Preface

Preface

Preface     

An improved BioHashing for human authentication

Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user specific biometric feature (BioHashing) has received much attention. These methods have significant functional advantages over sole biometrics i.e. zero equal error rate. The main drawback of the base BioHashing method proposed in the literature relies in exhibiting low performance when an “impostor” B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we introduce some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an “impostor” steals the Hash key.     

Influences of data filtering on human–computer interaction by gaze-contingent display and eye-tracking applications

We describe an interactive gaze-contingent display (GCD) applied to clinical applications; the system uses a simple texture hole to inhibit peripheral vision, to test and stress overt mechanisms of visual searching in normal subjects. The correct use of GCD in vision research is affected by tremor of the hole, due to system noise, nystagmus, eye blinking, calibration and subject reactivity. These issues compromise the execution of task. In order to obtain a stable GCD hole, we implemented a predictive gaze-contingent display (PGCD), fitting through dispersion of fixations and modulating a filter. The paper describes the PGCD and compare it with the common technique, providing evidence that humans fit exploration based on the characteristics of the computer system; in particular we found significant difference applying PGCD or a simple finite impulse response filter. We suggest that a correct human–computer interaction applied to neuropsychological context must be developed taking in consideration both technical point of view and human behavior.     

Relational Analysis and Precision via Probabilistic Abstract Interpretation

Within the context of a quantitative generalisation of the well established framework of Abstract Interpretation – i.e. Probabilistic Abstract Interpretation – we investigate a quantitative notion of precision which allows us to compare analyses on the basis of their expected exactness for a given program. We illustrate this approach by considering various types of numerical abstractions of the values of variables for independent analysis as well as weakly and fully relational analysis. We utilise for this a linear operator semantics of a simple imperative programming language. In this setting, fully relational dependencies are realised via the tensor product. Independent analyses and weakly relational analyses are realised as abstractions of the fully relational analysis.     

A multi-matcher system based on knuckle-based features

We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).

Machine learning multi-classifiers for peptide classification

In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier. Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously published in the literature.

Robust invariant sets for constrained storage systems

Several items are produced and stored into n buffers in order to supply an external demand without interruptions. We consider the classical problem of determining control laws and smallest buffer levels guaranteeing that an unknown bounded demand is always satisfied. A simple model with n decoupled integrators and n additive bounded disturbances is employed. The coupling arises from bounds on the total production capacity and on the total demand. Invariant set theory for linear and switched linear systems is exploited to compute robust positive invariant sets and controlled robust invariant sets for two commonly adopted scheduling policies. This paper provides the explicit expression of the invariant sets for any arbitrary n.     

Uniqueness and reciprocity in the boundary-initial value problem for a mixture of two elastic solids occupying an unbounded domain

Summary In this paper two uniqueness theorems for an anisotropic mixture of two linear elastic solids are established: The former concerns the mixed boundary-value problem, the latter the displacement problem. These theorems are proved for unbounded domains in the absence of artificial restrictions upon the behaviour of the unknown fields at infinity. A reciprocity theorem is also given.