Solving identity delegation problem in the e-government environment

At present, many countries allow citizens or entities to interact with the government outside the telematic environment through a legal representative who is granted powers of representation. However, if the interaction takes place through the Internet, only primitive mechanisms of representation are available, and these are mainly based on non-dynamic offline processes that do not enable quick and easy identity delegation. This paper proposes a system of dynamic delegation of identity between two generic entities that can solve the problem of delegated access to the telematic services provided by public authorities. The solution herein is based on the generation of a delegation token created from a proxy certificate that allows the delegating entity to delegate identity to another on the basis of a subset of its attributes as delegator, while also establishing in the delegation token itself restrictions on the services accessible to the delegated entity and the validity period of delegation. Further, the paper presents the mechanisms needed to either revoke a delegation token or to check whether a delegation token has been revoked. Implications for theory and practice and suggestions for future research are discussed.     

In vivo characterization of renal auto-antigens involved in human auto-immune diseases: the case of membranous glomerulonephritis

Renal auto-immune diseases represent a major source of morbidity in humans. For many years the knowledge on mechanisms of auto-immunity involving the kidney has been uniquely based on animal models. However, these findings often could not be readily translated to humans owing to notably difference in antigen expression by human podocytes. One example is Heymann nephritis (HN), the experimental model of human membranous glomerulonephritis (MGN), which is obtained in rats by injecting antibodies against megalin, a protein that is not present in human glomeruli. Human studies could not be done in the past since sequencing required too much material exceeding what obtainable from tissue biopsies in vivo. Research is now on the way to identify auto-antigens and isolate specific auto-antibodies in humans. New technology developments based on tissue microdissection and proteomical analysis have facilitated the recent discoveries, allowing direct analysis of human tissue in vivo. Major advances on the pathogenesis of MGN, the prototype for the formation and glomerular deposition of auto-antibodies, are now in progress. Two independent groups have, in fact, demonstrated the existence of specific IgG(4) against phospholipase A2 receptor, aldose reductase and Mn-superoxide dismutase in glomerular eluates and in plasma of a prominent part of patients with MGN, suggesting a major role of these proteins as auto-antigens in human MGN. This review will focalize these aspects outlining the contribution of proteomics in most recent developments.     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Fault detection for discrete event systems using Petri nets with unobservable transitions

In this paper we present a fault detection approach for discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. Our diagnosis approach is based on the notions of basis marking and justification, that allow us to characterize the set of markings that are consistent with the actual observation, and the set of unobservable transitions whose firing enable it. This approach applies to all net systems whose unobservable subnet is acyclic. If the net system is also bounded the proposed approach may be significantly simplified by moving the most burdensome part of the procedure off-line, thanks to the construction of a graph, called the basis reachability graph.     

Revisiting Zhang's 1D calibration algorithm

In recent years, the camera calibration using 1D patterns has been studied and improved by researchers all over the world. However, the progress in that area has been mainly in the sense of reducing the restrictions to the 1D pattern movement. On the other hand, the method's accuracy still demands improvements. In the present paper, the original technique proposed by Zhang is revisited and we demonstrate that the method's accuracy can be significantly improved, simply by analyzing and reformulating the problem. The numerical conditioning can be improved if a simple data normalization is performed. Furthermore, a non-linear solution based on the Partitioned Levenberg-Marquardt algorithm is proposed. That solution takes advantage of the problem's particular structure to reduce the computational complexity of the original method and to improve the accuracy. Tests using both synthetic and real images demonstrate that the calibration method using 1D patterns can be applied in practice, with accuracy comparable to other already traditional methods.     

ISS feedback control redesign for continuous stirred tank reactors

In this paper a new nonlinear feedback control law for continuous stirred tank reactors, obtained by exact Input/Output (I/O) linearization and Sontag's input‐to‐state stability feedback control redesign methods, is studied. By the new feedback control law, a significant attenuation of the disturbance effect is guaranteed, with evident improvement with respect to control laws obtained by the exact I/O linearization method. The performed simulations validate the theoretical results. Copyright © 2010 John Wiley & Sons, Ltd.     

2.5D Elastic graph matching

In this paper, we propose novel elastic graph matching (EGM) algorithms for face recognition assisted by the availability of 3D facial geometry. More specifically, we conceptually extend the EGM algorithm in order to exploit the 3D nature of human facial geometry for face recognition/verification. In order to achieve that, first we extend the matching module of the EGM algorithm in order to capitalize on the 2.5D facial data. Furthermore, we incorporate the 3D geometry into the multiscale analysis used and build a novel geodesic multiscale morphological pyramid of dilations/erosions in order to fill the graph jets. We show that the proposed advances significantly enhance the performance of EGM algorithms. We demonstrate the efficiency of the proposed advances in the face recognition/verification problem using photometric stereo.