Which notes are Vadi-Samvadi in Raga Rageshree?

Abstract: The notes which play the most important and second most important roles in expressing a raga are called Vadi and Samvadi swars respectively in (North) Indian Classical music. Like Bageshree, Bhairavi, Shankara, Hamir and Kalingra, Rageshree is another controversial raga so far as the choice of Vadi-Samvadi selection is concerned where there are two different opinions. In the present work, a two minute vocal recording of raga Rageshree is subjected to a careful statistical analysis. Our analysis is broken into three phases: first half, middle half and last half. Under a multinomial model set up holding appreciably in the first two phases, only one opinion is found acceptable. In the last phase the distribution seems to be quasi multinomial, characterized by an unstable nature of relative occurrence of pitch of all the notes and although the note whose relative occurrence of pitch suddenly shoots is the Vadi swar selected from our analysis of the first two phases, we take it as an outlier demanding a separate treatment like any other in statistics. Selection of Vadi-Samvadi notes in a quasi-multinomial set up is still an open research problem. An interesting musical cocktail is proposed, however, embedding several ideas like melodic property of notes, note combinations and pitch movements between notes, using some weighted combination of psychological and statistical stability of notes along with watching carefully the sudden shoot of one or more notes whenever there is enough evidence that multinomial model has broken down.


Fully distributed and fault tolerant task management based on diffusions

Abstract: The task management is a critical component for the computational grids. The aim is to assign tasks on nodes according to a global scheduling policy and a view of local resources of nodes. A peer-to-peer approach for the task management involves a better scalability for the grid and a higher fault tolerance. But some mechanisms have to be proposed to avoid the computation of replicated tasks that can reduce the efficiency and increase the load of nodes. In the same way, these mechanisms have to limit the number of exchanged messages to avoid the overload of the network. In a previous paper, we have proposed two methods for the task management called active and passive. These methods are based on a random walk: they are fully distributed and fault tolerant. Each node owns a local tasks states set updated thanks to a random walk and each node is in charge of the local assignment. Here, we propose three methods to improve the efficiency of the active method. These new methods are based on a circulating word. The nodes local tasks states sets are updated thanks to periodical diffusions along trees built from the circulating word. Particularly, we show that these methods increase the efficiency of the active method: they produce less replicated tasks. These three methods are also fully distributed and fault tolerant. On the other way, the circulating word can be exploited for other applications like the resources management or the nodes synchronization.


Strong Spatial Mixing and Approximating Partition Functions of Two-State Spin Systems without Hard Constrains

Abstract: We prove Gibbs distribution of two-state spin systems(also known as binary Markov random fields) without hard constrains on a tree exhibits strong spatial mixing(also known as strong correlation decay), under the assumption that, for arbitrary `external field', the absolute value of `inverse temperature' is small, or the `external field' is uniformly large or small. The first condition on `inverse temperature' is tight if the distribution is restricted to ferromagnetic or antiferromagnetic Ising models. Thanks to Weitz's self-avoiding tree, we extends the result for sparse on average graphs, which generalizes part of the recent work of Mossel and Sly\cite{MS08}, who proved the strong spatial mixing property for ferromagnetic Ising model. Our proof yields a different approach, carefully exploiting the monotonicity of local recursion. To our best knowledge, the second condition of `external field' for strong spatial mixing in this paper is first considered and stated in term of `maximum average degree' and `interaction energy'. As an application, we present an FPTAS for partition functions of two-state spin models without hard constrains under the above assumptions in a general family of graphs including interesting bounded degree graphs.


Justifications for Logic Programs under Answer Set Semantics

Abstract: The paper introduces the notion of off-line justification for Answer Set Programming (ASP). Justifications provide a graph-based explanation of the truth value of an atom w.r.t. a given answer set. The paper extends also this notion to provide justification of atoms during the computation of an answer set (on-line justification), and presents an integration of on-line justifications within the computation model of Smodels. Off-line and on-line justifications provide useful tools to enhance understanding of ASP, and they offer a basic data structure to support methodologies and tools for debugging answer set programs. A preliminary implementation has been developed in ASP-PROLOG. (To appear in Theory and Practice of Logic Programming (TPLP))


Hierarchy and equivalence of multi-letter quantum finite automata

Abstract: Multi-letter {\it quantum finite automata} (QFAs) were a new one-way QFA model proposed recently by Belovs, Rosmanis, and Smotrovs (LNCS, Vol. 4588, Springer, Berlin, 2007, pp. 60-71), and they showed that multi-letter QFAs can accept with no error some regular languages ($(a+b)^{*}b$) that are unacceptable by the one-way QFAs. In this paper, we continue to study multi-letter QFAs. We mainly focus on two issues: (1) we show that $(k+1)$-letter QFAs are computationally more powerful than $k$-letter QFAs, that is, $(k+1)$-letter QFAs can accept some regular languages that are unacceptable by any $k$-letter QFA. A comparison with the one-way QFAs is made by some examples; (2) we prove that a $k_{1}$-letter QFA ${\cal A}_1$ and another $k_{2}$-letter QFA ${\cal A}_2$ are equivalent if and only if they are $(n_{1}+n_{2})^{4}+k-1$-equivalent, and the time complexity of determining the equivalence of two multi-letter QFAs using this method is $O(n^{12}+k^{2}n^{4}+kn^{8})$, where $n_{1}$ and $n_{2}$ are the numbers of states of ${\cal A}_{1}$ and ${\cal A}_{2}$, respectively, and $k=\max(k_{1},k_{2})$. Some other issues are addressed for further consideration.


Stroke Fragmentation based on Geometry Features and HMM

Abstract: Stroke fragmentation is one of the key steps in pen-based interaction. In this letter, we present a unified HMM-based stroke fragmentation technique that can do segment point location and primitive type determination simultaneously. The geometry features included are used to evaluate local features, and the HMM model is utilized to measure the global drawing context. Experiments prove that the model can efficiently represent smooth curves as well as strokes made up of arbitrary lines and circular arcs.


Elagage d'un perceptron multicouches : utilisation de l'analyse de la variance de la sensibilit\'e des param\`etres

Abstract: The stucture determination of a neural network for the modelisation of a system remain the core of the problem. Within this framework, we propose a pruning algorithm of the network based on the use of the analysis of the sensitivity of the variance of all the parameters of the network. This algorithm will be tested on two examples of simulation and its performances will be compared with three other algorithms of pruning of the literature


Elementary epistemological features of machine intelligence

Abstract: Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of intelligence. The paper also establishes algebraic definitions of efficiency and accuracy of MI tests as their quality measure. The last part of the paper addresses the learning process with respect to the traditional epistemology and the epistemology of MI described here. The proposed views on MI positively correlate to the Hegelian monistic epistemology and contribute towards amalgamating idealistic deliberations with the AI theory, particularly in a local frame of reference.


Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Edge Crossings

Abstract: We provide linear-time algorithms for geometric graphs with sublinearly many crossings. That is, we provide algorithms running in O(n) time on connected geometric graphs having n vertices and k crossings, where k is smaller than n by an iterated logarithmic factor. Specific problems we study include Voronoi diagrams and single-source shortest paths. Our algorithms all run in linear time in the standard comparison-based computational model; hence, we make no assumptions about the distribution or bit complexities of edge weights, nor do we utilize unusual bit-level operations on memory words. Instead, our algorithms are based on a planarization method that "zeroes in" on edge crossings, together with methods for extending planar separator decompositions to geometric graphs with sublinearly many crossings. Incidentally, our planarization algorithm also solves an open computational geometry problem of Chazelle for triangulating a self-intersecting polygonal chain having n segments and k crossings in linear time, for the case when k is sublinear in n by an iterated logarithmic factor.


An Approximation of the Outage Probability for Multi-hop AF Fixed Gain Relay

Abstract: In this letter, we present a closed-form approximation of the outage probability for the multi-hop amplify-and-forward (AF) relaying systems with fixed gain in Rayleigh fading channel. The approximation is derived from the outage event for each hop. The simulation results show the tightness of the proposed approximation in low and high signal-to-noise ratio (SNR) region.


Decision trees are PAC-learnable from most product distributions: a smoothed analysis

Abstract: We consider the problem of PAC-learning decision trees, i.e., learning a decision tree over the n-dimensional hypercube from independent random labeled examples. Despite significant effort, no polynomial-time algorithm is known for learning polynomial-sized decision trees (even trees of any super-constant size), even when examples are assumed to be drawn from the uniform distribution on {0,1}^n. We give an algorithm that learns arbitrary polynomial-sized decision trees for {\em most product distributions}. In particular, consider a random product distribution where the bias of each bit is chosen independently and uniformly from, say, [.49,.51]. Then with high probability over the parameters of the product distribution and the random examples drawn from it, the algorithm will learn any tree. More generally, in the spirit of smoothed analysis, we consider an arbitrary product distribution whose parameters are specified only up to a [-c,c] accuracy (perturbation), for an arbitrarily small positive constant c.


EcoTRADE - a multi player network game of a tradable permit market for biodiversity credits

Abstract: EcoTRADE is a multi player network game of a virtual biodiversity credit market. Each player controls the land use of a certain amount of parcels on a virtual landscape. The biodiversity credits of a particular parcel depend on neighboring parcels, which may be owned by other players. The game can be used to study the strategies of players in experiments or classroom games and also as a communication tool for stakeholders participating in credit markets that include spatially interdependent credits.


Adaptive Uncertainty Resolution in Bayesian Combinatorial Optimization Problems

Abstract: In several applications such as databases, planning, and sensor networks, parameters such as selectivity, load, or sensed values are known only with some associated uncertainty. The performance of such a system (as captured by some objective function over the parameters) is significantly improved if some of these parameters can be probed or observed. In a resource constrained situation, deciding which parameters to observe in order to optimize system performance itself becomes an interesting and important optimization problem. This general problem is the focus of this paper. One of the most important considerations in this framework is whether adaptivity is required for the observations. Adaptive observations introduce blocking or sequential operations in the system whereas non-adaptive observations can be performed in parallel. One of the important questions in this regard is to characterize the benefit of adaptivity for probes and observation. We present general techniques for designing constant factor approximations to the optimal observation schemes for several widely used scheduling and metric objective functions. We show a unifying technique that relates this optimization problem to the outlier version of the corresponding deterministic optimization. By making this connection, our technique shows constant factor upper bounds for the benefit of adaptivity of the observation schemes. We show that while probing yields significant improvement in the objective function, being adaptive about the probing is not beneficial beyond constant factors.


Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

Abstract: We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed.


Decidability of the Equivalence of Multi-Letter Quantum Finite Automata

Abstract: Multi-letter {\it quantum finite automata} (QFAs) were a quantum variant of classical {\it one-way multi-head finite automata} (J. Hromkovi\v{c}, Acta Informatica 19 (1983) 377-384), and it has been shown that this new one-way QFAs (multi-letter QFAs) can accept with no error some regular languages $(a+b)^{*}b$ that are unacceptable by the previous one-way QFAs. In this paper, we study the decidability of the equivalence of multi-letter QFAs, and the main technical contributions are as follows: (1) We show that any two automata, a $k_{1}$-letter QFA ${\cal A}_1$ and a $k_{2}$-letter QFA ${\cal A}_2$, over the same input alphabet $\Sigma$ are equivalent if and only if they are $(n^2m^{k-1}-m^{k-1}+k)$-equivalent, where $m=|\Sigma|$ is the cardinality of $\Sigma$, $k=\max(k_{1},k_{2})$, and $n=n_{1}+n_{2}$, with $n_{1}$ and $n_{2}$ being the numbers of states of ${\cal A}_{1}$ and ${\cal A}_{2}$, respectively. When $k=1$, we obtain the decidability of equivalence of measure-once QFAs in the literature. It is worth mentioning that our technical method is essentially different from that for the decidability of the case of single input alphabet (i.e., $m=1$). (2) However, if we determine the equivalence of multi-letter QFAs by checking all strings of length not more than $ n^2m^{k-1}-m^{k-1}+k$, then the worst time complexity is exponential, i.e., $O(n^6m^{n^2m^{k-1}-m^{k-1}+2k-1})$. Therefore, we design a polynomial-time $O(m^{2k-1}n^{8}+km^kn^{6})$ algorithm for determining the equivalence of any two multi-letter QFAs. Here, the time complexity is concerning the number of states in the multi-letter QFAs, and $k$ is thought of as a constant.


Self-stabilizing K-out-of-L exclusion on tree network

Abstract: In this paper, we address the problem of K-out-of-L exclusion, a generalization of the mutual exclusion problem, in which there are $\ell$ units of a shared resource, and any process can request up to $\mathtt k$ units ($1\leq\mathtt k\leq\ell$). We propose the first deterministic self-stabilizing distributed K-out-of-L exclusion protocol in message-passing systems for asynchronous oriented tree networks which assumes bounded local memory for each process.


S\'election de la structure d'un perceptron multicouches pour la r\'eduction dun mod\`ele de simulation d'une scierie

Abstract: Simulation is often used to evaluate the relevance of a Directing Program of Production (PDP) or to evaluate its impact on detailed sc\'enarii of scheduling. Within this framework, we propose to reduce the complexity of a model of simulation by exploiting a multilayer perceptron. A main phase of the modeling of one system using a multilayer perceptron remains the determination of the structure of the network. We propose to compare and use various pruning algorithms in order to determine the optimal structure of the network used to reduce the complexity of the model of simulation of our case of application: a sawmill.


An analysis of a random algorithm for estimating all the matchings

Abstract: Counting the number of all the matchings on a bipartite graph has been transformed into calculating the permanent of a matrix obtained from the extended bipartite graph by Yan Huo, and Rasmussen presents a simple approach (RM) to approximate the permanent, which just yields a critical ratio O($n\omega(n)$) for almost all the 0-1 matrices, provided it's a simple promising practical way to compute this #P-complete problem. In this paper, the performance of this method will be shown when it's applied to compute all the matchings based on that transformation. The critical ratio will be proved to be very large with a certain probability, owning an increasing factor larger than any polynomial of $n$ even in the sense for almost all the 0-1 matrices. Hence, RM fails to work well when counting all the matchings via computing the permanent of the matrix. In other words, we must carefully utilize the known methods of estimating the permanent to count all the matchings through that transformation.


Improved Approximation for the Number of Hamiltonian Cycles in Dense Digraphs

Abstract: We propose an improved algorithm for counting the number of Hamiltonian cycles in a directed graph. The basic idea of the method is sequential acceptance/rejection, which is successfully used in approximating the number of perfect matchings in dense bipartite graphs. As a consequence, a new bound on the number of Hamiltonian cycles in a directed graph is proved, by using the ratio of the number of 1-factors. Based on this bound, we prove that our algorithm runs in expected time of $O(n^{8.5})$ for dense problems. This improves the Markov chain method, the most powerful existing method, a factor of at least $n^{4.5}(\log n)^{4}$ in running time. This class of dense problems is shown to be nontrivial in counting, in the sense that it is $#$P-Complete.


Emerge-Sort: Converging to Ordered Sequences by Simple Local Operators

Abstract: In this paper we examine sorting on the assumption that we do not know in advance which way to sort a sequence of numbers and we set at work simple local comparison and swap operators whose repeating application ends up in sorted sequences. These are the basic elements of Emerge-Sort, our approach to self-organizing sorting, which we then validate experimentally across a range of samples. Observing an O(n2) run-time behaviour, we note that the n/logn delay coefficient that differentiates Emerge-Sort from the classical comparison based algorithms is an instantiation of the price of anarchy we pay for not imposing a sorting order and for letting that order emerge through the local interactions.


Adversarial Scheduling in Evolutionary Game Dynamics

Abstract: Consider a system in which players at nodes of an underlying graph G repeatedly play Prisoner's Dilemma against their neighbors. The players adapt their strategies based on the past behavior of their opponents by applying the so-called win-stay lose-shift strategy. This dynamics has been studied in (Kittock 94), (Dyer et al. 2002), (Mossel and Roch, 2006). With random scheduling, starting from any initial configuration with high probability the system reaches the unique fixed point in which all players cooperate. This paper investigates the validity of this result under various classes of adversarial schedulers. Our results can be sumarized as follows: 1. An adversarial scheduler that can select both participants to the game can preclude the system from reaching the unique fixed point on most graph topologies. 2. A nonadaptive scheduler that is only allowed to choose one of the participants is no more powerful than a random scheduler. With this restriction even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is "reasonably fair". The results exemplify the adversarial scheduling approach we propose as a foundational basis for the generative approach to social science (Epstein 2007).


The Impact of New Technologies in Public Financial Management and Performance: Agenda for Public Financial Management Reformance in the Context of Global Best Practices

Abstract: Information and Communication Technologies (ICT) has practically penetrated into all spheres of life. Therefore a closer look at the impact of ICT in public financial management and performance is highly justified. Public finance is defined as a field of economics concerned with paying for collective or governmental activities, and with the administration and design of those activities. Activities will be viewed as services or more precisely as public services. We believe that there is need to consider performance from the perspective of effective performance and the perceived performance. In fact the real or effective performance might not correspond to the perceived performance. A service can be considered from the perspective of the decision-maker, who in our case could be a government or a collectivity. ICT can be employed in the three phases that concern the decision-maker: design, implementation and evaluation. The beneficiaries of a service can employ ICT in any of the three phases - awareness, exploitation and assessment - for guarantying a high level of efficiency. Each phase in the environment of a service will be presented as well as illustrations of how ICT can be employed in order to improve the end-result of each one of them. We believe that a high efficiency of each phase will produce a high global efficiency. It should be noted however that the effectiveness of any system is highly dependent on the human engagement in the system. Therefore, the impact of ICT in public financial management will be felt only if the decision-makers and the end-users of the services engage themselves in the success of the system. Instead of giving a catalog of services, the focus has been on the model (or methodology) to adopt in designing services for which ICT could enhance the implementation.


Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications

Abstract: In this paper, we propose a general cross-layer optimization framework in which we explicitly consider both the heterogeneous and dynamically changing characteristics of delay-sensitive applications and the underlying time-varying network conditions. We consider both the independently decodable data units (DUs, e.g. packets) and the interdependent DUs whose dependencies are captured by a directed acyclic graph (DAG). We first formulate the cross-layer design as a non-linear constrained optimization problem by assuming complete knowledge of the application characteristics and the underlying network conditions. The constrained cross-layer optimization is decomposed into several cross-layer optimization subproblems for each DU and two master problems. The proposed decomposition method determines the necessary message exchanges between layers for achieving the optimal cross-layer solution. However, the attributes (e.g. distortion impact, delay deadline etc) of future DUs as well as the network conditions are often unknown in the considered real-time applications. The impact of current cross-layer actions on the future DUs can be characterized by a state-value function in the Markov decision process (MDP) framework. Based on the dynamic programming solution to the MDP, we develop a low-complexity cross-layer optimization algorithm using online learning for each DU transmission. This online algorithm can be implemented in real-time in order to cope with unknown source characteristics, network dynamics and resource constraints. Our numerical results demonstrate the efficiency of the proposed online algorithm.


Unary finite automata vs. arithmetic progressions

Abstract: We point out a subtle error in the proof of Chrobak's theorem that every unary NFA can be represented as a union of arithmetic progressions that is at most quadratically large. We propose a correction for this and show how Martinez's polynomial time algorithm, which realizes Chrobak's theorem, can be made correct accordingly. We also show that Martinez's algorithm cannot be improved to have logarithmic space, unless L = NL.


Oscillations with TCP-like Flow Control in Networks of Queues

Abstract: We consider a set of flows passing through a set of servers. The injection rate into each flow is governed by a flow control that increases the injection rate when all the servers on the flow's path are empty and decreases the injection rate when some server is congested. We show that if each server's congestion is governed by the arriving traffic at the server then the system can *oscillate*. This is in contrast to previous work on flow control where congestion was modeled as a function of the flow injection rates and the system was shown to converge to a steady state that maximizes an overall network utility.


Obtaining Depth Maps From Color Images By Region Based Stereo Matching Algorithms

Abstract: In the paper, region based stereo matching algorithms are developed for extraction depth information from two color stereo image pair. A filter eliminating unreliable disparity estimation was used for increasing reliability of the disparity map. Obtained results by algorithms were represented and compared.


A Novel Clustering Algorithm Based on Quantum Random Walk

Abstract: The enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum random walk (QRW) with the problem of data clustering, and develop two clustering algorithms based on the one dimensional QRW. Then, the probability distributions on the positions induced by QRW in these algorithms are investigated, which also indicates the possibility of obtaining better results. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms are of fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.


Graph Polynomials: From Recursive Definitions To Subset Expansion Formulas

Abstract: Many graph polynomials, such as the Tutte polynomial, the interlace polynomial and the matching polynomial, have both a recursive definition and a defining subset expansion formula. In this paper we present a general, logic-based framework which gives a precise meaning to recursive definitions of graph polynomials. We then prove that in this framework every recursive definition of a graph polynomial can be converted into a subset expansion formula.


Distributed (Delta + 1)-coloring in linear (in Delta) time

Abstract: The distributed (Delta + 1)-coloring problem is one of most fundamental and well-studied problems of Distributed Algorithms. Starting with the work of Cole and Vishkin in 86, there was a long line of gradually improving algorithms published. The current state-of-the-art running time is O(Delta log Delta + log^* n), due to Kuhn and Wattenhofer, PODC'06. Linial (FOCS'87) has proved a lower bound of 1/2 \log^* n for the problem, and Szegedy and Vishwanathan (STOC'93) provided a heuristic argument that shows that algorithms from a wide family of locally iterative algorithms are unlikely to achieve running time smaller than \Theta(Delta log Delta). We present a deterministic (Delta + 1)-coloring distributed algorithm with running time O(Delta) + 1/2 log^* n. We also present a tradeoff between the running time and the number of colors, and devise an O(Delta * t)-coloring algorithm with running time O(Delta / t + \log^* n), for any parameter t, 1 < t < Delta^{1-epsilon}, for an arbitrarily small constant epsilon, 0 < epsilon < 1. On the way to this result we study a generalization of the notion of graph coloring, which is called defective coloring. In an m-defective p-coloring the vertices are colored with p colors so that each vertex has up to m neighbors with the same color. We show that an m-defective p-coloring with reasonably small m and p can be computed very efficiently. We also develop a technique to employ multiple defect colorings of various subgraphs of the original graph G for computing a (Delta+1)-coloring of G. We believe that these techniques are of independent interest.


An Extension of the Permutation Group Enumeration Technique (Collapse of the Polynomial Hierarchy: $\mathbf{NP = P}$)

Abstract: The distinguishing result of this paper is a $\mathbf{P}$-time enumerable partition of all the potential perfect matchings in a bipartite graph. This partition is a set of equivalence classes induced by the missing edges in the potential perfect matchings. We capture the behavior of these missing edges in a polynomially bounded representation of the exponentially many perfect matchings by a graph theoretic structure, called MinSet Sequence, where MinSet is a P-time enumerable structure derived from a graph theoretic counterpart of a generating set of the symmetric group. This leads to a polynomially bounded generating set of all the classes, enabling the enumeration of perfect matchings in polynomial time. The sequential time complexity of this $\mathbf{\#P}$-complete problem is shown to be $O(n^{45}\log n)$. And thus we prove a result even more surprising than $\mathbf{NP = P}$, that is, $\mathbf{\#P}=\mathbf{FP}$, where $\mathbf{FP}$ is the class of functions, $f: \{0, 1\}^* \rightarrow \mathbb{N} $, computable in polynomial time on a deterministic model of computation.


Conceptual approach through an annotation process for the representation and the information contents enhancement in economic intelligence (EI)

Abstract: In the era of the information society, the impact of the information systems on the economy of material and immaterial is certainly perceptible. With regards to the information resources of an organization, the annotation involved to enrich informational content, to track the intellectual activities on a document and to set the added value on information for the benefit of solving a decision-making problem in the context of economic intelligence. Our contribution is distinguished by the representation of an annotation process and its inherent concepts to lead the decisionmaker to an anticipated decision: the provision of relevant and annotated information. Such information in the system is made easy by taking into account the diversity of resources and those that are well annotated so formally and informally by the EI actors. A capital research framework consist of integrating in the decision-making process the annotator activity, the software agent (or the reasoning mechanisms) and the information resources enhancement.


Logic programs with propositional connectives and aggregates

Abstract: Answer set programming (ASP) is a logic programming paradigm that can be used to solve complex combinatorial search problems. Aggregates are an ASP construct that plays an important role in many applications. Defining a satisfactory semantics of aggregates turned out to be a difficult problem, and in this paper we propose a new approach, based on an analogy between aggregates and propositional connectives. First, we extend the definition of an answer set/stable model to cover arbitrary propositional theories; then we define aggregates on top of them both as primitive constructs and as abbreviations for formulas. Our definition of an aggregate combines expressiveness and simplicity, and it inherits many theorems about programs with nested expressions, such as theorems about strong equivalence and splitting.


Fast phylogeny reconstruction through learning of ancestral sequences

Abstract: Given natural limitations on the length DNA sequences, designing phylogenetic reconstruction methods which are reliable under limited information is a crucial endeavor. There have been two approaches to this problem: reconstructing partial but reliable information about the tree (\cite{Mo07, DMR08,DHJ06,GMS08}), and reaching "deeper" in the tree through reconstruction of ancestral sequences. In the latter category, \cite{DMR06} settled an important conjecture of M.Steel, showing that, under the CFN model of evolution, all trees on $n$ leaves with edge lengths bounded by the Ising model phase transition can be recovered with high probability from genomes of length $O(\log n)$ with a polynomial time algorithm. Their methods had a running time of $O(n^{10})$. Here we enhance our methods from \cite{DHJ06} with the learning of ancestral sequences and provide an algorithm for reconstructing a sub-forest of the tree which is reliable given available data, without requiring a-priori known bounds on the edge lengths of the tree. Our methods are based on an intuitive minimum spanning tree approach and run in $O(n^3)$ time. For the case of full reconstruction of trees with edges under the phase transition, we maintain the same sequence length requirements as \cite{DMR06}, despite the considerably faster running time.


A quasi-polynomial time approximation scheme for Euclidean capacitated vehicle routing

Abstract: In the capacitated vehicle routing problem, introduced by Dantzig and Ramser in 1959, we are given the locations of n customers and a depot, along with a vehicle of capacity k, and wish to find a minimum length collection of tours, each starting from the depot and visiting at most k customers, whose union covers all the customers. We give a quasi-polynomial time approximation scheme for the setting where the customers and the depot are on the plane, and distances are given by the Euclidean metric.


Multi-Agent Reinforcement Learning and Genetic Policy Sharing

Abstract: The effects of policy sharing between agents in a multi-agent dynamical system has not been studied extensively. I simulate a system of agents optimizing the same task using reinforcement learning, to study the effects of different population densities and policy sharing. I demonstrate that sharing policies decreases the time to reach asymptotic behavior, and results in improved asymptotic behavior.


Scarf is Ppad-Complete

Abstract: Scarf's lemma is one of the fundamental results in combinatorics, originally introduced to study the core of an N-person game. Over the last four decades, the usefulness of Scarf's lemma has been demonstrated in several important combinatorial problems seeking "stable" solutions. However, the complexity of the computational version of Scarf's lemma (SCARF) remained open. In this paper, we prove that SCARF is complete for the complexity class PPAD. This proves that SCARF is as hard as the computational versions of Brouwer's fixed point theorem and Sperner's lemma. Hence, there is no polynomial-time algorithm for SCARF unless PPAD \subseteq P. We also show that fractional stable paths problem and finding strong fractional kernels in digraphs are PPAD-hard.


Two Dimensional Connectivity for Vehicular Ad-Hoc Networks

Abstract: In this paper, we focus on two-dimensional connectivity in sparse vehicular ad hoc networks (VANETs). In this respect, we find thresholds for the arrival rates of vehicles at entrances of a block of streets such that the connectivity is guaranteed for any desired probability. To this end, we exploit a mobility model recently proposed for sparse VANETs, based on BCMP open queuing networks and solve the related traffic equations to find the traffic characteristics of each street and use the results to compute the exact probability of connectivity along these streets. Then, we use the results from percolation theory and the proposed fast algorithms for evaluation of bond percolation problem in a random graph corresponding to the block of the streets. We then find sufficiently accurate two dimensional connectivity-related parameters, such as the average number of intersections connected to each other and the size of the largest set of inter-connected intersections. We have also proposed lower bounds for the case of heterogeneous network with two transmission ranges. In the last part of the paper, we apply our method to several numerical examples and confirm our results by simulations.


Polyomino-Based Digital Halftoning

Abstract: In this work, we present a new method for generating a threshold structure. This kind of structure can be advantageously used in various halftoning algorithms such as clustered-dot or dispersed-dot dithering, error diffusion with threshold modulation, etc. The proposed method is based on rectifiable polyominoes -- a non-periodic hierarchical structure, which tiles the Euclidean plane with no gaps. Each polyomino contains a fixed number of discrete threshold values. Thanks to its inherent non-periodic nature combined with off-line optimization of threshold values, our polyomino-based threshold structure shows blue-noise spectral properties. The halftone images produced with this threshold structure have high visual quality. Although the proposed method is general, and can be applied on any polyomino tiling, we consider one particular case: tiling with G-hexominoes. We compare our polyomino-based threshold structure with the best known state-of-the-art methods for generation threshold matrices, and conclude considerable improvement achieved with our method.


The Wadge Hierarchy of Deterministic Tree Languages

Abstract: We provide a complete description of the Wadge hierarchy for deterministically recognisable sets of infinite trees. In particular we give an elementary procedure to decide if one deterministic tree language is continuously reducible to another. This extends Wagner's results on the hierarchy of omega-regular languages of words to the case of trees.


Identification of parameters underlying emotions and a classification of emotions

Abstract: The standard classification of emotions involves categorizing the expression of emotions. In this paper, parameters underlying some emotions are identified and a new classification based on these parameters is suggested.


Convex Sparse Matrix Factorizations

Abstract: We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the usual explicit upper bound on the dictionary size by a convex rank-reducing term similar to the trace norm. In particular, our formulation introduces an explicit trade-off between size and sparsity of the decomposition of rectangular matrices. Using a large set of synthetic examples, we compare the estimation abilities of the convex and non-convex approaches, showing that while the convex formulation has a single local minimum, this may lead in some cases to performance which is inferior to the local minima of the non-convex formulation.


A new metric for robustness with respect to virus spread

Abstract: The robustness of a network is depending on the type of attack we are considering. In this paper we focus on the spread of viruses on networks. It is common practice to use the epidemic threshold as a measure for robustness. Because the epidemic threshold is inversely proportional to the largest eigenvalue of the adjacency matrix, it seems easy to compare the robustness of two networks. We will show in this paper that the comparison of the robustness with respect to virus spread for two networks actually depends on the value of the effective spreading rate tau. For this reason we propose a new metric, the viral conductance, which takes into account the complete range of values tau can obtain. In this paper we determine the viral conductance of regular graphs, complete bi-partite graphs and a number of realistic networks.


Dynamic Complexity of Formal Languages

Abstract: The paper investigates the power of the dynamic complexity classes DynFO, DynQF and DynPROP over string languages. The latter two classes contain problems that can be maintained using quantifier-free first-order updates, with and without auxiliary functions, respectively. It is shown that the languages maintainable in DynPROP exactly are the regular languages, even when allowing arbitrary precomputation. This enables lower bounds for DynPROP and separates DynPROP from DynQF and DynFO. Further, it is shown that any context-free language can be maintained in DynFO and a number of specific context-free languages, for example all Dyck-languages, are maintainable in DynQF. Furthermore, the dynamic complexity of regular tree languages is investigated and some results concerning arbitrary structures are obtained: there exist first-order definable properties which are not maintainable in DynPROP. On the other hand any existential first-order property can be maintained in DynQF when allowing precomputation.


The convex hull of a regular set of integer vectors is polyhedral and effectively computable

Abstract: Number Decision Diagrams (NDD) provide a natural finite symbolic representation for regular set of integer vectors encoded as strings of digit vectors (least or most significant digit first). The convex hull of the set of vectors represented by a NDD is proved to be an effectively computable convex polyhedron.


Decomposition of Decidable First-Order Logics over Integers and Reals

Abstract: We tackle the issue of representing infinite sets of real- valued vectors. This paper introduces an operator for combining integer and real sets. Using this operator, we decompose three well-known logics extending Presburger with reals. Our decomposition splits a logic into two parts : one integer, and one decimal (i.e. on the interval [0,1]). We also give a basis for an implementation of our representation.


Control software analysis, part II: Closed-loop analysis

Abstract: The analysis and proper documentation of the properties of closed-loop control software presents many distinct aspects from the analysis of the same software running open-loop. Issues of physical system representations arise, and it is desired that such representations remain independent from the representations of the control program. For that purpose, a concurrent program representation of the plant and the control processes is proposed, although the closed-loop system is sufficiently serialized to enable a sequential analysis. While dealing with closed-loop system properties, it is also shown by means of examples how special treatment of nonlinearities extends from the analysis of control specifications to code analysis.


Accelerated Data-Flow Analysis

Abstract: Acceleration in symbolic verification consists in computing the exact effect of some control-flow loops in order to speed up the iterative fix-point computation of reachable states. Even if no termination guarantee is provided in theory, successful results were obtained in practice by different tools implementing this framework. In this paper, the acceleration framework is extended to data-flow analysis. Compared to a classical widening/narrowing-based abstract interpretation, the loss of precision is controlled here by the choice of the abstract domain and does not depend on the way the abstract value is computed. Our approach is geared towards precision, but we don't loose efficiency on the way. Indeed, we provide a cubic-time acceleration-based algorithm for solving interval constraints with full multiplication.


Convex Hull of Arithmetic Automata

Abstract: Arithmetic automata recognize infinite words of digits denoting decompositions of real and integer vectors. These automata are known expressive and efficient enough to represent the whole set of solutions of complex linear constraints combining both integral and real variables. In this paper, the closed convex hull of arithmetic automata is proved rational polyhedral. Moreover an algorithm computing the linear constraints defining these convex set is provided. Such an algorithm is useful for effectively extracting geometrical properties of the whole set of solutions of complex constraints symbolically represented by arithmetic automata.


Consensus Answers for Queries over Probabilistic Databases

Abstract: We address the problem of finding a "best" deterministic query answer to a query over a probabilistic database. For this purpose, we propose the notion of a consensus world (or a consensus answer) which is a deterministic world (answer) that minimizes the expected distance to the possible worlds (answers). This problem can be seen as a generalization of the well-studied inconsistent information aggregation problems (e.g. rank aggregation) to probabilistic databases. We consider this problem for various types of queries including SPJ queries, \Topk queries, group-by aggregate queries, and clustering. For different distance metrics, we obtain polynomial time optimal or approximation algorithms for computing the consensus answers (or prove NP-hardness). Most of our results are for a general probabilistic database model, called {\em and/xor tree model}, which significantly generalizes previous probabilistic database models like x-tuples and block-independent disjoint models, and is of independent interest.


Interoperability between Heterogeneous Federation Architectures: Illustration with SAML and WS-Federation

Abstract: Digital identity management intra and inter information systems, and, service oriented architectures, are the roots of identity federation. This kind of security architectures aims at enabling information system interoperability. Existing architectures, however, do not consider interoperability of heterogeneous federation architectures, which rely on different federation protocols.In this paper, we try to initiate an in-depth reflection on this issue, through the comparison of two main federation architecture specifications: SAML and WS-Federation. We firstly propose an overall outline of identity federation. We furthermore address the issue of interoperability for federation architectures using a different federation protocol. Afterwards, we compare SAML and WS-Federation. Eventually, we define the ways of convergence, and therefore, of interoperability.


Performance of a greedy algorithm for edge covering by cliques in interval graphs

Abstract: In this paper a greedy algorithm to detect conflict cliques in interval graphs and circular-arc graphs is analyzed. In a graph, a stable set requires that at most one vertex is chosen for each edge. It is equivalent to requiring that at most one vertex for each maximal clique is chosen. We show that this algorithm finds all maximal cliques for interval graphs, i.e. it can compute the convex hull of the stable set polytope. In case of circular-arc graphs, the algorithm is not able to detect all maximal cliques, yet remaining correct. This problem occurs in the context of railway scheduling. A train requests the allocation of a railway infrastructure resource for a specific time interval. As one is looking for conflict-free train schedules, the used resource allocation intervals in a schedule must not overlap. The conflict-free choices of used intervals for each resource correspond to stable sets in the interval graph associated to the allocation time intervals.


A Factor 3/2 Approximation for Generalized Steiner Tree Problem with Distances One and Two

Abstract: We design a 3/2 approximation algorithm for the Generalized Steiner Tree problem (GST) in metrics with distances 1 and 2. This is the first polynomial time approximation algorithm for a wide class of non-geometric metric GST instances with approximation factor below 2.


Equivalence of SQL Queries in Presence of Embedded Dependencies

Abstract: We consider the problem of finding equivalent minimal-size reformulations of SQL queries in presence of embedded dependencies [1]. Our focus is on select-project-join (SPJ) queries with equality comparisons, also known as safe conjunctive (CQ) queries, possibly with grouping and aggregation. For SPJ queries, the semantics of the SQL standard treat query answers as multisets (a.k.a. bags), whereas the stored relations may be treated either as sets, which is called bag-set semantics for query evaluation, or as bags, which is called bag semantics. (Under set semantics, both query answers and stored relations are treated as sets.) In the context of the above Query-Reformulation Problem, we develop a comprehensive framework for equivalence of CQ queries under bag and bag-set semantics in presence of embedded dependencies, and make a number of conceptual and technical contributions. Specifically, we develop equivalence tests for CQ queries in presence of arbitrary sets of embedded dependencies under bag and bag-set semantics, under the condition that chase [9] under set semantics (set-chase) on the inputs terminates. We also present equivalence tests for aggregate CQ queries in presence of embedded dependencies. We use our equivalence tests to develop sound and complete (whenever set-chase on the inputs terminates) algorithms for solving instances of the Query-Reformulation Problem with CQ queries under each of bag and bag-set semantics, as well as for instances of the problem with aggregate queries.


Unfolding Convex Polyhedra via Quasigeodesic Star Unfoldings

Abstract: We extend the notion of a star unfolding to be based on a simple quasigeodesic loop Q rather than on a point. This gives a new general method to unfold the surface of any convex polyhedron P to a simple, planar polygon: shortest paths from all vertices of P to Q are cut, and all but one segment of Q is cut.


An Efficient PTAS for Two-Strategy Anonymous Games

Abstract: We present a novel polynomial time approximation scheme for two-strategy anonymous games, in which the players' utility functions, although potentially different, do not differentiate among the identities of the other players. Our algorithm computes an $eps$-approximate Nash equilibrium of an $n$-player 2-strategy anonymous game in time $poly(n) (1/eps)^{O(1/eps^2)}$, which significantly improves upon the running time $n^{O(1/eps^2)}$ required by the algorithm of Daskalakis & Papadimitriou, 2007. The improved running time is based on a new structural understanding of approximate Nash equilibria: We show that, for any $eps$, there exists an $eps$-approximate Nash equilibrium in which either only $O(1/eps^3)$ players randomize, or all players who randomize use the same mixed strategy. To show this result we employ tools from the literature on Stein's Method.


Congestion Control Protocol for Wireless Sensor Networks Handling Prioritized Heterogeneous Traffic

Abstract: Heterogeneous applications could be assimilated within the same wireless sensor network with the aid of modern motes that have multiple sensor boards on a single radio board. Different types of data generated from such types of motes might have different transmission characteristics in terms of priority, transmission rate, required bandwidth, tolerable packet loss, delay demands etc. Considering a sensor network consisting of such multi-purpose nodes, in this paper we propose Prioritized Heterogeneous Traffic-oriented Congestion Control Protocol (PHTCCP) which ensures efficient rate control for prioritized heterogeneous traffic. Our protocol uses intra-queue and inter-queue priorities for ensuring feasible transmission rates of heterogeneous data. It also guarantees efficient link utilization by using dynamic transmission rate adjustment. Detailed analysis and simulation results are presented along with the description of our protocol to demonstrate its effectiveness in handling prioritized heterogeneous traffic in wireless sensor networks.


Characterizing Truthful Multi-Armed Bandit Mechanisms

Abstract: We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. If the advertisers bid their true private values, our problem is equivalent to the "multi-armed bandit problem", and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by "regret", the difference in social welfare between the algorithm and the benchmark which always selects the same "best" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that truthful mechanisms have certain strong structural properties -- essentially, they must separate exploration from exploitation -- and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret.


Urologic robots and future directions

Abstract: PURPOSE OF REVIEW: Robot-assisted laparoscopic surgery in urology has gained immense popularity with the daVinci system, but a lot of research teams are working on new robots. The purpose of this study is to review current urologic robots and present future development directions. RECENT FINDINGS: Future systems are expected to advance in two directions: improvements of remote manipulation robots and developments of image-guided robots. SUMMARY: The final goal of robots is to allow safer and more homogeneous outcomes with less variability of surgeon performance, as well as new tools to perform tasks on the basis of medical transcutaneous imaging, in a less invasive way, at lower costs. It is expected that improvements for a remote system could be augmented in reality, with haptic feedback, size reduction, and development of new tools for natural orifice translumenal endoscopic surgery. The paradigm of image-guided robots is close to clinical availability and the most advanced robots are presented with end-user technical assessments. It is also notable that the potential of robots lies much further ahead than the accomplishments of the daVinci system. The integration of imaging with robotics holds a substantial promise, because this can accomplish tasks otherwise impossible. Image-guided robots have the potential to offer a paradigm shift.


A New Trend in Optimization on Multi Overcomplete Dictionary toward Inpainting

Abstract: Recently, great attention was intended toward overcomplete dictionaries and the sparse representations they can provide. In a wide variety of signal processing problems, sparsity serves a crucial property leading to high performance. Inpainting, the process of reconstructing lost or deteriorated parts of images or videos, is an interesting application which can be handled by suitably decomposition of an image through combination of overcomplete dictionaries. This paper addresses a novel technique of such a decomposition and investigate that through inpainting of images. Simulations are presented to demonstrate the validation of our approach.


Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel Recognition in Continues Speech

Abstract: In this paper, we discuss the issues in automatic recognition of vowels in Persian language. The present work focuses on new statistical method of recognition of vowels as a basic unit of syllables. First we describe a vowel detection system then briefly discuss how the detected vowels can feed to recognition unit. According to pattern recognition, Support Vector Machines (SVM) as a discriminative classifier and Gaussian mixture model (GMM) as a generative model classifier are two most popular techniques. Current state-ofthe- art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. The main idea of the study is to combine probabilistic SVM and traditional GMM pattern classification with some characteristic of speech like band-pass energy to achieve better classification rate. This idea has been analytically formulated and tested on a FarsDat based vowel recognition system. The results show inconceivable increases in recognition accuracy. The tests have been carried out by various proposed vowel recognition algorithms and the results have been compared.


On the Expressive Power of 2-Stack Visibly Pushdown Automata

Abstract: Visibly pushdown automata are input-driven pushdown automata that recognize some non-regular context-free languages while preserving the nice closure and decidability properties of finite automata. Visibly pushdown automata with multiple stacks have been considered recently by La Torre, Madhusudan, and Parlato, who exploit the concept of visibility further to obtain a rich automata class that can even express properties beyond the class of context-free languages. At the same time, their automata are closed under boolean operations, have a decidable emptiness and inclusion problem, and enjoy a logical characterization in terms of a monadic second-order logic over words with an additional nesting structure. These results require a restricted version of visibly pushdown automata with multiple stacks whose behavior can be split up into a fixed number of phases. In this paper, we consider 2-stack visibly pushdown automata (i.e., visibly pushdown automata with two stacks) in their unrestricted form. We show that they are expressively equivalent to the existential fragment of monadic second-order logic. Furthermore, it turns out that monadic second-order quantifier alternation forms an infinite hierarchy wrt words with multiple nestings. Combining these results, we conclude that 2-stack visibly pushdown automata are not closed under complementation. Finally, we discuss the expressive power of B\"{u}chi 2-stack visibly pushdown automata running on infinite (nested) words. Extending the logic by an infinity quantifier, we can likewise establish equivalence to existential monadic second-order logic.


Strongly Multiplicative and 3-Multiplicative Linear Secret Sharing Schemes

Abstract: Strongly multiplicative linear secret sharing schemes (LSSS) have been a powerful tool for constructing secure multiparty computation protocols. However, it remains open whether or not there exist efficient constructions of strongly multiplicative LSSS from general LSSS. In this paper, we propose the new concept of a 3-multiplicative LSSS, and establish its relationship with strongly multiplicative LSSS. More precisely, we show that any 3-multiplicative LSSS is a strongly multiplicative LSSS, but the converse is not true; and that any strongly multiplicative LSSS can be efficiently converted into a 3-multiplicative LSSS. Furthermore, we apply 3-multiplicative LSSS to the computation of unbounded fan-in multiplication, which reduces its round complexity to four (from five of the previous protocol based on strongly multiplicative LSSS). We also give two constructions of 3-multiplicative LSSS from Reed-Muller codes and algebraic geometric codes. We believe that the construction and verification of 3-multiplicative LSSS are easier than those of strongly multiplicative LSSS. This presents a step forward in settling the open problem of efficient constructions of strongly multiplicative LSSS from general LSSS.


Kalinahia: Considering Quality of Service to Design and Execute Distributed Multimedia Applications

Abstract: One of the current challenges of Information Systems is to ensure semi-structured data transmission, such as multimedia data, in a distributed and pervasive environment. Information Sytems must then guarantee users a quality of service ensuring data accessibility whatever the hardware and network conditions may be. They must also guarantee information coherence and particularly intelligibility that imposes a personalization of the service. Within this framework, we propose a design method based on original models of multimedia applications and quality of service. We also define a supervision platform Kalinahia using a user centered heuristic allowing us to define at any moment which configuration of software components constitutes the best answers to users' wishes in terms of service.


Pattern Recognition and Memory Mapping using Mirroring Neural Networks

Abstract: In this paper, we present a new kind of learning implementation to recognize the patterns using the concept of Mirroring Neural Network (MNN) which can extract information from distinct sensory input patterns and perform pattern recognition tasks. It is also capable of being used as an advanced associative memory wherein image data is associated with voice inputs in an unsupervised manner. Since the architecture is hierarchical and modular it has the potential of being used to devise learning engines of ever increasing complexity.


Perturbation analysis of an M/M/1 queue in a diffusion random environment

Abstract: We study in this paper an $M/M/1$ queue whose server rate depends upon the state of an independent Ornstein-Uhlenbeck diffusion process $(X(t))$ so that its value at time $t$ is $\mu \phi(X(t))$, where $\phi(x)$ is some bounded function and $\mu>0$. We first establish the differential system for the conditional probability density functions of the couple $(L(t),X(t))$ in the stationary regime, where $L(t)$ is the number of customers in the system at time $t$. By assuming that $\phi(x)$ is defined by $\phi(x) = 1-\varepsilon ((x\wedge a/\varepsilon)\vee(-b/\varepsilon))$ for some positive real numbers $a$, $b$ and $\varepsilon$, we show that the above differential system has a unique solution under some condition on $a$ and $b$. We then show that this solution is close, in some appropriate sense, to the solution to the differential system obtained when $\phi$ is replaced with $\Phi(x)=1-\varepsilon x$ for sufficiently small $\varepsilon$. We finally perform a perturbation analysis of this latter solution for small $\varepsilon$. This allows us to check at the first order the validity of the so-called reduced service rate approximation, stating that everything happens as if the server rate were constant and equal to $\mu(1-\eps\E(X(t)))$.


Inference of Flow Statistics via Packet Sampling in the Internet

Abstract: We show in this note that by deterministic packet sampling, the tail of the distribution of the original flow size can be obtained by rescaling that of the sampled flow size. To recover information on the flow size distribution lost through packet sampling, we propose some heuristics based on measurements from different backbone IP networks. These heuristic arguments allow us to recover the complete flow size distribution.


An identification problem in an urn and ball model with heavy tailed distributions

Abstract: We consider in this paper an urn and ball problem with replacement, where balls are with different colors and are drawn uniformly from a unique urn. The numbers of balls with a given color are i.i.d. random variables with a heavy tailed probability distribution, for instance a Pareto or a Weibull distribution. We draw a small fraction $p\ll 1$ of the total number of balls. The basic problem addressed in this paper is to know to which extent we can infer the total number of colors and the distribution of the number of balls with a given color. By means of Le Cam's inequality and Chen-Stein method, bounds for the total variation norm between the distribution of the number of balls drawn with a given color and the Poisson distribution with the same mean are obtained. We then show that the distribution of the number of balls drawn with a given color has the same tail as that of the original number of balls. We finally establish explicit bounds between the two distributions when each ball is drawn with fixed probability $p$.


An $O({\log n\over \log\log n})$ Upper Bound on the Price of Stability for Undirected Shapley Network Design Games

Abstract: In this paper, we consider the Shapley network design game on undirected networks. In this game, we have an edge weighted undirected network $G(V,E)$ and $n$ selfish players where player $i$ wants to choose a path from source vertex $s_i$ to destination vertex $t_i$. The cost of each edge is equally split among players who pass it. The price of stability is defined as the ratio of the cost of the best Nash equilibrium to that of the optimal solution. We present an $O(\log n/\log\log n)$ upper bound on price of stability for the single sink case, i.e, $t_i=t$ for all $i$.


Feature Selection By KDDA For SVM-Based MultiView Face Recognition

Abstract: Applications such as face recognition that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features. Most of traditional Linear Discriminant Analysis suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" problem which is often encountered in FR tasks. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine classifier to deal with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on UMIST face database. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods and traditional linear classifiers.


Face Detection Using Adaboosted SVM-Based Component Classifier

Abstract: Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task. To obtain a set of effective SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database. Results indicate that the performance of the proposed method is overall superior to previous Adaboost approaches.


Learning Low Rank Matrices from O(n) Entries

Abstract: How many random entries of an n by m, rank r matrix are necessary to reconstruct the matrix within an accuracy d? We address this question in the case of a random matrix with bounded rank, whereby the observed entries are chosen uniformly at random. We prove that, for any d>0, C(r,d)n observations are sufficient. Finally we discuss the question of reconstructing the matrix efficiently, and demonstrate through extensive simulations that this task can be accomplished in nPoly(log n) operations, for small rank.


Approximating the least hypervolume contributor: NP-hard in general, but fast in practice

Abstract: The hypervolume indicator is an increasingly popular set measure to compare the quality of two Pareto sets. The basic ingredient of most hypervolume indicator based optimization algorithms is the calculation of the hypervolume contribution of single solutions regarding a Pareto set. We show that exact calculation of the hypervolume contribution is #P-hard while its approximation is NP-hard. The same holds for the calculation of the minimal contribution. We also prove that it is NP-hard to decide whether a solution has the least hypervolume contribution. Even deciding whether the contribution of a solution is at most $(1+\eps)$ times the minimal contribution is NP-hard. This implies that it is neither possible to efficiently find the least contributing solution (unless $P = NP$) nor to approximate it (unless $NP = BPP$). Nevertheless, in the second part of the paper we present a fast approximation algorithm for this problem. We prove that for arbitrarily given $\eps,\delta>0$ it calculates a solution with contribution at most $(1+\eps)$ times the minimal contribution with probability at least $(1-\delta)$. Though it cannot run in polynomial time for all instances, it performs extremely fast on various benchmark datasets. The algorithm solves very large problem instances which are intractable for exact algorithms (e.g., 10000 solutions in 100 dimensions) within a few seconds.


Asteroids in rooted and directed path graphs

Abstract: An asteroidal triple is a stable set of three vertices such that each pair is connected by a path avoiding the neighborhood of the third vertex. Asteroidal triples play a central role in a classical characterization of interval graphs by Lekkerkerker and Boland. Their result says that a chordal graph is an interval graph if and only if it contains no asteroidal triple. In this paper, we prove an analogous theorem for directed path graphs which are the intersection graphs of directed paths in a directed tree. For this purpose, we introduce the notion of a strong path. Two non-adjacent vertices are linked by a strong path if either they have a common neighbor or they are the endpoints of two vertex-disjoint chordless paths satisfying certain conditions. A strong asteroidal triple is an asteroidal triple such that each pair is linked by a strong path. We prove that a chordal graph is a directed path graph if and only if it contains no strong asteroidal triple. We also introduce a related notion of asteroidal quadruple, and conjecture a characterization of rooted path graphs which are the intersection graphs of directed paths in a rooted tree.


Geometric scaling: a simple preconditioner for certain linear systems with discontinuous coefficients

Abstract: Linear systems with large differences between coefficients ("discontinuous coefficients") arise in many cases in which partial differential equations(PDEs) model physical phenomena involving heterogeneous media. The standard approach to solving such problems is to use domain decomposition techniques, with domain boundaries conforming to the boundaries between the different media. This approach can be difficult to implement when the geometry of the domain boundaries is complicated or the grid is unstructured. This work examines the simple preconditioning technique of scaling the equations by dividing each equation by the Lp-norm of its coefficients. This preconditioning is called geometric scaling (GS). It has long been known that diagonal scaling can be useful in improving convergence, but there is no study on the general usefulness of this approach for discontinuous coefficients. GS was tested on several nonsymmetric linear systems with discontinuous coefficients derived from convection-diffusion elliptic PDEs with small to moderate convection terms. It is shown that GS improved the convergence properties of restarted GMRES and Bi-CGSTAB, with and without the ILUT preconditioner. GS was also shown to improve the distribution of the eigenvalues by reducing their concentration around the origin very significantly.


Optimal Succinctness for Range Minimum Queries

Abstract: For a static array A of n ordered objects, a range minimum query asks for the position of the minimum between two specified array indices. We show how to preprocess A into a scheme of size 2n+o(n) bits that allows to answer range minimum queries on A in constant time. This space is asymptotically optimal in the important setting where access to A is not permitted after the preprocessing step. Our scheme can be computed in linear time, using only n + o(n) additional bits at construction time. In interesting by-product is that we also improve on LCA-computation in BPS- or DFUDS-encoded trees.


Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts

Abstract: Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in each region based on its performance on samples from that region. The ensemble adapts dynamically by constantly adjusting these weights based on the current performance of the networks. The data set used is a collection of financial indicators with the goal of predicting the future platinum price. An ensemble with no weightings does not improve on the naive estimate of no weekly change; our weighting algorithm gives an average percentage error of 63% for twenty weeks of prediction.


The Violation Heap: A Relaxed Fibonacci-Like Heap

Abstract: We give a priority queue that achieves the same amortized bounds as Fibonacci heaps. Namely, find-min requires O(1) worst-case time, insert, meld and decrease-key require O(1) amortized time, and delete-min requires $O(\log n)$ amortized time. Our structure is simple and promises an efficient practical behavior when compared to other known Fibonacci-like heaps. The main idea behind our construction is to propagate rank updates instead of performing cascaded cuts following a decrease-key operation, allowing for a relaxed structure.


Minimax Trees in Linear Time

Abstract: A minimax tree is similar to a Huffman tree except that, instead of minimizing the weighted average of the leaves' depths, it minimizes the maximum of any leaf's weight plus its depth. Golumbic (1976) introduced minimax trees and gave a Huffman-like, $\Oh{n \log n}$-time algorithm for building them. Drmota and Szpankowski (2002) gave another $\Oh{n \log n}$-time algorithm, which checks the Kraft Inequality in each step of a binary search. In this paper we show how Drmota and Szpankowski's algorithm can be made to run in linear time on a word RAM with (\Omega (\log n))-bit words. We also discuss how our solution applies to problems in data compression, group testing and circuit design.


A Data Model for Integrating Heterogeneous Medical Data in the Health-e-Child Project

Abstract: There has been much research activity in recent times about providing the data infrastructures needed for the provision of personalised healthcare. In particular the requirement of integrating multiple, potentially distributed, heterogeneous data sources in the medical domain for the use of clinicians has set challenging goals for the healthgrid community. The approach advocated in this paper surrounds the provision of an Integrated Data Model plus links to/from ontologies to homogenize biomedical (from genomic, through cellular, disease, patient and population-related) data in the context of the EC Framework 6 Health-e-Child project. Clinical requirements are identified, the design approach in constructing the model is detailed and the integrated model described in the context of examples taken from that project. Pointers are given to future work relating the model to medical ontologies and challenges to the use of fully integrated models and ontologies are identified.


Ontology Assisted Query Reformulation Using Semantic and Assertion Capabilities of OWL-DL Ontologies

Abstract: End users of recent biomedical information systems are often unaware of the storage structure and access mechanisms of the underlying data sources and can require simplified mechanisms for writing domain specific complex queries. This research aims to assist users and their applications in formulating queries without requiring complete knowledge of the information structure of underlying data sources. To achieve this, query reformulation techniques and algorithms have been developed that can interpret ontology-based search criteria and associated domain knowledge in order to reformulate a relational query. These query reformulation algorithms exploit the semantic relationships and assertion capabilities of OWL-DL based domain ontologies for query reformulation. In this paper, this approach is applied to the integrated database schema of the EU funded Health-e-Child (HeC) project with the aim of providing ontology assisted query reformulation techniques to simplify the global access that is needed to millions of medical records across the UK and Europe.


On the Value of a Social Network

Abstract: In this paper we investigate the value of a social network with respect to the probability mechanism underlying its structure. Specifically, we compute the value for small world and scale free networks. We provide evidence in support of the value to be given by Zipfs law.


Sparse Component Analysis (SCA) in Random-valued and Salt and Pepper Noise Removal

Abstract: In this paper, we propose a new method for impulse noise removal from images. It uses the sparsity of images in the Discrete Cosine Transform (DCT) domain. The zeros in this domain give us the exact mathematical equation to reconstruct the pixels that are corrupted by random-value impulse noises. The proposed method can also detect and correct the corrupted pixels. Moreover, in a simpler case that salt and pepper noise is the brightest and darkest pixels in the image, we propose a simpler version of our method. In addition to the proposed method, we suggest a combination of the traditional median filter method with our method to yield better results when the percentage of the corrupted samples is high.


New parallel programming language design: a bridge between brain models and multi-core/many-core computers?

Abstract: The recurrent theme of this paper is that sequences of long temporal patterns as opposed to sequences of simple statements are to be fed into computation devices, being them (new proposed) models for brain activity or multi-core/many-core computers. In such models, parts of these long temporal patterns are already committed while other are predicted. This combination of matching patterns and making predictions appears as a key element in producing intelligent processing in brain models and getting efficient speculative execution on multi-core/many-core computers. A bridge between these far-apart models of computation could be provided by appropriate design of massively parallel, interactive programming languages. Agapia is a recently proposed language of this kind, where user controlled long high-level temporal structures occur at the interaction interfaces of processes. In this paper Agapia is used to link HTMs brain models with TRIPS multi-core/many-core architectures.


Shape Fitting on Point Sets with Probability Distributions

Abstract: A typical computational geometry problem begins: Consider a set P of n points in R^d. However, many applications today work with input that is not precisely known, for example when the data is sensed and has some known error model. What if we do not know the set P exactly, but rather we have a probability distribution mu_p governing the location of each point p in P? Consider a set of (non-fixed) points P, and let mu_P be the probability distribution of this set. We study several measures (e.g. the radius of the smallest enclosing ball, or the area of the smallest enclosing box) with respect to mu_P. The solutions to these problems do not, as in the traditional case, consist of a single answer, but rather a distribution of answers. We describe several data structures that approximate distributions of answers for shape fitting problems. We provide simple and efficient randomized algorithms for computing all of these data structures, which are easy to implement and practical. We provide some experimental results to assert this. We also provide more involved deterministic algorithms for some of these data structures that run in time polynomial in n and 1/eps, where eps is the approximation factor.


A Growing Self-Organizing Network for Reconstructing Curves and Surfaces

Abstract: Self-organizing networks such as Neural Gas, Growing Neural Gas and many others have been adopted in actual applications for both dimensionality reduction and manifold learning. Typically, in these applications, the structure of the adapted network yields a good estimate of the topology of the unknown subspace from where the input data points are sampled. The approach presented here takes a different perspective, namely by assuming that the input space is a manifold of known dimension. In return, the new type of growing self-organizing network presented gains the ability to adapt itself in way that may guarantee the effective and stable recovery of the exact topological structure of the input manifold.


The Korrontea Data Modeling

Abstract: Needs of multimedia systems evolved due to the evolution of their architecture which is now distributed into heterogeneous contexts. A critical issue lies in the fact that they handle, process, and transmit multimedia data. This data integrates several properties which should be considered since it holds a considerable part of its semantics, for instance the lips synchronization in a video. In this paper, we focus on the definition of a model as a basic abstraction for describing and modeling media in multimedia systems by taking into account their properties. This model will be used in software architecture in order to handle data in efficient way. The provided model is an interesting solution for the integration of media into applications; we propose to consider and to handle them in a uniform way. This model is proposed with synchronization policies to ensure synchronous transport of media. Therefore, we use it in a component model that we develop for the design and deployment of distributed multimedia systems.


Heterogeneous component interactions: Sensors integration into multimedia applications

Abstract: Resource-constrained embedded and mobile devices are becoming increasingly common. Since few years, some mobile and ubiquitous devices such as wireless sensor, able to be aware of their physical environment, appeared. Such devices enable proposing applications which adapt to user's need according the context evolution. It implies the collaboration of sensors and software components which differ on their nature and their communication mechanisms. This paper proposes a unified component model in order to easily design applications based on software components and sensors without taking care of their nature. Then it presents a state of the art of communication problems linked to heterogeneous components and proposes an interaction mechanism which ensures information exchanges between wireless sensors and software components.


Tree-width of hypergraphs and surface duality

Abstract: In Graph Minor III, Robertson and Seymour conjecture that the tree-width of a planar graph and that of its dual differ by at most one. We prove that given a hypergraph H on a surface of Euler genus k, the tree-width of H^* is at most the maximum of tw(H) + 1 + k and the maximum size of a hyperedge of H^*.


Analyse et structuration automatique des guides de bonnes pratiques cliniques : essai d'\'evaluation

Abstract: Health Practice Guideliens are supposed to unify practices and propose recommendations to physicians. This paper describes GemFrame, a system capable of semi-automatically filling an XML template from free texts in the clinical domain. The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and ac-tions/recommendations). Therefore, there is a need to compute the exact scope of condi-tions over text sequences expressing the re-quired actions. We present a system developped for this task. We show that it yields good performance when applied to the analysis of French practice guidelines. We conclude with a precise evaluation of the tool.


Branching Bisimilarity with Explicit Divergence

Abstract: We consider the relational characterisation of branching bisimilarity with explicit divergence. We prove that it is an equivalence and that it coincides with the original definition of branching bisimilarity with explicit divergence in terms of coloured traces. We also establish a correspondence with several variants of an action-based modal logic with until- and divergence modalities.


Binary Classification Based on Potentials

Abstract: We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector Machine methods.


Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines

Abstract: We investigate the performance of a simple signed distance function (SDF) based method by direct comparison with standard SVM packages, as well as K-nearest neighbor and RBFN methods. We present experimental results comparing the SDF approach with other classifiers on both synthetic geometric problems and five benchmark clinical microarray data sets. On both geometric problems and microarray data sets, the non-optimized SDF based classifiers perform just as well or slightly better than well-developed, standard SVM methods. These results demonstrate the potential accuracy of SDF-based methods on some types of problems.


Two conjectures such that the proof of any one of them will lead to the proof that P = NP

Abstract: In this paper we define a construct called a time-graph. A complete time-graph of order n is the cartesian product of a complete graph with n vertices and a linear graph with n vertices. A time-graph of order n is given by a subset of the set of edges E(n) of such a graph. The notion of a hamiltonian time-graph is defined in a natural way and we define the Hamiltonian time-graph problem (HAMTG) as : Given a time-graph is it hamiltonian ? We show that the Hamiltonian path problem (HAMP) can be transformed to HAMTG in polynomial time. We then define certain vector spaces of functions from E(n) and E(n)xE(n) to B = {0,1}, the field of two elements and derive certain properties of these spaces. We give two conjectures about these spaces and prove that if any one of these conjectures is true, we get a polynomial time algorithm for the Hamiltonian path problem. Since the Hamiltonian path problem is NP-complete we obtain the proof of P = NP provided any one of the two conjectures is true.


BiopSym: a simulator for enhanced learning of ultrasound-guided prostate biopsy

Abstract: This paper describes a simulator of ultrasound-guided prostate biopsies for cancer diagnosis. When performing biopsy series, the clinician has to move the ultrasound probe and to mentally integrate the real-time bi-dimensional images into a three-dimensional (3D) representation of the anatomical environment. Such a 3D representation is necessary to sample regularly the prostate in order to maximize the probability of detecting a cancer if any. To make the training of young physicians easier and faster we developed a simulator that combines images computed from three-dimensional ultrasound recorded data to haptic feedback. The paper presents the first version of this simulator.


Chain-Based Representations for Solid and Physical Modeling

Abstract: In this paper we show that the (co)chain complex associated with a decomposition of the computational domain, commonly called a mesh in computational science and engineering, can be represented by a block-bidiagonal matrix that we call the Hasse matrix. Moreover, we show that topology-preserving mesh refinements, produced by the action of (the simplest) Euler operators, can be reduced to multilinear transformations of the Hasse matrix representing the complex. Our main result is a new representation of the (co)chain complex underlying field computations, a representation that provides new insights into the transformations induced by local mesh refinements. Our approach is based on first principles and is general in that it applies to most representational domains that can be characterized as cell complexes, without any restrictions on their type, dimension, codimension, orientability, manifoldness, connectedness.


Approximate conditional distributions of distances between nodes in a two-dimensional sensor network

Abstract: When we represent a network of sensors in Euclidean space by a graph, there are two distances between any two nodes that we may consider. One of them is the Euclidean distance. The other is the distance between the two nodes in the graph, defined to be the number of edges on a shortest path between them. In this paper, we consider a network of sensors placed uniformly at random in a two-dimensional region and study two conditional distributions related to these distances. The first is the probability distribution of distances in the graph, conditioned on Euclidean distances; the other is the probability density function associated with Euclidean distances, conditioned on distances in the graph. We study these distributions both analytically (when feasible) and by means of simulations. To the best of our knowledge, our results constitute the first of their kind and open up the possibility of discovering improved solutions to certain sensor-network problems, as for example sensor localization.


Linearly Parameterized Bandits

Abstract: We consider bandit problems involving a large (possibly infinite) collection of arms, in which the expected reward of each arm is a linear function of an $r$-dimensional random vector $\mathbf{Z} \in \mathbb{R}^r$, where $r \geq 2$. The objective is to minimize the cumulative regret and Bayes risk. When the set of arms corresponds to the unit sphere, we prove that the regret and Bayes risk is of order $\Theta(r \sqrt{T})$, by establishing a lower bound for an arbitrary policy, and showing that a matching upper bound is obtained through a policy that alternates between exploration and exploitation phases. The phase-based policy is also shown to be effective if the set of arms satisfies a strong convexity condition. For the case of a general set of arms, we describe a near-optimal policy whose regret and Bayes risk admit upper bounds of the form $O(r \sqrt{T} \log^{3/2} T)$.


Automatic Construction of Lightweight Domain Ontologies for Chemical Engineering Risk Management

Abstract: The need for domain ontologies in mission critical applications such as risk management and hazard identification is becoming more and more pressing. Most research on ontology learning conducted in the academia remains unrealistic for real-world applications. One of the main problems is the dependence on non-incremental, rare knowledge and textual resources, and manually-crafted patterns and rules. This paper reports work in progress aiming to address such undesirable dependencies during ontology construction. Initial experiments using a working prototype of the system revealed promising potentials in automatically constructing high-quality domain ontologies using real-world texts.


XML Static Analyzer User Manual

Abstract: This document describes how to use the XML static analyzer in practice. It provides informal documentation for using the XML reasoning solver implementation. The solver allows automated verification of properties that are expressed as logical formulas over trees. A logical formula may for instance express structural constraints or navigation properties (like e.g. path existence and node selection) in finite trees. Logical formulas can be expressed using the syntax of XPath expressions, DTD, XML Schemas, and Relax NG definitions.


Questions & Answers for TEI Newcomers

Abstract: This paper provides an introduction to the Text Encoding Initia-tive (TEI), focused at bringing in newcomers who have to deal with a digital document project and are looking at the capacity that the TEI environment may have to fulfil his needs. To this end, we avoid a strictly technical presentation of the TEI and concentrate on the actual issues that such projects face, with parallel made on the situation within two institutions. While a quick walkthrough the TEI technical framework is provided, the papers ends up by showing the essential role of the community in the actual technical contributions that are being brought to the TEI.


A New Method for Knowledge Representation in Expert System's (XMLKR)

Abstract: Knowledge representation it is an essential section of a Expert Systems, Because in this section we have a framework to establish an expert system then we can modeling and use by this to design an expert system. Many method it is exist for knowledge representation but each method have problems, in this paper we introduce a new method of object oriented by XML language as XMLKR to knowledge representation, and we want to discuss advantage and disadvantage of this method.


Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008

Abstract: The 2008 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2008), sponsored by the NSF, DARPA, LinkedIn, and Yahoo!, was held at Stanford University, June 25--28. The goals of MMDS 2008 were (1) to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets; and (2) to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote cross-fertilization of ideas.


Business processes integration and performance indicators in a PLM

Abstract: In an economic environment more and more competitive, the effective management of information and knowledge is a strategic issue for industrial enterprises. In the global marketplace, companies must use reactive strategies and reduce their products development cycle. In this context, the PLM (Product Lifecycle Management) is considered as a key component of the information system. The aim of this paper is to present an approach to integrate Business Processes in a PLM system. This approach is implemented in automotive sector with second-tier subcontractor


Context-aware adaptation for group communication support applications with dynamic architecture

Abstract: In this paper, we propose a refinement-based adaptation approach for the architecture of distributed group communication support applications. Unlike most of previous works, our approach reaches implementable, context-aware and dynamically adaptable architectures. To model the context, we manage simultaneously four parameters that influence Qos provided by the application. These parameters are: the available bandwidth, the exchanged data communication priority, the energy level and the available memory for processing. These parameters make it possible to refine the choice between the various architectural configurations when passing from a given abstraction level to the lower level which implements it. Our approach allows the importance degree associated with each parameter to be adapted dynamically. To implement adaptation, we switch between the various configurations of the same level, and we modify the state of the entities of a given configuration when necessary. We adopt the direct and mediated Producer- Consumer architectural styles and graphs for architecture modelling. In order to validate our approach we elaborate a simulation model.


Architecture Logicielles pour des Applications h\'et\'erog\`enes, distribu\'ees et reconfigurables

Abstract: The recent apparition of mobile wireless sensor aware to their physical environment and able to process information must allow proposing applications able to take into account their physical context and to react according to the changes of the environment. It suppose to design applications integrating both software and hardware components able to communicate. Applications must use context information from components to measure the quality of the proposed services in order to adapt them in real time. This work is interested in the integration of sensors in distributed applications. It present a service oriented software architecture allowing to manage and to reconfigure applications in heterogeneous environment where entities of different nature collaborate: software components and wireless sensors.