Department of Holistic Systems
Today, everyone is talking about big-data analytics, video streaming, machine learning, web search, mobile apps, etc., and we all take it for granted that these "services" work perfectly. However, underneath such applications there are large systems, and individual components of these systems are often “only” a part of a big ecosystem of integrated building blocks needed to enable a functional service. In this respect, the HOST department addresses challenges potentially covering all components of the entire system, from data creation to visualisation of the results.
Using a combination of basic and applied research, we are targeting numerous applications, many in the field of sports and medicine, where we have a holistic view of the system and perform basic research, do experimental prototyping and run experiments in the intended environments.
Currently, machine learning is an important component of our research where we for example, in real- time, aim to analyse athlete performance and detect diseases in medical videos. In our holistic systems view, not only the accuracy of the machine learning analysis is of importance, but also the complete pipe-line integration and the system performance (e.g., resource consumption and scalability). We architect complete systems and optimise for particular application requirements, both functional and non-functional, in order to provide a best possible quality of the service and a lowest possible resource consumption. Finally, we have a goal to put the results into use for the society where we contribute to open source projects and have spun of new industries
People at Department of Holistic Systems
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Projects at Department of Holistic Systems
Interview training of child-welfare and law-enforcement professionals interviewing maltreated children supported via artificial avatars
The interdisciplinary FRIPRO project aims to improve interviews with maltreated children through a training program using realistic and interactive child avatars.
The department of Holistic Systems (HOST) at SimulaMet will be working with the Faculty of Social Sciences at OsloMet. The project will begin on the 1st of April 2021 and end on the 31st of March in 2024. It is funded by The Research Council of Norway with 12 million NOK and will include three Ph.D. positions.
Maltreatment and abuse of children is a significant societal problem that has serious and damaging effects on children’s behavior, psychological development, and adjustment. Detection and prevention of violence and sexual abuse against children is, therefore, a high priority for Child Protective Services (CPS) and law-enforcement professionals. The conversations and investigative interviews that are conducted with these children must be of high quality. However, both Norwegian and international research shows that despite investments in methodology, the current interview and conversation skills still need to be improved.
By using an empirically informed training system in highly realistic child avatars, this project aims to develop and maintain the advanced skills needed for interviewing maltreated children. They will use data from past investigative interviews with maltreated children and create a real-looking avatar that is capable of expressing emotion and spontaneous responses.
The planned avatar will be a combination of technologies from multiple areas in computer science including AI, computer vision, and natural language processing. The aim is for the child avatars to be a part of an interview-training program that will be implemented in cooperation with the CPS and the police. The training system will be evaluated by the project scientists to judge effectiveness in relation to real-world needs.
The project also involves collaborations with researchers from Griffith University in Australia and the University of Cambridge in the United Kingdom.
FFC: Female Football Centre
The Department of Holistic Systems at SimulaMet is collaborating with UiT - The Arctic University of Norway and Forzasys AS on the Female Football Centre (FFC), funded by the Tromsø Research Foundation.
The main goal of the centre is to gain new and fundamental insights into what affects the performance and overall health of female elite football players. A general objective is to devise novel methodologies for epidemiological research that might impact research fields in both sports and medicine. In particular, we aim to develop a non-invasive, privacy-preserving technology that enables us to continuously quantify and monitor athlete behavior where we derive analytic insights from different perspectives (e.g., biomechanics, sports-specific science, medicine, coaches, and athletes).
In the current gold standards for epidemiology, observational prospective cohort studies include that cohort subjects are followed in detail over a longer period. This is an error-prone and tedious task that has for a long time been carried out using pen and paper, and later doing a manual, tedious analysis. Making this entire process easier is the main responsibility of the researchers from the Department of Holistic Systems at SimulaMet.
In cooperation with UiT and Forzasys AS, SimulaMet has earlier developed and used an automatic performance monitoring system for athletes used by both national and elite series soccer teams. The goal is to quantify and develop accurate analysis technologies that enable a personalized assessment and performance development of elite athletes.
The automatic performance monitoring system collects athletes’ subjective parameters, like training load, wellness, injury, and illness, using a small questionnaire-app running on their mobile phones, and the data is transferred to a cloud-based backend system. Then, from a trainer-portal, the data can be automatically visualized for both individual players and team overviews.
In FFC, the objective is to extend the system further to include female-specific parameters and introduce more automatic analysis using, for example, machine learning. We will host and develop the system for all the teams participating in the project, and we will initiate automatic analyses that might be able to predict future overuse injuries or to help maybe to find the best development process of a player or a team.
Svein Arne Pettersen (head of research at the School of sports sciences, UiT) is the centre leader, and he has collaborated with the researchers at SimulaMet for a long time.
Read more about the new Female Football Centre (FFC)
FFC is funded by Tromsø Research Foundation.
Publications at Department of Holistic Systems
Journal Article
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Medical Image Analysis (2021).Status: Published
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Medical Image Analysis |
| 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 |
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
IEEE Access (2021).Status: Published
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | IEEE Access |
| Publisher | IEEE |
| Keywords | Medical image segmentation, ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG |
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
IEEE Journal of Biomedical and Health Informatics (2021).Status: Published
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
| 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 |
| Publisher | IEEE |
| Keywords | colonoscopy, conditional random field, generalization, Polyp segmentation, ResUNet++, test-time augmentation |
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Neural Computation 33 (2021): 1-45.Status: Published
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Neural Computation |
| Volume | 33 |
| Number | 1 |
| Pagination | 1–45 |
| Publisher | {MIT Press |
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 |
Automatic Polyp Segmentation using U-Net-ResNet50
In Medico MediaEval 2020. MediaEval 2020, 2021.Status: Published
Automatic Polyp Segmentation using U-Net-ResNet50
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Medico MediaEval 2020 |
| Publisher | MediaEval 2020 |
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
In 25th International Conference on Pattern Recognition . Springer, 2021.Status: Published
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | 25th International Conference on Pattern Recognition |
| Publisher | Springer |
| Keywords | Benchmarking, Convolutional neural network, deep learning, Polyp segmentation |
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In 27th International Conference on Multimedia Modeling. MMM 2021, 2021.Status: Published
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
| 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 | MMM 2021 |
Journal Article
Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge
Medical Image Analysis (2020).Status: Published
Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Medical Image Analysis |
| Date Published | 11/2020 |
| Publisher | Elsevier |
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm
IEEE Transactions on Neural Networks and Learning Systems (2020): 1-14.Status: Published
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Pagination | 1-14 |
| Publisher | IEEE |