Recent additions

  • User Expectations and Willingness to Share Self-collected Health Data 

    Woldaregay, Ashenafi Zebene; Henriksen, André; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Richard, Aude; Lovis, Christian; Årsand, Eirik; Rochat, Jessica; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
    The rapid improvement in mobile health technologies revolutionized what and how people can self-record and manage data. This massive amount of information accumulated by these technologies has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate people to share this data for the benefit of all. The purpose of this study is ...
  • Semi-CNN architecture for effective spatio-temporal Learning in action recognition 

    Leong, Mei Chee; Prasad, Dilip K.; Lee, Yong Tsui; Lin, Feng (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-12)
    This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters ...
  • User Expectations and Willingness to Share Self-Collected Health Data 

    Woldaregay, Ashenafi Zebene; Henriksen, André; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Richard, Aude; Lovis, Christian; Årsand, Eirik; Rochat, Jessica; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
    The rapid improvement in mobile health technologies revolutionized what and how people can self-record and manage data. This massive amount of information accumulated by these technologies has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate people to share this data for the benefit of all. The purpose of this study is ...
  • What Do We Know About the Use of Chatbots for Public Health? 

    Gabarron, Elia; Larbi, Dillys; Denecke, Kerstin; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
    <i>Background and objective</i>: The number of publications on the use of chatbots for health is recently increasing, however to our knowledge, there are no publications summarizing what is known about using chatbots for public health yet. The objective of this work is to provide an overview of the existing scientific literature on the use of chatbots for public health, for which purpose have chatbots ...
  • Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature 

    Mishra, Sambeet; Bordin, Chiara; Taharaguchi, Kota; Palu, Ivo (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03-02)
    Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can ...
  • Beneath the snow – Developing a wireless sensor node for remote locations in the Arctic 

    Tveito, Øystein (Master thesis; Mastergradsoppgave, 2020-05-15)
    In this thesis we describe how we designed, built, deployed, and improved upon a robust hardware- and software solution, tailor-made to this scientific question. During the course of this project, we created three distinct versions and we have conducted two deployments of the sensor nodes in the Arctic tundra. The node is able to measure CO2 , temperature, and humidity, in addition to monitoring an ...
  • Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings 

    Syed, Shaheen; Morseth, Bente; Hopstock, Laila Arnesdatter; Horsch, Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-04-03)
    Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms’ hyperparameters and classification performance, ...
  • Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review 

    Yeng, Prosper; Nweke, Livinus Obiora; Woldaregay, Ashenafi Zebene; Yang, Bian; Snekkenes, Einar Arthur (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03)
    Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the ...
  • Kvasir-SEG: A Segmented Polyp Dataset 

    Jha, Debesh; Pia H, Smedsrud; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-24)
    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: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced ...
  • Collision-free path finding for dynamic gaming and real time robot navigation 

    Bamal, Roopam (Peer reviewed; Book; Chapter, 2020-02-13)
    Collision-free path finding is crucial for multi-agent traversing environments like gaming systems. An efficient and accurate technique is proposed for avoiding collisions with potential obstacles in virtual and real time environments. Potential field is a coherent technique but it eventuates with various problems like static map usage and pre-calculated potential field map of the environment. It ...
  • Intelligent Offloading Distribution of High Definition Street Maps for Highly Automated Vehicles 

    Jomrich, Florian; Sharma, Aakash; Ruckelt, Tobias; Bohnstedt, Doreen; Steinmetz, Ralf (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-11-24)
    Highly automated vehicles will change our personal mobility in the future. To ensure the safety and the comfort of their passengers, the cars have to rely on as many information regarding their current surrounding traffic situation, as they can obtain. In addition to classical sensors like cameras or radar sensors, automated vehicles use data from a so called High Definition Street Map. Through such ...
  • Are object detection assessment criteria ready for maritime computer vision? 

    Prasad, Dilip K.; Dong, Huixu; Rajan, Deepu; Quek, Chai (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11-25)
    Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the ...
  • EDMON - a system architecture for real-time infection monitoring and outbreak detection based on self-recorded data from people with type 1 diabetes: system design and prototype implementation 

    Coucheron, Sverre; Woldaregay, Ashenafi Zebene; Årsand, Eirik; Botsis, Taxiarchis; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11)
    Infection incidences in people with diabetes can create sever health complications mainly due to the effect of stress hormones, such as cortisol and adrenaline, which increases glucose production and insulin resistance in the body. The proposed electronic disease surveillance monitoring network (EDMON) relies on self-recorded data from people with Type 1 diabetes and dedicated algorithms to detect ...
  • K-CUSUM: Cluster Detection Mechanism in EDMON 

    Yeng, Prosper; Woldaregay, Ashenafi Zebene; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-11)
    The main goal of the EDMON (Electronic Disease Monitoring Network) project is to detect the spread of contagious diseases at the earliest possible moment, and potentially before people know that they have been infected. The results shall be visualized on real-time maps as well as presented in digital communication. In this paper, a hybrid of K-nearness Neighbor (KNN) and cumulative sum (CUSUM), known ...
  • Acceptance barriers of using patients’ self-collected health data during medical consultation 

    Giordanengo, Alain; Årsand, Eirik; Grøttland, Astrid; Bradway, Meghan; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2019)
    Patients increasingly collect health-related data using mobile health apps and sensors. Studies have shown that this data can be beneficial for both clinicians and patients if used during medical consultations. However, such data is almost never used outside controlled situations or medical trials. This paper explains why the usage of self-collected health data is not widespread by identifying ...
  • Predicting Transaction Latency with Deep Learning in Proof-of-Work Blockchains 

    Tedeschi, Enrico; Nordmo, Tor-Arne Schmidt; Johansen, Dag; Johansen, Håvard D. (Peer reviewed; Chapter, 2019)
    Proof-of-work based cryptocurrencies, like Bitcoin, have a fee market where transactions are included in the blockchain according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience delays and evictions unless an enormous fee is paid. % In this paper, we present a novel ...
  • Cloudless Friend-to-Friend Middleware for Smartphones 

    Arnes, Jo Inge; Karlsen, Randi (Peer reviewed; Chapter; Bokkapittel, 2019-11-13)
    Using smartphones for peer-to-peer communication over the Internet is difficult without the aid of centralized services. These centralized services, which usually reside in the cloud, are necessary for brokering communication between peers, and all communication must pass through them. A reason for this is that smartphones lack publicly reachable IP addresses. Also, because people carry their ...
  • Efficient concurrent search trees using portable fine-grained locality 

    Ha, Hoai Phuong; Anshus, Otto; Umar, Ibrahim (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-01-14)
    Concurrent search trees are crucial data abstractions widely used in many important systems such as databases, file systems and data storage. Like other fundamental abstractions for energy-efficient computing, concurrent search trees should support both high concurrency and fine-grained data locality in a platform-independent manner. However, existing portable fine-grained locality-aware search trees ...
  • Vitamin D in relation to incident sarcopenia and changes in muscle parameters among older adults: The KORA-Age Study 

    Conzade, Romy; Grill, Eva; Bischoff-Ferrari, Heike A.; Ferrari, Uta; Horsch, Alexander; Peters, Annette; Thorand, Barbara (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-05-08)
    Summary We report low baseline 25-hydroxyvitamin D (25OHD) levels being associated with unfavorable changes in muscle mass and physical performance over three years, but not with incident sarcopenia. Future prospective studies are needed to assess causality and to address the issue of competing risks such as mortality in older cohorts. Introduction Effects of low serum 25-hydroxyvitamin D (25OHD) ...
  • SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology 

    Dey, Somdip; Singh, Amit Kumar; Prasad, Dilip K.; McDonald-Maier, Klaus D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-24)
    Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically ...

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