Abstract Platform and Transformations for Model-Driven Service-Oriented Development

In this paper, we discuss the use of abstract platforms and transformation for designing applications according to the principles of the service-oriented architecture. We illustrate our approach by discussing the use of the service discovery pattern at a platform-independent design level. We show how a trader service can be specified at a high-level of abstraction and incorporated in an abstract platform for service-oriented development. Designers can then build platform-independent models of applications by composing application parts with this abstract platform. Application parts can use the trader service to publish and discover service offers. We discuss how the abstract platform can be realized into two target platforms, namely Web Services (with UDDI) and CORBA (with the OMG trader)

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Query by String word spotting based on character bi-gram indexing

In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasetsComment: To be published in ICDAR201