A database for publications published by researchers and students at SimulaMet.
Status
Research area
Proceedings, refereed
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, |
Posters
Automatic Thumbnail Selection for Soccer using Machine Learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Accepted
Automatic Thumbnail Selection for Soccer using Machine Learning
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Poster |
| Year of Publication | 2022 |
| Place Published | NORA Annual Conference, Stavanger, Norway |
Talks, invited
AI-Based Video Production for Soccer
In FOKUS Media Web Symposium, 2022.Status: Accepted
AI-Based Video Production for Soccer
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Talks, invited |
| Year of Publication | 2022 |
| Location of Talk | FOKUS Media Web Symposium |
| URL | https://www.fokus.fraunhofer.de/go/mws |
7 Things They Don't Tell You About Streaming Analytics
In Demuxed, 2022.Status: Accepted
7 Things They Don't Tell You About Streaming Analytics
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Talks, invited |
| Year of Publication | 2022 |
| Location of Talk | Demuxed |
| URL | https://2022.demuxed.com/#speakers |
Proceedings, refereed
A Time-aware Tensor Decomposition for Tracking Evolving Patterns
In MLSP'23: IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 2023.Status: Accepted
A Time-aware Tensor Decomposition for Tracking Evolving Patterns
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | MLSP'23: IEEE International Workshop on Machine Learning for Signal Processing |
| Publisher | IEEE |
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron and LightGBM which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
| Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction |
Journal articles
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 |
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 |
Journal articles
Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes
Neural computation 32 (2020): 912-968.Status: Accepted
Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes
Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Neural computation |
| Volume | 32 |
| Number | 5 |
| Pagination | 912–968 |
| Publisher | {MIT Press |
Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes
Neural computation 32 (2020): 912-968.Status: Accepted
Equivalence projective simulation as 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 | 2020 |
| Journal | Neural computation |
| Volume | 32 |
| Number | 5 |
| Pagination | 912–968 |
| Publisher | {MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info … |