On the Complexity of Computing the Profinite Closure of a Rational Language

International audienceThe profinite topology is used in rational languages classification. In particular, several important decidability problems, related to the Malcev product, reduce to the computation of the closure of a rational language in the profinite topology. It is known that given a rational language by a deterministic automaton, computing a deterministic automaton accepting its profinite closure can be done with an exponential upper bound. This paper is dedicated the study of a lower bound for this problem: we prove that in some cases, if the alphabet contains at least three letters, it requires an exponential time

Seed: an easy to use random generator of recursive data structures for testing

Random testing represents a simple and tractable way for software assessment. This paper presents the \seed tool that can be used for the uniform random generation of recursive data structures such as labelled trees and logical formulas. We show how \seed can be used in several testing contexts, from model based testing to performance testing. Generated data structures are defined by grammar-like rules, given in an XML format, multiplying \seed possible applications. Seed is based on combinatorial techniques, and can generate uniformly at random k structures of size $n$ with a time complexity in O(n^2+ kn\log n). Finally, \seed is available as a free Java application and a great effort has been made to make it easy-to-use

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability

Towards Formalizing QoS of Web Services with Weighted Automata

Web services (WSs) are used more and more as components of distributed applications with a goal to resolve complex tasks that simple services cannot. This use of WSs is connected to the emergence of languages like WS-BPEL which allows describing the external behaviour of WSs on top of the service interfaces. The use of WSs as components of distributed applications implies the possibility to change a failing service for another which can do at least the same things as the replaced service. The composition issues are also of particular interest to WSs users. Different solutions have been proposed during the last years to check such properties, but, to our knowledge, none of them takes QoS aspects into account. This paper introduces underpinnings and a tool for verifying WSs substitutivity and well-formed composition while considering WSs costs such as the execution time of the different operations provided by WSs

Approximation based tree regular model checking

International audienceThis paper addresses the following general problem of tree regular model-checking: decide whether $\R^*(L)\cap L_p =\emptyset$ where $\R^*$ is the reflexive and transitive closure of a successor relation induced by a term rewriting system $\R$, and $L$ and $L_p$ are both regular tree languages. We develop an automatic approximation-based technique to handle this -- undecidable in general -- problem in most practical cases, extending a recent work by Feuillade, Genet and Viet Triem Tong. We also make this approach fully automatic for practical validation of security protocols

Approximations par réécriture pour deux problèmes indécidables

National audiencePour vérifier des propriétés de sûreté ou de (non-)atteignabilité par model-checking régulier, on se concentre sur une modélisation des configurations accessibles du système par des langages réguliers et des relations d'évolution par des systèmes de réécriture. Le problème d'atteignabilité est indécidable dans de nombreux formalismes, et l'approche générale consiste à étudier et/ou à combiner des cas particuliers. Nous nous intéressons à l'approche par approximation pour semi-décider ce problème de vérification. Dans ce papier, nous exploitons l'analyse d'atteignabilité pour deux problèmes indécidables pour les machines de Turing : vacuité d'un langage et appartenance d'un mot à un langage. Nous proposons une modélisation et montrons comment les approximations par réécriture permettent de semi-décider ces problèmes. Cette approche a été implantée et expérimentée

Random Generation of Positive TAGEDs wrt. the Emptiness Problem

Tree automata are a widely used formalism in Computer Science. Since their creation in the fifties, numerous more expressive extensions have been proposed. Unfortunately, the decision problems associated with these extensions are quite often undecidable or in prohibitive classes of algorithmic complexity (NP-complete or worse), and little work has gone into finding efficient heuristics for them. Beyond the inherent difficulty of those problems, a common hitch in this line of research is the experimental evaluation of new algorithms. As those extensions of tree automata have remained in chiefly theoretical spheres, there are no established testbeds from the "real world" against which to quantify the efficiency (or lack thereof) of new algorithms. Failing that, there is a need to generate suitable testbeds at random. Regrettably, there is little material in the literature regarding random generation of tree automata, and none at all regarding extensions such as Tree Automata with Global Equality and Disequality Constraints (TAGEDs). It should also be noted that what little material there is does not concern itself with the interest of the generated automata wrt. specific decision problems. In this report we present a scheme for random generation of positive TAGEDs, with a focus on making them interesting wrt. the Emptiness problem

Controlling and Assessing Correlations of Cost Matrices in Heterogeneous Scheduling

International audienceThis paper considers the problem of allocating independent tasks to unrelated machines such as to minimize the maximum completion time. Testing heuristics for this problem requires the generation of cost matrices that specify the execution time of each task on each machine. Numerous studies showed that the task and machine heterogeneities belong to the properties impacting heuristics performance the most. This study focuses on orthogonal properties, the average correlations between each pair of rows and each pair of columns, which is a proximity measure with uniform instances 1. Cost matrices generated with a novel generation method show the effect of these correlations on the performance of several heuristics from the literature. In particular, EFT performance depends on whether the tasks are more correlated than the machines and HLPT performs the best when both correlations are close to one