A database for publications published by researchers and students at SimulaMet.
Status
Research area
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 |
Towards a Lightweight Task Scheduling Framework for Cloud and Edge Platform
Internet of Things; Engineering Cyber Physical Human Systems (2023).Status: Accepted
Towards a Lightweight Task Scheduling Framework for Cloud and Edge Platform
Mobile devices are becoming ubiquitous in our daily lives, but they have limited computational capacity. Thanks to the advancement in the network infrastructure, task offloading from resource-constrained devices to the near edge and the cloud becomes possible and advantageous. Complete task offloading is now possible to almost limitless computing resources of public cloud platforms. Generally, the edge computing resources support latency-sensitive applications with limited computing resources, while the cloud supports latency-tolerant applications. This paper proposes one lightweight task-scheduling framework from cloud service provider perspective, for applications using both cloud and edge platforms. Here, the challenge is using edge and cloud resources efficiently when necessary. Such decisions have to be made quickly, with a small management overhead. Our framework aims at solving two research questions. They are: i) How to distribute tasks to the edge resource pools and multi-clouds? ii) How to manage these resource pools effectively with low overheads? To answer these two questions, we examine the performance of our proposed framework based on Reliable Server Pooling (RSerPool). We have shown via simulations that RSerPool, with the correct usage and configuration of pool member selection policies, can accomplish the cloud/edge setup resource selection task with a small overhead.
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, NorNet, SMIL: SimulaMet Interoperability Lab |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Internet of Things; Engineering Cyber Physical Human Systems |
Publisher | Elsevier |
Keywords | Cloud computing, Edge Computing, Reliable Server Pooling (RSerPool), Resource Pools, Task Scheduling |
Opportunistic CPU sharing in Mobile Edge Computing deploying the Cloud-RAN
IEEE Transactions on Network and Service Management (2023).Status: Accepted
Opportunistic CPU sharing in Mobile Edge Computing deploying the Cloud-RAN
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications, SMIL: SimulaMet Interoperability Lab |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE Transactions on Network and Service Management |
Publisher | IEEE |
Keywords | Cloud-RAN, Containers, Mobile edge computing, resource management |
DOI | 10.1109/TNSM.2023.3304067 |
Posters
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
Nordic AI Meet 2023, 2023.Status: Accepted
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
Diabetic retinopathy (DR) is a common complication of diabetes that damages the eye and potentially leads to blindness. The severity and treatment choice of DR depends on the presence of medical findings in fundus images. Much work has been done in developing complex machine learning (ML) models to automatically diagnose DR from fundus images. However, their high level of complexity increases the demand for techniques improving human understanding of the ML models. Explainable artificial intelligence (XAI) methods can detect weaknesses in ML models and increase trust among end users. In the medical field, it is crucial to explain ML models in order to apply them in the clinic. While a plethora of XAI methods exists, heatmaps are typically applied for explaining ML models for DR diagnosis. Heatmaps highlight image areas that are regarded as important for the model when making a prediction. Even though heatmaps are popular, they can be less appropriate in the medical field. Testing with Concept Activation Vectors (TCAV), providing explanations based on human-friendly concepts, can be a more suitable alternative for explaining models for DR diagnosis, but it has not been thoroughly investigated for DR models. We develop a deep neural network for diagnosing DR from fundus images and apply TCAV for explaining the resulting model. Concept generation with and without masking is compared. Based on diagnostic criteria for DR, we evaluate the model’s concept ranking for different severity levels of DR. TCAV can explain individual images to gain insight into a specific case, or an entire class to evaluate overall consistency with diagnostic standards. The most important concepts for the DR model agree with diagnostic criteria for DR. No large differences are detected between the two concept generation approaches. TCAV is a flexible explanation method where human-friendly concepts provide insights and trust in ML models for medical image analyses, and it shows promising results for DR grading.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2023 |
Place Published | Nordic AI Meet 2023 |
Keywords | concept-based explanations, diabetic retinopathy, Explainable artificial intelligence |
Proceedings, refereed
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.
Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns. We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.
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, meibomian gland dysfunction, proteomics |
A 10-Layer Model for Service Availability Risk Management
In 20th International Conference on Security and Cryptography, 2023.Status: Accepted
A 10-Layer Model for Service Availability Risk Management
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | 20th International Conference on Security and Cryptography |
Longitudinal Analysis of Inter-City Network Delays
In Network Traffic Measurement and Analysis Conference (TMA). IEEE/IFIP, 2023.Status: Accepted
Longitudinal Analysis of Inter-City Network Delays
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | Network Traffic Measurement and Analysis Conference (TMA) |
Publisher | IEEE/IFIP |
Keywords | big network data analysis, Internet measurements, longitudinal analysis, RTT delay |
How Large Is the Gap? Exploring MANRS and ISO27001 Security Management
In The 31st International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2023) . Split, Croatia: IEEE, 2023.Status: Accepted
How Large Is the Gap? Exploring MANRS and ISO27001 Security Management
Ensuring security of network traffic between op- erators is very important. This paper aims to enhance un- derstanding of the relationship between two crucial internet security standards, namely, Mutual Agreed Norms for Rout- ing Security (MANRS) and ISO/IEC 27001 (ISO27001). By examining the correlation between MANRS participation and ISO27001 certification, this study provides insightful analysis. To validate compliance, data from diverse sources such as CAIDA, PeeringDB, and RPKI is correlated. We assess ISO27001 controls that bear relevance to MANRS compliance and illustrate how implementing either framework leads to a reduced risk of security breaches. Moreover, a cost analysis reveals that the simultaneous implementation of MANRS and ISO27001 does not significantly increase costs or complexity.
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | The 31st International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2023) |
Publisher | IEEE |
Place Published | Split, Croatia |
Keywords | ISO27001, MANRS, risk analysis |
Evolved Cold-Potato routing experiences
In The 31st International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2023) . Split, Croatia: IEEE, 2023.Status: Accepted
Evolved Cold-Potato routing experiences
This paper presents a system designed to enhance Quality of Service (QoS) for a global video conferencing service. By a combination of off-the-shelf equipment and open-source software, the proposed system establishes a global service net- work with efficient cold-potato internal routing techniques.
Through 15 years of operational experience, we have developed egress selection heuristics to minimize latency and loss in real- time, end-to-end services. The use of legacy routers and switches guarantees stability and security, while the implementation of specially adapted Route Reflector software provides flexibility.
This study highlights the notable enhancements achieved in terms of accurate egress routing placement, latency reduction, and a significant decrease in customer support cases. By sharing our experiences, we aim to contribute valuable insights to the field of network optimization for real-time services.
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | The 31st International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2023) |
Publisher | IEEE |
Place Published | Split, Croatia |
Keywords | BGP, GeoIP, Routing |
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 |