Team Automata for Security: – A Survey –

In [Kleijn, J., Team Automata for CSCW – A Survey –, Petri Net Technology for Communication-Based Systems—Advances in Petri Nets, LNCS 2472, Springer, 2003, 295–320], Kleijn presented a survey of the use of team automata for the specification and analysis of phenomena from the field of computer supported cooperative work, in particular notions related to groupware systems. In this paper we present a survey of the use of team automata for the specification and analysis of some issues from the field of security. In particular, we show how team automata can adequately be used to model and verify various access control policies, multicast/broadcast communication protocols, and general (cryptographic) communication protocols.     

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1–8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience.     

A subject-specific software solution for the modeling and the visualization of muscles deformations

Today, to create and to simulate a virtual anatomical version of a subject is useful in the decision process of surgical treatments. The muscular activity is one of the factors which can contribute to abnormal movements such as in spasticity or static contracture. In this paper, we propose a numerical solution, based on the Finite Element (FE) method, able to estimate muscles deformations during contraction. Organized around a finite element solver and a volumetric environment, this solution is made of all the modeling and simulation processes from the discretization of the studied domain to the visualization of the results. The choices of materials and properties of the FE model are also presented such as the hyperelasticity, the contention model based on inter-meshes neighboring nodes pairing, and the estimation of nodal forces based on the subject-specific muscular forces and action lines.

Generalized ordering-search for learning directed probabilistic logical models

Recently, there has been an increasing interest in directed probabilistic logical models and a variety of formalisms for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their formalism can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm is based on the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on a genetics domain, blocks world domains and the Cora dataset. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.     

CEVCLUS: evidential clustering with instance-level constraints for relational data

Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecision and the uncertainty of the clustering process. In this context, the EVCLUS algorithm was proposed for partitioning objects described by a dissimilarity matrix. It is extended here so as to take pairwise constraints into account, by adding a term to its objective function. This term corresponds to a penalty term that expresses pairwise constraints in the belief function framework. Various synthetic and real datasets are considered to demonstrate the interest of the proposed method, called CEVCLUS, and two applications are presented. The performances of CEVCLUS are also compared to those of other constrained clustering algorithms.     

Hypercube sandwich approach to conferencing

This paper presents a novel cascaded conference network that provides distributed processing and signal transmission among members of disjoint sets of generic send/receive devices called conferees. It assumes an online request model in which idle groups of conferees may request the formation of a conference interconnection. Once a conference is established, all conferees remain connected until the entire conference is dissolved. The Hypercube Sandwich Network (HSN) consists of two components. A bidirectional permutation network is used for routing purposes to and from a hypercube of special processing elements for the purpose of conference formation. The HSN achieves strictly nonblocking performance for N conferees using O(Nlog N) processing elements, and this is shown to be tight to within a log ^1/4 N factor. Previous constructions required a quadratic number of processing elements for strictly nonblocking performance or could only provide wide-sense nonblocking conferencing. If the stronger requirement is made that the communication delay is logarithmic in the conference size, a simple algorithm is presented for wide-sense nonblocking conferencing in an HSN with O(N log N) processing elements.An earlier version of this paper was presented at the 1995 International Conference on Parallel Processing Techniques and Applications.     

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods. Recommended by: Patrick Valduriez     

A polynomial time computable metric between point sets

Measuring the similarity or distance between sets of points in a metric space is an important problem in machine learning and has also applications in other disciplines e.g. in computational geometry, philosophy of science, methods for updating or changing theories, . Recently Eiter and Mannila have proposed a new measure which is computable in polynomial time. However, it is not a distance function in the mathematical sense because it does not satisfy the trian gle inequality. We introduce a new measure which is a metric while being computable in polynomial time. We also present a variant which computes a normalised metric and a variant which can associate different weights with the points in the set. Received: 18 October 1999 / 8 January 2001     

Modelling and predicting partial orders from pairwise belief functions

In this paper, we introduce a generic way to represent and manipulate pairwise information about partial orders (representing rankings, preferences, ...) with belief functions. We provide generic and practical tools to make inferences from this pairwise information and illustrate their use on the machine learning problems that are label ranking and multi-label prediction. Our approach differs from most other quantitative approaches handling complete or partial orders, in the sense that partial orders are here considered as primary objects and not as incomplete specifications of ideal but unknown complete orders.