A database for publications published by researchers and students at SimulaMet.
Research area
Publication type
- All (370)
- Journal articles (136)
- Books (2)
- Edited books (1)
- Proceedings, refereed (165) Remove Proceedings, refereed <span class="counter">(165)</span> filter
- 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)
Proceedings, refereed
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
In Multimediaeval Benchmark 2019. CEUR Workshop Proceedings, 2019.Status: Published
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | Multimediaeval Benchmark 2019 |
Date Published | 10/2019 |
Publisher | CEUR Workshop Proceedings |
Medical Multimedia Systems and Applications
In Proceedings of the 27th ACM International Conference on Multimedia - MM '19. New York, NY, USA: ACM Press, 2019.Status: Published
Medical Multimedia Systems and Applications
In recent years, we have observed a rise of interest in the multimedia community towards research topics related to health. It can be observed that this goes into two interesting directions. One is personal health with a larger focus on well-being and everyday healthy living. The other direction focuses more on multimedia challenges within the health-care systems, for example, how can multimedia content produced in hospitals be used efficiently but also on the user perspective of patients and health-care personal. Challenges and requirements in this interesting and challenging direction are similar to classic multimedia research, but with some additional pitfalls and challenges. This tutorial aims to give a general introduction to the research area; to provide an overview of specific requirements, pitfalls and challenges; to discuss existing and possible future work; and to elaborate on how machine learning approaches can help in multimedia-related challenges to improve the health-care quality for patients and support medical experts in their daily work.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | Proceedings of the 27th ACM International Conference on Multimedia - MM '19 |
Pagination | 2711-2713 |
Date Published | 1072019 |
Publisher | ACM Press |
Place Published | New York, NY, USA |
ISBN Number | 9781450368896 |
URL | http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cf... |
DOI | 10.1145/3343031.3351319 |
ACM Multimedia BioMedia 2019 Grand Challenge Overview
In The ACM International Conference on Multimedia (ACM MM). New York, New York, USA: ACM Press, 2019.Status: Published
ACM Multimedia BioMedia 2019 Grand Challenge Overview
The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year’s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year’s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | The ACM International Conference on Multimedia (ACM MM) |
Pagination | 2563-2567 |
Date Published | 10/2019 |
Publisher | ACM Press |
Place Published | New York, New York, USA |
ISBN Number | 9781450368896 |
URL | http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cf... |
DOI | 10.1145/334303110.1145/3343031.3356058 |
Real-time Analysis of Physical Performance Parameters in Elite Soccer
In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.Status: Published
Real-time Analysis of Physical Performance Parameters in Elite Soccer
Technology is having vast impact on the sports in- dustry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of indi- vidual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform real- time monitoring and analysis of the players’ performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work.
Afilliation | Communication Systems, Machine Learning |
Project(s) | No Simula project, Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
Publisher | IEEE |
DOI | 10.1109/CBMI.2019.8877422 |
Proceedings, refereed
RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
In ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022.Status: Published
RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed. In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies. It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system. We implement and evaluate RCAD on real world measurements from a commercial mobile network. RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods.
Afilliation | Communication Systems, Machine Learning |
Project(s) | The Center for Resilient Networks and Applications, Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Pagination | 2682–2691 |
Publisher | ACM |
Proceedings, refereed
Challenges and Opportunities within Personal Life Archives
In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. New York, NY, USA: ACM Press, 2018.Status: Published
Challenges and Opportunities within Personal Life Archives
Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.
Afilliation | Communication Systems, Machine Learning |
Project(s) | No Simula project, Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval |
Pagination | 335-343 |
Date Published | 07/2018 |
Publisher | ACM Press |
Place Published | New York, NY, USA |
ISBN Number | 9781450350464 |
URL | http://dl.acm.org/citation.cfm?doid=3206025 |
DOI | 10.1145/3206025.3206040 |
Proceedings, refereed
Automatic Polyp Segmentation using U-Net-ResNet50
In Medico MediaEval 2020. MediaEval, 2021.Status: Published
Automatic Polyp Segmentation using U-Net-ResNet50
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Medico MediaEval 2020 |
Publisher | MediaEval |
Proceedings, refereed
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications
In CLEF 2020. Vol. 12260. Cham: Springer International Publishing, 2020.Status: Published
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | CLEF 2020 |
Volume | 12260 |
Pagination | 311 - 341 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-030-58218-0 |
ISSN Number | 0302-9743 |
URL | https://doi.org/10.1007/978-3-030-58219-7_22 |
DOI | 10.1007/978-3-030-58219-710.1007/978-3-030-58219-7_22 |
Proceedings, refereed
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
In EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference. IEEE, 2017.Status: Published
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference |
Publisher | IEEE |
DOI | 10.23919/EUSIPCO.2017.8081286 |
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
In ISCAS 2017: Proceedings of IEEE International Symposium on Circuits and Systems. IEEE, 2017.Status: Published
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | ISCAS 2017: Proceedings of IEEE International Symposium on Circuits and Systems |
Pagination | 314-317 |
Publisher | IEEE |
DOI | 10.1109/ISCAS.2017.8050303 |