Model checking memoryful linear-time logics over one-counter automata

Abstract: We study complexity of the model-checking problems for LTL with registers (also known as freeze LTL) and for first-order logic with data equality tests over one-counter automata. We consider several classes of one-counter automata (mainly deterministic vs. nondeterministic) and several logical fragments (restriction on the number of registers or variables and on the use of propositional variables for control locations). The logics have the ability to store a counter value and to test it later against the current counter value. We show that model checking over deterministic one-counter automata is PSPACE-complete with infinite and finite accepting runs. By constrast, we prove that model checking freeze LTL in which the until operator is restricted to the eventually operator over nondeterministic one-counter automata is undecidable even if only one register is used and with no propositional variable. As a corollary of our proof, this also holds for first-order logic with data equality tests restricted to two variables. This makes a difference with the facts that several verification problems for one-counter automata are known to be decidable with relatively low complexity, and that finitary satisfiability for the two logics are decidable. Our results pave the way for model-checking memoryful (linear-time) logics over other classes of operational models, such as reversal-bounded counter machines.


A branch-and-bound feature selection algorithm for U-shaped cost functions

Abstract: This paper presents the formulation of a combinatorial optimization problem with the following characteristics: i.the search space is the power set of a finite set structured as a Boolean lattice; ii.the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics, that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to SFFS, which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time.


Detection of parallel steps in programs with arrays

Abstract: The problem of detecting of information and logically independent (DILD) steps in programs is a key for equivalent program transformations. Here we are considering the problem of independence of loop iterations, the concentration of massive data processing and hence the most challenge construction for parallelizing. We introduced a separated form of loops when loop's body is a sequence of procedures each of them are used array's elements selected in a previous procedure. We prove that any loop may be algorithmically represented in this form and number of such procedures is invariant. We show that for this form of loop the steps connections are determined with some integer equations and hence the independence problem is algorithmically unsolvable if index expressions are more complex than cubical. We suggest a modification of index semantics that made connection equations trivial and loops iterations can be executed in parallel.


Anonymizing Graphs

Abstract: Motivated by recently discovered privacy attacks on social networks, we study the problem of anonymizing the underlying graph of interactions in a social network. We call a graph (k,l)-anonymous if for every node in the graph there exist at least k other nodes that share at least l of its neighbors. We consider two combinatorial problems arising from this notion of anonymity in graphs. More specifically, given an input graph we ask for the minimum number of edges to be added so that the graph becomes (k,l)-anonymous. We define two variants of this minimization problem and study their properties. We show that for certain values of k and l the problems are polynomial-time solvable, while for others they become NP-hard. Approximation algorithms for the latter cases are also given.


Anonymizing Unstructured Data

Abstract: In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. We define an optimization problem that arises from this definition of anonymity and provide O(klogk) and O(1)-approximation algorithms for the same. We demonstrate applicability of our algorithms to the America Online query log dataset.


Programming languages with algorithmically parallelizing problem

Abstract: The study consists of two parts. Objective of the first part is modern language constructions responsible for algorithmically insolvability of parallelizing problem. Second part contains several ways to modify the constructions to make the problem algorithmically solvable


Temporal Difference Updating without a Learning Rate

Abstract: We derive an equation for temporal difference learning from statistical principles. Specifically, we start with the variational principle and then bootstrap to produce an updating rule for discounted state value estimates. The resulting equation is similar to the standard equation for temporal difference learning with eligibility traces, so called TD(lambda), however it lacks the parameter alpha that specifies the learning rate. In the place of this free parameter there is now an equation for the learning rate that is specific to each state transition. We experimentally test this new learning rule against TD(lambda) and find that it offers superior performance in various settings. Finally, we make some preliminary investigations into how to extend our new temporal difference algorithm to reinforcement learning. To do this we combine our update equation with both Watkins' Q(lambda) and Sarsa(lambda) and find that it again offers superior performance without a learning rate parameter.


Kaltofen's division-free determinant algorithm differentiated for matrix adjoint computation

Abstract: Kaltofen has proposed a new approach in 1992 for computing matrix determinants without divisions. The algorithm is based on a baby steps/giant steps construction of Krylov subspaces, and computes the determinant as the constant term of a characteristic polynomial. For matrices over an abstract ring, by the results of Baur and Strassen, the determinant algorithm, actually a straight-line program, leads to an algorithm with the same complexity for computing the adjoint of a matrix. However, the latter adjoint algorithm is obtained by the reverse mode of automatic differentiation, hence somehow is not "explicit". We present an alternative (still closely related) algorithm for the adjoint thatcan be implemented directly, we mean without resorting to an automatic transformation. The algorithm is deduced by applying program differentiation techniques "by hand" to Kaltofen's method, and is completely decribed. As subproblem, we study the differentiation of programs that compute minimum polynomials of lineraly generated sequences, and we use a lazy polynomial evaluation mechanism for reducing the cost of Strassen's avoidance of divisions in our case.


Interpolation of Shifted-Lacunary Polynomials

Abstract: Given a "black box" function to evaluate an unknown rational polynomial f in Q[x] at points modulo a prime p, we exhibit algorithms to compute the representation of the polynomial in the sparsest shifted power basis. That is, we determine the sparsity t, the shift s (a rational), the exponents 0 <= e1 < e2 < ... < et, and the coefficients c1,...,ct in Q\{0} such that f(x) = c1(x-s)^e1+c2(x-s)^e2+...+ct(x-s)^et. The computed sparsity t is absolutely minimal over any shifted power basis. The novelty of our algorithm is that the complexity is polynomial in the (sparse) representation size, and in particular is logarithmic in deg(f). Our method combines previous celebrated results on sparse interpolation and computing sparsest shifts, and provides a way to handle polynomials with extremely high degree which are, in some sense, sparse in information.


On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach

Abstract: A lattice-theoretic framework is introduced that permits the study of the conditional independence (CI) implication problem relative to the class of discrete probability measures. Semi-lattices are associated with CI statements and a finite, sound and complete inference system relative to semi-lattice inclusions is presented. This system is shown to be (1) sound and complete for saturated CI statements, (2) complete for general CI statements, and (3) sound and complete for stable CI statements. These results yield a criterion that can be used to falsify instances of the implication problem and several heuristics are derived that approximate this "lattice-exclusion" criterion in polynomial time. Finally, we provide experimental results that relate our work to results obtained from other existing inference algorithms.


A triangle-based logic for affine-invariant querying of spatial and spatio-temporal data

Abstract: In spatial databases, incompatibilities often arise due to different choices of origin or unit of measurement (e.g., centimeters versus inches). By representing and querying the data in an affine-invariant manner, we can avoid these incompatibilities. In practice, spatial (resp., spatio-temporal) data is often represented as a finite union of triangles (resp., moving triangles). As two arbitrary triangles are equal up to a unique affinity of the plane, they seem perfect candidates as basic units for an affine-invariant query language. We propose a so-called "triangle logic", a query language that is affine-generic and has triangles as basic elements. We show that this language has the same expressive power as the affine-generic fragment of first-order logic over the reals on triangle databases. We illustrate that the proposed language is simple and intuitive. It can also serve as a first step towards a "moving-triangle logic" for spatio-temporal data.


Multi-Objective Model Checking of Markov Decision Processes

Abstract: We study and provide efficient algorithms for multi-objective model checking problems for Markov Decision Processes (MDPs). Given an MDP, M, and given multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a strategy \sigma for the controller such that, for all i, the probability that a trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We provide an algorithm that decides whether there exists such a strategy and if so produces it, and which runs in time polynomial in the size of the MDP. Such a strategy may require the use of both randomization and memory. We also consider more general multi-objective \omega -regular queries, which we motivate with an application to assume-guarantee compositional reasoning for probabilistic systems. Note that there can be trade-offs between different properties: satisfying property \varphi\_1 with high probability may necessitate satisfying \varphi\_2 with low probability. Viewing this as a multi-objective optimization problem, we want information about the "trade-off curve" or Pareto curve for maximizing the probabilities of different properties. We show that one can compute an approximate Pareto curve with respect to a set of \omega -regular properties in time polynomial in the size of the MDP. Our quantitative upper bounds use LP methods. We also study qualitative multi-objective model checking problems, and we show that these can be analysed by purely graph-theoretic methods, even though the strategies may still require both randomization and memory.


Practical language based on systems of definitions

Abstract: The article suggests a description of a system of tables with a set of special lists absorbing a semantics of data and reflects a fullness of data. It shows how their parallel processing can be constructed based on the descriptions. The approach also might be used for definition intermediate targets for data mining and unstructured data processing.


Non procedural language for parallel programs

Abstract: Probably building non procedural languages is the most prospective way for parallel programming just because non procedural means no fixed way for execution. The article consists of 3 parts. In first part we consider formal systems for definition a named datasets and studying an expression power of different subclasses. In the second part we consider a complexity of algorithms of building sets by the definitions. In third part we consider a fullness and flexibility of the class of program based data set definitions.


Number of wireless sensors needed to detect a wildfire

Abstract: The lack of extensive research in the application of inexpensive wireless sensor nodes for the early detection of wildfires motivated us to investigate the cost of such a network. As a first step, in this paper we present several results which relate the time to detection and the burned area to the number of sensor nodes in the region which is protected. We prove that the probability distribution of the burned area at the moment of detection is approximately exponential, given that some hypotheses hold: the positions of the sensor nodes are independent random variables uniformly distributed and the number of sensor nodes is large. This conclusion depends neither on the number of ignition points nor on the propagation model of the fire.


A complexity dichotomy for hypergraph partition functions

Abstract: We consider the complexity of counting homomorphisms from an $r$-uniform hypergraph $G$ to a symmetric $r$-ary relation $H$. We give a dichotomy theorem for $r>2$, showing for which $H$ this problem is in FP and for which $H$ it is #P-complete. This generalises a theorem of Dyer and Greenhill (2000) for the case $r=2$, which corresponds to counting graph homomorphisms. Our dichotomy theorem extends to the case in which the relation $H$ is weighted, and the goal is to compute the \emph{partition function}, which is the sum of weights of the homomorphisms. This problem is motivated by statistical physics, where it arises as computing the partition function for particle models in which certain combinations of $r$ sites interact symmetrically. In the weighted case, our dichotomy theorem generalises a result of Bulatov and Grohe (2005) for graphs, where $r=2$. When $r=2$, the polynomial time cases of the dichotomy correspond simply to rank-1 weights. Surprisingly, for all $r>2$ the polynomial time cases of the dichotomy have rather more structure. It turns out that the weights must be superimposed on a combinatorial structure defined by solutions of an equation over an Abelian group. Our result also gives a dichotomy for a closely related constraint satisfaction problem.


Conjectural Equilibrium in Water-filling Games

Abstract: This paper considers a non-cooperative game in which competing users sharing a frequency-selective interference channel selfishly optimize their power allocation in order to improve their achievable rates. Previously, it was shown that a user having the knowledge of its opponents' channel state information can make foresighted decisions and substantially improve its performance compared with the case in which it deploys the conventional iterative water-filling algorithm, which does not exploit such knowledge. This paper discusses how a foresighted user can acquire this knowledge by modeling its experienced interference as a function of its own power allocation. To characterize the outcome of the multi-user interaction, the conjectural equilibrium is introduced, and the existence of this equilibrium for the investigated water-filling game is proved. Interestingly, both the Nash equilibrium and the Stackelberg equilibrium are shown to be special cases of the generalization of conjectural equilibrium. We develop practical algorithms to form accurate beliefs and search desirable power allocation strategies. Numerical simulations indicate that a foresighted user without any a priori knowledge of its competitors' private information can effectively learn the required information, and induce the entire system to an operating point that improves both its own achievable rate as well as the rates of the other participants in the water-filling game.


A variant of Wiener's attack on RSA

Abstract: Wiener's attack is a well-known polynomial-time attack on a RSA cryptosystem with small secret decryption exponent d, which works if d<n^{0.25}, where n=pq is the modulus of the cryptosystem. Namely, in that case, d is the denominator of some convergent p_m/q_m of the continued fraction expansion of e/n, and therefore d can be computed efficiently from the public key (n,e). There are several extensions of Wiener's attack that allow the RSA cryptosystem to be broken when d is a few bits longer than n^{0.25}. They all have the run-time complexity (at least) O(D^2), where d=Dn^{0.25}. Here we propose a new variant of Wiener's attack, which uses results on Diophantine approximations of the form |\alpha - p/q| < c/q^2, and "meet-in-the-middle" variant for testing the candidates (of the form rq_{m+1} + sq_m) for the secret exponent. This decreases the run-time complexity of the attack to O(D log(D)) (with the space complexity O(D)).


Conversion/Preference Games

Abstract: We introduce the concept of Conversion/Preference Games, or CP games for short. CP games generalize the standard notion of strategic games. First we exemplify the use of CP games. Second we formally introduce and define the CP-games formalism. Then we sketch two `real-life' applications, namely a connection between CP games and gene regulation networks, and the use of CP games to formalize implied information in Chinese Wall security. We end with a study of a particular fixed-point construction over CP games and of the resulting existence of equilibria in possibly infinite games.


Approximation of a Fractional Order System by an Integer Order Model Using Particle Swarm Optimization Technique

Abstract: System identification is a necessity in control theory. Classical control theory usually considers processes with integer order transfer functions. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme is presented for approximation of such a real world fractional order process by an ideal integral order model. A population of integral order process models is generated and updated by PSO technique, the fitness function being the sum of squared deviations from the set of observations obtained from the actual fractional order process. Results show that the proposed scheme offers a high degree of accuracy.


A Swarm Intelligence Based Scheme for Complete and Fault-tolerant Identification of a Dynamical Fractional Order Process

Abstract: System identification refers to estimation of process parameters and is a necessity in control theory. Physical systems usually have varying parameters. For such processes, accurate identification is particularly important. Online identification schemes are also needed for designing adaptive controllers. Real processes are usually of fractional order as opposed to the ideal integral order models. In this paper, we propose a simple and elegant scheme of estimating the parameters for such a fractional order process. A population of process models is generated and updated by particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. Results show that the proposed scheme offers a high degree of accuracy even when the observations are corrupted to a significant degree. Additional schemes to improve the accuracy still further are also proposed and analyzed.


The Application of Stochastic Optimization Algorithms to the Design of a Fractional-order PID Controller

Abstract: The Proportional-Integral-Derivative Controller is widely used in industries for process control applications. Fractional-order PID controllers are known to outperform their integer-order counterparts. In this paper, we propose a new technique of fractional-order PID controller synthesis based on peak overshoot and rise-time specifications. Our approach is to construct an objective function, the optimization of which yields a possible solution to the design problem. This objective function is optimized using two popular bio-inspired stochastic search algorithms, namely Particle Swarm Optimization and Differential Evolution. With the help of a suitable example, the superiority of the designed fractional-order PID controller to an integer-order PID controller is affirmed and a comparative study of the efficacy of the two above algorithms in solving the optimization problem is also presented.


A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach

Abstract: Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.


Tuning PID and FOPID Controllers using the Integral Time Absolute Error Criterion

Abstract: Particle swarm optimization (PSO) is extensively used for real parameter optimization in diverse fields of study. This paper describes an application of PSO to the problem of designing a fractional-order proportional-integral-derivative (FOPID) controller whose parameters comprise proportionality constant, integral constant, derivative constant, integral order (lambda) and derivative order (delta). The presence of five optimizable parameters makes the task of designing a FOPID controller more challenging than conventional PID controller design. Our design method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. The digital realization of the deigned system utilizes the Tustin operator-based continued fraction expansion scheme. We carry out a simulation that illustrates the effectiveness of the proposed approach especially for realizing fractional-order plants. This paper also attempts to study the behavior of fractional PID controller vis-a-vis that of its integer order counterpart and demonstrates the superiority of the former to the latter.


A computational model of affects

Abstract: This article provides a simple logical structure, in which affective concepts (i.e. concepts related to emotions and feelings) can be defined. The set of affects defined is similar to the set of emotions covered in the OCC model (Ortony A., Collins A., and Clore G. L.: The Cognitive Structure of Emotions. Cambridge University Press, 1988), but the model presented in this article is fully computationally defined.


Balancing Exploration and Exploitation by an Elitist Ant System with Exponential Pheromone Deposition Rule

Abstract: The paper presents an exponential pheromone deposition rule to modify the basic ant system algorithm which employs constant deposition rule. A stability analysis using differential equation is carried out to find out the values of parameters that make the ant system dynamics stable for both kinds of deposition rule. A roadmap of connected cities is chosen as the problem environment where the shortest route between two given cities is required to be discovered. Simulations performed with both forms of deposition approach using Elitist Ant System model reveal that the exponential deposition approach outperforms the classical one by a large extent. Exhaustive experiments are also carried out to find out the optimum setting of different controlling parameters for exponential deposition approach and an empirical relationship between the major controlling parameters of the algorithm and some features of problem environment.


A Study of the Grunwald-Letnikov Definition for Minimizing the Effects of Random Noise on Fractional Order Differential Equations

Abstract: Of the many definitions for fractional order differintegral, the Grunwald-Letnikov definition is arguably the most important one. The necessity of this definition for the description and analysis of fractional order systems cannot be overstated. Unfortunately, the Fractional Order Differential Equation (FODE) describing such a systems, in its original form, highly sensitive to the effects of random noise components inevitable in a natural environment. Thus direct application of the definition in a real-life problem can yield erroneous results. In this article, we perform an in-depth mathematical analysis the Grunwald-Letnikov definition in depth and, as far as we know, we are the first to do so. Based on our analysis, we present a transformation scheme which will allow us to accurately analyze generalized fractional order systems in presence of significant quantities of random errors. Finally, by a simple experiment, we demonstrate the high degree of robustness to noise offered by the said transformation and thus validate our scheme.


A Novel Parser Design Algorithm Based on Artificial Ants

Abstract: This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates the calculation of the above mentioned sets and precedence relation tables. Further advantages of our scheme lie in i) ascertaining whether a given string belongs to the language represented by the grammar, and ii) finding out the shortest possible path from the given string to the start symbol S in case multiple routes exist.


Complete Identification of a Dynamic Fractional Order System Under Non-ideal Conditions Using Fractional Differintegral Definitions

Abstract: This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed. This method employs fractional calculus theory to find equations relating the parameters that are to be estimated, and then estimates the process parameters after solving the simultaneous equations. The data used for the calculations are intentionally corrupted to simulate real-life conditions. Results show that the proposed scheme offers a very high degree of accuracy even for erroneous data.


Extension of Max-Min Ant System with Exponential Pheromone Deposition Rule

Abstract: The paper presents an exponential pheromone deposition approach to improve the performance of classical Ant System algorithm which employs uniform deposition rule. A simplified analysis using differential equations is carried out to study the stability of basic ant system dynamics with both exponential and constant deposition rules. A roadmap of connected cities, where the shortest path between two specified cities are to be found out, is taken as a platform to compare Max-Min Ant System model (an improved and popular model of Ant System algorithm) with exponential and constant deposition rules. Extensive simulations are performed to find the best parameter settings for non-uniform deposition approach and experiments with these parameter settings revealed that the above approach outstripped the traditional one by a large extent in terms of both solution quality and convergence time.


A Novel Approach for Complete Identification of Dynamic Fractional Order Systems Using Stochastic Optimization Algorithms and Fractional Calculus

Abstract: This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed. This method employs fractional calculus theory to find equations relating the parameters that are to be estimated, and then estimates the process parameters after solving the simultaneous equations. The said simultaneous equations are generated and updated using particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. The data used for the calculations are intentionally corrupted to simulate real-life conditions. Results show that the proposed scheme offers a very high degree of accuracy even for erroneous data.


Entropy, Perception, and Relativity

Abstract: In this paper, I expand Shannon's definition of entropy into a new form of entropy that allows integration of information from different random events. Shannon's notion of entropy is a special case of my more general definition of entropy. I define probability using a so-called performance function, which is de facto an exponential distribution. Assuming that my general notion of entropy reflects the true uncertainty about a probabilistic event, I understand that our perceived uncertainty differs. I claim that our perception is the result of two opposing forces similar to the two famous antagonists in Chinese philosophy: Yin and Yang. Based on this idea, I show that our perceived uncertainty matches the true uncertainty in points determined by the golden ratio. I demonstrate that the well-known sigmoid function, which we typically employ in artificial neural networks as a non-linear threshold function, describes the actual performance. Furthermore, I provide a motivation for the time dilation in Einstein's Special Relativity, basically claiming that although time dilation conforms with our perception, it does not correspond to reality. At the end of the paper, I show how to apply this theoretical framework to practical applications. I present recognition rates for a pattern recognition problem, and also propose a network architecture that can take advantage of general entropy to solve complex decision problems.


Automatic Modular Abstractions for Linear Constraints

Abstract: We propose a method for automatically generating abstract transformers for static analysis by abstract interpretation. The method focuses on linear constraints on programs operating on rational, real or floating-point variables and containing linear assignments and tests. In addition to loop-free code, the same method also applies for obtaining least fixed points as functions of the precondition, which permits the analysis of loops and recursive functions. Our algorithms are based on new quantifier elimination and symbolic manipulation techniques. Given the specification of an abstract domain, and a program block, our method automatically outputs an implementation of the corresponding abstract transformer. It is thus a form of program transformation. The motivation of our work is data-flow synchronous programming languages, used for building control-command embedded systems, but it also applies to imperative and functional programming.


Characterizing Graphs of Zonohedra

Abstract: A classic theorem by Steinitz states that a graph G is realizable by a convex polyhedron if and only if G is 3-connected planar. Zonohedra are an important subclass of convex polyhedra having the property that the faces of a zonohedron are parallelograms and are in parallel pairs. In this paper we give characterization of graphs of zonohedra. We also give a linear time algorithm to recognize such a graph. In our quest for finding the algorithm, we prove that in a zonohedron P both the number of zones and the number of faces in each zone is O(square root{n}), where n is the number of vertices of P.


Efficient Energy Management Policies for Networks with Energy Harvesting Sensor Nodes

Abstract: We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies.


Edhibou: a Customizable Interface for Decision Support in a Semantic Portal

Abstract: The Semantic Web is becoming more and more a reality, as the required technologies have reached an appropriate level of maturity. However, at this stage, it is important to provide tools facilitating the use and deployment of these technologies by end-users. In this paper, we describe EdHibou, an automatically generated, ontology-based graphical user interface that integrates in a semantic portal. The particularity of EdHibou is that it makes use of OWL reasoning capabilities to provide intelligent features, such as decision support, upon the underlying ontology. We present an application of EdHibou to medical decision support based on a formalization of clinical guidelines in OWL and show how it can be customized thanks to an ontology of graphical components.


Cooperative interface of a swarm of UAVs

Abstract: After presenting the broad context of authority sharing, we outline how introducing more natural interaction in the design of the ground operator interface of UV systems should help in allowing a single operator to manage the complexity of his/her task. Introducing new modalities is one one of the means in the realization of our vision of next- generation GOI. A more fundamental aspect resides in the interaction manager which should help balance the workload of the operator between mission and interaction, notably by applying a multi-strategy approach to generation and interpretation. We intend to apply these principles to the context of the Smaart prototype, and in this perspective, we illustrate how to characterize the workload associated with a particular operational situation.


Document stream clustering: experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends

Abstract: We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.


Embedding Non-Ground Logic Programs into Autoepistemic Logic for Knowledge Base Combination

Abstract: In the context of the Semantic Web, several approaches to the combination of ontologies, given in terms of theories of classical first-order logic and rule bases, have been proposed. They either cast rules into classical logic or limit the interaction between rules and ontologies. Autoepistemic logic (AEL) is an attractive formalism which allows to overcome these limitations, by serving as a uniform host language to embed ontologies and nonmonotonic logic programs into it. For the latter, so far only the propositional setting has been considered. In this paper, we present three embeddings of normal and three embeddings of disjunctive non-ground logic programs under the stable model semantics into first-order AEL. While the embeddings all correspond with respect to objective ground atoms, differences arise when considering non-atomic formulas and combinations with first-order theories. We compare the embeddings with respect to stable expansions and autoepistemic consequences, considering the embeddings by themselves, as well as combinations with classical theories. Our results reveal differences and correspondences of the embeddings and provide useful guidance in the choice of a particular embedding for knowledge combination.


Instruction sequences for the production of processes

Abstract: Single-pass instruction sequences under execution are considered to produce behaviours to be controlled by some execution environment. Threads as considered in thread algebra model such behaviours: upon each action performed by a thread, a reply from its execution environment determines how the thread proceeds. Threads in turn can be looked upon as producing processes as considered in process algebra. We show that, by apposite choice of basic instructions, all processes that can only be in a finite number of states can be produced by single-pass instruction sequences.


CoZo+ - A Content Zoning Engine for textual documents

Abstract: Content zoning can be understood as a segmentation of textual documents into zones. This is inspired by [6] who initially proposed an approach for the argumentative zoning of textual documents. With the prototypical CoZo+ engine, we focus on content zoning towards an automatic processing of textual streams while considering only the actors as the zones. We gain information that can be used to realize an automatic recognition of content for pre-defined actors. We understand CoZo+ as a necessary pre-step towards an automatic generation of summaries and to make intellectual ownership of documents detectable.


Solving the P/NP Problem under Intrinsic Uncertainty

Abstract: Heisenberg's uncertainty principle states that it is not possible to compute both the position and momentum of an electron with absolute certainty. However, this computational limitation, which is central to quantum mechanics, has no counterpart in theoretical computer science. Here, I will show that we can distinguish between the complexity classes P and NP when we consider intrinsic uncertainty in our computations, and take uncertainty about whether a bit belongs to the program code or machine input into account. Given intrinsic uncertainty, every output is uncertain, and computations become meaningful only in combination with a confidence level. In particular, it is impossible to compute solutions with absolute certainty as this requires infinite run-time. Considering intrinsic uncertainty, I will present a function that is in NP but not in P, and thus prove that P is a proper subset of NP. I will also show that all traditional hard decision problems have polynomial-time algorithms that provide solutions with confidence under uncertainty.


Secure Arithmetic Computation with No Honest Majority

Abstract: We study the complexity of securely evaluating arithmetic circuits over finite rings. This question is motivated by natural secure computation tasks. Focusing mainly on the case of two-party protocols with security against malicious parties, our main goals are to: (1) only make black-box calls to the ring operations and standard cryptographic primitives, and (2) minimize the number of such black-box calls as well as the communication overhead. We present several solutions which differ in their efficiency, generality, and underlying intractability assumptions. These include: 1. An unconditionally secure protocol in the OT-hybrid model which makes a black-box use of an arbitrary ring $R$, but where the number of ring operations grows linearly with (an upper bound on) $\log|R|$. 2. Computationally secure protocols in the OT-hybrid model which make a black-box use of an underlying ring, and in which the number of ring operations does not grow with the ring size. These results extend a previous approach of Naor and Pinkas for secure polynomial evaluation (SIAM J. Comput., 35(5), 2006). 3. A protocol for the rings $\mathbb{Z}_m=\mathbb{Z}/m\mathbb{Z}$ which only makes a black-box use of a homomorphic encryption scheme. When $m$ is prime, the (amortized) number of calls to the encryption scheme for each gate of the circuit is constant. All of our protocols are in fact UC-secure in the OT-hybrid model and can be generalized to multiparty computation with an arbitrary number of malicious parties.


First-Order and Temporal Logics for Nested Words

Abstract: Nested words are a structured model of execution paths in procedural programs, reflecting their call and return nesting structure. Finite nested words also capture the structure of parse trees and other tree-structured data, such as XML. We provide new temporal logics for finite and infinite nested words, which are natural extensions of LTL, and prove that these logics are first-order expressively-complete. One of them is based on adding a "within" modality, evaluating a formula on a subword, to a logic CaRet previously studied in the context of verifying properties of recursive state machines (RSMs). The other logic, NWTL, is based on the notion of a summary path that uses both the linear and nesting structures. For NWTL we show that satisfiability is EXPTIME-complete, and that model-checking can be done in time polynomial in the size of the RSM model and exponential in the size of the NWTL formula (and is also EXPTIME-complete). Finally, we prove that first-order logic over nested words has the three-variable property, and we present a temporal logic for nested words which is complete for the two-variable fragment of first-order.


A Web-Based Resource Model for eScience: Object Reuse & Exchange

Abstract: Work in the Open Archives Initiative - Object Reuse and Exchange (OAI-ORE) focuses on an important aspect of infrastructure for eScience: the specification of the data model and a suite of implementation standards to identify and describe compound objects. These are objects that aggregate multiple sources of content including text, images, data, visualization tools, and the like. These aggregations are an essential product of eScience, and will become increasingly common in the age of data-driven scholarship. The OAI-ORE specifications conform to the core concepts of the Web architecture and the semantic Web, ensuring that applications that use them will integrate well into the general Web environment.


UNL-French deconversion as transfer & generation from an interlingua with possible quality enhancement through offline human interaction

Abstract: We present the architecture of the UNL-French deconverter, which "generates" from the UNL interlingua by first"localizing" the UNL form for French, within UNL, and then applying slightly adapted but classical transfer and generation techniques, implemented in GETA's Ariane-G5 environment, supplemented by some UNL-specific tools. Online interaction can be used during deconversion to enhance output quality and is now used for development purposes. We show how interaction could be delayed and embedded in the postedition phase, which would then interact not directly with the output text, but indirectly with several components of the deconverter. Interacting online or offline can improve the quality not only of the utterance at hand, but also of the utterances processed later, as various preferences may be automatically changed to let the deconverter "learn".


Optimization of automatically generated multi-core code for the LTE RACH-PD algorithm

Abstract: Embedded real-time applications in communication systems require high processing power. Manual scheduling devel-oped for single-processor applications is not suited to multi-core architectures. The Algorithm Architecture Matching (AAM) methodology optimizes static application implementation on multi-core architectures. The Random Access Channel Preamble Detection (RACH-PD) is an algorithm for non-synchronized access of Long Term Evolu-tion (LTE) wireless networks. LTE aims to improve the spectral efficiency of the next generation cellular system. This paper de-scribes a complete methodology for implementing the RACH-PD. AAM prototyping is applied to the RACH-PD which is modelled as a Synchronous DataFlow graph (SDF). An efficient implemen-tation of the algorithm onto a multi-core DSP, the TI C6487, is then explained. Benchmarks for the solution are given.


Classification dynamique d'un flux documentaire : une \'evaluation statique pr\'ealable de l'algorithme GERMEN

Abstract: Data-stream clustering is an ever-expanding subdomain of knowledge extraction. Most of the past and present research effort aims at efficient scaling up for the huge data repositories. Our approach focuses on qualitative improvement, mainly for "weak signals" detection and precise tracking of topical evolutions in the framework of information watch - though scalability is intrinsically guaranteed in a possibly distributed implementation. Our GERMEN algorithm exhaustively picks up the whole set of density peaks of the data at time t, by identifying the local perturbations induced by the current document vector, such as changing cluster borders, or new/vanishing clusters. Optimality yields from the uniqueness 1) of the density landscape for any value of our zoom parameter, 2) of the cluster allocation operated by our border propagation rule. This results in a rigorous independence from the data presentation ranking or any initialization parameter. We present here as a first step the only assessment of a static view resulting from one year of the CNRS/INIST Pascal database in the field of geotechnics.


Query Refinement by Multi Word Term expansions and semantic synonymy

Abstract: We developed a system, TermWatch (https://stid-bdd.iut.univ-metz.fr/TermWatch/index.pl), which combines a linguistic extraction of terms, their structuring into a terminological network with a clustering algorithm. In this paper we explore its ability in integrating the most promising aspects of the studies on query refinement: choice of meaningful text units to cluster (domain terms), choice of tight semantic relations with which to cluster terms, structuring of terms in a network enabling abetter perception of domain concepts. We have run this experiment on the 367 645 English abstracts of PASCAL 2005-2006 bibliographic database (http://www.inist.fr) and compared the structured terminological resource automatically build by TermWarch to the English segment of TermScience resource (http://termsciences.inist.fr/) containing 88 211 terms.


A Note on the Inversion Complexity of Boolean Functions in Boolean Formulas

Abstract: In this note, we consider the minimum number of NOT operators in a Boolean formula representing a Boolean function. In circuit complexity theory, the minimum number of NOT gates in a Boolean circuit computing a Boolean function $f$ is called the inversion complexity of $f$. In 1958, Markov determined the inversion complexity of every Boolean function and particularly proved that $\lceil \log_2(n+1) \rceil$ NOT gates are sufficient to compute any Boolean function on $n$ variables. As far as we know, no result is known for inversion complexity in Boolean formulas, i.e., the minimum number of NOT operators in a Boolean formula representing a Boolean function. The aim of this note is showing that we can determine the inversion complexity of every Boolean function in Boolean formulas by arguments based on the study of circuit complexity.


MMOGs as Social Experiments: the Case of Environmental Laws

Abstract: In this paper we argue that Massively Multiplayer Online Games (MMOGs), also known as Large Games are an interesting research tool for policy experimentation. One of the major problems with lawmaking is that testing the laws is a difficult enterprise. Here we show that the concept of an MMOG can be used to experiment with environmental laws on a large scale, provided that the MMOG is a real game, i.e., it is fun, addictive, presents challenges that last, etc.. We present a detailed game concept as an initial step.


Data Mining-based Fragmentation of XML Data Warehouses

Abstract: With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority.


When are two algorithms the same?

Abstract: People usually regard algorithms as more abstract than the programs that implement them. The natural way to formalize this idea is that algorithms are equivalence classes of programs with respect to a suitable equivalence relation. We argue that no such equivalence relation exists.


Persistent Queries

Abstract: We propose a syntax and semantics for interactive abstract state machines to deal with the following situation. A query is issued during a certain step, but the step ends before any reply is received. Later, a reply arrives, and later yet the algorithm makes use of this reply. By a persistent query, we mean a query for which a late reply might be used. Syntactically, our proposal involves issuing, along with a persistent query, a location where a late reply is to be stored. Semantically, it involves only a minor modification of the existing theory of interactive small-step abstract state machines.


Distributed Constrained Optimization with Semicoordinate Transformations

Abstract: Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve constrained optimization problems. This is done by translating the problem into an iterated game, where each agent controls a different variable of the problem, so that the joint probability distribution across the agents' moves gives an expected value of the objective function. The dynamics of the agents is designed to minimize a Lagrangian function of that joint distribution. Here we illustrate how the updating of the Lagrange parameters in the Lagrangian is a form of automated annealing, which focuses the joint distribution more and more tightly about the joint moves that optimize the objective function. We then investigate the use of ``semicoordinate'' variable transformations. These separate the joint state of the agents from the variables of the optimization problem, with the two connected by an onto mapping. We present experiments illustrating the ability of such transformations to facilitate optimization. We focus on the special kind of transformation in which the statistically independent states of the agents induces a mixture distribution over the optimization variables. Computer experiment illustrate this for $k$-sat constraint satisfaction problems and for unconstrained minimization of $NK$ functions.


\'Etude longitudinale d'une proc\'edure de mod\'elisation de connaissances en mati\`ere de gestion du territoire agricole

Abstract: This paper gives an introduction to this issue, and presents the framework and the main steps of the Rosa project. Four teams of researchers, agronomists, computer scientists, psychologists and linguists were involved during five years within this project that aimed at the development of a knowledge based system. The purpose of the Rosa system is the modelling and the comparison of farm spatial organizations. It relies on a formalization of agronomical knowledge and thus induces a joint knowledge building process involving both the agronomists and the computer scientists. The paper describes the steps of the modelling process as well as the filming procedures set up by the psychologists and linguists in order to make explicit and to analyze the underlying knowledge building process.


The Complexity of Propositional Implication

Abstract: The question whether a set of formulae G implies a formula f is fundamental. The present paper studies the complexity of the above implication problem for propositional formulae that are built from a systematically restricted set of Boolean connectives. We give a complete complexity classification for all sets of Boolean functions in the meaning of Post's lattice and show that the implication problem is efficentily solvable only if the connectives are definable using the constants {false,true} and only one of {and,or,xor}. The problem remains coNP-complete in all other cases. We also consider the restriction of G to singletons.


One useful logic that defines its own truth

Abstract: Existential fixed point logic (EFPL) is a natural fit for some applications, and the purpose of this talk is to attract attention to EFPL. The logic is also interesting in its own right as it has attractive properties. One of those properties is rather unusual: truth of formulas can be defined (given appropriate syntactic apparatus) in the logic. We mentioned that property elsewhere, and we use this opportunity to provide the proof.


Two Forms of One Useful Logic: Existential Fixed Point Logic and Liberal Datalog

Abstract: A natural liberalization of Datalog is used in the Distributed Knowledge Authorization Language (DKAL). We show that the expressive power of this liberal Datalog is that of existential fixed-point logic. The exposition is self-contained.


Modular difference logic is hard

Abstract: In connection with machine arithmetic, we are interested in systems of constraints of the form x + k \leq y + k'. Over integers, the satisfiability problem for such systems is polynomial time. The problem becomes NP complete if we restrict attention to the residues for a fixed modulus N.


How to turn a scripting language into a domain specific language for computer algebra

Abstract: We have developed two computer algebra systems, meditor [Jolly:2007] and JAS [Kredel:2006]. These CAS systems are available as Java libraries. For the use-case of interactively entering and manipulating mathematical expressions, there is a need of a scripting front-end for our libraries. Most other CAS invent and implement their own scripting interface for this purpose. We, however, do not want to reinvent the wheel and propose to use a contemporary scripting language with access to Java code. In this paper we discuss the requirements for a scripting language in computer algebra and check whether the languages Python, Ruby, Groovy and Scala meet these requirements. We conclude that, with minor problems, any of these languages is suitable for our purpose.


Resolution Trees with Lemmas: Resolution Refinements that Characterize DLL Algorithms with Clause Learning

Abstract: Resolution refinements called w-resolution trees with lemmas (WRTL) and with input lemmas (WRTI) are introduced. Dag-like resolution is equivalent to both WRTL and WRTI when there is no regularity condition. For regular proofs, an exponential separation between regular dag-like resolution and both regular WRTL and regular WRTI is given. It is proved that DLL proof search algorithms that use clause learning based on unit propagation can be polynomially simulated by regular WRTI. More generally, non-greedy DLL algorithms with learning by unit propagation are equivalent to regular WRTI. A general form of clause learning, called DLL-Learn, is defined that is equivalent to regular WRTL. A variable extension method is used to give simulations of resolution by regular WRTI, using a simplified form of proof trace extensions. DLL-Learn and non-greedy DLL algorithms with learning by unit propagation can use variable extensions to simulate general resolution without doing restarts. Finally, an exponential lower bound for WRTL where the lemmas are restricted to short clauses is shown.


A role-free approach to indexing large RDF data sets in secondary memory for efficient SPARQL evaluation

Abstract: Massive RDF data sets are becoming commonplace. RDF data is typically generated in social semantic domains (such as personal information management) wherein a fixed schema is often not available a priori. We propose a simple Three-way Triple Tree (TripleT) secondary-memory indexing technique to facilitate efficient SPARQL query evaluation on such data sets. The novelty of TripleT is that (1) the index is built over the atoms occurring in the data set, rather than at a coarser granularity, such as whole triples occurring in the data set; and (2) the atoms are indexed regardless of the roles (i.e., subjects, predicates, or objects) they play in the triples of the data set. We show through extensive empirical evaluation that TripleT exhibits multiple orders of magnitude improvement over the state of the art on RDF indexing, in terms of both storage and query processing costs.


Allocation of control and data channels for Large-Scale Wireless Sensor Networks

Abstract: Both IEEE 802.15.4 and 802.15.4a standards allow for dynamic channel allocation and use of multiple channels available at their physical layers but its MAC protocols are designed only for single channel. Also, sensor's transceivers such as CC2420 provide multiple channels and as shown in [1], [2] and [3] channel switch latency of CC2420 transceiver is just about 200$\mu$s. In order to enhance both energy efficiency and to shorten end to end delay, we propose, in this report, a spectrum-efficient frequency allocation schemes that are able to statically assign control channels and dynamically reuse data channels for Personal Area Networks (PANs) inside a Large-Scale WSN based on UWB technology.


Symbolic Backwards-Reachability Analysis for Higher-Order Pushdown Systems

Abstract: Higher-order pushdown systems (PDSs) generalise pushdown systems through the use of higher-order stacks, that is, a nested "stack of stacks" structure. These systems may be used to model higher-order programs and are closely related to the Caucal hierarchy of infinite graphs and safe higher-order recursion schemes. We consider the backwards-reachability problem over higher-order Alternating PDSs (APDSs), a generalisation of higher-order PDSs. This builds on and extends previous work on pushdown systems and context-free higher-order processes in a non-trivial manner. In particular, we show that the set of configurations from which a regular set of higher-order APDS configurations is reachable is regular and computable in n-EXPTIME. In fact, the problem is n-EXPTIME-complete. We show that this work has several applications in the verification of higher-order PDSs, such as linear-time model-checking, alternation-free mu-calculus model-checking and the computation of winning regions of reachability games.


A Model for Probabilistic Reasoning on Assume/Guarantee Contracts

Abstract: In this paper, we present a probabilistic adaptation of an Assume/Guarantee contract formalism. For the sake of generality, we assume that the extended state machines used in the contracts and implementations define sets of runs on a given set of variables, that compose by intersection over the common variables. In order to enable probabilistic reasoning, we consider that the contracts dictate how certain input variables will behave, being either non-deterministic, or probabilistic; the introduction of probabilistic variables leading us to tune the notions of implementation, refinement and composition. As shown in the report, this probabilistic adaptation of the Assume/Guarantee contract theory preserves compositionality and therefore allows modular reliability analysis, either with a top-down or a bottom-up approach.


Adaptive Base Class Boost for Multi-class Classification

Abstract: We develop the concept of ABC-Boost (Adaptive Base Class Boost) for multi-class classification and present ABC-MART, a concrete implementation of ABC-Boost. The original MART (Multiple Additive Regression Trees) algorithm has been very successful in large-scale applications. For binary classification, ABC-MART recovers MART. For multi-class classification, ABC-MART considerably improves MART, as evaluated on several public data sets.


The Application of Fuzzy Logic to Collocation Extraction

Abstract: Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost all the methods form a classical crisp set of collocation. We propose a fuzzy logic approach of collocation extraction to form a fuzzy set of collocations in which each word combination has a certain grade of membership for being collocation. Fuzzy logic provides an easy way to express natural language into fuzzy logic rules. Two existing methods; Mutual information and t-test have been utilized for the input of the fuzzy inference system. The resulting membership function could be easily seen and demonstrated. To show the utility of the fuzzy logic some word pairs have been examined as an example. The working data has been based on a corpus of about one million words contained in different novels constituting project Gutenberg available on www.gutenberg.org. The proposed method has all the advantages of the two methods, while overcoming their drawbacks. Hence it provides a better result than the two methods.


Distributed Algorithms for Computing Alternate Paths Avoiding Failed Nodes and Links

Abstract: A recent study characterizing failures in computer networks shows that transient single element (node/link) failures are the dominant failures in large communication networks like the Internet. Thus, having the routing paths globally recomputed on a failure does not pay off since the failed element recovers fairly quickly, and the recomputed routing paths need to be discarded. In this paper, we present the first distributed algorithm that computes the alternate paths required by some "proactive recovery schemes" for handling transient failures. Our algorithm computes paths that avoid a failed node, and provides an alternate path to a particular destination from an upstream neighbor of the failed node. With minor modifications, we can have the algorithm compute alternate paths that avoid a failed link as well. To the best of our knowledge all previous algorithms proposed for computing alternate paths are centralized, and need complete information of the network graph as input to the algorithm.


NB-FEB: An Easy-to-Use and Scalable Universal Synchronization Primitive for Parallel Programming

Abstract: This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures like CAS and LL/SC are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes (universality). NB-FEB is combinable, namely its memory requests to the same memory location can be combined into only one memory request, which consequently mitigates performance degradation due to synchronization "hot spots" (scalability). Since NB-FEB is a variant of the original full/empty bit that always returns a value instead of waiting for a conditional flag, it is as feasible as the original full/empty bit, which has been implemented in many computer systems (feasibility). The original full/empty bit is well-known as a special-purpose primitive for fast producer-consumer synchronization and has been used extensively in the specific domain of applications. In this paper, we show that NB-FEB can be deployed easily as a general-purpose primitive. Using NB-FEB, we construct a non-blocking software transactional memory system called NBFEB-STM, which can be used to handle concurrent threads conveniently. NBFEB-STM is space efficient: the space complexity of each object updated by $N$ concurrent threads/transactions is $\Theta(N)$, the optimal.


Applying Practice to Theory

Abstract: How can complexity theory and algorithms benefit from practical advances in computing? We give a short overview of some prior work using practical computing to attack problems in computational complexity and algorithms, informally describe how linear program solvers may be used to help prove new lower bounds for satisfiability, and suggest a research program for developing new understanding in circuit complexity.


Modeling Social Annotation: a Bayesian Approach

Abstract: Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users, can potentially be used to infer categorical knowledge, classify documents or recommend new relevant information. Traditional text inference methods do not make best use of social annotation, since they do not take into account variations in individual users' perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from Delicious in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.


Algorithmic Techniques for Several Optimization Problems Regarding Distributed Systems with Tree Topologies

Abstract: As the development of distributed systems progresses, more and more challenges arise and the need for developing optimized systems and for optimizing existing systems from multiple perspectives becomes more stringent. In this paper I present novel algorithmic techniques for solving several optimization problems regarding distributed systems with tree topologies. I address topics like: reliability improvement, partitioning, coloring, content delivery, optimal matchings, as well as some tree counting aspects. Some of the presented techniques are only of theoretical interest, while others can be used in practical settings.


Configuration spaces of convex and embedded polygons in the plane

Abstract: This paper studies the configuration spaces of linkages whose underlying graph is a single cycle. Assume that the edge lengths are such that there are no configurations in which all the edges lie along a line. The main results are that, modulo translations and rotations, each component of the space of convex configurations is homeomorphic to a closed Euclidean ball and each component of the space of embedded configurations is homeomorphic to a Euclidean space. This represents an elaboration on the topological information that follows from the convexification theorem of Connelly, Demaine, and Rote.


Fibonacci Index and Stability Number of Graphs: a Polyhedral Study

Abstract: The Fibonacci index of a graph is the number of its stable sets. This parameter is widely studied and has applications in chemical graph theory. In this paper, we establish tight upper bounds for the Fibonacci index in terms of the stability number and the order of general graphs and connected graphs. Tur\'an graphs frequently appear in extremal graph theory. We show that Tur\'an graphs and a connected variant of them are also extremal for these particular problems. We also make a polyhedral study by establishing all the optimal linear inequalities for the stability number and the Fibonacci index, inside the classes of general and connected graphs of order $n$.


Parallel execution of portfolio optimization

Abstract: Analysis of asset liability management (ALM) strategies especially for long term horizon is a crucial issue for banks, funds and insurance companies. Modern economic models, investment strategies and optimization criteria make ALM studies computationally very intensive task. It attracts attention to multiprocessor system and especially to the cheapest one: multi core PCs and PC clusters. In this article we are analyzing problem of parallel organization of portfolio optimization, results of using clusters for optimization and the most efficient cluster architecture for these kinds of tasks.


Airport Gate Assignment: New Model and Implementation

Abstract: Airport gate assignment is of great importance in airport operations. In this paper, we study the Airport Gate Assignment Problem (AGAP), propose a new model and implement the model with Optimization Programming language (OPL). With the objective to minimize the number of conflicts of any two adjacent aircrafts assigned to the same gate, we build a mathematical model with logical constraints and the binary constraints, which can provide an efficient evaluation criterion for the Airlines to estimate the current gate assignment. To illustrate the feasibility of the model we construct experiments with the data obtained from Continental Airlines, Houston Gorge Bush Intercontinental Airport IAH, which indicate that our model is both energetic and effective. Moreover, we interpret experimental results, which further demonstrate that our proposed model can provide a powerful tool for airline companies to estimate the efficiency of their current work of gate assignment.


Stability Bound for Stationary Phi-mixing and Beta-mixing Processes

Abstract: Most generalization bounds in learning theory are based on some measure of the complexity of the hypothesis class used, independently of any algorithm. In contrast, the notion of algorithmic stability can be used to derive tight generalization bounds that are tailored to specific learning algorithms by exploiting their particular properties. However, as in much of learning theory, existing stability analyses and bounds apply only in the scenario where the samples are independently and identically distributed. In many machine learning applications, however, this assumption does not hold. The observations received by the learning algorithm often have some inherent temporal dependence. This paper studies the scenario where the observations are drawn from a stationary phi-mixing or beta-mixing sequence, a widely adopted assumption in the study of non-i.i.d. processes that implies a dependence between observations weakening over time. We prove novel and distinct stability-based generalization bounds for stationary phi-mixing and beta-mixing sequences. These bounds strictly generalize the bounds given in the i.i.d. case and apply to all stable learning algorithms, thereby extending the use of stability-bounds to non-i.i.d. scenarios. We also illustrate the application of our phi-mixing generalization bounds to general classes of learning algorithms, including Support Vector Regression, Kernel Ridge Regression, and Support Vector Machines, and many other kernel regularization-based and relative entropy-based regularization algorithms. These novel bounds can thus be viewed as the first theoretical basis for the use of these algorithms in non-i.i.d. scenarios.


Best-Effort Strategies for Losing States

Abstract: We consider games played on finite graphs, whose goal is to obtain a trace belonging to a given set of winning traces. We focus on those states from which Player 1 cannot force a win. We explore and compare several criteria for establishing what is the preferable behavior of Player 1 from those states. Along the way, we prove several results of theoretical and practical interest, such as a characterization of admissible strategies, which also provides a simple algorithm for computing such strategies for various common goals, and the equivalence between the existence of positional winning strategies and the existence of positional subgame perfect strategies.


Artificial Intelligence Techniques for Steam Generator Modelling

Abstract: This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian and committee techniques were applied to these neural networks. Each of the AI methods considered was simulated in Matlab. The results of the simulations showed that all the AI methods were capable of predicting the Steam Generator data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system out performed the other methods in terms of accuracy and ease of implementation, while still achieving a fast execution time as well as a reasonable training time.


Efficient Multiplication of Dense Matrices over GF(2)

Abstract: We describe an efficient implementation of a hierarchy of algorithms for multiplication of dense matrices over the field with two elements (GF(2)). In particular we present our implementation -- in the M4RI library -- of Strassen-Winograd matrix multiplication and the "Method of the Four Russians" multiplication (M4RM) and compare it against other available implementations. Good performance is demonstrated on on AMD's Opteron and particulary good performance on Intel's Core 2 Duo. The open-source M4RI library is available stand-alone as well as part of the Sage mathematics software. In machine terms, addition in GF(2) is logical-XOR, and multiplication is logical-AND, thus a machine word of 64-bits allows one to operate on 64 elements of GF(2) in parallel: at most one CPU cycle for 64 parallel additions or multiplications. As such, element-wise operations over GF(2) are relatively cheap. In fact, in this paper, we conclude that the actual bottlenecks are memory reads and writes and issues of data locality. We present our empirical findings in relation to minimizing these and give an analysis thereof.


A Basic Framework for the Cryptanalysis of Digital Chaos-Based Cryptography

Abstract: Chaotic cryptography is based on the properties of chaos as source of entropy. Many different schemes have been proposed to take advantage of those properties and to design new strategies to encrypt information. However, the right and efficient use of chaos in the context of cryptography requires a thorough knowledge about the dynamics of the selected chaotic system. Indeed, if the final encryption system reveals enough information about the underlying chaotic system it could be possible for a cryptanalyst to get the key, part of the key or some information somehow equivalent to the key just analyzing those dynamical properties leaked by the cryptosystem. This paper shows what those dynamical properties are and how a cryptanalyst can use them to prove the inadequacy of an encryption system for the secure exchange of information. This study is performed through the introduction of a series of mathematical tools which should be the basic framework of cryptanalysis in the context of digital chaos-based cryptography.


Exact Exponential Time Algorithms for Max Internal Spanning Tree

Abstract: We consider the NP-hard problem of finding a spanning tree with a maximum number of internal vertices. This problem is a generalization of the famous Hamiltonian Path problem. Our dynamic-programming algorithms for general and degree-bounded graphs have running times of the form O*(c^n) (c <= 3). The main result, however, is a branching algorithm for graphs with maximum degree three. It only needs polynomial space and has a running time of O*(1.8669^n) when analyzed with respect to the number of vertices. We also show that its running time is 2.1364^k n^O(1) when the goal is to find a spanning tree with at least k internal vertices. Both running time bounds are obtained via a Measure & Conquer analysis, the latter one being a novel use of this kind of analyses for parameterized algorithms.


Action Theory Evolution

Abstract: Like any other logical theory, domain descriptions in reasoning about actions may evolve, and thus need revision methods to adequately accommodate new information about the behavior of actions. The present work is about changing action domain descriptions in propositional dynamic logic. Its contribution is threefold: first we revisit the semantics of action theory contraction that has been done in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness w.r.t. our semantics. Finally we state postulates for action theory contraction and assess the behavior of our operators w.r.t. them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction.


Ferrers Dimension and Boxicity

Abstract: This note explores the relation between the boxicity of undirected graphs and the Ferrers dimension of digraphs.


The Expressive Power of Binary Submodular Functions

Abstract: It has previously been an open problem whether all Boolean submodular functions can be decomposed into a sum of binary submodular functions over a possibly larger set of variables. This problem has been considered within several different contexts in computer science, including computer vision, artificial intelligence, and pseudo-Boolean optimisation. Using a connection between the expressive power of valued constraints and certain algebraic properties of functions, we answer this question negatively. Our results have several corollaries. First, we characterise precisely which submodular functions of arity 4 can be expressed by binary submodular functions. Next, we identify a novel class of submodular functions of arbitrary arities which can be expressed by binary submodular functions, and therefore minimised efficiently using a so-called expressibility reduction to the Min-Cut problem. More importantly, our results imply limitations on this kind of reduction and establish for the first time that it cannot be used in general to minimise arbitrary submodular functions. Finally, we refute a conjecture of Promislow and Young on the structure of the extreme rays of the cone of Boolean submodular functions.


A TLA+ Proof System

Abstract: We describe an extension to the TLA+ specification language with constructs for writing proofs and a proof environment, called the Proof Manager (PM), to checks those proofs. The language and the PM support the incremental development and checking of hierarchically structured proofs. The PM translates a proof into a set of independent proof obligations and calls upon a collection of back-end provers to verify them. Different provers can be used to verify different obligations. The currently supported back-ends are the tableau prover Zenon and Isabelle/TLA+, an axiomatisation of TLA+ in Isabelle/Pure. The proof obligations for a complete TLA+ proof can also be used to certify the theorem in Isabelle/TLA+.


Pilotage des processus collaboratifs dans les syst\`emes PLM. Quels indicateurs pour quelle \'evaluation des performances ?

Abstract: Les entreprises qui collaborent dans un processus de d\'eveloppement de produit ont besoin de mettre en oeuvre une gestion efficace des activit\'es collaborative. Malgr\'e la mise en place d'un PLM, les activit\'es collaborative sont loin d'\^etre aussi efficace que l'on pourrait s'y attendre. Cet article propose une analyse des probl\'ematiques de la collaboration avec un syst\`eme PLM. A partir de ces analyses, nous proposons la mise en place d'indicateurs et d'actions sur les processus visant \`a identifier puis att\'enuer les freins dans le travail collaboratif. ----- Companies that collaborate within the product development processes need to implement an effective management of their collaborative activities. Despite the implementation of a PLM system, the collaborative activities are not efficient as it might be expected. This paper presents an analysis of the problems related to the collaborative work using a PLM system, identified through a survey. From this analysis, we propose an approach for improving collaborative processes within a PLM system, based on monitoring indicators. This approach leads to identify and therefore to mitigate the brakes of the collaborative work.


Collaborative process control: Observation of tracks generated by PLM system

Abstract: This paper aims at analyzing the problems related to collaborative work using a PLM system. This research is mainly focused on the organisational aspects of SMEs involved in networks composed of large companies, subcontractors and other industrial partners. From this analysis, we propose the deployment of an approach based on an observation process of tracks generated by PLM system. The specific contributions are of two fold. First is to identify the brake points of collaborative work. The second, thanks to the exploitation of generated tracks, it allows reducing risks by reacting in real time to the incidents or dysfunctions that may occur. The overall system architecture based on services technology and supporting the proposed approach is described, as well as associated prototype developed using an industrial PLM system.


Characterization and collection of information from heterogeneous multimedia sources with users' parameters for decision support

Abstract: No single information source can be good enough to satisfy the divergent and dynamic needs of users all the time. Integrating information from divergent sources can be a solution to deficiencies in information content. We present how Information from multimedia document can be collected based on associating a generic database to a federated database. Information collected in this way is brought into relevance by integrating the parameters of usage and user's parameter for decision making. We identified seven different classifications of multimedia document.


Magic Fairy Tales as Source for Interface Metaphors

Abstract: The work is devoted to a problem of search of metaphors for interactive systems and systems based on Virtual Reality (VR) environments. The analysis of magic fairy tales as a source of metaphors for interface and virtual reality is offered. Some results of design process based on magic metaphors are considered.


Coalgebraic Automata Theory: Basic Results

Abstract: We generalize some of the central results in automata theory to the abstraction level of coalgebras and thus lay out the foundations of a universal theory of automata operating on infinite objects. Let F be any set functor that preserves weak pullbacks. We show that the class of recognizable languages of F-coalgebras is closed under taking unions, intersections, and projections. We also prove that if a nondeterministic F-automaton accepts some coalgebra it accepts a finite one of the size of the automaton. Our main technical result concerns an explicit construction which transforms a given alternating F-automaton into an equivalent nondeterministic one, whose size is exponentially bound by the size of the original automaton.


Land Cover Mapping Using Ensemble Feature Selection Methods

Abstract: Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices. For each ensemble, the classification accuracy was derived as well as a diversity measure purported to give a measure of the inensemble diversity. The correlation between ensemble classification accuracy and diversity measure was determined to establish the interplay between the two variables. From the findings of this paper, diversity measures as currently formulated do not provide an adequate means upon which to constitute ensembles for land cover mapping.


Disjunctive Databases for Representing Repairs

Abstract: This paper addresses the problem of representing the set of repairs of a possibly inconsistent database by means of a disjunctive database. Specifically, the class of denial constraints is considered. We show that, given a database and a set of denial constraints, there exists a (unique) disjunctive database, called canonical, which represents the repairs of the database w.r.t. the constraints and is contained in any other disjunctive database with the same set of minimal models. We propose an algorithm for computing the canonical disjunctive database. Finally, we study the size of the canonical disjunctive database in the presence of functional dependencies for both repairs and cardinality-based repairs.


The Church Problem for Countable Ordinals

Abstract: A fundamental theorem of Buchi and Landweber shows that the Church synthesis problem is computable. Buchi and Landweber reduced the Church Problem to problems about ω-games and used the determinacy of such games as one of the main tools to show its computability. We consider a natural generalization of the Church problem to countable ordinals and investigate games of arbitrary countable length. We prove that determinacy and decidability parts of the Bu}chi and Landweber theorem hold for all countable ordinals and that its full extension holds for all ordinals < \omega\^\omega.


Semantics and Evaluation of Top-k Queries in Probabilistic Databases

Abstract: We study here fundamental issues involved in top-k query evaluation in probabilistic databases. We consider simple probabilistic databases in which probabilities are associated with individual tuples, and general probabilistic databases in which, additionally, exclusivity relationships between tuples can be represented. In contrast to other recent research in this area, we do not limit ourselves to injective scoring functions. We formulate three intuitive postulates that the semantics of top-k queries in probabilistic databases should satisfy, and introduce a new semantics, Global-Topk, that satisfies those postulates to a large degree. We also show how to evaluate queries under the Global-Topk semantics. For simple databases we design dynamic-programming based algorithms, and for general databases we show polynomial-time reductions to the simple cases. For example, we demonstrate that for a fixed k the time complexity of top-k query evaluation is as low as linear, under the assumption that probabilistic databases are simple and scoring functions are injective.


Perfect Matchings via Uniform Sampling in Regular Bipartite Graphs

Abstract: In this paper we further investigate the well-studied problem of finding a perfect matching in a regular bipartite graph. The first non-trivial algorithm, with running time $O(mn)$, dates back to K\"{o}nig's work in 1916 (here $m=nd$ is the number of edges in the graph, $2n$ is the number of vertices, and $d$ is the degree of each node). The currently most efficient algorithm takes time $O(m)$, and is due to Cole, Ost, and Schirra. We improve this running time to $O(\min\{m, \frac{n^{2.5}\ln n}{d}\})$; this minimum can never be larger than $O(n^{1.75}\sqrt{\ln n})$. We obtain this improvement by proving a uniform sampling theorem: if we sample each edge in a $d$-regular bipartite graph independently with a probability $p = O(\frac{n\ln n}{d^2})$ then the resulting graph has a perfect matching with high probability. The proof involves a decomposition of the graph into pieces which are guaranteed to have many perfect matchings but do not have any small cuts. We then establish a correspondence between potential witnesses to non-existence of a matching (after sampling) in any piece and cuts of comparable size in that same piece. Karger's sampling theorem for preserving cuts in a graph can now be adapted to prove our uniform sampling theorem for preserving perfect matchings. Using the $O(m\sqrt{n})$ algorithm (due to Hopcroft and Karp) for finding maximum matchings in bipartite graphs on the sampled graph then yields the stated running time. We also provide an infinite family of instances to show that our uniform sampling result is tight up to poly-logarithmic factors (in fact, up to $\ln^2 n$).


Computing voting power in easy weighted voting games

Abstract: Weighted voting games are ubiquitous mathematical models which are used in economics, political science, neuroscience, threshold logic, reliability theory and distributed systems. They model situations where agents with variable voting weight vote in favour of or against a decision. A coalition of agents is winning if and only if the sum of weights of the coalition exceeds or equals a specified quota. The Banzhaf index is a measure of voting power of an agent in a weighted voting game. It depends on the number of coalitions in which the agent is the difference in the coalition winning or losing. It is well known that computing Banzhaf indices in a weighted voting game is NP-hard. We give a comprehensive classification of weighted voting games which can be solved in polynomial time. Among other results, we provide a polynomial ($O(k{(\frac{n}{k})}^k)$) algorithm to compute the Banzhaf indices in weighted voting games in which the number of weight values is bounded by $k$.


Origins of Modern Data Analysis Linked to the Beginnings and Early Development of Computer Science and Information Engineering

Abstract: The history of data analysis that is addressed here is underpinned by two themes, -- those of tabular data analysis, and the analysis of collected heterogeneous data. "Exploratory data analysis" is taken as the heuristic approach that begins with data and information and seeks underlying explanation for what is observed or measured. I also cover some of the evolving context of research and applications, including scholarly publishing, technology transfer and the economic relationship of the university to society.


A Transformation--Based Approach for the Design of Parallel/Distributed Scientific Software: the FFT

Abstract: We describe a methodology for designing efficient parallel and distributed scientific software. This methodology utilizes sequences of mechanizable algebra--based optimizing transformations. In this study, we apply our methodology to the FFT, starting from a high--level algebraic algorithm description. Abstract multiprocessor plans are developed and refined to specify which computations are to be done by each processor. Templates are then created that specify the locations of computations and data on the processors, as well as data flow among processors. Templates are developed in both the MPI and OpenMP programming styles. Preliminary experiments comparing code constructed using our methodology with code from several standard scientific libraries show that our code is often competitive and sometimes performs better. Interestingly, our code handled a larger range of problem sizes on one target architecture.


Phase transition for Local Search on planted SAT

Abstract: The Local Search algorithm (or Hill Climbing, or Iterative Improvement) is one of the simplest heuristics to solve the Satisfiability and Max-Satisfiability problems. It is a part of many satisfiability and max-satisfiability solvers, where it is used to find a good starting point for a more sophisticated heuristics, and to improve a candidate solution. In this paper we give an analysis of Local Search on random planted 3-CNF formulas. We show that if there is k<7/6 such that the clause-to-variable ratio is less than k ln(n) (n is the number of variables in a CNF) then Local Search whp does not find a satisfying assignment, and if there is k>7/6 such that the clause-to-variable ratio is greater than k ln(n)$ then the local search whp finds a satisfying assignment. As a byproduct we also show that for any constant r there is g such that Local Search applied to a random (not necessarily planted) 3-CNF with clause-to-variable ratio r produces an assignment that satisfies at least gn clauses less than the maximal number of satisfiable clauses.


Decentralized Overlay for Federation of Enterprise Clouds

Abstract: This chapter describes Aneka-Federation, a decentralized and distributed system that combines enterprise Clouds, overlay networking, and structured peer-to-peer techniques to create scalable wide-area networking of compute nodes for high-throughput computing. The Aneka-Federation integrates numerous small scale Aneka Enterprise Cloud services and nodes that are distributed over multiple control and enterprise domains as parts of a single coordinated resource leasing abstraction. The system is designed with the aim of making distributed enterprise Cloud resource integration and application programming flexible, efficient, and scalable. The system is engineered such that it: enables seamless integration of existing Aneka Enterprise Clouds as part of single wide-area resource leasing federation; self-organizes the system components based on a structured peer-to-peer routing methodology; and presents end-users with a distributed application composition environment that can support variety of programming and execution models. This chapter describes the design and implementation of a novel, extensible and decentralized peer-to-peer technique that helps to discover, connect and provision the services of Aneka Enterprise Clouds among the users who can use different programming models to compose their applications. Evaluations of the system with applications that are programmed using the Task and Thread execution models on top of an overlay of Aneka Enterprise Clouds have been described here.