Possibilistic Granular Count: Derivation and Extension to Granular Sum

Counting data in presence of uncertainty leads to granular counts that can be represented in terms of possibility distributions. The formula of granular count is derived on the basis of two weak assumptions that can be applied in a wide variety of problems involving uncertain data. The formulation is further extended to introduce the granular sum of counts, by taking into account the interactivity of granular counts. Numerical results show the differences in terms of specificity between granular sum and a direct application of the extension principle to sum granular counts

Fast Fuzzy Inference in Octave

Fuzzy relations are simple mathematical structures that enable a very general representation of fuzzy knowledge, and fuzzy relational calculus offers a powerful machinery for approximate reasoning. However, one of the most relevant limitations of approximate reasoning is the efficiency bottleneck. In this paper, we present two implementations for fast fuzzy inference through relational composition, with the twofold objective of being general and efficient. The two implementations are capable of working on full and sparse representations respectively. Further, a wrapper procedure is capable of automatically selecting the best implementation on the basis of the input features. We implemented the code in GNU Octave because it is a high-level language targeted to numerical computations. Experimental results show the impressive performance gain when the proposed implementation is used

Introducing fuzzy quantification in OWL 2 ontologies

In this paper, we briefly report our latest achievements in fuzzy granulation of OWL 2 ontologies. More precisely, we extend a previously presented method in order to address a new class of sentences with fuzzy quantifier

A fuzzy method for RNA-Seq differential expression analysis in presence of multireads

Background: When the reads obtained from high-throughput RNA sequencing are mapped against a reference database, a significant proportion of them - known as multireads - can map to more than one reference sequence. These multireads originate from gene duplications, repetitive regions or overlapping genes. Removing the multireads from the mapping results, in RNA-Seq analyses, causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences. Results: We present an innovative approach to deal with multireads and evaluate differential expression events, entirely based on fuzzy set theory. Since multireads cause uncertainty in the estimation of read counts during gene expression computation, they can also influence the reliability of differential expression analysis results, by producing false positives. Our method manages the uncertainty in gene expression estimation by defining the fuzzy read counts and evaluates the possibility of a gene to be differentially expressed with three fuzzy concepts: over-expression, same-expression and under-expression. The output of the method is a list of differentially expressed genes enriched with information about the uncertainty of the results due to the multiread presence. We have tested the method on RNA-Seq data designed for case-control studies and we have compared the obtained results with other existing tools for read count estimation and differential expression analysis. Conclusions: The management of multireads with the use of fuzzy sets allows to obtain a list of differential expression events which takes in account the uncertainty in the results caused by the presence of multireads. Such additional information can be used by the biologists when they have to select the most relevant differential expression events to validate with laboratory assays. Our method can be used to compute reliable differential expression events and to highlight possible false positives in the lists of differentially expressed genes computed with other tools

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

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Interpretability of Fuzzy Information Granules

Human-Centric Information Processing requires tight communication processes between users and computers. These two actors, however, traditionally use different paradigms for representing and manipulating information. Users are more inclined in managing perceptual information, usually expressed in natural language, whilst computers are formidable number-crunching systems, capable of manipulating information expressed in precise form. Fuzzy information granules could be used as a common interface for communicating information and knowledge, because of their ability of representing perceptual information in a computer manageable form. Nonetheless, this connection could be established only if information granules are interpretable, i.e. they are semantically co-intensive with human knowledge. Interpretable information granulation opens several methodological issues, regarding the representation and manipulation of information granules, the interpretability constraints and the granulation processes. By taking into account all such issues, effective Information Processing systems could be designed with a strong Human-Centric imprint