COORDINSPECTOR: a tool for extracting coordination data from legacy code

More and more current software systems rely on non trivial coordination logic for combining autonomous services typically running on different platforms and often owned by different organizations. Often, however, coordination data is deeply entangled in the code and, therefore, difficult to isolate and analyse separately. COORDINSPECTOR is a software tool which combines slicing and program analysis techniques to isolate all coordination elements from the source code of an existing application. Such a reverse engineering process provides a clear view of the actually invoked services as well as of the orchestration patterns which bind them together. The tool analyses Common Intermediate Language (CIL) code, the native language of Microsoft .Net Framework. Therefore, the scope of application of COORDINSPECTOR is quite large: potentially any piece of code developed in any of the programming languages which compiles to the .Net Framework. The tool generates graphical representations of the coordination layer together and identifies the underlying business process orchestrations, rendering them as Orc specifications

The referent : a new referential methodology and procedure in live drawing

My research in live drawing and new technologies uses a combination of a human figure in live in composition, overlaid with a digital projection of a second human figure. The aim is to explore, to amplify and thoroughly analyse the search for distinctive identities and graphic languages of representation for live and projected models.

Gráficos de probabilidade normal para avaliação de mecanismos de distribuição de sementes em semeadoras

Currently great emphasis is given for seed metering that assist rigorous demands in relation to longitudinal distribution of seeds, as well as to the index of fails in spacing laws, breaks and double seeds. The evaluation of these variable demands much time and work of attainment of data and processing. The objective of this work went propose to use of graphs of normal probability, facilitating the treatment of the data and decreasing the time of processing. The evaluation methodology consists in the counting of broken seeds, fail spacing and double seeds through the measure of the spacing among seeds, preliminary experiments through combinations of treatments had been carried through whose factors of variation were the level of the reservoir of seeds, the leveling of the seed metering, the speed of displacement and dosage of seeds. The evaluation was carried through in two parts, first through preliminary experiments for elaboration of the graphs of normal probability and later in experiments with bigger sampling for evaluation of the influence of the factors most important. It was done the evaluation of seed metering of rotating internal ring, and the amount of necessary data for the evaluation was very decreased through of the graphs of normal probability that facilitated to prioritize only the significant factors. The dosage of seeds was factor that more important because factor (D) have greater significance.

Alimentação de peixes: proposta para análise de itens registrados em conteúdos gástricos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

X̄ charts with variable sample size

The usual practice in using a control chart to monitor a process is to take samples of size n from the process every h hours. This article considers the properties of the X̄ chart when the size of each sample depends on what is observed in the preceding sample. The idea is that the sample should be large if the sample point of the preceding sample is close to but not actually outside the control limits and small if the sample point is close to the target. The properties of the variable sample size (VSS) X̄ chart are obtained using Markov chains. The VSS X̄ chart is substantially quicker than the traditional X̄ chart in detecting moderate shifts in the process.

Automatic segmentation of digital images applied in cardiac medical images

The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE.

Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

The use of elevation map histograms for topographic heterogeneity analysis

Surface defects are extremely important in mechanical characterization of several different materials. Therefore, the analysis of surface finishing is essential for a further simulation of surface mechanical properties in a customized project in materials science and technology. One of the methods commonly employed for such purpose is the statistical mapping of different sample surface regions using the depth from focus technique. The analysis is usually performed directly from the elevation maps which are obtained from the digital image processing. In this paper, the possibility of quantifying the surface heterogeneity of Silicon Carbide porous ceramics by elevation map histograms is presented. The advantage of this technique is that it allows the qualitative or quantitative verification of all surface image fields that cannot be done by using the Surface Plot plugin of image J™ platform commonly used in digital image processing. © 2012 Springer Science+Business Media, LLC.

Técnicas de obtenção da posição de relação central em pacientes edêntulos

Edentulism causes loss of horizontal intermaxillary relationship, which is defined by the condyle position in the joint cavities, called centric relation. The correct record of centric relation in edentulous pacients has a strong influence on the treatment final result and is considered one of the most difficult clinical steps to achieve success. The aim of this study was to describe the main centric relation recording methods for edentulous patients reported in the literature. The study described physiological methods such as swallowing and pull tongue back, manipulative and graphic methods. It is concluded that the combination of different methods facilitates the correct centric relation record. Dentists must have a good knowledge of those techniques to perform this procedure satisfactorily.

Alignment charts, construction and use /

"First published December 1944. Reprinted January 1946."

Lehrbuch der differentialrechnung und reihentheorie mit strenger begründung der infinitesimalrechnung.

Mode of access: Internet.

Nomographie, ou, Traité des abaques.

On cover: 2 éd.

Einführung in die Nomographie...

1.T. Die Funktionsleiter.--2.T. Die Zeichnung als Rechenmaschine.