Approximating Non-Uniform Sparsest Cut via Generalized Spectra

Abstract: We give an approximation algorithm for non-uniform sparsest cut with the following guarantee: For any $\epsilon,\delta \in (0,1)$, given cost and demand graphs with edge weights $C, D$ respectively, we can find a set $T\subseteq V$ with $\frac{C(T,V\setminus T)}{D(T,V\setminus T)}$ at most $\frac{1+\epsilon}{\delta}$ times the optimal non-uniform sparsest cut value, in time $2^{r/(\delta\epsilon)}\poly(n)$ provided $\lambda_r \ge \Phi^*/(1-\delta)$. Here $\lambda_r$ is the $r$'th smallest generalized eigenvalue of the Laplacian matrices of cost and demand graphs; $C(T,V\setminus T)$ (resp. $D(T,V\setminus T)$) is the weight of edges crossing the $(T,V\setminus T)$ cut in cost (resp. demand) graph and $\Phi^*$ is the sparsity of the optimal cut. In words, we show that the non-uniform sparsest cut problem is easy when the generalized spectrum grows moderately fast. To the best of our knowledge, there were no results based on higher order spectra for non-uniform sparsest cut prior to this work. Even for uniform sparsest cut, the quantitative aspects of our result are somewhat stronger than previous methods. Similar results hold for other expansion measures like edge expansion, normalized cut, and conductance, with the $r$'th smallest eigenvalue of the normalized Laplacian playing the role of $\lambda_r$ in the latter two cases. Our proof is based on an l1-embedding of vectors from a semi-definite program from the Lasserre hierarchy. The embedded vectors are then rounded to a cut using standard threshold rounding. We hope that the ideas connecting $\ell_1$-embeddings to Lasserre SDPs will find other applications. Another aspect of the analysis is the adaptation of the column selection paradigm from our earlier work on rounding Lasserre SDPs [GS11] to pick a set of edges rather than vertices. This feature is important in order to extend the algorithms to non-uniform sparsest cut.


Evaluation of Performance Measures for Classifiers Comparison

Abstract: The selection of the best classification algorithm for a given dataset is a very widespread problem, occuring each time one has to choose a classifier to solve a real-world problem. It is also a complex task with many important methodological decisions to make. Among those, one of the most crucial is the choice of an appropriate measure in order to properly assess the classification performance and rank the algorithms. In this article, we focus on this specific task. We present the most popular measures and compare their behavior through discrimination plots. We then discuss their properties from a more theoretical perspective. It turns out several of them are equivalent for classifiers comparison purposes. Futhermore. they can also lead to interpretation problems. Among the numerous measures proposed over the years, it appears that the classical overall success rate and marginal rates are the more suitable for classifier comparison task.


A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients

Abstract: In this paper, we introduce a Reduced Reference Image Quality Assessment (RRIQA) measure based on the natural image statistic approach. A new adaptive transform called "Tetrolet" is applied to both reference and distorted images. To model the marginal distribution of tetrolet coefficients Bessel K Forms (BKF) density is proposed. Estimating the parameters of this distribution allows to summarize the reference image with a small amount of side information. Five distortion measures based on the BKF parameters of the original and processed image are used to predict quality scores. A comparison between these measures is presented showing a good consistency with human judgment.


A Geometric Approach For Fully Automatic Chromosome Segmentation

Abstract: A fundamental task in human chromosome analysis is chromosome segmentation. Segmentation plays an important role in chromosome karyotyping. The first step in segmentation is to remove intrusive objects such as stain debris and other noises. The next step is detection of touching and overlapping chromosomes, and the final step is separation of such chromosomes. Common methods for separation between touching chromosomes are interactive and require human intervention for correct separation between touching and overlapping chromosomes. In this paper, a geometric-based method is used for automatic detection of touching and overlapping chromosomes and separating them. The proposed scheme performs segmentation in two phases. In the first phase, chromosome clusters are detected using three geometric criteria, and in the second phase, chromosome clusters are separated using a cut-line. Most of earlier methods did not work properly in case of chromosome clusters that contained more than two chromosomes. Our method, on the other hand, is quite efficient in separation of such chromosome clusters. At each step, one separation will be performed and this algorithm is repeated until all individual chromosomes are separated. Another important point about the proposed method is that it uses the geometric features of chromosomes which are independent of the type of images and it can easily be applied to any type of images such as binary images and does not require multispectral images as well. We have applied our method to a database containing 62 touching and partially overlapping chromosomes and a success rate of 91.9% is achieved.


An Empirical Study on End-users Productivity Using Model-based Spreadsheets

Abstract: Spreadsheets are widely used, and studies have shown that most end-user spreadsheets contain nontrivial errors. To improve end-users productivity, recent research proposes the use of a model-driven engineering approach to spreadsheets. In this paper we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved.


Spreadsheets on the Move: An Evaluation of Mobile Spreadsheets

Abstract: The power of mobile devices has increased dramatically in the last few years. These devices are becoming more sophisticated allowing users to accomplish a wide variety of tasks while on the move. The increasingly mobile nature of business has meant that more users will need access to spreadsheets while away from their desktop and laptop computers. Existing mobile applications suffer from a number of usability issues that make using spreadsheets in this way more difficult. This work represents the first evaluation of mobile spreadsheet applications. Through a pilot survey the needs and experiences of experienced spreadsheet users was examined. The range of spreadsheet apps available for the iOS platform was also evaluated in light of these users' needs.


A Distributed Scheduling Algorithm for High-Speed Switching Systems

Abstract: Given the rapid increase in traffic, greater demands have been put on research in high-speed switching systems. Such systems have to simultaneously meet several constraints, e.g., high throughput, low delay and low complexity. This makes it challenging to design an efficient scheduling algorithm, and has consequently drawn considerable research interest. However, previous results either cannot provide a 100% throughput guarantee without a speedup, or require a complex centralized scheduler. In this paper, we design a distributed 100% throughput algorithm for crosspoint buffered switches, called DISQUO, with very limited message passing. We prove that DISQUO can achieve 100% throughput for any admissible Bernoulli traffic, with a low time complexity of O(1) per port and a few bits message exchanging in every time slot. To the best of our knowledge, it is the first distributed algorithm that can provide a 100% throughput for a crosspoint buffered switch.


On Bounding Problems of Quantitative Information Flow

Abstract: Researchers have proposed formal definitions of quantitative information flow based on information theoretic notions such as the Shannon entropy, the min entropy, the guessing entropy, belief, and channel capacity. This paper investigates the hardness of precisely checking the quantitative information flow of a program according to such definitions. More precisely, we study the "bounding problem" of quantitative information flow, defined as follows: Given a program M and a positive real number q, decide if the quantitative information flow of M is less than or equal to q. We prove that the bounding problem is not a k-safety property for any k (even when q is fixed, for the Shannon-entropy-based definition with the uniform distribution), and therefore is not amenable to the self-composition technique that has been successfully applied to checking non-interference. We also prove complexity theoretic hardness results for the case when the program is restricted to loop-free boolean programs. Specifically, we show that the problem is PP-hard for all definitions, showing a gap with non-interference which is coNP-complete for the same class of programs. The paper also compares the results with the recently proved results on the comparison problems of quantitative information flow.


Vertex-centroid finite volume scheme on tetrahedral grids for conservation laws

Abstract: Vertex-centroid schemes are cell-centered finite volume schemes for conservation laws which make use of vertex values to construct high resolution schemes. The vertex values must be obtained through a consistent averaging (interpolation) procedure. A modified interpolation scheme is proposed which is better than existing schemes in giving positive weights in the interpolation formula. A simplified reconstruction scheme is also proposed which is also more accurate and efficient. For scalar conservation laws, we develop limited versions of the schemes which are stable in maximum norm by constructing suitable limiters. The schemes are applied to compressible flows governed by the Euler equations of inviscid gas dynamics.


Online Learning for Classification of Low-rank Representation Features and Its Applications in Audio Segment Classification

Abstract: In this paper, a novel framework based on trace norm minimization for audio segment is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the $\ell_1$-norm is used to extract low-rank features which are robust to white noise and gross corruption for audio segments. These low-rank features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this classifier, most methods find the weight and bias in batch-mode learning, which makes them inefficient for large-scale problems. In this paper, we propose an online framework using accelerated proximal gradient method. This framework has a main advantage in memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of general proximal gradient method was omitted in our approach. Experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.


A Sufficient Condition for Hanna Neumann Property of Submonoids of a Free Monoid

Abstract: Using automata-theoretic approach, Giambruno and Restivo have investigated on the intersection of two finitely generated submonoids of the free monoid over a finite alphabet. In particular, they have obtained Hanna Neumann property for a special class of submonoids generated by finite prefix sets. This work continues their work and provides a sufficient condition for Hanna Neumann property for the entire class of submonoids generated by finite prefix sets. In this connection, a general rank formula for the submonoids which are accepted by semi-flower automata is also obtained.


Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data

Abstract: Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATA- EAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clusters with perform well in terms of the Silhouette Coefficients cluster measure.


Bounded-Distance Network Creation Games

Abstract: A network creation game simulates a decentralized and non-cooperative building of a communication network. Informally, there are $n$ players sitting on the network nodes, which attempt to establish a reciprocal communication by activating, incurring a certain cost, any of their incident links. The goal of each player is to have all the other nodes as close as possible in the resulting network, while buying as few links as possible. According to this intuition, any model of the game must then appropriately address a balance between these two conflicting objectives. Motivated by the fact that a player might have a strong requirement about its centrality in the network, in this paper we introduce a new setting in which if a player maintains its (either maximum or average) distance to the other nodes within a given associated bound, then its cost is simply equal to the number of activated edges, otherwise its cost is unbounded. We study the problem of understanding the structure of associated pure Nash equilibria of the resulting games, that we call MaxBD and SumBD, respectively. For both games, we show that computing the best response of a player is an NP-hard problem. Next, we show that when distance bounds associated with players are non-uniform, then equilibria can be arbitrarily bad. On the other hand, for MaxBD, we show that when nodes have a uniform bound $R$ on the maximum distance, then the Price of Anarchy (PoA) is lower and upper bounded by 2 and $O(n^{\frac{1}{\lfloor\log_3 R\rfloor+1}})$ for $R \geq 3$, while for the interesting case R=2, we are able to prove that the PoA is $\Omega(\sqrt{n})$ and $O(\sqrt{n \log n})$. For the uniform SumBD we obtain similar (asymptotically) results, and moreover we show that the PoA becomes constant as soon as the bound on the average distance is $n^{\omega(\frac{1}{\sqrt{\log n}})}$.


Refactoring Composite to Visitor and Inverse Transformation in Java

Abstract: We describe how to use refactoring tools to transform a Java program conforming to the Composite design pattern into a program conforming to the Visitor design pattern with the same external behavior. We also describe the inverse transformation. We use the refactoring tool provided by IntelliJ IDEA.


Computing Bits of Algebraic Numbers

Abstract: We initiate the complexity theoretic study of the problem of computing the bits of (real) algebraic numbers. This extends the work of Yap on computing the bits of transcendental numbers like \pi, in Logspace. Our main result is that computing a bit of a fixed real algebraic number is in C=NC1\subseteq Logspace when the bit position has a verbose (unary) representation and in the counting hierarchy when it has a succinct (binary) representation. Our tools are drawn from elementary analysis and numerical analysis, and include the Newton-Raphson method. The proof of our main result is entirely elementary, preferring to use the elementary Liouville's theorem over the much deeper Roth's theorem for algebraic numbers. We leave the possibility of proving non-trivial lower bounds for the problem of computing the bits of an algebraic number given the bit position in binary, as our main open question. In this direction we show very limited progress by proving a lower bound for rationals.


Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization

Abstract: The rapid advances in the field of optimization methods in many pure and applied science pose the difficulty of keeping track of the developments as well as selecting an appropriate technique that best suits the problem in-hand. From a practitioner point of view is rightful to wander "which optimization method is the best for my problem?". Looking at the optimization process as a "system" of intercon- nected parts, in this paper are collected some ideas about how to tackle an optimization problem using a class of tools from evolutionary computations called Genetic Algorithms. Despite the number of optimization techniques available nowadays the author of this paper thinks that Genetic Algorithms still play a central role for their versatility, robustness, theoretical framework and simplicity of use. The paper can be considered a "collection of tips" (from literature and personal experience) for the non-computer-scientist that has to deal with optimization problems both in the science and engineering practice. No original methods or algorithms are proposed.


A Multipath Transport Protocol for Future Internet


A Scalable Multiclass Algorithm for Node Classification

Abstract: We introduce a scalable algorithm, MUCCA, for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a game-theoretic formulation of the problem in which the test labels are expressed as a Nash Equilibrium of a certain game. However, in order to achieve scalability, we find the equilibrium on a spanning tree of the original graph. Experiments on real-world data reveal that MUCCA is much faster than its competitors while achieving a similar predictive performance.


A note on the paper "Minimizing total tardiness on parallel machines with preemptions" by Kravchenko and Werner [2010]

Abstract: In this note, we point out two major errors in the paper "Minimizing total tardiness on parallel machines with preemptions" by Kravchenko and Werner [2010]. More precisely, they proved that both problems P|pmtn|sum(Tj) and P|rj, pj = p, pmtn|sum(Tj) are NP-Hard. We give a counter-example to their proofs, letting the complexity of these two problems open.


New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria

Abstract: In this paper, we are interested in parallel identical machine scheduling problems with preemption and release dates in case of a regular criterion to be minimized. We show that solutions having a permutation flow shop structure are dominant if there exists an optimal solution with completion times scheduled in the same order as the release dates, or if there is no release date. We also prove that, for a subclass of these problems, the completion times of all jobs can be ordered in an optimal solution. Using these two results, we provide new results on polynomially solvable problems and hence refine the boundary between P and NP for these problems.


On the Complexity of the Generalized MinRank Problem

Abstract: We study the complexity of solving the \emph{generalized MinRank problem}, i.e. computing the set of points where the evaluation of a polynomial matrix has rank at most $r$. A natural algebraic representation of this problem gives rise to a \emph{determinantal ideal}: the ideal generated by all minors of size $r+1$ of the matrix. We give new complexity bounds for solving this problem using Gr\"obner bases algorithms under genericity assumptions on the input matrix. In particular, these complexity bounds allow us to identify families of generalized MinRank problems for which the arithmetic complexity of the solving process is polynomial in the number of solutions. We also provide an algorithm to compute a rational parametrization of the variety of a 0-dimensional and radical system of bi-degree $(D,1)$. We show that its complexity can be bounded by using the complexity bounds for the generalized MinRank problem.


Subexponential fixed-parameter tractability of cluster editing

Abstract: In the Correlation Clustering, also known as Cluster Editing, we are given an undirected n-vertex graph G and a positive integer k. The task is to decide if G can be transformed into a cluster graph, i.e., a disjoint union of cliques, by changing at most k adjacencies, i.e. by adding/deleting at most k edges. We give a subexponential algorithm that, in time 2^O(sqrt(pk)) + n^O(1) decides whether G can be transformed into a cluster graph with p cliques by changing at most k adjacencies. We complement our algorithmic findings by the following tight lower bounds on the asymptotic behaviour of our algorithm. We show that, unless ETH fails, for any constant 0 < s <= 1, there is p = Theta(k^s) such that there is no algorithm deciding in time 2^o(sqrt(pk)) n^O(1) whether G can be transformed into a cluster graph with p cliques by changing at most k adjacencies.


Causality, Knowledge and Coordination in Distributed Systems

Abstract: Effecting coordination across remote sites in a distributed system is an essential part of distributed computing, and also an inherent challenge. In 1978, an analysis of communication in asynchronous systems was suggested by Leslie Lamport. Lamport's analysis determines a notion of temporal precedence, a sort of weak notion of time, which is otherwise missing in asynchronous systems. This notion has been extensively utilized in various applications. Yet the analysis is limited to systems that are asynchronous. In this thesis we go beyond by investigating causality in synchronous systems. In such systems, the boundaries of causal influence are not charted out exclusively by message passing. Here time itself, passing at a uniform (or almost uniform) rate for all processes, is also a medium by which causal influence may fan out. This thesis studies, and characterizes, the combinations of time and message passing that govern causal influence in synchronous systems. It turns out that knowledge based analysis [FHMV] provides a well tailored formal framework within which causal notions can be studied. As we show, the formal notion of knowledge is highly appropriate for characterizing causal influence in terms of information flow, broadening the analysis of Chandy and Misra in [ChM]. We define several generic classes of coordination problems that pose various temporal ordering requirements on the participating processes. These coordination problems provide natural generalizations of real life requirements. We then analyze the causal conditions that underlie suitable solutions to these problems. The analysis is conducted in two stages: first, the temporal ordering requirements are reduced to epistemic conditions. Then, these epistemic conditions are characterized in terms of the causal communication patterns that are necessary and sufficient to bring them about.


What is an OS?

Abstract: While the engineering of operating systems is well understood, their formal structure and properties are not. The latter needs a clear definition of the purpose of an OS and an identification of the core. In this paper I offer definitions of the OS, processes and files, and present a few useful principles. The principles allow us to identify work like closure and continuation algorithms, in programming languages that is useful for the OS problem. The definitions and principles should yield a symbolic, albeit semiquantitative, framework that encompasses practice. Towards that end I specialise the definitions to describe conventional OSes and identify the core operations for a single computer OS that can be used to express their algorithms. The assumptions underlying the algorithms offer the design space framework. The paging and segmentation algorithms for conventional OSes are extracted from the framework as a check. Among the insights the emerge is that an OS is a constructive proof of equivalence between models of computation. Clear and useful definitions and principles are the first step towards a fully quantitative structure of an OS.


Intractability of the Minimum-Flip Supertree problem and its variants

Abstract: Computing supertrees is a central problem in phylogenetics. The supertree method that is by far the most widely used today was introduced in 1992 and is called Matrix Representation with Parsimony analysis (MRP). Matrix Representation using Flipping (MRF)}, which was introduced in 2002, is an interesting variant of MRP: MRF is arguably more relevant that MRP and various efficient implementations of MRF have been presented. From a theoretical point of view, implementing MRF or MRP is solving NP-hard optimization problems. The aim of this paper is to study the approximability and the fixed-parameter tractability of the optimization problem corresponding to MRF, namely Minimum-Flip Supertree. We prove strongly negative results.


A Constructive Approach to Reduced-Form Auctions with Applications to Multi-Item Mechanism Design

Abstract: We provide a constructive proof of Border's theorem [Bor91, HR15a] and its generalization to reduced-form auctions with asymmetric bidders [Bor07, MV10, CKM13]. Given a reduced form, we identify a subset of Border constraints that are necessary and sufficient to determine its feasibility. Importantly, the number of these constraints is linear in the total number of bidder types. In addition, we provide a characterization result showing that every feasible reduced form can be induced by an ex-post allocation rule that is a distribution over ironings of the same total ordering of the union of all bidders' types. We show how to leverage our results for single-item reduced forms to design auctions with heterogeneous items and asymmetric bidders with valuations that are additive over items. Appealing to our constructive Border's theorem, we obtain polynomial-time algorithms for computing the revenue-optimal auction. Appealing to our characterization of feasible reduced forms, we characterize feasible multi-item allocation rules.


Self-Index based on LZ77 (thesis)

Abstract: Domains like bioinformatics, version control systems, collaborative editing systems (wiki), and others, are producing huge data collections that are very repetitive. That is, there are few differences between the elements of the collection. This fact makes the compressibility of the collection extremely high. For example, a collection with all different versions of a Wikipedia article can be compressed up to the 0.1% of its original space, using the Lempel-Ziv 1977 (LZ77) compression scheme. Many of these repetitive collections handle huge amounts of text data. For that reason, we require a method to store them efficiently, while providing the ability to operate on them. The most common operations are the extraction of random portions of the collection and the search for all the occurrences of a given pattern inside the whole collection. A self-index is a data structure that stores a text in compressed form and allows to find the occurrences of a pattern efficiently. On the other hand, self-indexes can extract any substring of the collection, hence they are able to replace the original text. One of the main goals when using these indexes is to store them within main memory. In this thesis we present a scheme for random text extraction from text compressed with a Lempel-Ziv parsing. Additionally, we present a variant of LZ77, called LZ-End, that efficiently extracts text using space close to that of LZ77. The main contribution of this thesis is the first self-index based on LZ77/LZ-End and oriented to repetitive texts, which outperforms the state of the art (the RLCSA self-index) in many aspects. Finally, we present a corpus of repetitive texts, coming from several application domains. We aim at providing a standard set of texts for research and experimentation, hence this corpus is publicly available.


TEMANEJO - a debugger for task based parallel programming models

Abstract: We present the program Temanejo, a debugger for task based parallelisation models such as StarSs. The challenge in debugging StarSs applications lies in the fact that tasks are scheduled at runtime, i.e dynamically in accordance to the data dependencies between them. Our tool assists the programmer in the debugging process by visualising the task dependency graph and allowing to control the scheduling of tasks. The toolset consists of the library Ayudame which communicates with the StarSs runtime on one side and of the debugger Temanejo on the other side which communicates with Ayudame. Temanejo provides a graphical user interface with which the application can be analysed and controlled.


Selective data collection in vehicular networks for traffic control applications

Abstract: Vehicular sensor network (VSN) is an emerging technology, which combines wireless communication offered by vehicular ad hoc networks (VANET) with sensing devices installed in vehicles. VSN creates a huge opportunity to extend the road-side sensor infrastructure of existing traffic control systems. The efficient use of the wireless communication medium is one of the basic issues in VSN applications development. This paper introduces a novel method of selective data collection for traffic control applications, which provides a significant reduction in data amounts transmitted through VSN. The underlying idea is to detect the necessity of data transfers on the basis of uncertainty determination of the traffic control decisions. According to the proposed approach, sensor data are transmitted from vehicles to the control node only at selected time moments. Data collected in VSN are processed using on-line traffic simulation technique, which enables traffic flow prediction, performance evaluation of control strategies and uncertainty estimation. If precision of the resulting information is insufficient, the optimal control strategy cannot be derived without ambiguity. As a result the control decision becomes uncertain and it is a signal informing that new traffic data from VSN are necessary to provide more precise prediction and to reduce the uncertainty of decision. The proposed method can be applied in traffic control systems of different types e.g. traffic signals, variable speed limits, and dynamic route guidance. The effectiveness of this method is illustrated in an experimental study on traffic control at signalised intersection.


Circular-Arc Cartograms

Abstract: We present a new circular-arc cartogram model in which countries are drawn as polygons with circular arcs instead of straight-line segments. Given a political map and values associated with each country in the map, a cartogram is a distorted map in which the areas of the countries are proportional to the corresponding values. In the circular-arc cartogram model straight-line segments can be replaced by circular arcs in order to modify the areas of the polygons, while the corners of the polygons remain fixed. The countries in circular-arc cartograms have the aesthetically pleasing appearance of clouds or snowflakes, depending on whether their edges are bent outwards or inwards. This makes it easy to determine whether a country has grown or shrunk, just by its overall shape. We show that determining whether a given map and given area-values can be realized as a circular-arc cartogram is an NP-hard problem. Next we describe a heuristic method for constructing circular-arc cartograms, which uses a max-flow computation on the dual graph of the map, along with a computation of the straight skeleton of the underlying polygonal decomposition. Our method is implemented and produces cartograms that, while not yet perfectly accurate, achieve many of the desired areas in our real-world examples.


Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction

Abstract: Nowadays, computer scientists have shown the interest in the study of social insect's behaviour in neural networks area for solving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data.


The Price of Matching Selfish Vertices

Abstract: We analyze the setting of minimum-cost perfect matchings with selfish vertices through the price of anarchy (PoA) and price of stability (PoS) lens. The underlying solution concept used for this analysis is the Gale-Shapley stable matching notion, where the preferences are determined so that each player (vertex) wishes to minimize the cost of her own matching edge.


On the Complexity of the Equivalence Problem for Probabilistic Automata

Abstract: Checking two probabilistic automata for equivalence has been shown to be a key problem for efficiently establishing various behavioural and anonymity properties of probabilistic systems. In recent experiments a randomised equivalence test based on polynomial identity testing outperformed deterministic algorithms. In this paper we show that polynomial identity testing yields efficient algorithms for various generalisations of the equivalence problem. First, we provide a randomized NC procedure that also outputs a counterexample trace in case of inequivalence. Second, we show how to check for equivalence two probabilistic automata with (cumulative) rewards. Our algorithm runs in deterministic polynomial time, if the number of reward counters is fixed. Finally we show that the equivalence problem for probabilistic visibly pushdown automata is logspace equivalent to the Arithmetic Circuit Identity Testing problem, which is to decide whether a polynomial represented by an arithmetic circuit is identically zero.


Towards realistic modeling of IP-level routing topology dynamics

Abstract: Many works have studied the Internet topology, but few have investigated the question of how it evolves over time. This paper focuses on the Internet routing IP-level topology and proposes a first step towards realistic modeling of its dynamics. We study periodic measurements of routing trees from a single monitor to a fixed destination set and identify invariant properties of its dynamics. We then propose a simple model for the underlying mechanisms of the topology dynamics. Simulations show that it effectively captures the observed behaviors, thus providing key insights of relevant mechanisms governing the Internet routing dynamics.


Abstracting Path Conditions for Effective Symbolic Execution

Abstract: We present an algorithm for tests generation tools based on symbolic execution. The algorithm is supposed to help in situations, when a tool is repeatedly failing to cover some code by tests. The algorithm then provides the tool a necessary condition strongly narrowing space of program paths, which must be checked for reaching the uncovered code. We also discuss integration of the algorithm into the tools and we provide experimental results showing a potential of the algorithm to be valuable in the tools, when properly implemented there.


Transformation Networks: How Innovation and the Availability of Technology can Increase Economic Performance

Abstract: A transformation network describes how one set of resources can be transformed into another via technological processes. Transformation networks in economics are useful because they can highlight areas for future innovations, both in terms of new products, new production techniques, or better efficiency. They also make it easy to detect areas where an economy might be fragile. In this paper, we use computational simulations to investigate how the density of a transformation network affects the economic performance, as measured by the gross domestic product (GDP), of an artificial economy. Our results show that on average, the GDP of our economy increases as the density of the transformation network increases. We also find that while the average performance increases, the maximum possible performance decreases and the minimum possible performance increases.


Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions

Abstract: We propose a new, nonparametric approach to estimating the value function in reinforcement learning. This approach makes use of a recently developed representation of conditional distributions as functions in a reproducing kernel Hilbert space. Such representations bypass the need for estimating transition probabilities, and apply to any domain on which kernels can be defined. Our approach avoids the need to approximate intractable integrals since expectations are represented as RKHS inner products whose computation has linear complexity in the sample size. Thus, we can efficiently perform value function estimation in a wide variety of settings, including finite state spaces, continuous states spaces, and partially observable tasks where only sensor measurements are available. A second advantage of the approach is that we learn the conditional distribution representation from a training sample, and do not require an exhaustive exploration of the state space. We prove convergence of our approach either to the optimal policy, or to the closest projection of the optimal policy in our model class, under reasonable assumptions. In experiments, we demonstrate the performance of our algorithm on a learning task in a continuous state space (the under-actuated pendulum), and on a navigation problem where only images from a sensor are observed. We compare with least-squares policy iteration where a Gaussian process is used for value function estimation. Our algorithm achieves better performance in both tasks.


Simulation-based study of MPTCP (Multipath TCP)

Abstract: Our current world is revolutionned by the networks which are interconnecting any machine to any other one. Nowadays equipments are plugged to the network by the way of many different network adapters (Ethernet, wifi, GSM), and network equipments are more and more interconnected to each other creating a highly mesh network. Thus, redundant paths are created between any two endpoints enabling multipath or the use of multiple paths to handle a communication. The benefits of multipath are the enhancement of robustness against failure, and minimizing communication cost. In the present work, we will talk about some solutions of multipath applied to the transport level. We will expose the most important problem facing multipath which is packet reordering, and we will also expose results of our simulations.


NACS: non-overlapping AP's caching scheme to reduce handoff in 802.11 wireless LAN

Abstract: With the escalation of the IEEE 802.11 based wireless networks, voice over IP and analogous applications are also used over wireless networks. Recently, the wireless LAN systems are spaciously deployed for public Internet services. In public wireless LAN systems, reliable user authentication and mobility support are indispensable issues. When a mobile device budges out the range of one access point (AP) and endeavor to connect to new AP, it performs handoff. Contemporarily, PNC and SNC were proposed to propagate the MN context to the entire neighboring AP's on the wireless network with the help of neighbor graph. In this paper, we proposed a non-overlapping AP's caching scheme (NACS), which propagates the mobile node context to those AP's which do not overlap with the current AP. To capture the topology of non-overlapping AP's in the wireless network, non-overlapping graph (NOG) is generated at each AP. Simulation results shows that NACS reduces the signaling cost of propagating the MN context to the neighbor AP's in the wireless network.


Unfolding Orthogonal Polyhedra with Quadratic Refinement: The Delta-Unfolding Algorithm

Abstract: We show that every orthogonal polyhedron homeomorphic to a sphere can be unfolded without overlap while using only polynomially many (orthogonal) cuts. By contrast, the best previous such result used exponentially many cuts. More precisely, given an orthogonal polyhedron with n vertices, the algorithm cuts the polyhedron only where it is met by the grid of coordinate planes passing through the vertices, together with Theta(n^2) additional coordinate planes between every two such grid planes.


3D Finite Element Analysis of HMA Overlay Mix Design to Control Reflective Cracking

Abstract: This study examines the effectiveness of HMA overlay design strategies for the purpose of controlling the development of reflective cracking. A parametric study was conducted using a 3D Finite Element (FE) model of a rigid pavement section including Linear Viscoelastic (LVE) material properties for the Hot Mix Asphalt (HMA) overlay and non-uniform tire-pavement contact stresses. Several asphalt mixtures were tested in the surface, intermediate, and leveling course of the HMA overlay. Results obtained show that no benefits can be anticipated by using either Polymer-Modified (PM) or Dense-Graded (DG) mixtures instead of Standard Binder (SB) mixtures in the surface or intermediate course. For the leveling course, the use of a PM asphalt binder was found beneficial in terms of mitigating reflective cracking. As compared to the SB mix, the use of PM asphalt mixture in the leveling course reduced the level of longitudinal tensile stress at the bottom of the HMA overlay above the PCC joint by approximately 30%.


Deciding Whether a Regular Language is Generated by a Splicing System

Abstract: Splicing as a binary word/language operation is inspired by the DNA recombination under the action of restriction enzymes and ligases, and was first introduced by Tom Head in 1987. Shortly thereafter, it was proven that the languages generated by (finite) splicing systems form a proper subclass of the class of regular languages. However, the question of whether or not one can decide if a given regular language is generated by a splicing system remained open. In this paper we give a positive answer to this question. Namely, we prove that, if a language is generated by a splicing system, then it is also generated by a splicing system whose size is a function of the size of the syntactic monoid of the input language, and which can be effectively constructed.


A Unit Commitment Model with Demand Response for the Integration of Renewable Energies

Abstract: The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast of output its error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms and analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increases with the forecast error. The effect of a sudden decrease in wind power is also analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total operation cost significantly; however the substitution of thermal power plants by wind farms or PV systems is not expected to be very high. Finally, the effects of the demand response in the case of a sudden decrease in wind power are analyzed. We confirm that the number of operating thermal power plants is reduced by the demand response. A power utility has to continue thermal power plants for ensuring supply-demand balance; some of these plants can be decommissioned after installing a large number of wind farms or PV systems, if the demand response is applied using an appropriate price structure.


Cryptanalyzing a chaos-based image encryption algorithm using alternate structure

Abstract: Recently, a chaos-based image encryption algorithm using alternate structure (IEAS) was proposed. This paper focuses on differential cryptanalysis of the algorithm and finds that some properties of IEAS can support a differential attack to recover equivalent secret key with a little small number of known plain-images. Detailed approaches of the cryptanalysis for cryptanalyzing IEAS of the lower round number are presented and the breaking method can be extended to the case of higher round number. Both theoretical analysis and experiment results are provided to support vulnerability of IEAS against differential attack. In addition, some other security defects of IEAS, including insensitivity with respect to changes of plain-images and insufficient size of key space, are also reported.


Combining Adaptive Coding and Modulation with Hierarchical Modulation in Satcom Systems

Abstract: We investigate the design of a broadcast system in order to maximise the throughput. This task is usually challenging due to the channel variability. Forty years ago, Cover introduced and compared two schemes: time sharing and superposition coding. Even if the second scheme was proved to be optimal for some channels, modern satellite communications systems such as DVB-SH and DVB-S2 mainly rely on time sharing strategy to optimize the throughput. They consider hierarchical modulation, a practical implementation of superposition coding, but only for unequal error protection or backward compatibility purposes. We propose in this article to combine time sharing and hierarchical modulation together and show how this scheme can improve the performance in terms of available rate. We introduce the hierarchical 16-APSK to boost the performance of the DVB-S2 standard. We also evaluate various strategies to group the receivers in pairs when using hierarchical modulation. Finally, we show in a realistic use case based on DVB-S2 that the combined scheme can provide throughput gains greater than 10% compared to the best time sharing strategy.


Coded Path Protection: Efficient Conversion of Sharing to Coding

Abstract: Link failures in wide area networks are common and cause significant data losses. Mesh-based protection schemes offer high capacity efficiency but they are slow and require complex signaling. Additionally, real-time reconfiguration of a cross-connect threatens their transmission integrity. On the other hand, coding-based protection schemes are proactive. Therefore, they have higher restoration speed, lower signaling complexity, and higher transmission integrity. This paper introduces a coding-based protection scheme, named Coded Path Protection (CPP). In CPP, a backup copy of the primary data is encoded with other data streams, resulting in capacity savings. This paper presents an optimal and simple capacity placement and coding group formation algorithm. The algorithm converts the sharing structure of any solution of a Shared Path Protection (SPP) technique into a coding structure with minimum extra capacity. We conducted quantitative and qualitative comparisons of our technique with the SPP and, another technique, known as p-cycle protection. Simulation results confirm that the CPP is significantly faster than the SPP and p-cycle techniques. CPP incurs marginal extra capacity on top of SPP. Its capacity efficiency is lower than the p-cycle technique for dense networks but can be higher for sparse networks. In addition, unlike p-cycle protection, CPP is inherently suitable for the wavelength continuity constraint in optical networks.


Analysis of Bitcoin Pooled Mining Reward Systems

Abstract: In this paper we describe the various scoring systems used to calculate rewards of participants in Bitcoin pooled mining, explain the problems each were designed to solve and analyze their respective advantages and disadvantages.


Online Proceedings of the 11th International Colloquium on Implementation of Constraint LOgic Programming Systems (CICLOPS 2011), Lexington, KY, U.S.A., July 10, 2011

Abstract: These are the revised versions of the papers presented at CICLOPS 2011, a workshop colocated with ICLP 2011.


Lazy Pointer Analysis

Abstract: Flow- and context-sensitive pointer analysis is generally considered too expensive for large programs; most tools relax one or both of the requirements for scalability. We formulate a flow- and context-sensitive points-to analysis that is lazy in the following sense: points-to information is computed only for live pointers and its propagation is sparse (restricted to live ranges of respective pointers). Further, our analysis (a) uses strong liveness, effectively including dead code elimination; (b) afterwards calculates must-points-to information from may-points-to information instead of using a mutual fixed-point; and (c) uses value-based termination of call strings during interprocedural analysis (which reduces the number of call strings significantly). A naive implementation of our analysis within GCC-4.6.0 gave analysis time and size of points-to measurements for SPEC2006. Using liveness reduced the amount of points-to information by an order of magnitude with no loss of precision. For all programs under 30kLoC we found that the results were much more precise than gcc's analysis. What comes as a pleasant surprise however, is the fact that below this cross-over point, our naive linked-list implementation is faster than a flow- and context-insensitive analysis which is primarily used for efficiency. We speculate that lazy flow- and context-sensitive analyses may be not only more precise, but also more efficient, than current approaches.


Quest-V: A Virtualized Multikernel for High-Confidence Systems

Abstract: This paper outlines the design of `Quest-V', which is implemented as a collection of separate kernels operating together as a distributed system on a chip. Quest-V uses virtualization techniques to isolate kernels and prevent local faults from affecting remote kernels. This leads to a high-confidence multikernel approach, where failures of system subcomponents do not render the entire system inoperable. A virtual machine monitor for each kernel keeps track of shadow page table mappings that control immutable memory access capabilities. This ensures a level of security and fault tolerance in situations where a service in one kernel fails, or is corrupted by a malicious attack. Communication is supported between kernels using shared memory regions for message passing. Similarly, device driver data structures are shareable between kernels to avoid the need for complex I/O virtualization, or communication with a dedicated kernel responsible for I/O. In Quest-V, device interrupts are delivered directly to a kernel, rather than via a monitor that determines the destination. Apart from bootstrapping each kernel, handling faults and managing shadow page tables, the monitors are not needed. This differs from conventional virtual machine systems in which a central monitor, or hypervisor, is responsible for scheduling and management of host resources amongst a set of guest kernels. In this paper we show how Quest-V can implement novel fault isolation and recovery techniques that are not possible with conventional systems. We also show how the costs of using virtualization for isolation of system services does not add undue overheads to the overall system performance.


Comments on "Capacity with explicit delay guarantees for generic sources over correlated Rayleigh channel"

Abstract: We address a major flaw in the abovementioned paper, which proposes to calculate effective capacity of random channels by the use of central limit theorem. We analytically show that the authors are incorrect in finding the effective capacity by first taking the limit of cumulative random process rather than taking the limit of moment generating function of the same process. We later quantify our results over a correlated ON-OFF process.


Tight Bounds for Distributed Functional Monitoring

Abstract: We resolve several fundamental questions in the area of distributed functional monitoring, initiated by Cormode, Muthukrishnan, and Yi (SODA, 2008). In this model there are $k$ sites each tracking their input and communicating with a central coordinator that continuously maintain an approximate output to a function $f$ computed over the union of the inputs. The goal is to minimize the communication. We show the randomized communication complexity of estimating the number of distinct elements up to a $1+\eps$ factor is $\tilde{\Omega}(k/\eps^2)$, improving the previous $\Omega(k + 1/\eps^2)$ bound and matching known upper bounds up to a logarithmic factor. For the $p$-th frequency moment $F_p$, $p > 1$, we improve the previous $\Omega(k + 1/\eps^2)$ communication bound to $\tilde{\Omega}(k^{p-1}/\eps^2)$. We obtain similar improvements for heavy hitters, empirical entropy, and other problems. We also show that we can estimate $F_p$, for any $p > 1$, using $\tilde{O}(k^{p-1}\poly(\eps^{-1}))$ communication. This greatly improves upon the previous $\tilde{O}(k^{2p+1}N^{1-2/p} \poly(\eps^{-1}))$ bound of Cormode, Muthukrishnan, and Yi for general $p$, and their $\tilde{O}(k^2/\eps + k^{1.5}/\eps^3)$ bound for $p = 2$. For $p = 2$, our bound resolves their main open question. Our lower bounds are based on new direct sum theorems for approximate majority, and yield significant improvements to problems in the data stream model, improving the bound for estimating $F_p, p > 2,$ in $t$ passes from $\tilde{\Omega}(n^{1-2/p}/(\eps^{2/p} t))$ to $\tilde{\Omega}(n^{1-2/p}/(\eps^{4/p} t))$, giving the first bound for estimating $F_0$ in $t$ passes of $\Omega(1/(\eps^2 t))$ bits of space that does not use the gap-hamming problem.


Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU

Abstract: In this paper we present a new pseudorandom number generator (PRNG) on graphics processing units (GPU). This PRNG is based on the so-called chaotic iterations. It is firstly proven to be chaotic according to the Devaney's formulation. We thus propose an efficient implementation for GPU that successfully passes the BigCrush tests, deemed to be the hardest battery of tests in TestU01. Experiments show that this PRNG can generate about 20 billion of random numbers per second on Tesla C1060 and NVidia GTX280 cards. It is then established that, under reasonable assumptions, the proposed PRNG can be cryptographically secure. A chaotic version of the Blum-Goldwasser asymmetric key encryption scheme is finally proposed.


A Complexity Approach for Steganalysis

Abstract: In this proposal for the Journ\`ees Codes et St\'eganographie 2012, we define a new rigorous approach for steganalysis based on the complexity theory. It is similar to the definitions of security that can be found for hash functions, PRNG, and so on. We propose here a notion of \emph{secure hiding} and we give a first secure hiding scheme.


Combining One-Class Classifiers via Meta-Learning

Abstract: Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.


Solving simple stochastic games with few coin toss positions

Abstract: Gimbert and Horn gave an algorithm for solving simple stochastic games with running time O(r! n) where n is the number of positions of the simple stochastic game and r is the number of its coin toss positions. Chatterjee et al. pointed out that a variant of strategy iteration can be implemented to solve this problem in time 4^r r^{O(1)} n^{O(1)}. In this paper, we show that an algorithm combining value iteration with retrograde analysis achieves a time bound of O(r 2^r (r log r + n)), thus improving both time bounds. While the algorithm is simple, the analysis leading to this time bound is involved, using techniques of extremal combinatorics to identify worst case instances for the algorithm.


Observability of Strapdown INS Alignment: A Global Perspective

Abstract: Alignment of the strapdown inertial navigation system (INS) has strong nonlinearity, even worse when maneuvers, e.g., tumbling techniques, are employed to improve the alignment. There is no general rule to attack the observability of a nonlinear system, so most previous works addressed the observability of the corresponding linearized system by implicitly assuming that the original nonlinear system and the linearized one have identical observability characteristics. Strapdown INS alignment is a nonlinear system that has its own characteristics. Using the inherent properties of strapdown INS, e.g., the attitude evolution on the SO(3) manifold, we start from the basic definition and develop a global and constructive approach to investigate the observability of strapdown INS static and tumbling alignment, highlighting the effects of the attitude maneuver on observability. We prove that strapdown INS alignment, considering the unknown constant sensor biases, will be completely observable if the strapdown INS is rotated successively about two different axes and will be nearly observable for finite known unobservable states (no more than two) if it is rotated about a single axis. Observability from a global perspective provides us with insights into and a clearer picture of the problem, shedding light on previous theoretical results on strapdown INS alignment that were not comprehensive or consistent.. The reporting of inconsistencies calls for a review of all linearization-based observability studies in the vast literature. Extensive simulations with constructed ideal observers and an extended Kalman filter are carried out, and the numerical results accord with the analysis. The conclusions can also assist in designing the optimal tumbling strategy and the appropriate state observer in practice to maximize the alignment performance.


On Position Translation Vector

Abstract: The paper derives a new "position translation vector" (PTV) with remarkably simpler rate equation, and proves its connections with Savage's PTV.


Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy

Abstract: After the discovery that fixed points of loopy belief propagation coincide with stationary points of the Bethe free energy, several researchers proposed provably convergent algorithms to directly minimize the Bethe free energy. These algorithms were formulated only for non-zero temperature (thus finding fixed points of the sum-product algorithm) and their possible extension to zero temperature is not obvious. We present the zero-temperature limit of the double-loop algorithm by Heskes, which converges a max-product fixed point. The inner loop of this algorithm is max-sum diffusion. Under certain conditions, the algorithm combines the complementary advantages of the max-product belief propagation and max-sum diffusion (LP relaxation): it yields good approximation of both ground states and max-marginals.


POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

Abstract: Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. The novel algorithmic framework POWERPLAY (2011) continually searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Wow-effects are achieved by continually making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. POWERPLAY's search orders candidate pairs of tasks and solver modifications by their conditional computational (time & space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. The computational costs of validating new tasks need not grow with task repertoire size. POWERPLAY's ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Goedel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. POWERPLAY may be viewed as a greedy but practical implementation of basic principles of creativity. A first experimental analysis can be found in separate papers [53,54].


A Preprocessor Based on Clause Normal Forms and Virtual Substitutions to Parallelize Cylindrical Algebraic Decomposition

Abstract: The Cylindrical Algebraic Decomposition (CAD) algorithm is a comprehensive tool to perform quantifier elimination over real closed fields. CAD has doubly exponential running time, making it infeasible for practical purposes. We propose to use the notions of clause normal forms and virtual substitutions to develop a preprocessor for CAD, that will enable an input-level parallelism. We study the performance of CAD in the presence of the preprocessor by extensive experimentation. Since parallelizability of CAD depends on the structure of given prenex formula, we introduce some structural notions to study the performance of CAD with the proposed preprocessor.


2P-Med: Building a Personalization Platform for Mediation Systems

Abstract: Nowadays, with the increasing number of integrated data sources, there is a real trend to personalize mediation systems to improve user satisfaction. To make these systems user sensitive, we propose a personalization platform called 2P-Med. 2P-Med allows personalizing any mediation system used in any domain following a cyclic process. The process includes building and managing adequate user profiles and sources profiles, content and quality matching, source selection, adapting the mediator responses to user preferences and handling user feedbacks. In this paper, we describe 2P-Med architecture and highlight its main functionalities. We also illustrate the operation of the platform through personalizing source selection in a travel planning assistant.


Enhancing Support for Knowledge Works: A relatively unexplored vista of computing research

Abstract: Let us envision a new class of IT systems, the "Support Systems for Knowledge Works" or SSKW. An SSKW can be defined as a system built for providing comprehensive support to human knowledge-workers while performing instances of complex knowledge-works of a particular type within a particular domain of professional activities To get an idea what an SSKW-enabled work environment can be like, let us look into a hypothetical scenario that depicts the interaction between a physician and a patient-care SSKW during the activity of diagnosing a patient.


Improving the Efficiency of Approximate Inference for Probabilistic Logical Models by means of Program Specialization

Abstract: We consider the task of performing probabilistic inference with probabilistic logical models. Many algorithms for approximate inference with such models are based on sampling. From a logic programming perspective, sampling boils down to repeatedly calling the same queries on a knowledge base composed of a static part and a dynamic part. The larger the static part, the more redundancy there is in these repeated calls. This is problematic since inefficient sampling yields poor approximations. We show how to apply logic program specialization to make sampling-based inference more efficient. We develop an algorithm that specializes the definitions of the query predicates with respect to the static part of the knowledge base. In experiments on real-world data we obtain speedups of up to an order of magnitude, and these speedups grow with the data-size.


AdCell: Ad Allocation in Cellular Networks

Abstract: With more than four billion usage of cellular phones worldwide, mobile advertising has become an attractive alternative to online advertisements. In this paper, we propose a new targeted advertising policy for Wireless Service Providers (WSPs) via SMS or MMS- namely {\em AdCell}. In our model, a WSP charges the advertisers for showing their ads. Each advertiser has a valuation for specific types of customers in various times and locations and has a limit on the maximum available budget. Each query is in the form of time and location and is associated with one individual customer. In order to achieve a non-intrusive delivery, only a limited number of ads can be sent to each customer. Recently, new services have been introduced that offer location-based advertising over cellular network that fit in our model (e.g., ShopAlerts by AT&T) . We consider both online and offline version of the AdCell problem and develop approximation algorithms with constant competitive ratio. For the online version, we assume that the appearances of the queries follow a stochastic distribution and thus consider a Bayesian setting. Furthermore, queries may come from different distributions on different times. This model generalizes several previous advertising models such as online secretary problem \cite{HKP04}, online bipartite matching \cite{KVV90,FMMM09} and AdWords \cite{saberi05}. ...


Cache-Oblivious Implicit Predecessor Dictionaries with the Working Set Property

Abstract: In this paper we present an implicit dynamic dictionary with the working-set property, supporting insert(e) and delete(e) in O(log n) time, predecessor(e) in O(log l_{p(e)}) time, successor(e) in O(log l_{s(e)}) time and search(e) in O(log min(l_{p(e)},l_{e}, l_{s(e)})) time, where n is the number of elements stored in the dictionary, l_{e} is the number of distinct elements searched for since element e was last searched for and p(e) and s(e) are the predecessor and successor of e, respectively. The time-bounds are all worst-case. The dictionary stores the elements in an array of size n using no additional space. In the cache-oblivious model the log is base B and the cache-obliviousness is due to our black box use of an existing cache-oblivious implicit dictionary. This is the first implicit dictionary supporting predecessor and successor searches in the working-set bound. Previous implicit structures required O(log n) time.


A Study on Using Uncertain Time Series Matching Algorithms in MapReduce Applications

Abstract: In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Moreover, under a hypothesis, another algorithm is proposed to classify applications under similar CPU utilization patterns. Three widely used text processing applications (WordCount, Distributed Grep, and Terasort) and another application (Exim Mainlog parsing) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on 5-node Map-Reduce platform


Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation

Abstract: Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia "Fermi" class of GPGPUs. A new "padded jagged diagonals storage" (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme. In our test scenarios the pJDS format cuts the overall spMVM memory footprint on the GPGPU by up to 70%, and achieves 95% to 130% of the ELLPACK-R performance. Using a suitable performance model we identify performance bottlenecks on the node level that invalidate some types of matrix structures for efficient multi-GPGPU parallelization. For appropriate sparsity patterns we extend previous work on distributed-memory parallel spMVM to demonstrate a scalable hybrid MPI-GPGPU code, achieving efficient overlap of communication and computation.


Optimal Inverter VAR Control in Distribution Systems with High PV Penetration

Abstract: The intent of the study detailed in this paper is to demonstrate the benefits of inverter var control on a fast timescale to mitigate rapid and large voltage fluctuations due to the high penetration of photovoltaic generation and the resulting reverse power flow. Our approach is to formulate the volt/var control as a radial optimal power flow (OPF) problem to minimize line losses and energy consumption, subject to constraints on voltage magnitudes. An efficient solution to the radial OPF problem is presented and used to study the structure of optimal inverter var injection and the net benefits, taking into account the additional cost of inverter losses when operating at non-unity power factor. This paper will illustrate how, depending on the circuit topology and its loading condition, the inverter's optimal reactive power injection is not necessarily monotone with respect to their real power output. The results are demonstrated on a distribution feeder on the Southern California Edison system that has a very light load and a 5 MW photovoltaic (PV) system installed away from the substation.


A Study of CAPTCHAs for Securing Web Services

Abstract: Atomizing various Web activities by replacing human to human interactions on the Internet has been made indispensable due to its enormous growth. However, bots also known as Web-bots which have a malicious intend and pretending to be humans pose a severe threat to various services on the Internet that implicitly assume a human interaction. Accordingly, Web service providers before allowing access to such services use various Human Interaction Proof's (HIPs) to authenticate that the user is a human and not a bot. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a class of HIPs tests and are based on Artificial Intelligence. These tests are easier for humans to qualify and tough for bots to simulate. Several Web services use CAPTCHAs as a defensive mechanism against automated Web-bots. In this paper, we review the existing CAPTCHA schemes that have been proposed or are being used to protect various Web services. We classify them in groups and compare them with each other in terms of security and usability. We present general method used to generate and break text-based and image-based CAPTCHAs. Further, we discuss various security and usability issues in CAPTCHA design and provide guidelines for improving their robustness and usability.


Detecting Threat E-mails using Bayesian Approach

Abstract: Fraud and terrorism have a close connect in terms of the processes that enables and promote them. In the era of Internet, its various services that include Web, e-mail, social networks, blogs, instant messaging, chats, etc. are used in terrorism not only for communication but also for i) creation of ideology, ii) resource gathering, iii) recruitment, indoctrination and training, iv) creation of terror network, and v) information gathering. A major challenge for law enforcement and intelligence agencies is efficient and accurate gathering of relevant and growing volume of crime data. This paper reports on use of established Na\"ive Bayesian filter for classification of threat e-mails. Efficiency in filtering threat e-mail by use of three different Na\"ive Bayesian filter approaches i.e. single keywords, weighted multiple keywords and weighted multiple keywords with keyword context matching are evaluated on a threat e-mail corpus created by extracting data from sources that are very close to terrorism.


SPAM -- Technological and Legal Aspects

Abstract: In this paper an attempt is made to review technological, economical and legal aspects of the spam in detail. The technical details will include different techniques of spam control e.g., filtering techniques, Genetic Algorithm, Memory Based Classifier, Support Vector Machine Method, etc. The economic aspect includes Shaping/Rate Throttling Approach/Economic Filtering and Pricing/Payment based spam control. Finally, the paper discusses the legal provisions for the control of spam. The scope of the legal options is limited to USA, European Union, New Zealand, Canada, Britain and Australia.


Tight lower bounds for online labeling problem

Abstract: We consider the file maintenance problem (also called the online labeling problem) in which n integer items from the set {1,...,r} are to be stored in an array of size m >= n. The items are presented sequentially in an arbitrary order, and must be stored in the array in sorted order (but not necessarily in consecutive locations in the array). Each new item must be stored in the array before the next item is received. If r<=m then we can simply store item j in location j but if r>m then we may have to shift the location of stored items to make space for a newly arrived item. The algorithm is charged each time an item is stored in the array, or moved to a new location. The goal is to minimize the total number of such moves done by the algorithm. This problem is non-trivial when n=<m<r. In the case that m=Cn for some C>1, algorithms for this problem with cost O(log(n)^2) per item have been given [IKR81, Wil92, BCD+02]. When m=n, algorithms with cost O(log(n)^3) per item were given [Zha93, BS07]. In this paper we prove lower bounds that show that these algorithms are optimal, up to constant factors. Previously, the only lower bound known for this range of parameters was a lower bound of \Omega(log(n)^2) for the restricted class of smooth algorithms [DSZ05a, Zha93]. We also provide an algorithm for the sparse case: If the number of items is polylogarithmic in the array size then the problem can be solved in amortized constant time per item.


Discretization of Parametrizable Signal Manifolds

Abstract: Transformation-invariant analysis of signals often requires the computation of the distance from a test pattern to a transformation manifold. In particular, the estimation of the distances between a transformed query signal and several transformation manifolds representing different classes provides essential information for the classification of the signal. In many applications the computation of the exact distance to the manifold is costly, whereas an efficient practical solution is the approximation of the manifold distance with the aid of a manifold grid. In this paper, we consider a setting with transformation manifolds of known parameterization. We first present an algorithm for the selection of samples from a single manifold that permits to minimize the average error in the manifold distance estimation. Then we propose a method for the joint discretization of multiple manifolds that represent different signal classes, where we optimize the transformation-invariant classification accuracy yielded by the discrete manifold representation. Experimental results show that sampling each manifold individually by minimizing the manifold distance estimation error outperforms baseline sampling solutions with respect to registration and classification accuracy. Performing an additional joint optimization on all samples improves the classification performance further. Moreover, given a fixed total number of samples to be selected from all manifolds, an asymmetric distribution of samples to different manifolds depending on their geometric structures may also increase the classification accuracy in comparison with the equal distribution of samples.


Learning Smooth Pattern Transformation Manifolds

Abstract: Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. In order to construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold building problem, namely, approximation and classification. For the approximation problem, we propose a greedy method that constructs a representative pattern by selecting analytic atoms from a continuous dictionary manifold. We present a DC (Difference-of-Convex) optimization scheme that is applicable to a wide range of transformation and dictionary models, and demonstrate its application to transformation manifolds generated by rotation, translation and anisotropic scaling of a reference pattern. Then, we generalize this approach to a setting with multiple transformation manifolds, where each manifold represents a different class of signals. We present an iterative multiple manifold building algorithm such that the classification accuracy is promoted in the learning of the representative patterns. Experimental results suggest that the proposed methods yield high accuracy in the approximation and classification of data compared to some reference methods, while the invariance to geometric transformations is achieved due to the transformation manifold model.


Abstracting Path Conditions

Abstract: We present a symbolic-execution-based algorithm that for a given program and a given program location produces a nontrivial necessary condition on input values to drive the program execution to the given location. We also propose an application of necessary conditions in contemporary bug-finding and test-generation tools. Experimental results show that the presented technique can significantly improve performance of the tools.


Transferring Voice using SMS over GSM Network

Abstract: The paper presents a methodology of transmitting voice in SMS (Short Message Service) over GSM network. Usually SMS contents are text based and limited to 140 bytes. It supports national and international roaming, but also supported by other telecommunication such as TDMA (Time Division Multiple Access), CDMA (Code Division Multiple Access) as well. It can sent/ receive simultaneously with other services. Such features make it favorable for this methodology. For this an application is developed using J2ME platform which is supported by all mobile phones in the world. This algorithm's test is conducted on N95 having Symbian Operating System (OS).


A Comprehensive Performance Analysis of Proactive, Reactive and Hybrid MANETs Routing Protocols

Abstract: A mobile Ad-hoc network (MANET) is a dynamic multi hop wireless network established by a group of nodes in which there is no central administration. Due to mobility of nodes and dynamic network topology, the routing is one of the most important challenges in ad-hoc networks. Several routing algorithms for MANETs have been proposed by the researchers which have been classified into various categories, however, the most prominent categories are proactive, reactive and hybrid. The performance comparison of routing protocols for MANETs has been presented by other researcher also, however, none of these works considers proactive, reactive and hybrid protocols together. In this paper, the performance of proactive (DSDV), reactive (DSR and AODV) and hybrid (ZRP) routing protocols has been compared. The performance differentials are analyzed on the basis of throughput, average delay, routing overhead and number of packets dropped with a variation of number of nodes, pause time and mobility.


Rank-profile revealing Gaussian elimination and the CUP matrix decomposition

Abstract: Transforming a matrix over a field to echelon form, or decomposing the matrix as a product of structured matrices that reveal the rank profile, is a fundamental building block of computational exact linear algebra. This paper surveys the well known variations of such decompositions and transformations that have been proposed in the literature. We present an algorithm to compute the CUP decomposition of a matrix, adapted from the LSP algorithm of Ibarra, Moran and Hui (1982), and show reductions from the other most common Gaussian elimination based matrix transformations and decompositions to the CUP decomposition. We discuss the advantages of the CUP algorithm over other existing algorithms by studying time and space complexities: the asymptotic time complexity is rank sensitive, and comparing the constants of the leading terms, the algorithms for computing matrix invariants based on the CUP decomposition are always at least as good except in one case. We also show that the CUP algorithm, as well as the computation of other invariants such as transformation to reduced column echelon form using the CUP algorithm, all work in place, allowing for example to compute the inverse of a matrix on the same storage as the input matrix.


Web Accessibility - A timely recognized challenge

Abstract: Web Accessibility for disabled people has posed a challenge to the civilized societies that claim to uphold the principles of equal opportunity and nondiscrimination. Certain concrete measures have been taken to narrow down the digital divide between normal and disabled users of Internet technology. The efforts have resulted in enactment of legislations and laws and mass awareness about the discriminatory nature of the accessibility issue, besides the efforts have resulted in the development of commensurate technological tools to develop and test the Web accessibility. World Wide Web consortium's (W3C) Web Accessibility Initiative (WAI) has framed a comprehensive document comprising of set of guidelines to make the Web sites accessible to the users with disabilities. This paper is about the issues and aspects surrounding Web Accessibility. The details and scope are kept limited to comply with the aim of the paper which is to create awareness and to provide basis for in-depth investigation.


Phishing - A Growing Threat to E-Commerce

Abstract: In today's business environment, it is difficult to imagine a workplace without access to the web, yet a variety of email born viruses, spyware, adware, Trojan horses, phishing attacks, directory harvest attacks, DoS attacks, and other threats combine to attack businesses and customers. This paper is an attempt to review phishing - a constantly growing and evolving threat to Internet based commercial transactions. Various phishing approaches that include vishing, spear phishng, pharming, keyloggers, malware, web Trojans, and others will be discussed. This paper also highlights the latest phishing analysis made by Anti-Phishing Working Group (APWG) and Korean Internet Security Center.


Security Survey of Internet Browsers Data Managers

Abstract: The paper analyses current versions of top three used Internet browsers and compare their security levels to a research done in 2006. The security is measured by analyzing how user data is stored. Data recorded during different browsing sessions and by different password management functions it is considered sensitive data. The paper describes how the browser protects the sensitive data and how an attacker or a forensic analyst can access it.


Semantics and Algorithms for Parametric Monitoring

Abstract: Analysis of execution traces plays a fundamental role in many program analysis approaches, such as runtime verification, testing, monitoring, and specification mining. Execution traces are frequently parametric, i.e., they contain events with parameter bindings. Each parametric trace usually consists of many meaningful trace slices merged together, each slice corresponding to one parameter binding. This gives a semantics-based solution to parametric trace analysis. A general-purpose parametric trace slicing technique is introduced, which takes each event in the parametric trace and dispatches it to its corresponding trace slices. This parametric trace slicing technique can be used in combination with any conventional, non-parametric trace analysis technique, by applying the later on each trace slice. As an instance, a parametric property monitoring technique is then presented. The presented parametric trace slicing and monitoring techniques have been implemented and extensively evaluated. Measurements of runtime overhead confirm that the generality of the discussed techniques does not come at a performance expense when compared with existing parametric trace monitoring systems.


Dynamic Composition of Evolving Process Types

Abstract: Classical approaches like process algebras or labelled transition systems deal with static composition to model non-trivial concurrent or distributed systems; this is not sufficient for systems with dynamic architecture and with variable number of components. We introduce a method to guide the modelling and the dynamic composition of processes to build large distributed systems with dynamic adhoc architecture. The modelling and the composition are based on an event-based approach that favour the decoupling of the system components. The composition uses the sharing of abstract communication channels. The method is appropriate to deal with evolving processes (with mobility, mutation). The event-B method is used for practical support. A fauna and its evolution are considered as a working system; this system presents some specificities, its behaviour is not foreseeable, it has an adhoc (not statically fixed) architecture.


An Overview of Context-Aware Vertical Handover Schemes in Heterogeneous Networks

Abstract: As wireless access technologies grow rapidly, the recent studies have focused on granting mobile users the ability of roaming across different wireless networks in a seamless manner thus offering seamless mobility. The different characteristics of each wireless technology with regards to QoS brought many challenges for provisioning the continuous services (audio/video streaming) in a seamless way. In this paper, we intend to review the existing context-aware methods which offered solutions for service continuity. We looked at the types of context information used in each solution. Through this study, it is clear that context awareness plays a significant role in handover process in order to satisfy users demanding seamless services. Therefore, the goal of this paper is to compare the existing methods grouped as general, IMS based, and WLAN/WiMAX solutions in terms of several criteria, such as interworking architecture, service continuity, and QoS provisioning.


A study on Modeling of Dependency between Configuration Parameters and Overall Energy Consumption in Wireless Sensor Network (WSN)

Abstract: In this paper, we study a new approach to model the overall Energy Consumption (EC) in Wireless Sensor Networks (WSN). First, we extract parameters involving in the EC of WSNs. The dependency between configuration parameters and the average residual energy of a specific application is then investigated. Our approach has three key steps: profiling, parameter reduction, and modeling. In profiling, a sensor network simulator is re-run 800 times with different values of the configuration parameters in order to profile the average residual energy in nodes. In the parameter reduction, three statistical analyses (p-value, linear and non-linear correlation) are applied to the outcome of profiled experiments in order to separate the effective parameters on WSN residual energy. Finally, linear regression is used to model the relation between the chosen effective parameters and the residual energy. The evaluation based on running the simulator for another 200 times with different values of the effective parameters shows that the model can predict the residual energy of nodes in WSN with average error of less than 13%.


Modeling overall energy consumption in Wireless Sensor Networks

Abstract: Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. Optimizing a specific component of the total energy cost does not help in reducing the total energy cost as this reduction may be negated by an increase in the energy consumption of other components of the application. Recently we proposed Hierarchy Energy Driven Architecture as a robust architecture that takes into account all principal energy constituents of wireless sensor network applications. Based on the proposed architecture, this paper presents a single overall model and proposes a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of a wireless sensor network application, optimizing its constituent's operations, and designing more energy-efficient applications. The paper also presents simulation results to demonstrate the feasibility of our model and energy formulation


Informatics Perspectives on Decision Taking, a Case Study on Resolving Process Product Ambiguity

Abstract: A decision is an act or event of decision taking. Decision making always includes decision taking, the latter not involving significant exchanges with non-deciding agents. A decision outcome is a piece of storable information constituting the result of a decision. Decision outcomes are typed, for instance: plan, command, assertion, or boolean reply to a question. Decision outcomes are seen by an audience and autonomous actions from the audience is supposed to realize the putting into effect of a decision outcome, thus leading to so-called decision effects. Decision outcomes are supposedly expected by the decider. Using a model or a theory concerning the causal chain leading from a decision outcome to one or more decision effects may support a decision taker decision taker in predicting plausible decision effects for candidate decision outcomes. Decision taking is positioned amidst many related notions including: decision making, decision process, decision making process, decision process making, decision engineering, decision progression, and decision progression production.


Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models

Abstract: A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the non-adaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.


Memory-Constrained Algorithms for Simple Polygons

Abstract: A constant-workspace algorithm has read-only access to an input array and may use only O(1) additional words of $O(\log n)$ bits, where $n$ is the size of the input. We assume that a simple $n$-gon is given by the ordered sequence of its vertices. We show that we can find a triangulation of a plane straight-line graph in $O(n^2)$ time. We also consider preprocessing a simple polygon for shortest path queries when the space constraint is relaxed to allow $s$ words of working space. After a preprocessing of $O(n^2)$ time, we are able to solve shortest path queries between any two points inside the polygon in $O(n^2/s)$ time.


Query Answering under Matching Dependencies for Data Cleaning: Complexity and Algorithms

Abstract: Matching dependencies (MDs) have been recently introduced as declarative rules for entity resolution (ER), i.e. for identifying and resolving duplicates in relational instance $D$. A set of MDs can be used as the basis for a possibly non-deterministic mechanism that computes a duplicate-free instance from $D$. The possible results of this process are the clean, "minimally resolved instances" (MRIs). There might be several MRIs for $D$, and the "resolved answers" to a query are those that are shared by all the MRIs. We investigate the problem of computing resolved answers. We look at various sets of MDs, developing syntactic criteria for determining (in)tractability of the resolved answer problem, including a dichotomy result. For some tractable classes of MDs and conjunctive queries, we present a query rewriting methodology that can be used to retrieve the resolved answers. We also investigate connections with "consistent query answering", deriving further tractability results for MD-based ER.


Management of Data Replication for PC Cluster-based Cloud Storage System

Abstract: Storage systems are essential building blocks for cloud computing infrastructures. Although high performance storage servers are the ultimate solution for cloud storage, the implementation of inexpensive storage system remains an open issue. To address this problem, the efficient cloud storage system is implemented with inexpensive and commodity computer nodes that are organized into PC cluster based datacenter. Hadoop Distributed File System (HDFS) is an open source cloud based storage platform and designed to be deployed in low-cost hardware. PC Cluster based Cloud Storage System is implemented with HDFS by enhancing replication management scheme. Data objects are distributed and replicated in a cluster of commodity nodes located in the cloud. This system provides optimum replica number as well as weighting and balancing among the storage server nodes. The experimental results show that storage can be balanced depending on the available disk space, expected availability and failure probability of each node in PC cluster.


Random Context and Semi-Conditional Insertion-Deletion Systems

Abstract: In this article we introduce the operations of insertion and deletion working in a random-context and semi-conditional manner. We show that the conditional use of rules strictly increase the computational power. In the case of semi-conditional insertion-deletion systems context-free insertion and deletion rules of one symbol are sufficient to get the computational completeness. In the random context case our results expose an asymmetry between the computational power of insertion and deletion rules: systems of size $(2,0,0; 1,1,0)$ are computationally complete, while systems of size $(1,1,0;2,0,0)$ (and more generally of size $(1,1,0;p,1,1)$) are not. This is particularly interesting because other control mechanisms like graph-control or matrix control used together with insertion-deletion systems do not present such asymmetry.


Usage Des Mesures Pour La G\'en\'eration Des R\`egles d'Associations Cycliques

Abstract: The online analytical processing (OLAP) does not provide any explanation of correlations discovered between data. Thus, the coupling of OLAP and data mining, especially association rules, is considered as an efficient solution to this problem. In this context, we mainly focus on a particular class of association rules which is the cyclic association rules. These rules aimed to discover patterns that display regular variation over user-defined intervals. Generally,the generated patterns do not take an advantage from the specificities of the multidimensional context namely, the consideration of the measures and their aggregations. In this paper, we introduce a novel method for extracting cyclic association rules from measures, and we redefine the evaluation metrics of association rules quality inspired of the temporal summarizability of measures concept through the integration of appropriate aggregation functions. To prove the usefulness of our approach, we conduct an empirical study on a real data warehouse.