The Experts below are selected from a list of 487116 Experts worldwide ranked by ideXlab platform
Aitor Soroa - One of the best experts on this subject based on the ideXlab platform.
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exploiting Domain Information for word sense disambiguation of medical documents
Journal of the American Medical Informatics Association, 2012Co-Authors: Mark Stevenson, Eneko Agirre, Aitor SoroaAbstract:OBJECTIVE: Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the Domain of the target documents. The authors' goal is to improve these methods by using Information about the topic of the document in which the ambiguous term appears. DESIGN: The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. MEASUREMENTS: A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. RESULTS AND DISCUSSION: The authors report a significant improvement when combining those key terms with local context, showing that Domain Information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency.
Vince D Calhoun - One of the best experts on this subject based on the ideXlab platform.
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dynamic coherence analysis of resting fmri data to jointly capture state based phase frequency and time Domain Information
NeuroImage, 2015Co-Authors: Maziar Yaesoubi, Elena A Allen, Robyn L Miller, Vince D CalhounAbstract:Abstract Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain.
Paal E Engelstad - One of the best experts on this subject based on the ideXlab platform.
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data leakage prevention for secure cross Domain Information exchange
IEEE Communications Magazine, 2017Co-Authors: Kyrre Wahl Kongsgard, Nils Agne Nordbotten, Federico Mancini, Raymond Haakseth, Paal E EngelstadAbstract:Cross-Domain Information exchange is an increasingly important capability for conducting efficient and secure operations, both within coalitions and within single nations. A data guard is a common cross-Domain sharing solution that inspects the security labels of exported data objects and validates that they are such that they can be released according to policy. While we see that guard solutions can be implemented with high assurance, we find that obtaining an equivalent level of assurance in the correctness of the security labels easily becomes a hard problem in practical scenarios. Thus, a weakness of the guard-based solution is that there is often limited assurance in the correctness of the security labels. To mitigate this, guards make use of content checkers such as dirty word lists as a means of detecting mislabeled data. To improve the overall security of such cross-Domain solutions, we investigate more advanced content checkers based on the use of machine learning. Instead of relying on manually specified dirty word lists, we can build data-driven methods that automatically infer the words associated with classified content. However, care must be taken when constructing and deploying these methods as naive implementations are vulnerable to manipulation attacks. In order to provide a better context for performing classification, we monitor the incoming Information flow and use the audit trail to construct controlled environments. The usefulness of this deployment scheme is demonstrated using a real collection of classified and unclassified documents.
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automatic security classification by machine learning for cross Domain Information exchange
Military Communications Conference, 2015Co-Authors: Hugo Lewi Hammer, Kyrre Wahl Kongsgard, Nils Agne Nordbotten, Aleksander Bai, Anis Yazidi, Paal E EngelstadAbstract:Cross-Domain Information exchange is necessary to obtain Information superiority in the military Domain, and should be based on assigning appropriate security labels to the Information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled Information is produced every day. Humans find that doing the security labeling of such Information is labor-intensive and time consuming. At the same time there is an Information explosion observed where more and more unlabeled Information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an Information object correspondingly. This paper presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an Information object.
Takashi Totsuka - One of the best experts on this subject based on the ideXlab platform.
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combining frequency and spatial Domain Information for fast interactive image noise removal
International Conference on Computer Graphics and Interactive Techniques, 1996Co-Authors: Anil N Hirani, Takashi TotsukaAbstract:Scratches on old films must be removed since these are more noticeable on higher definition and digital televisions. Wires that suspend actors or cars must be carefully erased during post production of special effects shots. Both of these are time consuming tasks but can be addressed by the following image restoration process: given the locations of noisy pixels to be replaced and a prototype image, restore those noisy pixels in a natural way. We call it image noise removal and this paper describes its fast iterative algorithm. Most existing algorithms for removing image noise use either frequency Domain Information (e.g low pass filtering) or spatial Domain Information (e.g median filtering or stochastic texture generation). The few that do combine the two Domains place the limitation that the image be band limited and the band limits be known. Our algorithm works in both spatial and frequency Domains without placing the limitations about band limits, making it possible to fully exploit advantages from each Domain. While global features and large textures are captured in frequency Domain, local continuity and sharpness are maintained in spatial Domain. With a judicious choice of operations and Domains in which they work, our dual-Domain approach can reconstruct many contiguous noisy pixels in areas with large patterns while maintaining continuity of features such as lines. In addition, the image intensity does not have to be uniform. These are significant advantages over existing algorithms. Our algorithm is based on a general framework of projection onto convex sets (POCS). Any image analysis technique that can be described as a closed convex set can be cleanly plugged into the iteration loop of our algorithm. This is another important advantage of our algorithm. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; Display Algorithms; I.3.6 [Computer Graphics]: Methodology and Techniques – Interaction techniques; I.4.4 [Image Processing]: Restoration; I.4.9 [Image Processing]: Applications. Additional
Mark Stevenson - One of the best experts on this subject based on the ideXlab platform.
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exploiting Domain Information for word sense disambiguation of medical documents
Journal of the American Medical Informatics Association, 2012Co-Authors: Mark Stevenson, Eneko Agirre, Aitor SoroaAbstract:OBJECTIVE: Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the Domain of the target documents. The authors' goal is to improve these methods by using Information about the topic of the document in which the ambiguous term appears. DESIGN: The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. MEASUREMENTS: A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. RESULTS AND DISCUSSION: The authors report a significant improvement when combining those key terms with local context, showing that Domain Information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency.