Search results for " artificial intelligence"
showing 10 items of 1992 documents
Spatial noise-aware temperature retrieval from infrared sounder data
2020
In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using…
A Fuzzy Chance-constraint Programming Model for a Home Health Care Routing Problem with Fuzzy Demand
2017
Clausal coordination in Finnish Sign Language
2016
This paper deals with the coordination of clauses in Finnish Sign Language (FinSL). Building on conversational data, the paper first shows that linking in conjunctive coordination in FinSL is primarily asyndetic, whereas in adversative and disjunctive coordination FinSL prefers syndetic linking. Secondly, the paper investigates the nonmanual prosody of coordination: nonmanual activity is shown both to mark the juncture of the coordinand clauses and to draw their contours. Finally, the paper addresses certain forms of clausal coordination in FinSL that are sign language-specific. It is suggested that the sign language-specific properties of coordination are caused both by the fact that signe…
The Average State Complexity of the Star of a Finite Set of Words Is Linear
2008
We prove that, for the uniform distribution over all sets Xof m(that is a fixed integer) non-empty words whose sum of lengths is n, $\mathcal{D}_X$, one of the usual deterministic automata recognizing X*, has on average $\mathcal{O}(n)$ states and that the average state complexity of X*is i¾?(n). We also show that the average time complexity of the computation of the automaton $\mathcal{D}_X$ is $\mathcal{O}(n\log n)$, when the alphabet is of size at least three.
An Interactive Framework for Offline Data-Driven Multiobjective Optimization
2020
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the d…
Visual saliency detection in colour images based on density estimation
2017
International audience; A simple and effective method for visual saliency detection in colour images is presented. The method is based on the common observation that local salient regions exhibit distinct geometric and and texture patterns from neighbouring regions. We model the colour distribution of local image patches with a Gaussian density and measure the saliency of each patch as the statistical distance from that density. Experimental results with public datasets and comparison with other state-of-the-art methods show the effectiveness of our method.
K-nearest neighbor driving active contours to delineate biological tumor volumes
2019
Abstract An algorithm for tumor delineation in positron emission tomography (PET) is presented. Segmentation is achieved by a local active contour algorithm, integrated and optimized with the k-nearest neighbor (KNN) classification method, which takes advantage of the stratified k-fold cross-validation strategy. The proposed approach is evaluated considering the delineation of cancers located in different body districts (i.e. brain, head and neck, and lung), and considering different PET radioactive tracers. Data are pre-processed in order to be expressed in terms of standardized uptake value, the most widely used PET quantification index. The algorithm uses an initial, operator selected re…
Foreign Aid to Education in Sub-Saharan Africa : How Useful Is It?
1988
International audience
Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation
2016
The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…
A privacy enhanced device access protocol for an IoT context
2013
In this paper, we present the case for a device authentication protocol that authenticates a device/service class rather than an individual device. The devices in question are providing services available to the public. The proposed protocol is an online protocol, and it uses a pseudo-random temporary identity scheme to provide user privacy. This allows the Internet-of-Things device to have full assurance of the user, with respect to the request service, while permitting the user to remain anonymous with respect to the device. The user can then enjoy identity and location privacy in addition to untraceability with respect to device access. Copyright © 2013 John Wiley & Sons, Ltd.