Change detection in urban areas from remote sensing data: a multidimensional classification scheme

Change detection (CD) from remote sensing data is a very challenging research problem, especially when we analyse an urban scene. Urban scenes are composed of many different types of objects, both natural and man-made. The building class is one of the important and most complex classes to analyse, important because it is useful for so many applications and complex because it exhibits many changes due to human activity and natural catastrophes. For these reasons, we focus our study on building change detection (BCD). In this paper we propose a classification scheme for BCD research according to several important dimensions including objective, input data, temporal resolution, analysis unit, target output unit, building features, processing technique, change categories, and assessment of results. This classification scheme can guide practitioners in choosing appropriate change detection methods to achieve their goals as well as informing new research efforts. Based on this multidimensional characterisation of BCD, we offer a number of suggestions for further work to be done in this field.     

Listing all maximal cliques in large graphs on vertex-centric model

The Journal of Supercomputing - Maximal Clique Enumeration (MCE), which consists to enumerate all maximal complete subgraphs in a given graph, is a fundamental problem in graph theory, and it is...     

SemWebDL: A privacy-preserving Semantic Web infrastructure for digital libraries

Recent advances in digital libraries have been closely intertwined with advances in Internet technologies. With the advent of the Web, digital libraries have been able to reach constituencies previously unanticipated. Because of the wide deployability of Web-accessible digital libraries, the potential for privacy violations has also grown tremendously. The much touted Semantic Web, with its agent, service, and ontology technologies, is slated to take the Web to another qualitative level in advances. Unfortunately, these advances may also open doors for privacy violations in ways never seen before. We propose a Semantic Web infrastructure, called SemWebDL, that enables the dynamic composition of disparate and autonomous digital libraries while preserving user privacy. In the proposed infrastructure, users will be able to pose more qualitative queries that may require the ad hoc collaboration of multiple digital libraries. In addition to the Semantic Web-based infrastructure, the quality of the response would rest on extraneous information in the form of a profile. We introduce the concept of communities to enable subject-based cooperation and search speedup. Further, digital libraries heterogeneity and autonomy are transcended by a layered Web-service-based infrastructure. Semantic Web-based digital library providers would advertise to Web services, which in turn are organized in communities accessed by users. For the purpose of privacy preservation, we devise a three-tier privacy model consisting of user privacy, Web service privacy, and digital library privacy that offers autonomy of perspectives for privacy definition and violation. We propose an approach that seamlessly interoperates with potentially conflicting privacy definitions and policies at the different levels of the Semantic Web-based infrastructure. A key aspect in the approach is the use of reputations for outsourcing Web services. A Web service reputation is associated with its behavior with regard to privacy preservation. We developed a technique that uses attribute ontologies and information flow difference to collect, evaluate, and disseminate the reputation of Web services.