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
Approximate Bayesian Inference Based on Expected Evaluation
Bayesian Analysis 1, no. 1 (2023).Status: Published
Approximate Bayesian Inference Based on Expected Evaluation
Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL.
Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Bayesian Analysis |
| Volume | 1 |
| Issue | 1 |
| Date Published | Jan-01-2023 |
| Publisher | Project euclid |
| URL | https://projecteuclid.org/journals/bayesian-analysis/volume--1/issue--1/... |
| DOI | 10.1214/23-BA1368 |
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
Algorithms, no. 1 (2023): 30.Status: Published
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Algorithms |
| Issue | 1 |
| Pagination | 30 |
| Date Published | Jan-01-2023 |
| Publisher | MDPI |
| URL | https://www.mdpi.com/1999-4893/16/1/30 |
| DOI | 10.3390/a16010030 |
A multi-center polyp detection and segmentation dataset for generalisability assessment
Nature Scientific Data 10 (2023).Status: Published
A multi-center polyp detection and segmentation dataset for generalisability assessment
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Nature Scientific Data |
| Volume | 10 |
| Publisher | Nature |
| URL | https://doi.org/10.1038/s41597-023-01981-y |
| DOI | 10.1038/s41597-023-01981-y |
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Trauma, Violence, & Abuse (2023).Status: Accepted
Live Streaming Technology and Online Child Sexual Exploitation and Abuse - A Scoping Review
Livestreaming of child sexual abuse is an established form of online child sexual exploitation
and abuse. However, only a limited body of research has examined this issue. The Covid-19
pandemic has accelerated internet use and user knowledge of livestreaming services
emphasising the importance of understanding this crime. In this scoping review, existing
literature was brought together through an iterative search of eight databases containing peer-
reviewed journal articles, as well as grey literature. Records were eligible for inclusion if the
primary focus was on livestream technology and online child sexual exploitation and abuse,
the child being defined as eighteen years or younger. Fourteen of the 2,218 records were
selected. The data were charted and divided into four categories: victims, offenders,
legislation, and technology. Limited research, differences in terminology, study design, and
population inclusion criteria present a challenge to drawing general conclusions on the
current state of livestreaming of child sexual abuse. The records show that victims are
predominantly female. The average livestream offender was found to be older than the
average online child sexual abuse offender. Therefore, it is unclear whether the findings are
representative of the global population of livestream offenders. Furthermore, there appears to
be a gap in what the records show on platforms and payment services used and current digital
trends. The lack of a legal definition and privacy considerations pose a challenge to
investigation, detection, and prosecution. The available data allow some insights into a
potentially much larger issue.
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Trauma, Violence, & Abuse |
| Publisher | SAGE Publications |
Proceedings, refereed
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Accepted
Multimedia datasets: challenges and future possibilities
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | International conference on multimedia modeling |
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
In Nordic Artificial Intelligence Research and Development. Springer, 2023.Status: Published
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | Nordic Artificial Intelligence Research and Development |
| Pagination | 81-93 |
| Publisher | Springer |
Book
The Influence of Delay on Cloud Gaming Quality of Experience
Cham: Springer, 2022.Status: Published
The Influence of Delay on Cloud Gaming Quality of Experience
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book |
| Year of Publication | 2022 |
| Edition | 1 |
| Number of Pages | 156 |
| Date Published | 06/2022 |
| Publisher | Springer |
| Place Published | Cham |
| ISBN Number | 978-3-030-99868-4 |
| URL | https://link.springer.com/book/10.1007/978-3-030-99869-1 |
| DOI | 10.1109/QoMEX55416.2022.9900908 |
Book Chapter
Smittestopp Backend
In Smittestopp − A Case Study on Digital Contact Tracing, 29-62. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp Backend
An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 29 - 62 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2.pdf |
| DOI | 10.1007/978-3-031-05466-2_3 |
Smittestopp analytics: Analysis of position data
In Smittestopp − A Case Study on Digital Contact Tracing, 63-79. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp analytics: Analysis of position data
Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 63 - 79 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2_4 |
| DOI | 10.1007/978-3-031-05466-2_4 |
Journal Article
Predicting an unstable tear film through artificial intelligence
Scientific Reports 12, no. 1 (2022): 21416.Status: Published
Predicting an unstable tear film through artificial intelligence
Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue | 1 |
| Pagination | 21416 |
| Date Published | 12/2022 |
| Publisher | Springer Nature |
| URL | https://www.nature.com/articles/s41598-022-25821-y |
| DOI | 10.1038/s41598-022-25821-y |
