Artikler, rapporter og annet (informatikk): Recent submissions
Now showing items 341-360 of 483
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Masking the Effects of Delays in Human-to-Human Remote Interaction
(Chapter; Bokkapittel, 2014)Humans can interact remotely with each other through computers. Systems supporting this include teleconferencing, games and virtual environments. There are delays from when a human does an action until it is reflected remotely. When delays are too large, they will result in inconsistencies in what the state of the interaction is as seen by each participant. The delays can be reduced, but they cannot ... -
Wearable Sensors with Possibilities for Data Exchange: Analyzing Statusand Needs of Different Actors in Mobile Health Monitoring Systems
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-31)<i>Background</i> - Wearable devices with an ability to collect various type of physiological data are increasingly becoming seamlessly integrated into everyday life of people. In the area of electronic health (eHealth), many of these devices provide remote transfer of health data, as a result of the increasing need for ambulatory monitoring of patients. This has a potential to reduce the cost ... -
Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in Type 1 diabetes
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-05-01)<p><i>Background - </i>Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading ... -
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-06-06)We have attempted to reproduce the results in <i>Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs</i>, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS ... -
Recommendations with a Nudge
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-06-13)In areas such as health, environment, and energy consumption, there is a need to do better. A common goal in society is to get people to behave in ways that are sustainable for the environment or support a healthier lifestyle. Nudging is a term known from economics and political theory, for influencing decisions and behavior using suggestions, positive reinforcement, and other non-coercive means. ... -
Convolutional Neural Network for Breathing Phase Detection in Lung Sounds
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-04-15)We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the ... -
Improved maximal strength is not associated with improvements in sprint time or jump height in high-level female football players: a clusterrendomized controlled trial
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-09-17)<i>Background</i> - Maximal strength increments are reported to result in improvements in sprint speed and jump height in elite male football players. Although similar effects are expected in females, this is yet to be elucidated. The aim of this study was to examine the effect of maximal strength training on sprint speed and jump height in high-level female football players.<p> <p><i>Methods</i> ... -
Performance optimization and modeling of fine-grained irregular communication in UPC
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-03-03)The Unified Parallel C (UPC) programming language offers parallelism via logically partitioned shared memory, which typically spans physically disjoint memory subsystems. One convenient feature of UPC is its ability to automatically execute between-thread data movement, such that the entire content of a shared data array appears to be freely accessible by all the threads. The programmer friendliness, ... -
Interdisciplinary optimism? Sentiment analysis of Twitter data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-31)Interdisciplinary research has faced many challenges, including institutional, cultural and practical ones, while it has also been reported as a ‘career risk’ and even ‘career suicide’ for researchers pursuing such an education and approach. Yet, the propagation of challenges and risks can easily lead to a feeling of anxiety and disempowerment in researchers, which we think is counterproductive to ... -
Validity of the Polar M430 Activity Monitor in Free-Living Conditions: Validation Study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-08-16)<i>Background</i>: Accelerometers, often in conjunction with heart rate sensors, are extensively used to track physical activity (PA) in research. Research-grade instruments are often expensive and have limited battery capacity, limited storage, and high participant burden. Consumer-based activity trackers are equipped with similar technology and designed for long-term wear, and can therefore ... -
App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018)<p><i>Background</i>: Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored.</p> <p><i>Objective</i>: To study the gap between literature ... -
META-pipe cloud setup and execution
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-01-18)META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the ... -
Uni- and triaxial accelerometric signals agree during daily routine, but show differences between sports
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-10-10)Accelerometers objectively monitor physical activity, and ongoing research suggests they can also detect patterns of body movement. However, different types of signal (uniaxial, captured by older studies, vs. the newer triaxial) and or/device (validated Actigraph used by older studies, vs. others) may lead to incomparability of results from different time periods. Standardization is desirable. We ... -
Norwegian e-Infrastructure for Life Sciences (NeLS)
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-06-29)The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may ... -
A systematic review of cluster detection mechanisms in syndromic surveillance: Towards developing a framework of cluster detection mechanisms for EDMON system
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018)Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing ... -
Shrinkage estimation of rate statistics
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-10-08)This paper presents a simple shrinkage estimator of rates based on Bayesian methods. Our focus is on crime rates as a motivating example. The estimator shrinks each town’s observed crime rate toward the country-wide average crime rate according to town size. By realistic simulations we confirm that the proposed estimator outperforms the maximum likelihood estimator in terms of global risk. We also ... -
Performance principles for trusted computing with intel SGX
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-14)Cloud providers offering Software-as-a-Service (SaaS) are increasingly being trusted by customers to store sensitive data. Companies often monetize such personal data through curation and analysis, providing customers with personalized application experiences and targeted advertisements. Personal data is often accompanied by strict privacy and security policies, requiring data processing to be ... -
Deep learning and hand-crafted feature based approaches for polyp detection in medical videos
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-23)Video analysis including classification, segmentation or tagging is one of the most challenging but also interesting topics multimedia research currently try to tackle. This is often related to videos from surveillance cameras or social media. In the last years, also medical institutions produce more and more video and image content. Some areas of medical image analysis, like radiology or brain ... -
Dissecting deep neural networks for better medical image classification and classification understanding
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-23)Neural networks, in the context of deep learning, show much promise in becoming an important tool with the purpose assisting medical doctors in disease detection during patient examinations. However, the current state of deep learning is something of a "black box", making it very difficult to understand what internal processes lead to a given result. This is not only true for non-technical users but ... -
Design and development of a context-aware knowledge-based module for identifying relevant information and information gaps in patients with type 1 diabetes self-collected health data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-11)<p><i>Background</i>: Patients with diabetes use an increasing number of self-management tools in their daily life. However, health institutions rarely use the data generated by these services mainly due to (1) the lack of data reliability, and (2) medical workers spending too much time extracting relevant information from the vast amount of data produced. This work is part of the FullFlow project, ...