NR vitenarkiv: Recent submissions
Now showing items 241-260 of 291
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Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation
(Chapter, 2020)We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes ... -
Estimating Traffic in Urban Areas from Very-High Resolution Aerial Images
(Journal article; Peer reviewed, 2021)Traffic estimation from very-high-resolution remote-sensing imagery has received increasing interest during the last few years. In this article, we propose an automatic system for estimation of the annual average daily ... -
Dense dilated convolutions merging network for land cover classification
(Journal article; Peer reviewed, 2020)Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the ... -
Acoustic classification in multifrequency echosounder data using deep convolutional neural networks
(Journal article; Peer reviewed, 2020)Acoustic target classification is the process of assigning observed acoustic backscattering intensity to an acoustic category. A deep learning strategy for acoustic target classification using a convolutional network is ... -
Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh
(Journal article; Peer reviewed, 2020)Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is ... -
Large-Scale Vegetation Height Mapping from Sentinel Data Using Deep Learning
(Chapter, 2020)The deep learning revolution in computer vision has enabled a potential for creating new value chains for Earth observation that significantly enhances the analysis of satellite data for tasks like land cover mapping, ... -
Learning latent representations of bank customers with the Variational Autoencoder
(Journal article; Peer reviewed, 2020)Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In ... -
Deep generative models for reject inference in credit scoring
(Journal article; Peer reviewed, 2020)Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of ... -
Security in Android smartphone, Confidentiality in IoT-enabled Smart Grids, and Fault-tolerant privacy-preserving in AMS
(NR-notat;, Research report, 2020)The report summarizes the three journal papers which address security in android smartphone, confidentiality in IoT-enabled smart grids, and fault-tolerant privacy-preserving in AMS, respectively. -
Optimized Anticipatory Adaptive Security Models for IoT-enabled Smart Grids
(Research report, 2020)The objective of this deliverable is to improve the accuracy of the adaptive mechanisms for different IoTs processing capabilities by applying high-level optimization using machine learning and AI approaches. In adaptive ... -
Anticipatory Adaptive Security for IoT-based Smart Grids Infrastructure and Value-added Services
(NR-notat;, Research report, 2020)The report describes the research and development of adaptive security addressing the protection of "IoT-based smart grids" against evolutionary threats and attacks through the prediction and advanced behavioural analysis ... -
Teknologiens mange sider i synshemmedes arbeidsliv
(Report at the Norwegian Computing Center;, Research report, 2020) -
Reproducible Data Management and Analysis using R
(Chapter, 2020)Standardizing and documenting computational analyses is necessary to ensure reproducible results. We describe an R-based implementation of data management and preprocessing that is well integrated with the analysis tools ... -
Teknologiens ambivalens for arbeidstakere med nedsatt syn
(Report at the Norwegian Computing Center;, Research report, 2020) -
Risikomodell for vannskader på bygninger og sensitivitet i klimaframskrivninger
(NR-notat;, Research report, 2020) -
Signals of Death - Post-Diagnostic Single Gene Expression Trajectories in Breast Cancer - A Proof of Concept
(Chapter, 2020)Using the time-dependent dynamics of gene expression from immune cells in blood, we aimed to explore single gene expression trajectories as biomarkers for death after a diagnosis of breast cancer introducing a new statistical ... -
On the number of bins in a rank histogram
(Journal article; Peer reviewed, 2020) -
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values
(Journal article; Peer reviewed, 2020) -
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees
(Chapter, 2022)It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from ... -
Partially linear monotone methods with automatic variable selection and monotonicity direction discovery
(Journal article; Peer reviewed, 2020)