A database for publications published by researchers and students at SimulaMet.
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- Journal articles (211)
- Books (7)
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- Proceedings, refereed (235) Remove Proceedings, refereed <span class="counter">(235)</span> filter
- Book chapters (6)
- Talks, keynote (17)
- PhD theses (1)
- Proceedings, non-refereed (18)
- Posters (11)
- Technical reports (11)
- Manuals (1)
- Talks, invited (159)
- Talks, contributed (18)
- Public outreach (46)
- Miscellaneous (12)
Proceedings, refereed
Measurement of software development effort estimation bias: Avoiding biased measures of estimation bias
In 11th International Conference on Software Engineering and Applications (SEA 2022), 2022.Status: Accepted
Measurement of software development effort estimation bias: Avoiding biased measures of estimation bias
In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort – estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 11th International Conference on Software Engineering and Applications (SEA 2022) |
Investigative Interviews using a Multimodal Virtual Avatar
In American Psychology-Law Society Conference 2022. Denver USA,: American Psychology-Law Society, 2022.Status: Accepted
Investigative Interviews using a Multimodal Virtual Avatar
To meet best-practice standards, we are developing an interactive virtual avatar aiming as a training tool to raise interviewing skills of child-welfare and law-enforcement professionals. Therefore, we present the “Ilma” avatar that recognizes interviewers’ behavior during open-ended, closed and leading questions, and which can automatically respond to the conversation. We conducted a user study in which master students (N=3) and child protective workers (N=8) interviewed “Ilma” and rated their perception of the interaction. The results show that the participants valued the interaction and found the avatar useful. Thus, it has great potential to be an effective training tool.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | American Psychology-Law Society Conference 2022 |
| Publisher | American Psychology-Law Society |
| Place Published | Denver USA, |
Multimedia Streaming Analytics: Quo Vadis?
In ACM Mile High Video. ACM, 2022.Status: Accepted
Multimedia Streaming Analytics: Quo Vadis?
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | ACM Mile High Video |
| Publisher | ACM |
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
In MediaEval 2021. Working Notes Proceedings of the MediaEval 2021 Workshop ed. CEUR Workshop Proceedings, 2022.Status: Accepted
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
This paper presents the submission of team Medical-XAI for the Medico: Transparency in Medical Image Segmentation task held at MediaEval 2021. We propose an unsupervised method that utilizes tools from the field of explainable artificial intelligence to create segmentation masks. We extract heat maps, which are useful in order to explain how the `black box' model predicts the category of a certain image, and the segmentation masks are directly derived from the heat maps. Our results show that the created masks can capture the relevant findings to a certain extent using only a small amount of image-level labeled data for the classification model and no segmentation masks at all for the training. This is promising for addressing different challenges within the intersection of artificial intelligence for medicine such as availability of data, cost of labeling and interpretable and explainable results.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | MediaEval 2021 |
| Edition | Working Notes Proceedings of the MediaEval 2021 Workshop |
| Publisher | CEUR Workshop Proceedings |
| Keywords | clustering, Explainable artificial intelligence, Global Features, Grad-CAM, Image segmentation, Medical imaging, Polyp Detection, Self-supervised learning |
| URL | https://2021.multimediaeval.com/paper12.pdf |
Proceedings, refereed
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
In 27th International Conference on Multimedia Modeling. Springer, 2021.Status: Published
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | 27th International Conference on Multimedia Modeling |
| Publisher | Springer |
| Keywords | Accelerometer, Activity recognition, Audio, dataset, Sensor fusion |
Reliable Server Pooling Based Workload Offloading with Mobile Edge Computing: A Proof-of-Concept
In Proceedings of the 2nd International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 35th International Conference on Advanced Information Networking and Applications (AINA). Toronto, Ontario/Canada: Springer, 2021.Status: Published
Reliable Server Pooling Based Workload Offloading with Mobile Edge Computing: A Proof-of-Concept
In recent times, mobile broadband devices have become almost ubiquitous. However, battery-powered devices (such as smartphones), have limitations on energy consumption, computation power and storage space. Cloud computing, and in particular with the upcoming 5G networks, Mobile Edge Computing (MEC) can provide compute and storage services at the vicinity of the user (with a low communication latency). However, the complexity lies in how to simply and efficiently realise MEC services, with the auxiliary public (multi-)cloud resources? In this paper, we propose a proof-of-concept of using Reliable Server Pooling (RSerPool) as a light-weight layer of managing resource pools and handling application sessions with these pools. Our approach is simple, efficient, has low overhead and is available as open-source. Here, we demonstrate the usefulness of our approach by measuring in a test setup, with a 4G testbed connected to MEC and public multi-cloud resources.
| Afilliation | Communication Systems |
| Project(s) | NorNet, The Center for Resilient Networks and Applications, SMIL: SimulaMet Interoperability Lab, 5G-VINNI: 5G Verticals INNovation Infrastructure , Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 2nd International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing (M2EC) in conjunction with the 35th International Conference on Advanced Information Networking and Applications (AINA) |
| Publisher | Springer |
| Place Published | Toronto, Ontario/Canada |
| Keywords | 5G, Evolved Packet Core (EPC), Mobile Edge Computing (MEC), Multi-Cloud Computing, Network Function Virtualisation (NFV), Reliable Server Pooling (RSerPool) |
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 |
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
In DDW, 2021.Status: Accepted
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
Convolutional neural networks (CNNs) are increasingly used to improve and automate processes in gastroenterology, like the detection of polyps during a colonoscopy. An important input to these methods is images and videos. Up until now, no well-defined, common understanding or standard regarding the resolution of the images and video frames has been defined, and to reduce processing time and resource requirements, images are today almost always down-sampled. However, how such down-sampling and the image resolution influence the performance in context with medical data is unknown. In this work, we investigate how the resolution relates to the performance of convolutional neural networks. This can help set standards for image or video characteristics for future CNN based models in gastrointestinal endoscopy.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | DDW |
DivergentNets: Medical Image Segmentation by Network Ensemble
In EndoCV, 2021.Status: Accepted
DivergentNets: Medical Image Segmentation by Network Ensemble
Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation has gained attention in the medical community. Segmentation has the advantage of being more accurate than per-frame classification or object detection as it can show the affected area in greater detail. For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches. First, a segmentation model named TriUNet composed of three separate UNet models. Second, we combine TriUNet with an ensemble of well-known segmentation models, namely UNet++, FPN, DeepLabv3, and DeepLabv3+, into a model called DivergentNets to produce more generalizable medical image segmentation masks. In addition, we propose a modified Dice loss that calculates loss only for a single class when performing multi-class segmentation, forcing the model to focus on what is most important. Overall, the proposed methods achieved the best average scores for each respective round in the challenge, with TriUNet being the winning model in Round I and DvergentNets being the winning model in Round II of the segmentation generalization challenge at EndoCV 2021
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | EndoCV |