Computer-aided Interactive Classification: Applications of VIBE

Tools like the VIBE visualization system permit human analysts to use both an understanding of a data set's content and a recognition of structure that the visualization reveals. But what happens when a database's semantics are hidden from the analyst? What guidelines or heuristics can he or she use to reveal the "correct" underlying structure? Results of two experiments conducted at the University of Pittsburgh support the claim that VIBE analysts can uncover a meaningful clustering even without semantic clues. In one experiment artificial data sets were created in which some of the variables discriminate one or more clusters and the other half contribute only random noise. Variable selection guidelines based on computed discrimination value were used in an attempt to distinguish between the signal and noise variables. In a second experiment, a human analyst's encoding of 714 short phrases to 23 overlapping and inter-related categories was stripped of meaningful titles and relabeled with integers. A VIBE analyst was able to highlight relationships among the 23 categories solely on the basisof co-assignment of the phrases

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas

Assessment of the Food Habits of the Moroccan Dorcas Gazelle in M’Sabih Talaa, West Central Morocco, Using the trnL Approach

Food habits of the Moroccan dorcas gazelle, Gazella dorcas massaesyla, previously investigated in the 1980s using microhistological fecal analysis, in the M’Sabih Talaa Reserve, west central Morocco, were re-evaluated over three seasons (spring, summer and autumn 2009) using the trnL approach to determine the diet composition and its seasonal variation from fecal samples. Taxonomic identification was carried out using the identification originating from the database built from EMBL and the list of plant species within the reserve. The total taxonomic richness in the reserve was 130 instead of 171 species in the 1980s. The diet composition revealed to be much more diversified (71 plant taxa belonging to 57 genus and 29 families) than it was 22 years ago (29 identified taxa). Thirty-four taxa were newly identified in the diet while 13 reported in 1986–87 were not found. Moroccan dorcas gazelle showed a high preference to Acacia gummifera, Anagallis arvensis, Glebionis coronaria, Cladanthus arabicus, Diplotaxis tenuisiliqua, Erodium salzmannii, Limonium thouini, Lotus arenarius and Zizyphus lotus. Seasonal variations occurred in both number (40–41 taxa in spring-summer and 49 taxa in autumn vs. respectively 23–22 and 26 in 1986–1987) and taxonomic type of eaten plant taxa. This dietary diversification could be attributed either to the difference in methods of analysis, trnL approach having a higher taxonomic resolution, or a potential change in nutritional quality of plants over time

Russula

Fung

Helvella

Fung