Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Group Reverse Nearest Neighbor Search using Modified Skip Graph

The reverse nearest neighbor search is used for spatial queries. The reverse nearest neighbor search, the object in high dimensional space has a certain region where all objects inside the region will think of query object as their nearest neighbor. The existing methods for reverse nearest neighbor search are limited to the single query point, which is inefficient for the high dimensional spatial databases etc. Therefore, in this paper we proposed a group reverse nearest neighbor search which can find multiple query objects in a specific region. In this paper we proposed method for group reverse nearest neighbor queries using modified skip graph

Genetic variation in neotropical butterflies is associated with sampling scale, species distributions, and historical forest dynamics

Previous studies of butterfly diversification in the Neotropics have focused on Amazonia and the tropical Andes, while southern regions of the continent have received little attention. To address the gap in knowledge about the Lepidoptera of temperate South America, we analysed over 3000 specimens representing nearly 500 species from Argentina for a segment of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Representing 42% of the country's butterfly fauna, collections targeted species from the Atlantic and Andean forests, and biodiversity hotspots that were previously connected but are now isolated. We assessed COI effectiveness for species discrimination and identification and how its performance was affected by geographic distances and taxon coverage. COI data also allowed to study patterns of genetic variation across Argentina, particularly between populations in the Atlantic and Andean forests. Our results show that COI discriminates species well, but that identification success is reduced on average by ~20% as spatial and taxonomic coverage rises. We also found that levels of genetic variation are associated with species' spatial distribution type, a pattern which might reflect differences in their dispersal and colonization abilities. In particular, intraspecific distance between populations in the Atlantic and Andean forests was significantly higher in species with disjunct distributions than in those with a continuous range. All splits between lineages in these forests dated to the Pleistocene, but divergence dates varied considerably, suggesting that historical connections between the Atlantic and Andean forests have differentially affected their shared butterfly fauna. Our study supports the fact that large-scale assessments of mitochondrial DNA variation are a powerful tool for evolutionary studies.Fil: Attiná, Natalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Núñez Bustos, Ezequiel Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Lijtmaer, Dario Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Hebert, Paul David Neil. University of Guelph; CanadáFil: Tubaro, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Lavinia Oblanca, Pablo Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina. Universidad Nacional de Río Negro; Argentin

Methods of Hierarchical Clustering

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference

Privacy protection in location based services

This thesis takes a multidisciplinary approach to understanding the characteristics of Location Based Services (LBS) and the protection of location information in these transactions. This thesis reviews the state of the art and theoretical approaches in Regulations, Geographic Information Science, and Computer Science. Motivated by the importance of location privacy in the current age of mobile devices, this thesis argues that failure to ensure privacy protection under this context is a violation to human rights and poses a detriment to the freedom of users as individuals. Since location information has unique characteristics, existing methods for protecting other type of information are not suitable for geographical transactions. This thesis demonstrates methods that safeguard location information in location based services and that enable geospatial analysis. Through a taxonomy, the characteristics of LBS and privacy techniques are examined and contrasted. Moreover, mechanisms for privacy protection in LBS are presented and the resulting data is tested with different geospatial analysis tools to verify the possibility of conducting these analyses even with protected location information. By discussing the results and conclusions of these studies, this thesis provides an agenda for the understanding of obfuscated geospatial data usability and the feasibility to implement the proposed mechanisms in privacy concerning LBS, as well as for releasing crowdsourced geographic information to third-parties