Next steps in preparing for Geoinformation Society. A tentative for strategic educational approach. Angela Ioni??1, Aurel B?loi2, Maria Vi?an2 1 Research Institute for Artificial Intelligence ,,Mihai Dr?g?nescu", Romanian Academy 2 Intergraph Computer Services s.r.l. Rom?nia The Knowledge Society with its many facets including Spatially Enabled Society and Government or Geoinformation Society is the new step in the evolution of Information Society associated with a new characteristic named creativity. In this society there is a continuous and intensive demand for competence in order to overcome the barriers in relation with resources, knowledge and skills, institutions, attitudes and beliefs, assessment, and an imperious need to create a new culture. In different studies has been highlighted the large range of obstacles hindering the distribution of geoinformation and GIS throughout different education systems in Europe. Though Geoinformation has been mentioned as a priority in the IST (Information Society Technology) research programme, in any document's of different policies or action plans there is no any mention of it as priority. More than, no mention concerning any ideas in connection with education in order to assure the appropriate amount of skills and to create the competencies. This indicate a major gap in policy making largely created by the subsidiary nature of education in the European activities with deference to national policies and programmes. Romania is not an exception. This paper examines the outcomes in terms of curriculum opportunities and realities for the use of geoinformation in schools and in teacher education and in research. The importance of professional development for teachers and the widespread use and implementation European standards is specifically highlighted. In additional will be presented the targets for next, short, medium and long term in order to create the premises of a new culture connected with geoinformation and their applications in the new step of society.
Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones.
The digital era brings up on one hand massive amounts of available data, and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, bio-medical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also evaluated in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of massmedia broadcast materials. Thus, the present paper proposes a GPU based approach for parallel implementation of the Jaro-Winkler string similarity metric computation. Further on, a thresholding-based algorithm is also implemented using GPU for matching records over large datasets. The global GPU RAM memory is used to store multiple string lines as raw data. In the case of a single string, its comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Moreover, based on the computed similarity metrics, an adaptive neural network approach guided by a novelty detection classifier together with a naive neural network implementation are proposed to increase the accuracy of the records matching procedure. Timing considerations and the computational complexity are detailed for the proposed approaches compared with state-of-the-art CPU and GPU approaches. A speed-up factor of 21.6 was obtained for the GPU based JaroWinkler implementation compared with the general purpose processor one, whereas improved accuracy for the records matching procedure was delivered using machine learning approaches.
The digital era brings up on one hand massive amounts of available data and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.
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