Recent additions

  • Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities 

    Ngo, Phuong; Tejedor Hernandez, Miguel Angel; Tayefi, Maryam; Chomutare, Taridzo; Godtliebsen, Fred (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-12)
    <p><i>Background.</i> Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce ...
  • Features extraction of wind ramp events from a virtual wind park 

    Mishra, Sambeet; Oren, Esin; Bordin, Chiara; Wen, Fushuan; Palu, Ivo (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-26)
    In the European renewable energy portfolio, wind has a sizeable share in the total energy production. The Nordic and Baltic energy systems in particular are benefiting from wind energy to reach the greenhouse gas emissions reduction objectives set by the EU. The wind energy production varies with time, and this intermittent characteristic imposes a challenge for full utilization of renewable energy ...
  • 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 (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-28)
    Artificial 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 label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article ...
  • Neural network based country wise risk prediction of COVID-19 

    Pal, Ratnabali; Sekh, Arif Ahmed; Kar, Samarjit; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-16)
    The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the ...
  • Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort 

    Holsbø, Einar; Olsen, Karina Standahl (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-15)
    Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood ...
  • Predicting breast cancer metastasis from whole-blood transcriptomic measurements 

    Holsbø, Einar; Perduca, Vittorio; Bongo, Lars Ailo; Lund, Eiliv; Birmelé, Etienne (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-20)
    <i>Objective</i> - In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast ...
  • Smart Energy and power systems modelling: an IoT and Cyber-Physical Systems perspective, in the context of Energy Informatics 

    Bordin, Chiara; Håkansson, Anne; Mishra, Sambeet (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-02)
    This paper aims at identifying the key role of ”Smart Energy and Power Systems Modelling”, within the context of Energy Informatics. The main objective is to describe how the specific subject of ”Smart Energy and Power Systems Modelling” can give a key contribution within the novel domain of Energy Informatics, by successfully linking and integrating the different disciplines involved. First the ...
  • Neural Network Based Country Wise Risk Prediction of COVID-19 

    Pal, Ratnabali; Sekh, Arif Ahmed; Kar, Samarjit; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-16)
    The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the ...
  • How mHealth can facilitate collaboration in diabetes care: qualitative analysis of codesign workshops 

    Bradway, Meghan; Morris, Rebecca L.; Giordanengo, Alain; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-30)
    <i>Background</i> - Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’ needs and expectations for a PGD-sharing system.<p> ...
  • Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions 

    Bordin, Chiara; Skjelbred, Hans Ivar; Kong, Jiehong; Yang, Zhirong (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-02)
    This paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show the most common topics that have been addressed in the scientific literature in the last years. The objective is to provide recommendations for novel research directions that can ...
  • A low-cost set CRDT based on causal lengths 

    Yu, Weihai; Rostad, Sigbjørn (Chapter; Bokkapittel, 2020-04)
    CRDTs, or Conflict-free Replicated Data Types, are data abstractions that guarantee convergence for replicated data. Set is one of the most fundamental and widely used data types. Existing general-purpose set CRDTs associate every element in the set with causal contexts as meta data. Manipulation of causal contexts can be complicated and costly. We present a new set CRDT, CLSet (causal-length set), ...
  • Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms 

    Ha, Hoai Phuong; Tran, Vi Ngoc-Nha; Umar, Ibrahim; Atalar, Aras; Gidenstam, Anders; Renaud-Goud, Paul; Tsigas, Philippas; Walulya, Ivan (Research report; Forskningsrapport; Preprint; Manuskript, 2016)
    This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of ...
  • White-box methodologies, programming abstractions and libraries 

    Ha, Hoai Phuong; Tran, Ngoc Nha Vi; Umar, Ibrahim; Atalar, Aras; Gidenstam, Anders; Renaud-Goud, Paul; Tsigas, Philippas (Research report; Forskningsrapport; Preprint; Manuskript, 2015)
    This deliverable reports the results of white-box methodologies and early results ofthe first prototype of libraries and programming abstractions as available by projectmonth 18 by Work Package 2 (WP2). It reports i) the latest results of Task 2.2on white-box methodologies, programming abstractions and libraries for developingenergy-efficient data structures and algorithms ...
  • Report on the final prototype of programming abstractions for energy-efficient inter-process communication 

    Ha, Hoai Phuong; Tran, Vi Ngoc-Nha; Umar, Ibrahim; Atalar, Aras; Gidenstam, Anders; Renaud-Goud, Paul; Tsigas, Philippas; Walulya, Ivan (Research report; Forskningsrapport; Preprint; Manuskript, 2016)
    Work package 2 (WP2) aims to develop libraries for energy-efficient inter-processcommunication and data sharing on the EXCESS platforms. The Deliverable D2.4reports on the final prototype of programming abstractions for energy-efficient inter-process communication. Section 1 is the updated overview of the prototype of pro-gramming abstraction and devised power/energy models. The Section 2-6 contain ...
  • Models for energy consumption of data structures and algorithms 

    Ha, Hoai Phuong; Tran, Ngoc Nha Vi; Umar, Ibrahim; Tsigas, Philippas; Gidenstam, Anders; Renaud-Goud, Paul; Walulya, Ivan; Atalar, Aras (Research report; Forskningsrapport; Preprint; Manuskript, 2014)
    This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been ...
  • The NOOP experimental Python programming environment 

    Andersen, Anders (Journal article; Tidsskriftartikkel; Preprint; Manuskript, 2014)
    Python is a dynamic language well suited to build a run-time providing adaptive support to distributed applications. Python has dynamic typing where variables are given a type when they are assigned a value. To introduce type safety, interfaces, and a component model in Python NOOP introduces a type language and a way to apply typing to functions (and methods). This type system is described in the ...
  • Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System 

    Woldaregay, Ashenafi Zebene; Launonen, Ilkka Kalervo; Årsand, Eirik; Albers, David; Holubova, Anna; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)
    <i>Background</i>: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key ...
  • A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism 

    Woldaregay, Ashenafi Zebene; Launonen, Ilkka Kalervo; Albers, David; Igual, Jorge; Årsand, Eirik; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-08-12)
    <i>Background</i>: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged ...
  • Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review 

    Larbi, Dillys; Randine, Pietro; Årsand, Eirik; Antypas, Konstantinos; Bradway, Meghan; Gabarron, Elia (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-07-06)
    <i>Background</i>: There is growing evidence that apps and digital interventions have a positive impact on diabetes self-management. Standard self-management for patients with diabetes could therefore be supplemented by apps and digital interventions to increase patients’ skills. Several initiatives, models, and frameworks suggest how health apps and digital interventions could be evaluated, but ...
  • Scalability of Distributed Version Control Systems 

    Murphy, Mike; Bjørndalen, John Markus; Anshus, Otto (Journal article; Tidsskriftartikkel, 2017-11-26)
    <p>Distributed version control systems are popular for storing source code, but they are notoriously ill suited for storing large binary files. <p>We report on the results from a set of experiments designed to characterize the behavior of some widely used distributed version control systems with respect to scaling. The experiments measured commit times and repository sizes when storing single files ...

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