work). The evidence supporting these risks and mitigation were extracted from 619 and 389 primary sources, respectively. Given the amount of evidence found, we have reported only those with higher empirical support. The raw data, including all the values obtained, are available online as complementary material.
Conclusions: Interest in GSD, its potential risks, and possible mitigation strategies, remains high. This update has allowed increasing the level of empirical support provided by the findings of the original work.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management, EDOS: Effective Digitalization of Public Sector |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Journal of Software: Evolution and Process |
| Publisher | John Wiley & Sons Ltd |
Leveraging Network-Centric Strategic Goals in Capabilities
J. Miltary Studies (2021).Status: Accepted
Leveraging Network-Centric Strategic Goals in Capabilities
The vision of network-centric operations is to increase operational capabilities through networked collaboration. NATO and its member nations state this vision in strategic documents at a very high level of abstraction. While suitable for giving an overall feel, current documentation renders the steps toward implementing those visions largely unsupported. We outline a method, based on agile requirements engineering, for converting high-level strategic visions into capabilities, whose form lend themselves to incremental implementation. We illustrate the use of this method in two cases that concern both operational capabilities and technical capabilities. We also show how the method enables one to prioritize which capabilities to develop first. We conclude that some form of explicit methodology to span the gap between strategic visions and implementing those visions is necessary.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | J. Miltary Studies |
| Publisher | De Gruyter |
Posters
Assessment of sperm motility according to WHO classification using convolutional neural networks
ESHRE: ESHRE, 2021.Status: Accepted
Assessment of sperm motility according to WHO classification using convolutional neural networks
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Poster |
| Year of Publication | 2021 |
| Publisher | ESHRE |
| Place Published | ESHRE |
Assessment of sperm motility according to WHO classification using convolutional neural networks
ESHRE: ESHRE, 2021.Status: Accepted
Assessment of sperm motility according to WHO classification using convolutional neural networks
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Poster |
| Year of Publication | 2021 |
| Publisher | ESHRE |
| Place Published | ESHRE |
Proceedings, non-refereed
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
In Endocv2021 (ISBI), 2021.Status: Accepted
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
Medical image segmentation is a crucial task in medical image analysis. Despite near expert-label performance with the application of the deep learning method in medical image segmentation, the generalization of such models in the clinical environment remains a significant challenge. Transfer learning from a large medical dataset from the same domain is a common technique to address generalizability. However, it is difficult to find a similar large medical dataset. To address generalizability in polyp segmentation, we have used an ensemble of four MultiResUNet architectures, each trained on the combination of the different centered datasets provided by the challenge organizers. Our method achieved a decent performance of 0.6172 ± 0.0778 for the multi-centered dataset. Our study shows that significant work needs to be done to develop a computer-aided diagnosis system to detect and localize polyp of the multi-center datasets, which is essential for improving the quality of the colonoscopy.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, non-refereed |
| Year of Publication | 2021 |
| Conference Name | Endocv2021 (ISBI) |
| Keywords | colonoscopy, Convolutional neural network, health informatics, Polyp segmentation |
Proceedings, refereed
Identification of spermatozoa by unsupervised learning from video data
In ESHRE , 2021.Status: Accepted
Identification of spermatozoa by unsupervised learning from video data
Identification of individual sperm is essential to assess a given sperm sample's motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | ESHRE |
Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
In DDW, 2021.Status: Accepted
Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | DDW |