Speed Scaling on Parallel Processors with Migration

Abstract: We study the problem of scheduling a set of jobs with release dates, deadlines and processing requirements (or works), on parallel speed-scaled processors so as to minimize the total energy consumption. We consider that both preemption and migration of jobs are allowed. An exact polynomial-time algorithm has been proposed for this problem, which is based on the Ellipsoid algorithm. Here, we formulate the problem as a convex program and we propose a simpler polynomial-time combinatorial algorithm which is based on a reduction to the maximum flow problem. Our algorithm runs in $O(nf(n)logP)$ time, where $n$ is the number of jobs, $P$ is the range of all possible values of processors' speeds divided by the desired accuracy and $f(n)$ is the complexity of computing a maximum flow in a layered graph with O(n) vertices. Independently, Albers et al. \cite{AAG11} proposed an $O(n^2f(n))$-time algorithm exploiting the same relation with the maximum flow problem. We extend our algorithm to the multiprocessor speed scaling problem with migration where the objective is the minimization of the makespan under a budget of energy.


Magnifying Lens Abstraction for Stochastic Games with Discounted and Long-run Average Objectives

Abstract: Turn-based stochastic games and its important subclass Markov decision processes (MDPs) provide models for systems with both probabilistic and nondeterministic behaviors. We consider turn-based stochastic games with two classical quantitative objectives: discounted-sum and long-run average objectives. The game models and the quantitative objectives are widely used in probabilistic verification, planning, optimal inventory control, network protocol and performance analysis. Games and MDPs that model realistic systems often have very large state spaces, and probabilistic abstraction techniques are necessary to handle the state-space explosion. The commonly used full-abstraction techniques do not yield space-savings for systems that have many states with similar value, but does not necessarily have similar transition structure. A semi-abstraction technique, namely Magnifying-lens abstractions (MLA), that clusters states based on value only, disregarding differences in their transition relation was proposed for qualitative objectives (reachability and safety objectives). In this paper we extend the MLA technique to solve stochastic games with discounted-sum and long-run average objectives. We present the MLA technique based abstraction-refinement algorithm for stochastic games and MDPs with discounted-sum objectives. For long-run average objectives, our solution works for all MDPs and a sub-class of stochastic games where every state has the same value.


Partial-Observation Stochastic Games: How to Win when Belief Fails

Abstract: In two-player finite-state stochastic games of partial observation on graphs, in every state of the graph, the players simultaneously choose an action, and their joint actions determine a probability distribution over the successor states. We consider reachability objectives where player 1 tries to ensure a target state to be visited almost-surely or positively. On the basis of information, the game can be one-sided with either (a)player 1 or (b)player 2 having partial observation, or two-sided with both players having partial observation. On the basis of randomization (a)players may not be allowed to use randomization (pure strategies), or (b)may choose a probability distribution over actions but the actual random choice is not visible (actions invisible), or (c)may use full randomization. Our results for pure strategies are as follows: (1)For one-sided games with player 2 perfect observation we show that belief-based strategies are not sufficient, and present an exponential upper bound on memory both for almost-sure and positive winning strategies; we show that the problem of deciding the existence of almost-sure and positive winning strategies for player 1 is EXPTIME-complete and present symbolic algorithms that avoid the explicit exponential construction. (2)For one-sided games with player 1 perfect observation we show that non-elementary memory is both necessary and sufficient for both almost-sure and positive winning strategies. (3)We show that for the two-sided case finite memory strategies are sufficient for both positive and almost-sure winning. We establish the equivalence of the almost-sure winning problem for pure strategies with randomized strategies with actions invisible. Our equivalence result exhibit serious flaws in previous results in the literature: we show a non-elementary memory lower bound for almost-sure winning whereas an exponential upper bound was claimed.


Bounded Rationality in Concurrent Parity Games

Abstract: We consider 2-player games played on a finite state space for infinite rounds. The games are concurrent: in each round, the two players choose their moves simultaneously; the current state and the moves determine the successor. We consider omega-regular winning conditions given as parity objectives. We consider the qualitative analysis problems: the computation of the almost-sure and limit-sure winning set of states, where player 1 can ensure to win with probability 1 and with probability arbitrarily close to 1, respectively. In general the almost-sure and limit-sure winning strategies require both infinite-memory and infinite-precision. We study the bounded-rationality problem for qualitative analysis of concurrent parity games, where the strategy set player 1 is restricted to bounded-resource strategies. In terms of precision, strategies can be deterministic, uniform, finite-precision or infinite-precision; and in terms of memory, strategies can be memoryless, finite-memory or infinite-memory. We present a precise and complete characterization of the qualitative winning sets for all combinations of classes of strategies. In particular, we show that uniform memoryless strategies are as powerful as finite-precision infinite-memory strategies, and infinite-precision memoryless strategies are as powerful as infinite-precision finite-memory strategies. We show that the winning sets can be computed in O(n^{2d+3}) time, where n is the size of the game and 2d is the number of priorities, and our algorithms are symbolic. The membership problem of whether a state belongs to a winning set can be decided in NP cap coNP. While this complexity is the same as for the simpler class of turn-based games, where in each state only one of the players has a choice of moves, our algorithms, that are obtained by characterization of the winning sets as mu-calculus formulas, are considerably more involved.


ForOpenCL: Transformations Exploiting Array Syntax in Fortran for Accelerator Programming

Abstract: Emerging GPU architectures for high performance computing are well suited to a data-parallel programming model. This paper presents preliminary work examining a programming methodology that provides Fortran programmers with access to these emerging systems. We use array constructs in Fortran to show how this infrequently exploited, standardized language feature is easily transformed to lower-level accelerator code. The transformations in ForOpenCL are based on a simple mapping from Fortran to OpenCL. We demonstrate, using a stencil code solving the shallow-water fluid equations, that the performance of the ForOpenCL compiler-generated transformations is comparable with that of hand-optimized OpenCL code.


Lower bounds in differential privacy

Abstract: This is a paper about private data analysis, in which a trusted curator holding a confidential database responds to real vector-valued queries. A common approach to ensuring privacy for the database elements is to add appropriately generated random noise to the answers, releasing only these {\em noisy} responses. In this paper, we investigate various lower bounds on the noise required to maintain different kind of privacy guarantees.


The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems

Abstract: We study the matroid secretary problems with submodular valuation functions. In these problems, the elements arrive in random order. When one element arrives, we have to make an immediate and irrevocable decision on whether to accept it or not. The set of accepted elements must form an {\em independent set} in a predefined matroid. Our objective is to maximize the value of the accepted elements. In this paper, we focus on the case that the valuation function is a non-negative and monotonically non-decreasing submodular function. We introduce a general algorithm for such {\em submodular matroid secretary problems}. In particular, we obtain constant competitive algorithms for the cases of laminar matroids and transversal matroids. Our algorithms can be further applied to any independent set system defined by the intersection of a {\em constant} number of laminar matroids, while still achieving constant competitive ratios. Notice that laminar matroids generalize uniform matroids and partition matroids. On the other hand, when the underlying valuation function is linear, our algorithm achieves a competitive ratio of 9.6 for laminar matroids, which significantly improves the previous result.


Study of a Hybrid - Analog TV and Ethernet- Home Data Link using a Coaxial Cable

Abstract: The paper presents an implementation and compatibility tests of a simple home network implemented in a nonconventional manner using a CATV coaxial cable. Reusing the cable, normally designated to supply RF modulated TV signals from cable TV networks, makes possible to add data services as well. A short presentation of the technology is given with an investigation of the main performances obtained using this technique. The measurements revealed that this simple solution makes possible to have both TV and data services with performances close to traditional home data services: cable modems or ADSL, with minimal investments. This technology keeps also open the possibility for future improvements of the network: DVB-C or Data via Cable Modems.


Approximate Pure Nash Equilibria in Weighted Congestion Games: Existence, Efficient Computation, and Structure

Abstract: We consider structural and algorithmic questions related to the Nash dynamics of weighted congestion games. In weighted congestion games with linear latency functions, the existence of (pure Nash) equilibria is guaranteed by potential function arguments. Unfortunately, this proof of existence is inefficient and computing equilibria is such games is a {\sf PLS}-hard problem. The situation gets worse when superlinear latency functions come into play; in this case, the Nash dynamics of the game may contain cycles and equilibria may not even exist. Given these obstacles, we consider approximate equilibria as alternative solution concepts. Do such equilibria exist? And if so, can we compute them efficiently? We provide positive answers to both questions for weighted congestion games with polynomial latency functions by exploiting an "approximation" of such games by a new class of potential games that we call $\Psi$-games. This allows us to show that these games have $d!$-approximate equilibria, where $d$ is the maximum degree of the latency functions. Our main technical contribution is an efficient algorithm for computing O(1)-approximate equilibria when $d$ is a constant. For games with linear latency functions, the approximation guarantee is $\frac{3+\sqrt{5}}{2}+O(\gamma)$ for arbitrarily small $\gamma>0$; for latency functions with maximum degree $d\geq 2$, it is $d^{2d+o(d)}$. The running time is polynomial in the number of bits in the representation of the game and $1/\gamma$. As a byproduct of our techniques, we also show the following structural statement for weighted congestion games with polynomial latency functions of maximum degree $d\geq 2$: polynomially-long sequences of best-response moves from any initial state to a $d^{O(d^2)}$-approximate equilibrium exist and can be efficiently identified in such games as long as $d$ is constant.


Complexity of Metric Dimension on Planar Graphs

Abstract: The metric dimension of a graph $G$ is the size of a smallest subset $L \subseteq V(G)$ such that for any $x,y \in V(G)$ with $x\not= y$ there is a $z \in L$ such that the graph distance between $x$ and $z$ differs from the graph distance between $y$ and $z$. Even though this notion has been part of the literature for almost 40 years, prior to our work the computational complexity of determining the metric dimension of a graph was still very unclear. In this paper, we show tight complexity boundaries for the Metric Dimension problem. We achieve this by giving two complementary results. First, we show that the Metric Dimension problem on planar graphs of maximum degree $6$ is NP-complete. Then, we give a polynomial-time algorithm for determining the metric dimension of outerplanar graphs.


The parallel versus branching recurrences in computability logic

Abstract: This paper shows that the basic logic induced by the parallel recurrence of Computability Logic is a proper superset of the basic logic induced by the branching recurrence. The latter is known to be precisely captured by the cirquent calculus system CL15, conjectured by Japaridze to remain sound---but not complete---with parallel recurrence instead of branching recurrence. The present result is obtained by positively verifying that conjecture. A secondary result of the paper is showing that parallel recurrence is strictly weaker than branching recurrence in the sense that, while the latter logically implies the former, vice versa does not hold.


iBGP and Constrained Connectivity

Abstract: We initiate the theoretical study of the problem of minimizing the size of an iBGP overlay in an Autonomous System (AS) in the Internet subject to a natural notion of correctness derived from the standard "hot-potato" routing rules. For both natural versions of the problem (where we measure the size of an overlay by either the number of edges or the maximum degree) we prove that it is NP-hard to approximate to a factor better than $\Omega(\log n)$ and provide approximation algorithms with ratio $\tilde{O}(\sqrt{n})$. In addition, we give a slightly worse $\tilde{O}(n^{2/3})$-approximation based on primal-dual techniques that has the virtue of being both fast and good in practice, which we show via simulations on the actual topologies of five large Autonomous Systems. The main technique we use is a reduction to a new connectivity-based network design problem that we call Constrained Connectivity. In this problem we are given a graph $G=(V,E)$, and for every pair of vertices $u,v \in V$ we are given a set $S(u,v) \subseteq V$ called the safe set of the pair. The goal is to find the smallest subgraph $H$ of $G$ in which every pair of vertices $u,v$ is connected by a path contained in $S(u,v)$. We show that the iBGP problem can be reduced to the special case of Constrained Connectivity where $G = K_n$ and safe sets are defined geometrically based on the IGP distances in the AS. We also believe that Constrained Connectivity is an interesting problem in its own right, so provide stronger hardness results ($2^{\log^{1-\epsilon} n}$-hardness of approximation) and integrality gaps ($n^{1/3 - \epsilon}$) for the general case. On the positive side, we show that Constrained Connectivity turns out to be much simpler for some interesting special cases other than iBGP: when safe sets are symmetric and hierarchical, we give a polynomial time algorithm that computes an optimal solution.


Computing the Distance between Piecewise-Linear Bivariate Functions

Abstract: We consider the problem of computing the distance between two piecewise-linear bivariate functions $f$ and $g$ defined over a common domain $M$. We focus on the distance induced by the $L_2$-norm, that is $\|f-g\|_2=\sqrt{\iint_M (f-g)^2}$. If $f$ is defined by linear interpolation over a triangulation of $M$ with $n$ triangles, while $g$ is defined over another such triangulation, the obvious na\"ive algorithm requires $\Theta(n^2)$ arithmetic operations to compute this distance. We show that it is possible to compute it in $\O(n\log^4 n)$ arithmetic operations, by reducing the problem to multi-point evaluation of a certain type of polynomials. We also present an application to terrain matching.


A Variation of the Box-Counting Algorithm Applied to Colour Images

Abstract: The box counting method for fractal dimension estimation had not been applied to large or colour images thus far due to the processing time required. In this letter we present a fast, easy to implement and very easily expandable to any number of dimensions variation, the box merging method. It is applied here in RGB images which are considered as sets in 5-D space.


Approximation algorithms for two-state anti-ferromagnetic spin systems on bounded degree graphs

Abstract: In a seminal paper (Weitz, 2006), Weitz gave a deterministic fully polynomial approximation scheme for count- ing exponentially weighted independent sets (equivalently, approximating the partition function of the hard-core model from statistical physics) on graphs of degree at most d, up to the critical activity for the uniqueness of the Gibbs measure on the infinite d-regular tree. More recently Sly (Sly, 2010) showed that this is optimal in the sense that if there is an FPRAS for the hard-core partition function on graphs of maximum degree d for activities larger than the critical activity on the infinite d-regular tree then NP = RP. In this paper, we extend Weitz's approach to derive a deterministic fully polynomial approx- imation scheme for the partition function of the anti-ferromagnetic Ising model with arbitrary field on graphs of maximum degree d, up to the corresponding critical point on the d-regular tree. The main ingredient of our result is a proof that for two-state anti-ferromagnetic spin systems on the d-regular tree, weak spatial mixing implies strong spatial mixing. This in turn uses a message-decay argument which extends a similar approach proposed recently for the hard-core model by Restrepo et al (Restrepo et al, 2011) to the case of the anti-ferromagnetic Ising model with arbitrary field. By a standard correspondence, these results translate to arbitrary two-state anti-ferromagnetic spin systems with soft constraints.


Data Stability in Clustering: A Closer Look

Abstract: We consider the model introduced by Bilu and Linial (2010), who study problems for which the optimal clustering does not change when distances are perturbed. They show that even when a problem is NP-hard, it is sometimes possible to obtain efficient algorithms for instances resilient to certain multiplicative perturbations, e.g. on the order of $O(\sqrt{n})$ for max-cut clustering. Awasthi et al. (2010) consider center-based objectives, and Balcan and Liang (2011) analyze the $k$-median and min-sum objectives, giving efficient algorithms for instances resilient to certain constant multiplicative perturbations. Here, we are motivated by the question of to what extent these assumptions can be relaxed while allowing for efficient algorithms. We show there is little room to improve these results by giving NP-hardness lower bounds for both the $k$-median and min-sum objectives. On the other hand, we show that constant multiplicative resilience parameters can be so strong as to make the clustering problem trivial, leaving only a narrow range of resilience parameters for which clustering is interesting. We also consider a model of additive perturbations and give a correspondence between additive and multiplicative notions of stability. Our results provide a close examination of the consequences of assuming stability in data.


A Linear Time Algorithm for Seeds Computation

Abstract: A seed in a word is a relaxed version of a period in which the occurrences of the repeating subword may overlap. We show a linear-time algorithm computing a linear-size representation of all the seeds of a word (the number of seeds might be quadratic). In particular, one can easily derive the shortest seed and the number of seeds from our representation. Thus, we solve an open problem stated in the survey by Smyth (2000) and improve upon a previous O(n log n) algorithm by Iliopoulos, Moore, and Park (1996). Our approach is based on combinatorial relations between seeds and subword complexity (used here for the first time in context of seeds). In the previous papers, the compact representation of seeds consisted of two independent parts operating on the suffix tree of the word and the suffix tree of the reverse of the word, respectively. Our second contribution is a simpler representation of all seeds which avoids dealing with the reversed word. A preliminary version of this work, with a much more complex algorithm constructing the earlier representation of seeds, was presented at the 23rd Annual ACM-SIAM Symposium of Discrete Algorithms (SODA 2012).


Funding Games: the Truth but not the Whole Truth

Abstract: We introduce the Funding Game, in which $m$ identical resources are to be allocated among $n$ selfish agents. Each agent requests a number of resources $x_i$ and reports a valuation $\tilde{v}_i(x_i)$, which verifiably {\em lower}-bounds $i$'s true value for receiving $x_i$ items. The pairs $(x_i, \tilde{v}_i(x_i))$ can be thought of as size-value pairs defining a knapsack problem with capacity $m$. A publicly-known algorithm is used to solve this knapsack problem, deciding which requests to satisfy in order to maximize the social welfare. We show that a simple mechanism based on the knapsack {\it highest ratio greedy} algorithm provides a Bayesian Price of Anarchy of 2, and for the complete information version of the game we give an algorithm that computes a Nash equilibrium strategy profile in $O(n^2 \log^2 m)$ time. Our primary algorithmic result shows that an extension of the mechanism to $k$ rounds has a Price of Anarchy of $1 + \frac{1}{k}$, yielding a graceful tradeoff between communication complexity and the social welfare.


A CONVERT compiler of REC for PDP-8

Abstract: REC (REGULAR EXPRESSION COMPILER) is a programming language of simple structure developed originally for the PDP-8 computer of the Digital Equipment, Corporation, but readily adaptable to any other general purpose computer. It has been used extensively in teaching Algebra and Numerical Analysis in the Escuela Superior de F\'isica y Matem\'aticas of the Instituto Polit\'ecnico Nacional. Moreover, the fact that the same control language, REC, is equally applicable and equally efficient over the whole range of computer facilities available to the students gives a very welcome coherence to the entire teaching program, including the course of Mathematical Logic which is devoted to the theoretical aspects of such matters. REC; derives its appeal from the fact that computers can be regarded reasonably well as Turing Machines. The REC notation is simply a manner of writing regular expression, somewhat more amenable to programming the Turing Machine which they control. If one does not wish to think so strictly in terms of Turing Machines, REC expressions still provide a means of defining the flow of control in a program which is quite convenient for many applications.


On the Approximability and Hardness of Minimum Topic Connected Overlay and Its Special Instances

Abstract: In the context of designing a scalable overlay network to support decentralized topic-based pub/sub communication, the Minimum Topic-Connected Overlay problem (Min-TCO in short) has been investigated: Given a set of t topics and a collection of n users together with the lists of topics they are interested in, the aim is to connect these users to a network by a minimum number of edges such that every graph induced by users interested in a common topic is connected. It is known that Min-TCO is NP-hard and approximable within O(log t) in polynomial time. In this paper, we further investigate the problem and some of its special instances. We give various hardness results for instances where the number of topics in which an user is interested in is bounded by a constant, and also for the instances where the number of users interested in a common topic is constant. For the latter case, we present a first constant approximation algorithm. We also present some polynomial-time algorithms for very restricted instances of Min-TCO.


Private Data Release via Learning Thresholds

Abstract: This work considers computationally efficient privacy-preserving data release. We study the task of analyzing a database containing sensitive information about individual participants. Given a set of statistical queries on the data, we want to release approximate answers to the queries while also guaranteeing differential privacy---protecting each participant's sensitive data. Our focus is on computationally efficient data release algorithms; we seek algorithms whose running time is polynomial, or at least sub-exponential, in the data dimensionality. Our primary contribution is a computationally efficient reduction from differentially private data release for a class of counting queries, to learning thresholded sums of predicates from a related class. We instantiate this general reduction with a variety of algorithms for learning thresholds. These instantiations yield several new results for differentially private data release. As two examples, taking {0,1}^d to be the data domain (of dimension d), we obtain differentially private algorithms for: (*) Releasing all k-way conjunctions. For any given k, the resulting data release algorithm has bounded error as long as the database is of size at least d^{O(\sqrt{k\log(k\log d)})}. The running time is polynomial in the database size. (*) Releasing a (1-\gamma)-fraction of all parity queries. For any \gamma \geq \poly(1/d), the algorithm has bounded error as long as the database is of size at least \poly(d). The running time is polynomial in the database size. Several other instantiations yield further results for privacy-preserving data release. Of the two results highlighted above, the first learning algorithm uses techniques for representing thresholded sums of predicates as low-degree polynomial threshold functions. The second learning algorithm is based on Jackson's Harmonic Sieve algorithm [Jackson 1997].


Maximum Matchings via Glauber Dynamics

Abstract: In this paper we study the classic problem of computing a maximum cardinality matching in general graphs $G = (V, E)$. The best known algorithm for this problem till date runs in $O(m \sqrt{n})$ time due to Micali and Vazirani \cite{MV80}. Even for general bipartite graphs this is the best known running time (the algorithm of Karp and Hopcroft \cite{HK73} also achieves this bound). For regular bipartite graphs one can achieve an $O(m)$ time algorithm which, following a series of papers, has been recently improved to $O(n \log n)$ by Goel, Kapralov and Khanna (STOC 2010) \cite{GKK10}. In this paper we present a randomized algorithm based on the Markov Chain Monte Carlo paradigm which runs in $O(m \log^2 n)$ time, thereby obtaining a significant improvement over \cite{MV80}. We use a Markov chain similar to the \emph{hard-core model} for Glauber Dynamics with \emph{fugacity} parameter $\lambda$, which is used to sample independent sets in a graph from the Gibbs Distribution \cite{V99}, to design a faster algorithm for finding maximum matchings in general graphs. Our result crucially relies on the fact that the mixing time of our Markov Chain is independent of $\lambda$, a significant deviation from the recent series of works \cite{GGSVY11,MWW09, RSVVY10, S10, W06} which achieve computational transition (for estimating the partition function) on a threshold value of $\lambda$. As a result we are able to design a randomized algorithm which runs in $O(m\log^2 n)$ time that provides a major improvement over the running time of the algorithm due to Micali and Vazirani. Using the conductance bound, we also prove that mixing takes $\Omega(\frac{m}{k})$ time where $k$ is the size of the maximum matching.


Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent

Abstract: For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent (SGD) is as good as that of the parameters which minimize the empirical cost. However, to our knowledge, despite its optimal asymptotic convergence rate, averaged SGD (ASGD) received little attention in recent research on large scale learning. One possible reason is that it may take a prohibitively large number of training samples for ASGD to reach its asymptotic region for most real problems. In this paper, we present a finite sample analysis for the method of Polyak and Juditsky (1992). Our analysis shows that it indeed usually takes a huge number of samples for ASGD to reach its asymptotic region for improperly chosen learning rate. More importantly, based on our analysis, we propose a simple way to properly set learning rate so that it takes a reasonable amount of data for ASGD to reach its asymptotic region. We compare ASGD using our proposed learning rate with other well known algorithms for training large scale linear classifiers. The experiments clearly show the superiority of ASGD.


Matching Pursuits with Random Sequential Subdictionaries

Abstract: Matching pursuits are a class of greedy algorithms commonly used in signal processing, for solving the sparse approximation problem. They rely on an atom selection step that requires the calculation of numerous projections, which can be computationally costly for large dictionaries and burdens their competitiveness in coding applications. We propose using a non adaptive random sequence of subdictionaries in the decomposition process, thus parsing a large dictionary in a probabilistic fashion with no additional projection cost nor parameter estimation. A theoretical modeling based on order statistics is provided, along with experimental evidence showing that the novel algorithm can be efficiently used on sparse approximation problems. An application to audio signal compression with multiscale time-frequency dictionaries is presented, along with a discussion of the complexity and practical implementations.


Fuzzy Topological Systems

Abstract: Dialectica categories are a very versatile categorical model of linear logic. These have been used to model many seemingly different things (e.g., Petri nets and Lambek's calculus). In this note, we expand our previous work on fuzzy petri nets to deal with fuzzy topological systems. One basic idea is to use as the dualizing object in the Dialectica categories construction, the unit real interval [0,1], which has all the properties of a {\em lineale}. The second basic idea is to generalize Vickers's notion of a topological system.


Learning Hypergraph Labeling for Feature Matching

Abstract: This study poses the feature correspondence problem as a hypergraph node labeling problem. Candidate feature matches and their subsets (usually of size larger than two) are considered to be the nodes and hyperedges of a hypergraph. A hypergraph labeling algorithm, which models the subset-wise interaction by an undirected graphical model, is applied to label the nodes (feature correspondences) as correct or incorrect. We describe a method to learn the cost function of this labeling algorithm from labeled examples using a graphical model training algorithm. The proposed feature matching algorithm is different from the most of the existing learning point matching methods in terms of the form of the objective function, the cost function to be learned and the optimization method applied to minimize it. The results on standard datasets demonstrate how learning over a hypergraph improves the matching performance over existing algorithms, notably one that also uses higher order information without learning.


Routing in Undirected Graphs with Constant Congestion

Abstract: Given an undirected graph G=(V,E), a collection (s_1,t_1),...,(s_k,t_k) of k source-sink pairs, and an integer c, the goal in the Edge Disjoint Paths with Congestion problem is to connect maximum possible number of the source-sink pairs by paths, so that the maximum load on any edge (called edge congestion) does not exceed c. We show an efficient randomized algorithm to route $\Omega(OPT/\poly\log k)$ source-sink pairs with congestion at most 14, where OPT is the maximum number of pairs that can be simultaneously routed on edge-disjoint paths. The best previous algorithm that routed $\Omega(OPT/\poly\log n)$ pairs required congestion $\poly(\log \log n)$, and for the setting where the maximum allowed congestion is bounded by a constant c, the best previous algorithms could only guarantee the routing of $OPT/n^{O(1/c)}$ pairs.


Lower Bounds for Number-in-Hand Multiparty Communication Complexity, Made Easy

Abstract: In this paper we prove lower bounds on randomized multiparty communication complexity, both in the \emph{blackboard model} (where each message is written on a blackboard for all players to see) and (mainly) in the \emph{message-passing model}, where messages are sent player-to-player. We introduce a new technique for proving such bounds, called \emph{symmetrization}, which is natural, intuitive, and often easy to use. For example, for the problem where each of $k$ players gets a bit-vector of length $n$, and the goal is to compute the coordinate-wise XOR of these vectors, we prove a tight lower bounds of $\Omega(nk)$ in the blackboard model. For the same problem with AND instead of XOR, we prove a lower bounds of roughly $\Omega(nk)$ in the message-passing model (assuming $k \le n/3200$) and $\Omega(n \log k)$ in the blackboard model. We also prove lower bounds for bit-wise majority, for a graph-connectivity problem, and for other problems; the technique seems applicable to a wide range of other problems as well. All of our lower bounds allow randomized communication protocols with two-sided error. We also use the symmetrization technique to prove several direct-sum-like results for multiparty communication.


Aspects of Entertainment Distribution in an Intelligent Home Environment

Abstract: The paper presents an implementation and tests of a simple home entertainment distribution architecture (server + multiple clients) implemented using two conventional cabling architectures: CATV coaxial cable and conventional Ethernet. This architecture is created taking into account the "Home gateway" concept present in most attempts to solve the problem of the "Intelligent home". A short presentation of the experimental is given with an investigation of the main performances obtained using this architecture. The experiments revealed that this simple solution makes possible to have entertainment and data services with performances close to traditional data services in a cost-effective architecture


An IDE to Build and Check Task Flow Models

Abstract: This paper presents the Eclipse plug-ins for the Task Flow model in the Discovery Method. These plug-ins provide an IDE for the Task Algebra compiler and the model-checking tools. The Task Algebra is the formal representation for the Task Model and it is based on simple and compound tasks. The model-checking techniques were developed to validate Task Models represented in the algebra.


A Higher-Order Cheeger's Inequality

Abstract: A basic fact in algebraic graph theory is that the number of connected components in an undirected graph is equal to the multiplicity of the eigenvalue 1 in the normalized adjacency matrix of the graph. In particular, the graph is disconnected if and only if there are at least two eigenvalues equal to 1. Cheeger's inequality provides an "approximate" version of the latter fact, and it states that a graph has a sparse cut (it is "almost disconnected") if and only if there are at least two eigenvalues that are close to one. It has been conjectured that an analogous characterization holds for higher multiplicities, that is there are $k$ eigenvalues close to 1 if and only if the vertex set can be partitioned into $k$ subsets, each defining a sparse cut. In this paper we resolve this conjecture. Our result provides a theoretical justification for clustering algorithms that use the top $k$ eigenvector to embed the vertices into $\R^k$, and then apply geometric considerations to the embedding.


A Fuzzy View on k-Means Based Signal Quantization with Application in Iris Segmentation

Abstract: This paper shows that the k-means quantization of a signal can be interpreted both as a crisp indicator function and as a fuzzy membership assignment describing fuzzy clusters and fuzzy boundaries. Combined crisp and fuzzy indicator functions are defined here as natural generalizations of the ordinary crisp and fuzzy indicator functions, respectively. An application to iris segmentation is presented together with a demo program.


Exploring New Directions in Iris Recognition

Abstract: A new approach in iris recognition based on Circular Fuzzy Iris Segmentation (CFIS) and Gabor Analytic Iris Texture Binary Encoder (GAITBE) is proposed and tested here. CFIS procedure is designed to guarantee that similar iris segments will be obtained for similar eye images, despite the fact that the degree of occlusion may vary from one image to another. Its result is a circular iris ring (concentric with the pupil) which approximates the actual iris. GAITBE proves better encoding of statistical independence between the iris codes extracted from different irides using Hilbert Transform. Irides from University of Bath Iris Database are binary encoded on two different lengths (768 / 192 bytes) and tested in both single-enrollment and multi-enrollment identification scenarios. All cases illustrate the capacity of the newly proposed methodology to narrow down the distribution of inter-class matching scores, and consequently, to guarantee a steeper descent of the False Accept Rate.


On the Feasibility of Maintenance Algorithms in Dynamic Graphs

Abstract: Near ubiquitous mobile computing has led to intense interest in dynamic graph theory. This provides a new and challenging setting for algorithmics and complexity theory. For any graph-based problem, the rapid evolution of a (possibly disconnected) graph over time naturally leads to the important complexity question: is it better to calculate a new solution from scratch or to adapt the known solution on the prior graph to quickly provide a solution of guaranteed quality for the changed graph? In this paper, we demonstrate that the former is the best approach in some cases, but that there are cases where the latter is feasible. We prove that, under certain conditions, hard problems cannot even be approximated in any reasonable complexity bound --- i.e., even with a large amount of time, having a solution to a very similar graph does not help in computing a solution to the current graph. To achieve this, we formalize the idea as a maintenance algorithm. Using r-Regular Subgraph as the primary example we show that W[1]-hardness for the parameterized approximation problem implies the non-existence of a maintenance algorithm for the given approximation ratio. Conversely we show that Vertex Cover, which is fixed-parameter tractable, has a 2-approximate maintenance algorithm. The implications of NP-hardness and NPO-hardness are also explored.


Topographic Feature Extraction for Bengali and Hindi Character Images

Abstract: Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shape-based graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.


Proposed Quality Evaluation Framework to Incorporate Quality Aspects in Web Warehouse Creation

Abstract: Web Warehouse is a read only repository maintained on the web to effectively handle the relevant data. Web warehouse is a system comprised of various subsystems and process. It supports the organizations in decision making. Quality of data store in web warehouse can affect the quality of decision made. For a valuable decision making it is required to consider the quality aspects in designing and modelling of a web warehouse. Thus data quality is one of the most important issues of the web warehousing system. Quality must be incorporated at different stages of the web warehousing system development. It is necessary to enhance existing data warehousing system to increase the data quality. It results in the storage of high quality data in the repository and efficient decision making. In this paper a Quality Evaluation Framework is proposed keeping in view the quality dimensions associated with different phases of a web warehouse. Further more, the proposed framework is validated empirically with the help of quantitative analysis.


Restructuring Compressed Texts without Explicit Decompression

Abstract: We consider the problem of {\em restructuring} compressed texts without explicit decompression. We present algorithms which allow conversions from compressed representations of a string $T$ produced by any grammar-based compression algorithm, to representations produced by several specific compression algorithms including LZ77, LZ78, run length encoding, and some grammar based compression algorithms. These are the first algorithms that achieve running times polynomial in the size of the compressed input and output representations of $T$. Since most of the representations we consider can achieve exponential compression, our algorithms are theoretically faster in the worst case, than any algorithm which first decompresses the string for the conversion.


Second moment method for a family of boolean CSP

Abstract: The estimation of phase transitions in random boolean Constraint Satisfaction Problems (CSP) is based on two fundamental tools: the first and second moment methods. While the first moment method on the number of solutions permits to compute upper bounds on any boolean CSP, the second moment method used for computing lower bounds proves to be more tricky and in most cases gives only the trivial lower bound 0. In this paper, we define a subclass of boolean CSP covering the monotone versions of many known NP-Complete boolean CSPs. We give a method for computing non trivial lower bounds for any member of this subclass. This is achieved thanks to an application of the second moment method to some selected solutions called characteristic solutions that depend on the boolean CSP considered. We apply this method with a finer analysis to establish that the threshold $r_{k}$ (ratio : #constrains/#variables) of monotone 1-in-k-SAT is $\log k/k\leq r_{k}\leq\log^{2}k/k$.


Face Recognition using Curvelet Transform

Abstract: Face recognition has been studied extensively for more than 20 years now. Since the beginning of 90s the subject has became a major issue. This technology is used in many important real-world applications, such as video surveillance, smart cards, database security, internet and intranet access. This report reviews recent two algorithms for face recognition which take advantage of a relatively new multiscale geometric analysis tool - Curvelet transform, for facial processing and feature extraction. This transform proves to be efficient especially due to its good ability to detect curves and lines, which characterize the human's face. An algorithm which is based on the two algorithms mentioned above is proposed, and its performance is evaluated on three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech. k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are used, along with Principal Component Analysis (PCA) for dimensionality reduction. This algorithm shows good results, and it even outperforms other algorithms in some cases.


Modelling Distributed Shape Priors by Gibbs Random Fields of Second Order

Abstract: We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express simple shapes and spatial relations between them simultaneously. This allows to model and recognise complex shapes as spatial compositions of simpler parts.


A Survey on how Description Logic Ontologies Benefit from Formal Concept Analysis

Abstract: Although the notion of a concept as a collection of objects sharing certain properties, and the notion of a conceptual hierarchy are fundamental to both Formal Concept Analysis and Description Logics, the ways concepts are described and obtained differ significantly between these two research areas. Despite these differences, there have been several attempts to bridge the gap between these two formalisms, and attempts to apply methods from one field in the other. The present work aims to give an overview on the research done in combining Description Logics and Formal Concept Analysis.


Label-Specific Training Set Construction from Web Resource for Image Annotation

Abstract: Recently many research efforts have been devoted to image annotation by leveraging on the associated tags/keywords of web images as training labels. A key issue to resolve is the relatively low accuracy of the tags. In this paper, we propose a novel semi-automatic framework to construct a more accurate and effective training set from these web media resources for each label that we want to learn. Experiments conducted on a real-world dataset demonstrate that the constructed training set can result in higher accuracy for image annotation.


Optimization with Demand Oracles

Abstract: We study \emph{combinatorial procurement auctions}, where a buyer with a valuation function $v$ and budget $B$ wishes to buy a set of items. Each item $i$ has a cost $c_i$ and the buyer is interested in a set $S$ that maximizes $v(S)$ subject to $\Sigma_{i\in S}c_i\leq B$. Special cases of combinatorial procurement auctions are classical problems from submodular optimization. In particular, when the costs are all equal (\emph{cardinality constraint}), a classic result by Nemhauser et al shows that the greedy algorithm provides an $\frac e {e-1}$ approximation. Motivated by many papers that utilize demand queries to elicit the preferences of agents in economic settings, we develop algorithms that guarantee improved approximation ratios in the presence of demand oracles. We are able to break the $\frac e {e-1}$ barrier: we present algorithms that use only polynomially many demand queries and have approximation ratios of $\frac 9 8+\epsilon$ for the general problem and $\frac 9 8$ for maximization subject to a cardinality constraint. We also consider the more general class of subadditive valuations. We present algorithms that obtain an approximation ratio of $2+\epsilon$ for the general problem and 2 for maximization subject to a cardinality constraint. We guarantee these approximation ratios even when the valuations are non-monotone. We show that these ratios are essentially optimal, in the sense that for any constant $\epsilon>0$, obtaining an approximation ratio of $2-\epsilon$ requires exponentially many demand queries.


Geometric Packing under Non-uniform Constraints

Abstract: We study the problem of discrete geometric packing. Here, given weighted regions (say in the plane) and points (with capacities), one has to pick a maximum weight subset of the regions such that no point is covered more than its capacity. We provide a general framework and an algorithm for approximating the optimal solution for packing in hypergraphs arising out of such geometric settings. Using this framework we get a flotilla of results on this problem (and also on its dual, where one wants to pick a maximum weight subset of the points when the regions have capacities). For example, for the case of fat triangles of similar size, we show an O(1)-approximation and prove that no \PTAS is possible.


Lower Bound for Envy-Free and Truthful Makespan Approximation on Related Machines

Abstract: We study problems of scheduling jobs on related machines so as to minimize the makespan in the setting where machines are strategic agents. In this problem, each job $j$ has a length $l_{j}$ and each machine $i$ has a private speed $t_{i}$. The running time of job $j$ on machine $i$ is $t_{i}l_{j}$. We seek a mechanism that obtains speed bids of machines and then assign jobs and payments to machines so that the machines have incentive to report true speeds and the allocation and payments are also envy-free. We show that 1. A deterministic envy-free, truthful, individually rational, and anonymous mechanism cannot approximate the makespan strictly better than $2-1/m$, where $m$ is the number of machines. This result contrasts with prior work giving a deterministic PTAS for envy-free anonymous assignment and a distinct deterministic PTAS for truthful anonymous mechanism. 2. For two machines of different speeds, the unique deterministic scalable allocation of any envy-free, truthful, individually rational, and anonymous mechanism is to allocate all jobs to the quickest machine. This allocation is the same as that of the VCG mechanism, yielding a 2-approximation to the minimum makespan. 3. No payments can make any of the prior published monotone and locally efficient allocations that yield better than an $m$-approximation for $\qcmax$ \cite{aas, at,ck10, dddr, kovacs} a truthful, envy-free, individually rational, and anonymous mechanism.


Solving the At-Most-Once Problem with Nearly Optimal Effectiveness

Abstract: We present and analyze a wait-free deterministic algorithm for solving the at-most-once problem: how m shared-memory fail-prone processes perform asynchronously n jobs at most once. Our algorithmic strategy provides for the first time nearly optimal effectiveness, which is a measure that expresses the total number of jobs completed in the worst case. The effectiveness of our algorithm equals n-2m+2. This is up to an additive factor of m close to the known effectiveness upper bound n-m+1 over all possible algorithms and improves on the previously best known deterministic solutions that have effectiveness only n-log m o(n). We also present an iterative version of our algorithm that for any $m = O\left(\sqrt[3+\epsilon]{n/\log n}\right)$ is both effectiveness-optimal and work-optimal, for any constant $\epsilon > 0$. We then employ this algorithm to provide a new algorithmic solution for the Write-All problem which is work optimal for any $m=O\left(\sqrt[3+\epsilon]{n/\log n}\right)$.


Budget Feasible Mechanism Design via Random Sampling

Abstract: Budget feasible mechanism considers algorithmic mechanism design questions where there is a budget constraint on the total payment of the mechanism. An important question in the field is that under which valuation domains there exist budget feasible mechanisms that admit `small' approximations (compared to a socially optimal solution). Singer \cite{PS10} showed that additive and submodular functions admit a constant approximation mechanism. Recently, Dobzinski, Papadimitriou, and Singer \cite{DPS11} gave an $O(\log^2n)$ approximation mechanism for subadditive functions and remarked that: "A fundamental question is whether, regardless of computational constraints, a constant-factor budget feasible mechanism exists for subadditive function." In this paper, we give the first attempt to this question. We give a polynomial time $O(\frac{\log n}{\log\log n})$ sub-logarithmic approximation ratio mechanism for subadditive functions, improving the best known ratio $O(\log^2 n)$. Further, we connect budget feasible mechanism design to the concept of approximate core in cooperative game theory, and show that there is a mechanism for subadditive functions whose approximation is, via a characterization of the integrality gap of a linear program, linear to the largest value to which an approximate core exists. Our result implies in particular that the class of XOS functions, which is a superclass of submodular functions, admits a constant approximation mechanism. We believe that our work could be a solid step towards solving the above fundamental problem eventually, and possibly, with an affirmative answer.


An Ontology-driven Framework for Supporting Complex Decision Process

Abstract: The study proposes a framework of ONTOlogy-based Group Decision Support System (ONTOGDSS) for decision process which exhibits the complex structure of decision-problem and decision-group. It is capable of reducing the complexity of problem structure and group relations. The system allows decision makers to participate in group decision-making through the web environment, via the ontology relation. It facilitates the management of decision process as a whole, from criteria generation, alternative evaluation, and opinion interaction to decision aggregation. The embedded ontology structure in ONTOGDSS provides the important formal description features to facilitate decision analysis and verification. It examines the software architecture, the selection methods, the decision path, etc. Finally, the ontology application of this system is illustrated with specific real case to demonstrate its potentials towards decision-making development.


Linear-Time Poisson-Disk Patterns

Abstract: We present an algorithm for generating Poisson-disc patterns taking O(N) time to generate $N$ points. The method is based on a grid of regions which can contain no more than one point in the final pattern, and uses an explicit model of point arrival times under a uniform Poisson process.


Computing q-gram Frequencies on Collage Systems

Abstract: Collage systems are a general framework for representing outputs of various text compression algorithms. We consider the all $q$-gram frequency problem on compressed string represented as a collage system, and present an $O((q+h\log n)n)$-time $O(qn)$-space algorithm for calculating the frequencies for all $q$-grams that occur in the string. Here, $n$ and $h$ are respectively the size and height of the collage system.


Computing q-gram Non-overlapping Frequencies on SLP Compressed Texts

Abstract: Length-$q$ substrings, or $q$-grams, can represent important characteristics of text data, and determining the frequencies of all $q$-grams contained in the data is an important problem with many applications in the field of data mining and machine learning. In this paper, we consider the problem of calculating the {\em non-overlapping frequencies} of all $q$-grams in a text given in compressed form, namely, as a straight line program (SLP). We show that the problem can be solved in $O(q^2n)$ time and $O(qn)$ space where $n$ is the size of the SLP. This generalizes and greatly improves previous work (Inenaga & Bannai, 2009) which solved the problem only for $q=2$ in $O(n^4\log n)$ time and $O(n^3)$ space.


Security Analysis of two Distance-Bounding Protocols

Abstract: In this paper, we analyze the security of two recently proposed distance bounding protocols called the Hitomi and the NUS protocols. Our results show that the claimed security of both protocols has been overestimated. Namely, we show that the Hitomi protocol is susceptible to a full secret key disclosure attack which not only results in violating the privacy of the protocol but also can be exploited for further attacks such as impersonation, ma?a fraud and terrorist fraud attacks. Our results also demonstrates that the probability of success in a distance fraud attack against the NUS protocol can be increased up to (3/4)^n and even slightly more, if the adversary is furnished with some computational capabilities.


Compression via Matroids: A Randomized Polynomial Kernel for Odd Cycle Transversal

Abstract: The Odd Cycle Transversal problem (OCT) asks whether a given graph can be made bipartite by deleting at most $k$ of its vertices. In a breakthrough result Reed, Smith, and Vetta (Operations Research Letters, 2004) gave a $\BigOh(4^kkmn)$ time algorithm for it, the first algorithm with polynomial runtime of uniform degree for every fixed $k$. It is known that this implies a polynomial-time compression algorithm that turns OCT instances into equivalent instances of size at most $\BigOh(4^k)$, a so-called kernelization. Since then the existence of a polynomial kernel for OCT, i.e., a kernelization with size bounded polynomially in $k$, has turned into one of the main open questions in the study of kernelization. This work provides the first (randomized) polynomial kernelization for OCT. We introduce a novel kernelization approach based on matroid theory, where we encode all relevant information about a problem instance into a matroid with a representation of size polynomial in $k$. For OCT, the matroid is built to allow us to simulate the computation of the iterative compression step of the algorithm of Reed, Smith, and Vetta, applied (for only one round) to an approximate odd cycle transversal which it is aiming to shrink to size $k$. The process is randomized with one-sided error exponentially small in $k$, where the result can contain false positives but no false negatives, and the size guarantee is cubic in the size of the approximate solution. Combined with an $\BigOh(\sqrt{\log n})$-approximation (Agarwal et al., STOC 2005), we get a reduction of the instance to size $\BigOh(k^{4.5})$, implying a randomized polynomial kernelization.


A Satisfiability Algorithm for AC$^0$

Abstract: We consider the problem of efficiently enumerating the satisfying assignments to $\AC^0$ circuits. We give a zero-error randomized algorithm which takes an $\AC^0$ circuit as input and constructs a set of restrictions which partition $\{0,1\}^n$ so that under each restriction the value of the circuit is constant. Let $d$ denote the depth of the circuit and $cn$ denote the number of gates. This algorithm runs in time $|C| 2^{n(1-\mu_{c.d})}$ where $|C|$ is the size of the circuit for $\mu_{c,d} \ge 1/\bigO[\lg c + d \lg d]^{d-1}$ with probability at least $1-2^{-n}$. As a result, we get improved exponential time algorithms for $\AC^0$ circuit satisfiability and for counting solutions. In addition, we get an improved bound on the correlation of $\AC^0$ circuits with parity. As an important component of our analysis, we extend the H{\aa}stad Switching Lemma to handle multiple $\kcnf$s and $\kdnf$s.


Homomorphic Self-repairing Codes for Agile Maintenance of Distributed Storage Systems

Abstract: Distributed data storage systems are essential to deal with the need to store massive volumes of data. In order to make such a system fault-tolerant, some form of redundancy becomes crucial, incurring various overheads - most prominently in terms of storage space and maintenance bandwidth requirements. Erasure codes, originally designed for communication over lossy channels, provide a storage efficient alternative to replication based redundancy, however entailing high communication overhead for maintenance, when some of the encoded fragments need to be replenished in news ones after failure of some storage devices. We propose as an alternative a new family of erasure codes called self-repairing codes (SRC) taking into account the peculiarities of distributed storage systems, specifically the maintenance process. SRC has the following salient features: (a) encoded fragments can be repaired directly from other subsets of encoded fragments by downloading less data than the size of the complete object, ensuring that (b) a fragment is repaired from a fixed number of encoded fragments, the number depending only on how many encoded blocks are missing and independent of which specific blocks are missing. This paper lays the foundations by defining the novel self-repairing codes, elaborating why the defined characteristics are desirable for distributed storage systems. Then homomorphic self-repairing codes (HSRC) are proposed as a concrete instance, whose various aspects and properties are studied and compared - quantitatively or qualitatively with respect to other codes including traditional erasure codes as well as other recent codes designed specifically for storage applications.


Stable, scalable, decentralized P2P file sharing with non-altruistic peers

Abstract: P2P systems provide a scalable solution for distributing large files in a network. The file is split into many chunks, and peers contact other peers to collect missing chunks to eventually complete the entire file. The so-called `rare chunk' phenomenon, where a single chunk becomes rare and prevents peers from completing the file, is a threat to the stability of such systems. Practical systems such as BitTorrent overcome this issue by requiring a global search for the rare chunk, which necessitates a centralized mechanism. We demonstrate a new system based on an approximate rare-chunk rule, allowing for completely distributed file sharing while retaining scalability and stability. We assume non-altruistic peers and the seed is required to make only a minimal contribution.


Type Expressiveness and Its Application in Separation of Behavior Programming and Data Management Programming

Abstract: A new behavior descriptive entity type called spec is proposed, which combines the traditional interface with test rules and test cases, to completely specify the desired behavior of each method, and to enforce the behavior-wise correctness of all compiled units. Using spec, a new programming paradigm is proposed, which allows the separation programming space into 1) a behavior domain to aggregate all behavior programming in the format of specs, 2) a object domain to bind each concrete spec to its data representation in a particular address space, and 3) a realization domain to connect the behavior domain and the object domain. Such separation guarantees the strictness of behavior satisfaction at compile time, while allows flexibility of dynamical binding of actual implementation at runtime. A new convention call type expressiveness to allow data exchange between different programming languages and between different software environments is also proposed.


Fingerprint recognition using standardized fingerprint model

Abstract: Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models...are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we use the final fingerprint to match with others in FVC2004 fingerprint database (DB4) to show the capability of the model.


Facial Expression Classification Based on Multi Artificial Neural Network and Two Dimensional Principal Component Analysis

Abstract: Facial expression classification is a kind of image classification and it has received much attention, in recent years. There are many approaches to solve these problems with aiming to increase efficient classification. One of famous suggestions is described as first step, project image to different spaces; second step, in each of these spaces, images are classified into responsive class and the last step, combine the above classified results into the final result. The advantages of this approach are to reflect fulfill and multiform of image classified. In this paper, we use 2D-PCA and its variants to project the pattern or image into different spaces with different grouping strategies. Then we develop a model which combines many Neural Networks applied for the last step. This model evaluates the reliability of each space and gives the final classification conclusion. Our model links many Neural Networks together, so we call it Multi Artificial Neural Network (MANN). We apply our proposal model for 6 basic facial expressions on JAFFE database consisting 213 images posed by 10 Japanese female models.


Cooperative oligopoly games: a probabilistic approach

Abstract: We analyze the core of a cooperative Cournot game. We assume that when contemplating a deviation, the members of a coalition assign positive probability over all possible coalition structures that the non-members can form. We show that when the number of firms in the market is sufficiently large then the core of the underlying cooperative game is non-empty. Moreover, we show that the core of our game is a subset of the \gamma - core.


Strategic delegation in a sequential model with multiple stages

Abstract: We analyze strategic delegation in a Stackelberg model with an arbitrary number, n, of firms. We show that the n-1 last movers delegate their production decisions to managers whereas the first mover does not. Equilibrium incentive rates are increasing in the order with which managers select quantities. Letting u_i^* denote the equilibrium payoff of the firm whose manager moves in the i-th place, we show that u_n^*>u_{n-1}^*>...>u_2^*>u_1^*. We also compare the delegation outcome of our game with that of a Cournot oligopoly and show that the late (early) moving firms choose higher (lower) incentive rates than the Cournot firms.


Estimation of Characteristics of a Software Team for Implementing Effective Inspection Process through Inspection Performance Metric

Abstract: The continued existence of any software industry depends on its capability to develop nearly zero-defect product, which is achievable through effective defect management. Inspection has proven to be one of the promising techniques of defect management. Introductions of metrics like, Depth of Inspection (DI, a process metric) and Inspection Performance Metric (IPM, a people metric) enable one to have an appropriate measurement of inspection technique. This article elucidates a mathematical approach to estimate the IPM value without depending on shop floor defect count at every time. By applying multiple linear regression models, a set of characteristic coefficients of the team is evaluated. These coefficients are calculated from the empirical projects that are sampled from the teams of product-based and service-based IT industries. A sample of three verification projects indicates a close match between the IPM values obtained from the defect count (IPMdc) and IPM values obtained using the team coefficients using the mathematical model (IPMtc). The IPM values observed onsite and IPM values produced by our model which are strongly matching, support the predictive capability of IPM through team coefficients. Having finalized the value of IPM that a company should achieve for a project, it can tune the inspection influencing parameters to realize the desired quality level of IPM. Evaluation of team coefficients resolves several defect-associated issues, which are related to the management, stakeholders, outsourcing agents and customers. In addition, the coefficient vector will further aid the strategy of PSP and TSP


Large Formal Wikis: Issues and Solutions

Abstract: We present several steps towards large formal mathematical wikis. The Coq proof assistant together with the CoRN repository are added to the pool of systems handled by the general wiki system described in \cite{DBLP:conf/aisc/UrbanARG10}. A smart re-verification scheme for the large formal libraries in the wiki is suggested for Mizar/MML and Coq/CoRN, based on recently developed precise tracking of mathematical dependencies. We propose to use features of state-of-the-art filesystems to allow real-time cloning and sandboxing of the entire libraries, allowing also to extend the wiki to a true multi-user collaborative area. A number of related issues are discussed.


Licensing the Mizar Mathematical Library

Abstract: The Mizar Mathematical Library (MML) is a large corpus of formalised mathematical knowledge. It has been constructed over the course of many years by a large number of authors and maintainers. Yet the legal status of these efforts of the Mizar community has never been clarified. In 2010, after many years of loose deliberations, the community decided to investigate the issue of licensing the content of the MML, thereby clarifying and crystallizing the status of the texts, the text's authors, and the library's long-term maintainers. The community has settled on a copyright and license policy that suits the peculiar features of Mizar and its community. In this paper we discuss the copyright and license solutions. We offer our experience in the hopes that the communities of other libraries of formalised mathematical knowledge might take up the legal and scientific problems that we addressed for Mizar.


Cloud Computing Future Framework for e-management of NGO's

Abstract: Cloud computing is an emerging new computing paradigm for delivering computing services. This computing approach relies on a number of existing technologies, e.g., the Internet, virtualization, grid computing, Web services, etc. Cloud Computing aims to provide scalable and inexpensive on-demand computing infrastructures with good quality of service levels. It represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to consumers from the cloud. It helps an organization in saving costs and creating new business opportunities.This paper provides a framework, Education Cloud for the e- management of NGO's. The Education Cloud can transform a nonprofit, or an entire sector of nonprofits, achieves its mission and creates lasting impact in its communities. This paper also presents the case study of Kalgidhar trust, Baru Sahib, Himachal Pradesh, NGO which is using the education as the tool to solve the social issues.


An Agent-based Strategy for Deploying Analysis Models into Specification and Design for Distributed APS Systems

Abstract: Despite the extensive use of the agent technology in the Supply Chain Management field, its integration with Advanced Planning and Scheduling (APS) tools still represents a promising field with several open research questions. Specifically, the literature falls short in providing an integrated framework to analyze, specify, design and implement simulation experiments covering the whole simulation cycle. Thus, this paper proposes an agent-based strategy to convert the 'analysis' models into 'specification' and 'design' models combining two existing methodologies proposed in the literature. The first one is a recent and unique approach dedicated to the 'analysis' of agent-based APS systems. The second one is a well-established methodological framework to 'specify' and 'design' agent-based supply chain systems. The proposed conversion strategy is original and is the first one allowing simulation analysts to integrate the whole simulation development process in the domain of distributed APS.


Feed drive modelling for the simulation of tool path tracking in multi-axis High Speed Machining

Abstract: Within the context of High Speed Machining, it is essential to manage the trajectory generation to achieve both high surface quality and high productivity. As feed drives are one part of the set Machine tool - Numerical Controller, it is necessary to improve their performances to optimize feed drive dynamics during trajectory follow up. Hence, this paper deals with the modelling of the feed drive in the case of multi axis machining. This model can be used for the simulation of axis dynamics and tool-path tracking to tune parameters and optimize new frameworks of command strategies. A procedure of identification based on modern NC capabilities is presented and applied to industrial HSM centres. Efficiency of this modelling is assessed by experimental verifications on various representative trajectories. After implementing a Generalized Predictive Control, reliable simulations are performed thanks to the model. These simulations can then be used to tune parameters of this new framework according to the tool-path geometry.


Quantum information approach to normal representation of extensive games

Abstract: We modify the concept of quantum strategic game to make it useful for extensive form games. We prove that our modification allows to consider the normal representation of any finite extensive game using the fundamental concepts of quantum information. The Selten's Horse game and the general form of two-stage extensive game with perfect information are studied to illustrate a potential application of our idea. In both examples we use Eisert-Wilkens-Lewenstein approach as well as Marinatto-Weber approach to quantization of games.


Probabilistic Methods on Erdos Problems

Abstract: The paper reviews and tries to describe the reference set method, which is a method of combinatorial optimization that gives upper bounds on parameters.


E-DTN : A Multi-Interface Energy DTN Gateway

Abstract: To overcome the problem of unavailability of grid power in rural India, we explore the possibility of powering WSN gateways using a bicycle dynamo. The "Data mule" bicycle generates its own power to ensure a self sustainable data transfer for information dissemination to small and marginal farmers. Our multi-interface WSN gateway is equipped with Bluetooth, Wi-Fi and GPRS technologies. To achieve our goal, we exploit the DTN stack in the energy sense and introduce necessary modifications to its configuration.


Semantic annotation of requirements for automatic UML class diagram generation

Abstract: The increasing complexity of software engineering requires effective methods and tools to support requirements analysts' activities. While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In this context, we propose a tool for transforming text documents describing users' requirements to an UML model. The presented tool uses Natural Language Processing (NLP) and semantic rules to generate an UML class diagram. The main contribution of our tool is to provide assistance to designers facilitating the transition from a textual description of user requirements to their UML diagrams based on GATE (General Architecture of Text) by formulating necessary rules that generate new semantic annotations.


From decision to action : intentionality, a guide for the specification of intelligent agents' behaviour

Abstract: This article introduces a reflexion about behavioural specification for interactive and participative agent-based simulation in virtual reality. Within this context, it is neces sary to reach a high level of expressivness in order to enforce interactions between the designer and the behavioural model during the in-line prototyping. This requires to consider the need of semantic very early in the design process. The Intentional agent model is here exposed as a possible answer. It relies on a mixed imperative and declarative approach which focuses on the link between decision and action. The design of a tool able to simulate virtual environment implying agents based on this model is discuss


A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

Abstract: Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.


Real-time retrieval for case-based reasoning in interactive multiagent-based simulations

Abstract: The aim of this paper is to present the principles and results about case-based reasoning adapted to real- time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section pre- sents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other auton- omous agents or users' avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project.


Computing Strong Game-Theoretic Strategies in Jotto

Abstract: We develop a new approach that computes approximate equilibrium strategies in Jotto, a popular word game. Jotto is an extremely large two-player game of imperfect information; its game tree has many orders of magnitude more states than games previously studied, including no-limit Texas hold 'em. To address the fact that the game is so large, we propose a novel strategy representation called oracular form, in which we do not explicitly represent a strategy, but rather appeal to an oracle that quickly outputs a sample move from the strategy's distribution. Our overall approach is based on an extension of the fictitious play algorithm to this oracular setting. We demonstrate the superiority of our computed strategies over the strategies computed by a benchmark algorithm, both in terms of head-to-head and worst-case performance.


Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques

Abstract: In remote sensing, image fusion technique is a useful tool used to fuse high spatial resolution panchromatic images (PAN) with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion (F) while preserving the spectral information in the multispectral image (MS).There are many PAN sharpening techniques or Pixel-Based image fusion techniques that have been developed to try to enhance the spatial resolution and the spectral property preservation of the MS. This paper attempts to undertake the study of image fusion, by using two types of pixel-based image fusion techniques i.e. Arithmetic Combination and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques. The first type includes Brovey Transform (BT), Color Normalized Transformation (CN) and Multiplicative Method (MLT). The second type include High-Pass Filter Additive Method (HPFA), High-Frequency-Addition Method (HFA) High Frequency Modulation Method (HFM) and The Wavelet transform-based fusion method (WT). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), Entropy(En), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively.


Compressive Mechanism: Utilizing Sparse Representation in Differential Privacy

Abstract: Differential privacy provides the first theoretical foundation with provable privacy guarantee against adversaries with arbitrary prior knowledge. The main idea to achieve differential privacy is to inject random noise into statistical query results. Besides correctness, the most important goal in the design of a differentially private mechanism is to reduce the effect of random noise, ensuring that the noisy results can still be useful. This paper proposes the \emph{compressive mechanism}, a novel solution on the basis of state-of-the-art compression technique, called \emph{compressive sensing}. Compressive sensing is a decent theoretical tool for compact synopsis construction, using random projections. In this paper, we show that the amount of noise is significantly reduced from $O(\sqrt{n})$ to $O(\log(n))$, when the noise insertion procedure is carried on the synopsis samples instead of the original database. As an extension, we also apply the proposed compressive mechanism to solve the problem of continual release of statistical results. Extensive experiments using real datasets justify our accuracy claims.


Object Oriented Information Computing over WWW

Abstract: Traditional search engines on World Wide Web (WWW) focus essentially on relevance ranking at the page level. But this lead to missing innumerable structured information about real-world objects embedded in static Web pages and online Web databases. Page-level information retrieval (IR) can unfortunately lead to highly inaccurate relevance ranking in answering object-oriented queries. On the other hand, Object Oriented Information Computing (OOIC) is promising and greatly reduces the complexity of the system while improving reusability and manageability. The most distinguishing requirement of today's complex heterogeneous systems is the need of the computing system to instantly adapt to vigorously changing conditions. OOIC allows reflecting the dynamic characteristics of the applications by instantiating objects dynamically. In this paper, major challenges of OOIC as well as its rudiments are recapped. The review includes the insight to PopRank Model and comparison analysis of conventional page rank based IR with OOIC


SD-AODV: A Protocol for Secure and Dynamic Data Dissemination in Mobile Ad Hoc Network

Abstract: Security remains as a major concern in the mobile ad hoc networks. This paper presents a new protocol SD-AODV, which is an extension of the exiting protocol AODV. The proposed protocol is made secure and dynamic against three main types of routing attacks- wormhole attack, byzantine attack and blackhole attack. SD-AODV protocol was evaluated through simulation experiments done on Glomosim and performance of the network was measured in terms of packet delivery fraction, average end-to-end delay, global throughput and route errors of a mobile ad hoc network where a defined percentage of nodes behave maliciously. Experimentally it was found that the performance of the network did not degrade in the presence of the above said attacks indicating that the proposed protocol was secure against these attacks.


Discovering Knowledge using a Constraint-based Language

Abstract: Discovering pattern sets or global patterns is an attractive issue from the pattern mining community in order to provide useful information. By combining local patterns satisfying a joint meaning, this approach produces patterns of higher level and thus more useful for the data analyst than the usual local patterns, while reducing the number of patterns. In parallel, recent works investigating relationships between data mining and constraint programming (CP) show that the CP paradigm is a nice framework to model and mine such patterns in a declarative and generic way. We present a constraint-based language which enables us to define queries addressing patterns sets and global patterns. The usefulness of such a declarative approach is highlighted by several examples coming from the clustering based on associations. This language has been implemented in the CP framework.


Randomisation and Derandomisation in Descriptive Complexity Theory

Abstract: We study probabilistic complexity classes and questions of derandomisation from a logical point of view. For each logic L we introduce a new logic BPL, bounded error probabilistic L, which is defined from L in a similar way as the complexity class BPP, bounded error probabilistic polynomial time, is defined from PTIME. Our main focus lies on questions of derandomisation, and we prove that there is a query which is definable in BPFO, the probabilistic version of first-order logic, but not in Cinf, finite variable infinitary logic with counting. This implies that many of the standard logics of finite model theory, like transitive closure logic and fixed-point logic, both with and without counting, cannot be derandomised. Similarly, we present a query on ordered structures which is definable in BPFO but not in monadic second-order logic, and a query on additive structures which is definable in BPFO but not in FO. The latter of these queries shows that certain uniform variants of AC0 (bounded-depth polynomial sized circuits) cannot be derandomised. These results are in contrast to the general belief that most standard complexity classes can be derandomised. Finally, we note that BPIFP+C, the probabilistic version of fixed-point logic with counting, captures the complexity class BPP, even on unordered structures.


Optimal symmetric Tardos traitor tracing schemes

Abstract: For the Tardos traitor tracing scheme, we show that by combining the symbol-symmetric accusation function of Skoric et al. with the improved analysis of Blayer and Tassa we get further improvements. Our construction gives codes that are up to 4 times shorter than Blayer and Tassa's, and up to 2 times shorter than the codes from Skoric et al. Asymptotically, we achieve the theoretical optimal codelength for Tardos' distribution function and the symmetric score function. For large coalitions, our codelengths are asymptotically about 4.93% of Tardos' original codelengths, which also improves upon results from Nuida et al.


What can we learn from slow self-avoiding adaptive walks by an infinite radius search algorithm?

Abstract: Slow self-avoiding adaptive walks by an infinite radius search algorithm (Limax) are analyzed as themselves, and as the network they form. The study is conducted on several NK problems and two HIFF problems. We find that examination of such "slacker" walks and networks can indicate relative search difficulty within a family of problems, help identify potential local optima, and detect presence of structure in fitness landscapes. Hierarchical walks are used to differentiate rugged landscapes which are hierarchical (e.g. HIFF) from those which are anarchic (e.g. NK). The notion of node viscidity as a measure of local optimum potential is introduced and found quite successful although more work needs to be done to improve its accuracy on problems with larger K.


Systematic Abstraction of Abstract Machines

Abstract: We describe a derivational approach to abstract interpretation that yields novel and transparently sound static analyses when applied to well-established abstract machines for higher-order and imperative programming languages. To demonstrate the technique and support our claim, we transform the CEK machine of Felleisen and Friedman, a lazy variant of Krivine's machine, and the stack-inspecting CM machine of Clements and Felleisen into abstract interpretations of themselves. The resulting analyses bound temporal ordering of program events; predict return-flow and stack-inspection behavior; and approximate the flow and evaluation of by-need parameters. For all of these machines, we find that a series of well-known concrete machine refactorings, plus a technique of store-allocated continuations, leads to machines that abstract into static analyses simply by bounding their stores. We demonstrate that the technique scales up uniformly to allow static analysis of realistic language features, including tail calls, conditionals, side effects, exceptions, first-class continuations, and even garbage collection. In order to close the gap between formalism and implementation, we provide translations of the mathematics as running Haskell code for the initial development of our method.


Evaluation of servo, geometric and dynamic error sources on five axis high-speed machine tool

Abstract: Many sources of errors exist in the manufacturing process of complex shapes. Some approximations occur at each step from the design geometry to the machined part. The aim of the paper is to present a method to evaluate the effect of high speed and high dynamic load on volumetric errors at the tool center point. The interpolator output signals and the machine encoder signals are recorded and compared to evaluate the contouring errors resulting from each axis follow-up error. The machine encoder signals are also compared to the actual tool center point position as recorded with a non-contact measuring instrument called CapBall to evaluate the total geometric errors. The novelty of the work lies in the method that is proposed to decompose the geometric errors in two categories: the quasi-static geometric errors independent from the speed of the trajectory and the dynamic geometric errors, dependent on the programmed feed rate and resulting from the machine structure deflection during the acceleration of its axes. The evolution of the respective contributions for contouring errors, quasi-static geometric errors and dynamic geomet- ric errors is experimentally evaluated and a relation between programmed feed rate and dynamic errors is highlighted.


On Consensus under Polynomial Protocols

Abstract: In this paper we explore the possibility of using computational algebraic methods to analyze a class of consensus protocols. We state some necessary conditions for convergence under consensus protocols that are polynomials.


Privacy-Enhanced Methods for Comparing Compressed DNA Sequences

Abstract: In this paper, we study methods for improving the efficiency and privacy of compressed DNA sequence comparison computations, under various querying scenarios. For instance, one scenario involves a querier, Bob, who wants to test if his DNA string, $Q$, is close to a DNA string, $Y$, owned by a data owner, Alice, but Bob does not want to reveal $Q$ to Alice and Alice is willing to reveal $Y$ to Bob \emph{only if} it is close to $Q$. We describe a privacy-enhanced method for comparing two compressed DNA sequences, which can be used to achieve the goals of such a scenario. Our method involves a reduction to set differencing, and we describe a privacy-enhanced protocol for set differencing that achieves absolute privacy for Bob (in the information theoretic sense), and a quantifiable degree of privacy protection for Alice. One of the important features of our protocols, which makes them ideally suited to privacy-enhanced DNA sequence comparison problems, is that the communication complexity of our solutions is proportional to a threshold that bounds the cardinality of the set differences that are of interest, rather than the cardinality of the sets involved (which correlates to the length of the DNA sequences). Moreover, in our protocols, the querier, Bob, can easily compute the set difference only if its cardinality is close to or below a specified threshold.


Optimizing Index Deployment Order for Evolving OLAP (Extended Version)

Abstract: Query workloads and database schemas in OLAP applications are becoming increasingly complex. Moreover, the queries and the schemas have to continually \textit{evolve} to address business requirements. During such repetitive transitions, the \textit{order} of index deployment has to be considered while designing the physical schemas such as indexes and MVs. An effective index deployment ordering can produce (1) a prompt query runtime improvement and (2) a reduced total deployment time. Both of these are essential qualities of design tools for quickly evolving databases, but optimizing the problem is challenging because of complex index interactions and a factorial number of possible solutions. We formulate the problem in a mathematical model and study several techniques for solving the index ordering problem. We demonstrate that Constraint Programming (CP) is a more flexible and efficient platform to solve the problem than other methods such as mixed integer programming and A* search. In addition to exact search techniques, we also studied local search algorithms to find near optimal solution very quickly. Our empirical analysis on the TPC-H dataset shows that our pruning techniques can reduce the size of the search space by tens of orders of magnitude. Using the TPC-DS dataset, we verify that our local search algorithm is a highly scalable and stable method for quickly finding a near-optimal solution.


K-sort: A new sorting algorithm that beats Heap sort for n <= 70 lakhs!

Abstract: Sundararajan and Chakraborty (2007) introduced a new version of Quick sort removing the interchanges. Khreisat (2007) found this algorithm to be competing well with some other versions of Quick sort. However, it uses an auxiliary array thereby increasing the space complexity. Here, we provide a second version of our new sort where we have removed the auxiliary array. This second improved version of the algorithm, which we call K-sort, is found to sort elements faster than Heap sort for an appreciably large array size (n <= 70,00,000) for uniform U[0, 1] inputs.


Improving AODV Performance using Dynamic Density Driven Route Request Forwarding

Abstract: Ad-hoc routing protocols use a number of algorithms for route discovery. Some use flooding in which a route request packet (RREQ) is broadcasted from a source node to other nodes in the network. This often leads to unnecessary retransmissions, causing congestion and packet collisions in the network, a phenomenon called a broadcast storm. This paper presents a RREQ message forwarding scheme for AODV that reduces routing overheads. This has been called AODV_EXT. Its performance is compared to that of AODV, DSDV, DSR and OLSR protocols. Simulation results show that AODV_EXT achieves 3% energy efficiency, 19.5% improvement in data throughput and 69.5% reduction in the number of dropped packets for a network of 50 nodes. Greater efficiency is achieved in high density network and marginal improvement in networks with a small number of nodes.


A Review on Elliptic Curve Cryptography for Embedded Systems

Abstract: Importance of Elliptic Curves in Cryptography was independently proposed by Neal Koblitz and Victor Miller in 1985.Since then, Elliptic curve cryptography or ECC has evolved as a vast field for public key cryptography (PKC) systems. In PKC system, we use separate keys to encode and decode the data. Since one of the keys is distributed publicly in PKC systems, the strength of security depends on large key size. The mathematical problems of prime factorization and discrete logarithm are previously used in PKC systems. ECC has proved to provide same level of security with relatively small key sizes. The research in the field of ECC is mostly focused on its implementation on application specific systems. Such systems have restricted resources like storage, processing speed and domain specific CPU architecture.


QoS Routing and Performance Evaluation for Mobile Ad Hoc Networks using OLSR Protocol

Abstract: Mobile Ad-Hoc network is a collection of mobile nodes in communication without using infrastructure. As the real-time applications used in today's wireless network grow, we need some schemes to provide more suitable service for them. We know that most of actual schemes do not perform well on traffic which is not strictly CBR. Therefore, in this paper we have studied the impact, respectively, of mobility models and the density of nodes on the performances (End-to-End Delay, Throughput and Packet Delivery ratio) of routing protocol (Optimized Link State Routing) OLSR by using in the first a real-time VBR (MPEG-4) and secondly the Constant Bit Rate (CBR) traffic. Finally we compare the performance on both cases. Experimentally, we considered the three mobility models as follows Random Waypoint, Random Direction and Mobgen Steady State. The experimental results illustrate that the behavior of OLSR change according to the model and the used traffics.


On Polynomial Kernels for Structural Parameterizations of Odd Cycle Transversal

Abstract: The Odd Cycle Transversal problem (OCT) asks whether a given graph can be made bipartite (i.e., 2-colorable) by deleting at most l vertices. We study structural parameterizations of OCT with respect to their polynomial kernelizability, i.e., whether instances can be efficiently reduced to a size polynomial in the chosen parameter. It is a major open problem in parameterized complexity whether Odd Cycle Transversal admits a polynomial kernel when parameterized by l. On the positive side, we show a polynomial kernel for OCT when parameterized by the vertex deletion distance to the class of bipartite graphs of treewidth at most w (for any constant w); this generalizes the parameter feedback vertex set number (i.e., the distance to a forest). Complementing this, we exclude polynomial kernels for OCT parameterized by the distance to outerplanar graphs, conditioned on the assumption that NP \not \subseteq coNP/poly. Thus the bipartiteness requirement for the treewidth w graphs is necessary. Further lower bounds are given for parameterization by distance from cluster and co-cluster graphs respectively, as well as for Weighted OCT parameterized by the vertex cover number (i.e., the distance from an independent set).