• Enhancing investigative interview training using a child avatar system: a comparative study of interactive environments 

      Zohaib Hassan, Syed; Shafiee Sabet, Saeed; Riegler, Michael Alexander; Baugerud, Gunn Astrid; Ko, Hayley Manalang; Salehi, Pegah; Røed, Ragnhild Klingenberg; Sinkerud Johnson, Miriam; Halvorsen, Pål (Peer reviewed; Journal article, 2023)
      The impact of investigative interviews by police and Child Protective Services (CPS) on abused children can be profound, making effective training vital. Quality in these interviews often falls short and current training ...
    • Enhancing questioning skills through child avatar chatbot training with feedback 

      Røed, Ragnhild Klingenberg; Baugerud, Gunn Astrid; Zohaib Hassan, Syed; Shafiee Sabet, Saeed; Salehi, Pegah; Powell, Martine B.; Riegler, Michael Alexander; Halvorsen, Pål; Sinkerud Johnson, Miriam (Peer reviewed; Journal article, 2023)
      Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the ...
    • An evaluation of using transformer networks for ECG Analysis 

      Yawar, Syeda Ambreen (Master thesis, 2023)
      Electrocardiogram (ECG) is a simulated recording of heart activity in electrical signals. It carries essential clinical information in the form of amplitude and timing. It is used to monitor and analyze the functionality ...
    • Explaining deep neural networks for knowledge discovery in electrocardiogram analysis 

      Hicks, Steven; Isaksen, Jonas L; Thambawita, Vajira L B; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Strumke, Inga; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Halvorsen, Pål; Maleckar, Mary Margot Catherine; Riegler, Michael; Kanters, Jørgen K (Scientific Reports;11, Article number: 10949 (2021), Peer reviewed; Journal article, 2021-05-26)
      Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any ...
    • File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments 

      Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Riegler, Michael Alexander; Halvorsen, Pål; Johansen, Dag (Information;Volume 12, Issue 10, Peer reviewed; Journal article, 2021-10-18)
      In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform ...
    • Flexible device compositions and dynamic resource sharing in PCIe interconnected clusters using Device Lending 

      Markussen, Jonas Sæther; Bjørlykke Kristiansen, Lars; Borgli, Rune Johan; Stensland, Håkon Kvale; Seifert, Friedrich; Riegler, Michael Alexander; Griwodz, Carsten; Halvorsen, Pål (Cluster Computing volume;Volume 23, issue 2, Journal article; Peer reviewed, 2019-09-21)
      Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers ...
    • GANEx: A complete pipeline of training, inference and benchmarking GAN experiments 

      Thambawita, Vajira; Hammer, Hugo Lewi; Riegler, Michael Alexander; Halvorsen, Pål (International Workshop on Content-Based Multimedia Indexing, CBMI;, Conference object, 2019-10-21)
      Deep learning (DL) is one of the standard methods in the field of multimedia research to perform data classification, detection, segmentation and generation. Within DL, generative adversarial networks (GANs) represents a ...
    • GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos 

      Monakhov, Vladimir; Thambawita, Vajira L B; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)
      The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep ...
    • HOST-ATS: automatic thumbnail selection with dashboard-controlled ML pipeline and dynamic user survey 

      Husa, Andreas; Midoglu, Cise; Hammou, Malek; Halvorsen, Pål; Riegler, Michael (MMSys: ACM Multimedia Systems;MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Conference object, 2022)
      We present HOST-ATS, a holistic system for the automatic selection and evaluation of soccer video thumbnails, which is composed of a dashboard-controlled machine learning (ML) pipeline, and a dynamic user survey. The ML ...
    • Huldra: a framework for collecting crowdsourced feedback on multimedia assets 

      Hammou, Malek; Midoglu, Cise; Hicks, Steven; Storås, Andrea; Sabet, Saeed; Strumke, Inga; Riegler, Michael; Halvorsen, Pål (MMSys: ACM Multimedia Systems;MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Conference object, 2022)
      Collecting crowdsourced feedback to evaluate, rank, or score multimedia content can be cumbersome and time-consuming. Most of the existing survey tools are complicated, hard to customize, or tailored for a specific asset ...
    • HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD) 

      Hicks, Steven; Stautland, Andrea; Fasmer, Ole Bernt; Førland, Wenche; Hammer, Hugo Lewi; Halvorsen, Pål; Mjeldheim, Kristin; Ødegaard, Ketil Joachim; Osnes, Berge; Syrstad, Vigdis Elin Giæver; Riegler, Michael; Jakobsen, Petter (MMSys: ACM Multimedia Systems;MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference, Conference object, 2021-09-22)
      Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirementsthat come with ...
    • HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy 

      Borgli, Hanna; Thambawita, Vajira; Smedsrud, Pia H; Hicks, Steven; Jha, Debesh; Eskeland, Sigrun Losada; Randel, Kristin Ranheim; Pogorelov, Konstantin; Lux, Mathias; Dang Nguyen, Duc Tien; Johansen, Dag; Griwodz, Carsten; Stensland, Håkon Kvale; Garcia-Ceja, Enrique; Schmidt, Peter T; Hammer, Hugo Lewi; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas (Scientific Data;7, Article number: 283 (2020), Journal article; Peer reviewed, 2020-08-28)
      Artifcial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually ...
    • Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence 

      Storås, Andrea; Magnø, Morten Schjerven; Fineide, Fredrik; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)
      Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ...
    • Impact of different type of Child Avatar Interactions on user Quality of Experience 

      Hals, Alexander William Ingvarsson (ACIT;2022, Master thesis, 2022)
      Conducting an interview and communicating with children that have experienced traumatic situations can be difficult. Norway’s Child Protective services received in 2017 over 58.580 reports about child maltreatment and ...
    • Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images 

      Thambawita, Vajira L B; Strumke, Inga; Hicks, Steven; Halvorsen, Pål; Parasa, Sravanthi; Riegler, Michael (Diagnostics;Volume 11 / Issue 12, Peer reviewed; Journal article, 2021-11-24)
      Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)- based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies ...
    • An Investigation into using Deep Convolutional Neural Networks for ECG Analysis 

      Hameed, Mohammad Awais (Master thesis, 2023)
      In this day and age, the fascination surrounding deep learning and AI is at its absolute peak. Both in terms of hype and controversy the current interest level is unprecedented, with exciting developments happening at a ...
    • Kvasir-Capsule, a video capsule endoscopy dataset 

      Smedsrud, Pia H; Thambawita, Vajira L B; Hicks, Steven; Gjestang, Henrik; Olsen Nedrejord, Oda; Næss, Espen; Borgli, Hanna; Jha, Debesh; Berstad, Tor Jan; Eskeland, Sigrun Losada; Lux, Mathias; Espeland, Håvard; Petlund, Andreas; Dang Nguyen, Duc Tien; Garcia, Enrique; Johansen, Dag; Schmidt, Peter Thelin; Toth, Ervin; Hammer, Hugo Lewi; de Lange, Thomas; Riegler, Michael Alexander; Halvorsen, Pål (Scientific Data;8, Article number: 142 (2021), Peer reviewed; Journal article, 2021-05-27)
      Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work ...
    • Kvasir-SEG: A Segmented Polyp Dataset 

      Jha, Debesh; Pia H, Smedsrud; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas; Johansen, Dag; Johansen, Håvard D. (Lecture Notes in Computer Science;Volume 11962, Conference object, 2019-12-24)
      Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: ...
    • LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification 

      Jha, Debesh; Yazidi, Anis; Riegler, Michael Alexander; Johansen, Dag; Johansen, Håvard D.; Halvorsen, Pål (Lecture Notes in Computer Science;Volume 12606, Conference object, 2021-02-21)
      Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. ...
    • Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction 

      Hicks, Steven; Andersen, Jorunn Marie; Witczak, Oliwia; Lasantha Bandara Thambawita, Vajira; Halvorsen, Pål; Hammer, Hugo Lewi; Haugen, Trine B.; Riegler, Michael Alexander (Scientific Reports;9, Article number: 16770 (2019), Journal article; Peer reviewed, 2019-10-24)
      Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are ...