Document Type De�nition (DTD) Metrics

In this paper, we present two complexity metrics for the assessment of schema quality written in Document Type De�finition (DTD) language. Both "Entropy (E) metric: E(DTD)" and "Distinct Structured Element Repetition Scale (DSERS) metric: DSERS(DTD)" are intended to measure the structural complexity of schemas in DTD language. These metrics exploit a directed graph representation of schema document and consider the complexity of schema due to its similar structured elements and the occurrences of these elements. The empirical and theoretical validations of these metrics prove the robustness of the metrics

Comparative Analysis of XML Schema Languages for Improved Entropy Metric

The eXtensible Markup Language (XML) is a data set to represent data in a format that is both human readable and machine readable. For XML documents to provide understanding about data exchange between applications, XML schema documents should be validated against the schema language. Most existing schema metrics were implemented differently in Document Type Definition (DTD), XML Schema Definition (XSD) and Regular Language for Next Generation (RNG) but never compare XML schema languages on any metric. Hence this paper compared three different schema languages on Improved Entropy Metric (IEM) using the Number of Attributes (NA), Number of Equivalence Class (NEC), Frequency Occurrence of Class (FOCi) and Number of Elements (NE). The proposed metric was applied on real schemas documents data are acquired from Web Service Description Language (WSDL) and implemented in DTD, XSD and RNG. The result showed that RNG reduce complexity of class elements, reflect strong support for class elements to appear in any order which showed more reusability and flexibility traits and overall understanding of the schema documents becomes much easier because RNG can be algorithmically converted and partner with other schema languages therefore this reduces maintenance effort. Keywords— XML Schema Language, Schema Documents, Schema Metric

Entropy as a Measure of Quality of XML Schema Document

In this paper, a metric for the assessment of the structural complexity of eXtensible Markup Language schema document is formulated. The present metric ‘Schema Entropy is based on entropy concept and intended to measure the complexity of the schema documents written in W3C XML Schema Language due to diversity in the structures of its elements. The SE is useful in evaluating the efficiency of the design of Schemas. A good design reduces the maintainability efforts. Therefore, our metric provides valuable information about the reliability and maintainability of systems. In this respect, this metric is believed to be a valuable contribution for improving the quality of XML-based systems. It is demonstrated with examples and validated empirically through actual test cases

Matrix powers algorithms for trust evaluation in PKI architectures

This paper deals with the evaluation of trust in public-key infrastructures. Different trust models have been proposed to interconnect the various PKI components in order to propagate the trust between them. In this paper we provide a new polynomial algorithm using linear algebra to assess trust relationships in a network using different trust evaluation schemes. The advantages are twofold: first the use of matrix computations instead of graph algorithms provides an optimized computational solution; second, our algorithm can be used for generic graphs, even in the presence of cycles. Our algorithm is designed to evaluate the trust using all existing (finite) trust paths between entities as a preliminary to any exchanges between PKIs. This can give a precise evaluation of trust, and accelerate for instance cross-certificate validation

Statistically Motivated Second Order Pooling

Second-order pooling, a.k.a.~bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models. Our approach is motivated by a statistical analysis of the network's activations, relying on operations that lead to a Gaussian-distributed final representation, as inherently used by first-order deep networks. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.Comment: Accepted to ECCV 2018. Camera ready version. 14 page, 5 figures, 3 table

Measuring and Evaluating a Design Complexity Metric for XML Schema Documents

The eXtensible Markup Language (XML) has been gaining extraordinary acceptance from many diverse enterprise software companies for their object repositories, data interchange, and development tools. Further, many different domains, organizations and content providers have been publishing and exchanging information via internet by the usage of XML and standard schemas. Efficient implementation of XML in these domains requires well designed XML schemas. In this point of view, design of XML schemas plays an extremely important role in software development process and needs to be quantified for ease of maintainability. In this paper, an attempt has been made to evaluate the quality of XML schema documents (XSD) written in W3C XML Schema language. We propose a metric, which measures the complexity due to the internal architecture of XSD components, and due to recursion. This is the single metric, which cover all major factors responsible for complexity of XSD. The metric has been empirically and theoretically validated, demonstrated with examples and supported by comparison with other well known structure metrics applied on XML schema documents

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets