Proceedings Fifth Transformation Tool Contest

Abstract: The aim of the Transformation Tool Contest (TTC) series is to compare the expressiveness, the usability and the performance of graph and model transformation tools along a number of selected case studies. Participants want to learn about the pros and cons of each tool considering different applications. A deeper understanding of the relative merits of different tool features will help to further improve graph and model transformation tools and to indicate open problems. TTC 2011 involved 25 offline case study solutions: 12 solutions to the Hello World case, 2 solutions to the GMF Model Migration case, 5 solutions to the Compiler Optimization case, and 7 solutions to the Reengineering (i.e., Program Understanding) case. This volume contains the submissions that have passed an additional (post-workshop) reviewing round.


Full Restoration of Visual Encrypted Color Images

Abstract: While strictly black and white images have been the basis for visual cryptography, there has been a lack of an easily implemented format for colour images. This paper establishes a simple, yet secure way of implementing visual cryptography with colour, assuming a binary data representation.


Parametrized Stochastic Multi-armed Bandits with Binary Rewards

Abstract: In this paper, we consider the problem of multi-armed bandits with a large, possibly infinite number of correlated arms. We assume that the arms have Bernoulli distributed rewards, independent across time, where the probabilities of success are parametrized by known attribute vectors for each arm, as well as an unknown preference vector, each of dimension $n$. For this model, we seek an algorithm with a total regret that is sub-linear in time and independent of the number of arms. We present such an algorithm, which we call the Two-Phase Algorithm, and analyze its performance. We show upper bounds on the total regret which applies uniformly in time, for both the finite and infinite arm cases. The asymptotics of the finite arm bound show that for any $f \in \omega(\log(T))$, the total regret can be made to be $O(n \cdot f(T))$. In the infinite arm case, the total regret is $O(\sqrt{n^3 T})$.


Efficient Regression in Metric Spaces via Approximate Lipschitz Extension

Abstract: We present a framework for performing efficient regression in general metric spaces. Roughly speaking, our regressor predicts the value at a new point by computing a Lipschitz extension --- the smoothest function consistent with the observed data --- after performing structural risk minimization to avoid overfitting. We obtain finite-sample risk bounds with minimal structural and noise assumptions, and a natural speed-precision tradeoff. The offline (learning) and online (prediction) stages can be solved by convex programming, but this naive approach has runtime complexity $O(n^3)$, which is prohibitive for large datasets. We design instead a regression algorithm whose speed and generalization performance depend on the intrinsic dimension of the data, to which the algorithm adapts. While our main innovation is algorithmic, the statistical results may also be of independent interest.


A Low-Energy Fast Cyber Foraging Mechanism for Mobile Devices

Abstract: The ever increasing demands for using resource-constrained mobile devices for running more resource intensive applications nowadays has initiated the development of cyber foraging solutions that offload parts or whole computational intensive tasks to more powerful surrogate stationary computers and run them on behalf of mobile devices as required. The choice of proper mix of mobile devices and surrogates has remained an unresolved challenge though. In this paper, we propose a new decision-making mechanism for cyber foraging systems to select the best locations to run an application, based on context metrics such as the specifications of surrogates, the specifications of mobile devices, application specification, and communication network specification. Experimental results show faster response time and lower energy consumption of benched applications compared to when applications run wholly on mobile devices and when applications are offloaded to surrogates blindly for execution.


Greening File Distribution: Centralized or Distributed?

Abstract: Despite file-distribution applications are responsible for a major portion of the current Internet traffic, so far little effort has been dedicated to study file distribution from the point of view of energy efficiency. In this paper, we present a first approach at the problem of energy efficiency for file distribution. Specifically, we first demonstrate that the general problem of minimizing energy consumption in file distribution in heterogeneous settings is NP-hard. For homogeneous settings, we derive tight lower bounds on energy consumption, and we design a family of algorithms that achieve these bounds. Our results prove that collaborative p2p schemes achieve up to 50% energy savings with respect to the best available centralized file distribution scheme. Through simulation, we demonstrate that in more realistic cases (e.g., considering network congestion, and link variability across hosts) we validate this observation, since our collaborative algorithms always achieve significant energy savings with respect to the power consumption of centralized file distribution systems.


Large Scale Spectral Clustering Using Approximate Commute Time Embedding

Abstract: Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale systems. Recently, many methods have been proposed to accelerate the computational time of spectral clustering. These approximate methods usually involve sampling techniques by which a lot information of the original data may be lost. In this work, we propose a fast and accurate spectral clustering approach using an approximate commute time embedding, which is similar to the spectral embedding. The method does not require using any sampling technique and computing any eigenvector at all. Instead it uses random projection and a linear time solver to find the approximate embedding. The experiments in several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.


Grid Security and Integration with Minimal Performance Degradation

Abstract: Computational grids are believed to be the ultimate framework to meet the growing computational needs of the scientific community. Here, the processing power of geographically distributed resources working under different ownerships, having their own access policy, cost structure and the likes, is logically coupled to make them perform as a unified resource. The continuous increase of availability of high-bandwidth communication as well as powerful computers built of low-cost components further enhance chances of computational grids becoming a reality. However, the question of grid security remains one of the important open research issues. Here, we present some novel ideas about how to implement grid security, without appreciable performance degradation in grids. A suitable alternative to the computationally expensive encryption is suggested, which uses a key for message authentication. Methods of secure transfer and exchange of the required key(s) are also discussed.


Non-monotony and Boolean automata networks

Abstract: This paper aims at setting the keystone of a prospective theoretical study on the role of non-monotone interactions in biological regulation networks. Focusing on discrete models of these networks, namely, Boolean automata networks, we propose to analyse the contribution of non-monotony to the diversity and complexity in their dynamical behaviours. More precisely, in this paper, we start by detailing some motivations, both mathematical and biological, for our interest in non-monotony, and we discuss how it may account for phenomena that cannot be produced by monotony only. Then, to build some understanding in this direction, we propose some preliminary results on the dynamical behaviour of some specific non-monotone Boolean automata networks called XOR circulant networks.


On the Transience of Linear Max-Plus Dynamical Systems

Abstract: We study the transients of linear max-plus dynamical systems. For that, we consider for each irreducible max-plus matrix A, the weighted graph G(A) such that A is the adjacency matrix of G(A). Based on a novel graph-theoretic counterpart to the number-theoretic Brauer's theorem, we propose two new methods for the construction of arbitrarily long paths in G(A) with maximal weight. That leads to two new upper bounds on the transient of a linear max-plus system which both improve on the bounds previously given by Even and Rajsbaum (STOC 1990, Theory of Computing Systems 1997), by Bouillard and Gaujal (Research Report 2000), and by Soto y Koelemeijer (PhD Thesis 2003), and are, in general, incomparable with Hartmann and Arguelles' bound (Mathematics of Operations Research 1999). With our approach, we also show how to improve the latter bound by a factor of two. A significant benefit of our bounds is that each of them turns out to be linear in the size of the system in various classes of linear max-plus system whereas the bounds previously given are all at least quadratic. Our second result concerns the relationship between matrix and system transients: We prove that the transient of an NxN matrix A is, up to some constant, equal to the transient of an A-linear system with an initial vector whose norm is quadratic in N. Finally, we study the applicability of our results to the well-known Full Reversal algorithm whose behavior can be described as a min-plus linear system.


From nominal sets binding to functions and lambda-abstraction: connecting the logic of permutation models with the logic of functions

Abstract: Permissive-Nominal Logic (PNL) extends first-order predicate logic with term-formers that can bind names in their arguments. It takes a semantics in (permissive-)nominal sets. In PNL, the forall-quantifier or lambda-binder are just term-formers satisfying axioms, and their denotation is functions on nominal atoms-abstraction. Then we have higher-order logic (HOL) and its models in ordinary (i.e. Zermelo-Fraenkel) sets; the denotation of forall or lambda is functions on full or partial function spaces. This raises the following question: how are these two models of binding connected? What translation is possible between PNL and HOL, and between nominal sets and functions? We exhibit a translation of PNL into HOL, and from models of PNL to certain models of HOL. It is natural, but also partial: we translate a restricted subsystem of full PNL to HOL. The extra part which does not translate is the symmetry properties of nominal sets with respect to permutations. To use a little nominal jargon: we can translate names and binding, but not their nominal equivariance properties. This seems reasonable since HOL---and ordinary sets---are not equivariant. Thus viewed through this translation, PNL and HOL and their models do different things, but they enjoy non-trivial and rich subsystems which are isomorphic.


Redundant Wavelets on Graphs and High Dimensional Data Clouds

Abstract: In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level linear operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform to image denoising, and show that it achieves denoising results that are close to those obtained with the BM3D algorithm.


Sensing Matrix Setting Schemes for Cognitive Networks and Their Performance Analysis

Abstract: Powerful spectrum decision schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. One of the key effecting factor on the CR network throughput is the spectrum sensing sequence used by each secondary user. In this paper, secondary users' throughput maximization through finding an appropriate sensing matrix (SM) is investigated. To this end, first the average throughput of the CR network is evaluated for a given SM. Then, an optimization problem based on the maximization of the network throughput is formulated in order to find the optimal SM. As the optimum solution is very complicated, to avoid its major challenges, three novel sub optimum solutions for finding an appropriate SM are proposed for various cases including perfect and non-perfect sensing. Despite of having less computational complexities as well as lower consumed energies, the proposed solutions perform quite well compared to the optimum solution (the optimum SM). The structure and performance of the proposed SM setting schemes are discussed in detail and a set of illustrative simulation results is presented to validate their efficiencies.


Linear Relation on General Ergodic T-Function

Abstract: We find linear (as well as quadratic) relations in a very large class of T-functions. The relations may be used in analysis of T-function-based stream ciphers.


Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data

Abstract: Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, predicting outcomes, and so on. In this work we investigate the properties of learning and inference of real world data collected via mobile phones over time. In particular, we look at the dynamic learning process over time, and how the ability to predict individual parameters and social links is incrementally enhanced with the accumulation of additional data. To do this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year, and is one of the most comprehensive mobile phone datasets gathered in academia to date. We develop several models that predict social and individual properties from sensed mobile phone data, including detection of life-partners, ethnicity, and whether a person is a student or not. Then, for this set of diverse learning tasks, we investigate how the prediction accuracy evolves over time, as new data is collected. Finally, based on gained insights, we propose a method for advance prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. This has practical implications, like informing the design of mobile data collection campaigns, or evaluating analysis strategies.


A self-portrait of young Leonardo

Abstract: One of the most famous drawings by Leonardo da Vinci is a self-portrait in red chalk, where he looks quite old. In fact, there is a sketch in one of his notebooks, partially covered by written notes, that can be a self-portrait of the artist when he was young. The use of image processing, to remove the handwritten text and improve the image, allows a comparison of the two portraits.


Facial Asymmetry and Emotional Expression

Abstract: This report is about facial asymmetry, its connection to emotional expression, and methods of measuring facial asymmetry in videos of faces. The research was motivated by two factors: firstly, there was a real opportunity to develop a novel measure of asymmetry that required minimal human involvement and that improved on earlier measures in the literature; and secondly, the study of the relationship between facial asymmetry and emotional expression is both interesting in its own right, and important because it can inform neuropsychological theory and answer open questions concerning emotional processing in the brain. The two aims of the research were: first, to develop an automatic frame-by-frame measure of facial asymmetry in videos of faces that improved on previous measures; and second, to use the measure to analyse the relationship between facial asymmetry and emotional expression, and connect our findings with previous research of the relationship.


An Insight into Spreadsheet User Behaviour through an Analysis of EuSpRIG Website Statistics

Abstract: The European Spreadsheet Risks Interest Group (EuSpRIG) has maintained a website almost since its inception in 2000. We present here longitudinal and cross-sectional statistics from the website log in order to shed some light upon end-user activity in the EuSpRIG domain.


On the Critical Delays of Mobile Networks under L\'{e}vy Walks and L\'{e}vy Flights

Abstract: Delay-capacity tradeoffs for mobile networks have been analyzed through a number of research work. However, L\'{e}vy mobility known to closely capture human movement patterns has not been adopted in such work. Understanding the delay-capacity tradeoff for a network with L\'{e}vy mobility can provide important insights into understanding the performance of real mobile networks governed by human mobility. This paper analytically derives an important point in the delay-capacity tradeoff for L\'{e}vy mobility, known as the critical delay. The critical delay is the minimum delay required to achieve greater throughput than what conventional static networks can possibly achieve (i.e., $O(1/\sqrt{n})$ per node in a network with $n$ nodes). The L\'{e}vy mobility includes L\'{e}vy flight and L\'{e}vy walk whose step size distributions parametrized by $\alpha \in (0,2]$ are both heavy-tailed while their times taken for the same step size are different. Our proposed technique involves (i) analyzing the joint spatio-temporal probability density function of a time-varying location of a node for L\'{e}vy flight and (ii) characterizing an embedded Markov process in L\'{e}vy walk which is a semi-Markov process. The results indicate that in L\'{e}vy walk, there is a phase transition such that for $\alpha \in (0,1)$, the critical delay is always $\Theta (n^{1/2})$ and for $\alpha \in [1,2]$ it is $\Theta(n^{\frac{\alpha}{2}})$. In contrast, L\'{e}vy flight has the critical delay $\Theta(n^{\frac{\alpha}{2}})$ for $\alpha\in(0,2]$.


GMF: A Model Migration Case for the Transformation Tool Contest

Abstract: Using a real-life evolution taken from the Graphical Modeling Framework, we invite submissions to explore ways in which model transformation and migration tools can be used to migrate models in response to metamodel adaptation.


Compiler Optimization: A Case for the Transformation Tool Contest

Abstract: An optimizing compiler consists of a front end parsing a textual programming language into an intermediate representation (IR), a middle end performing optimizations on the IR, and a back end lowering the IR to a target representation (TR) built of operations supported by the target hardware. In modern compiler construction graph-based IRs are employed. Optimization and lowering tasks can then be implemented with graph transformation rules. This case provides two compiler tasks to evaluate the participating tools regarding performance.


Program Understanding: A Reengineering Case for the Transformation Tool Contest

Abstract: In Software Reengineering, one of the central artifacts is the source code of the legacy system in question. In fact, in most cases it is the only definitive artifact, because over the time the code has diverged from the original architecture and design documents. The first task of any reengineering project is to gather an understanding of the system's architecture. Therefore, a common approach is to use parsers to translate the source code into a model conforming to the abstract syntax of the programming language the system is implemented in which can then be subject to querying. Despite querying, transformations can be used to generate more abstract views on the system's architecture. This transformation case deals with the creation of a state machine model out of a Java syntax graph. It is derived from a task that originates from a real reengineering project.


HelloWorld! An Instructive Case for the Transformation Tool Contest

Abstract: This case comprises several primitive tasks that can be solved straight away with most transformation tools. The aim is to cover the most important kinds of primitive operations on models, i.e. create, read, update and delete (CRUD). To this end, tasks such as a constant transformation, a model-to-text transformation, a very basic migration transformation or diverse simple queries or in-place operations on graphs have to be solved. The motivation for this case is that the results expectedly will be very instructive for beginners. Also, it is really hard to compare transformation languages along complex cases, because the complexity of the respective case might hide the basic language concepts and constructs.


Solving the TTC 2011 Model Migration Case with Edapt

Abstract: This paper gives an overview of the Edapt solution to the GMF model migration case of the Transformation Tool Contest 2011.


Solving the TTC 2011 Model Migration Case with UML-RSDS

Abstract: In this paper we apply the UML-RSDS notation and tools to the GMF model migration case study and explain how to use the UML-RSDS tools.


Solving the TTC 2011 Compiler Optimization Case with GrGen.NET

Abstract: The challenge of the Compiler Optimization Case is to perform local optimizations and instruction selection on the graph-based intermediate representation of a compiler. The case is designed to compare participating tools regarding their performance. We tackle this task employing the general purpose graph rewrite system GrGen.NET (www.grgen.net).


Solving the TTC 2011 Compiler Optimization Case with QVTR-XSLT

Abstract: In this short paper we present our solution for the Compiler Optimization case study of the Transformation Tool Contest (TTC) 2011 using the QVTR-XSLT tool. The tool supports editing and execution of the graphical notation of QVT Relations language


Solving the TTC 2011 Compiler Optimization Task with metatools

Abstract: The authors' "metatools" are a collection of tools for generic programming. This includes generating Java sources from mathematically well-founded specifications, as well as the creation of strictly typed document object models for XML encoded texts. In this context, almost every computer-internal structure is treated as a "model", and every computation is a kind of model transformation. This concept differs significantly from "classical model transformation" executed by specialized tools and languages. Therefore it seemed promising to the organizers of the TTC 2011, as well as to the authors, to apply metatools to one of the challenges, namely to the "compiler optimization task". This is a report on the resulting experiences.


Solving the TTC 2011 Compiler Optimization Case with GReTL

Abstract: This paper discusses the GReTL solution of the TTC 2011 Compiler Optimization case. The submitted solution covers both the constant folding task and the instruction selection task. The verifier for checking the validity of the graph is also implemented, and some additional test graphs are provided as requested by the extension.


Solving the TTC 2011 Compiler Optimization Case with GROOVE

Abstract: This report presents a partial solution to the Compiler Optimization case study using GROOVE. We explain how the input graphs provided with the case study were adapted into a GROOVE representation and we describe an initial solution for Task 1. This solution allows us to automatically reproduce the steps of the constant folding example given in the case description. We did not solve Task 2.


Solving the TTC 2011 Reengineering Case with GReTL

Abstract: This paper discusses the GReTL reference solution of the TTC 2011 Reengineering case. Given a Java syntax graph, a simple state machine model has to be extracted. The submitted solution covers both the core task and the two extension tasks.


Solving the TTC 2011 Reengineering Case with VIATRA2

Abstract: The current paper presents a solution of the Program Understanding: A Reengineering Case for the Transformation Tool Contest using the VIATRA2 model transformation tool.


Solving the TTC 2011 Reengineering Case with Edapt

Abstract: This paper gives an overview of the Edapt solution to the reengineering case of the Transformation Tool Contest 2011.


Solving the TTC 2011 Reengineering Case with MOLA and Higher-Order Transformations

Abstract: The Reengineering Case of the Transformation Tool Contest 2011 deals with automatic extraction of state machine from Java source code. The transformation task involves complex, non-local matching of model elements. This paper contains the solution of the task using model transformation language MOLA. The MOLA solution uses higher-order transformations (HOT-s) to generate a part of the required MOLA program. The described HOT approach allows creating reusable, complex model transformation libraries for generic tasks without modifying an implementation of a model transformation language. Thus model transformation users who are not the developers of the language can achieve the desired functionality more easily.


Solving the TTC 2011 Reengineering Case with GrGen.NET

Abstract: The challenge of the Reengineering Case is to extract a state machine model out of the abstract syntax graph of a Java program. The extracted state machine offers a reduced view on the full program graph and thus helps to understand the program regarding the question of interest. We tackle this task employing the general purpose graph rewrite system GrGen.NET (www.grgen.net).


Solving the TTC 2011 Reengineering Case with Henshin

Abstract: This paper presents the Henshin solution to the Model Transformations for Program Understanding case study as part of the Transformation Tool Contest 2011.


Saying Hello World with Edapt - A Solution to the TTC 2011 Instructive Case

Abstract: This paper gives an overview of the Edapt solution to the hello world case of the Transformation Tool Contest 2011.


Saying Hello World with GROOVE - A Solution to the TTC 2011 Instructive Case

Abstract: This report presents a solution to the Hello World case study of TTC 2011 using GROOVE. We provide and explain the grammar that we used to solve the case study. Every requested question of the case study was solved by a single rule application.


Saying Hello World with MOLA - A Solution to the TTC 2011 Instructive Case

Abstract: This paper describes the solution of Hello World transformations in MOLA transformation language. Transformations implementing the task are relatively straightforward and easily inferable from the task specification. The required additional steps related to model import and export are also described.


Saying Hello World with Henshin - A Solution to the TTC 2011 Instructive Case

Abstract: This paper gives an overview of the Henshin solution to the Hello World case study of the Transformation Tool Contest 2011, intended to show basic language concepts and constructs.


Saying Hello World with GrGen.NET - A Solution to the TTC 2011 Instructive Case

Abstract: We introduce the graph transformation tool GrGen.NET (www.grgen.net) by solving the Hello World Case of the Transformation Tool Contest 2011 which consists of a collection of small transformation tasks; for each task a section is given explaining our implementation.


Saying Hello World with VIATRA2 - A Solution to the TTC 2011 Instructive Case

Abstract: The paper presents a solution of the Hello World! An Instructive Case for the Transformation Tool Contest using the VIATRA2 model transformation tool.


Saying HelloWorld with QVTR-XSLT - A Solution to the TTC 2011 Instructive Case

Abstract: In this short paper we present our solution for the Hello World case study of the Transformation Tool Contest (TTC) 2011 using the QVTR-XSLT tool. The tool supports editing and execution of the graphical notation of QVT Relations language. The case study consists of a set of simple transformation tasks which covers the basic functions required for a transformation language, such as creating, reading/querying, updating and deleting of model elements. We design a transformation for each of the tasks.


Saying Hello World with GReTL - A Solution to the TTC 2011 Instructive Case

Abstract: This paper discusses the GReTL solution of the TTC 2011 Hello World case. The submitted solution covers all tasks including the optional ones.


Saying Hello World with UML-RSDS - A Solution to the 2011 Instructive Case

Abstract: In this paper we apply the UML-RSDS notation and tools to the "Hello World" case studies and explain the underlying development process for this model transformation approach.


Saying Hello World with Epsilon - A Solution to the 2011 Instructive Case

Abstract: Epsilon is an extensible platform of integrated and task-specific languages for model management. With solutions to the 2011 TTC Hello World case, this paper demonstrates some of the key features of the Epsilon Object Language (an extension and reworking of OCL), which is at the core of Epsilon. In addition, the paper introduces several of the task-specific languages provided by Epsilon including the Epsilon Generation Language (for model-to-text transformation), the Epsilon Validation Language (for model validation) and Epsilon Flock (for model migration).


On Strong Graph Partitions and Universal Steiner Trees

Abstract: We study the problem of constructing universal Steiner trees for undirected graphs. Given a graph $G$ and a root node $r$, we seek a single spanning tree $T$ of minimum {\em stretch}, where the stretch of $T$ is defined to be the maximum ratio, over all terminal sets $X$, of the cost of the minimal sub-tree $T_X$ of $T$ that connects $X$ to $r$ to the cost of an optimal Steiner tree connecting $X$ to $r$ in $G$. Universal Steiner trees (USTs) are important for data aggregation problems where computing the Steiner tree from scratch for every input instance of terminals is costly, as for example in low energy sensor network applications. We provide a polynomial time \ust\ construction for general graphs with $2^{O(\sqrt{\log n})}$-stretch. We also give a polynomial time $\polylog(n)$-stretch construction for minor-free graphs. One basic building block of our algorithms is a hierarchy of graph partitions, each of which guarantees small strong diameter for each cluster and bounded neighbourhood intersections for each node. We show close connections between the problems of constructing USTs and building such graph partitions. Our construction of partition hierarchies for general graphs is based on an iterative cluster merging procedure, while the one for minor-free graphs is based on a separator theorem for such graphs and the solution to a cluster aggregation problem that may be of independent interest even for general graphs. To our knowledge, this is the first subpolynomial-stretch ($o(n^\epsilon)$ for any $\epsilon > 0$) UST construction for general graphs, and the first polylogarithmic-stretch UST construction for minor-free graphs.


Enhancement of Image Resolution by Binarization

Abstract: Image segmentation is one of the principal approaches of image processing. The choice of the most appropriate Binarization algorithm for each case proved to be a very interesting procedure itself. In this paper, we have done the comparison study between the various algorithms based on Binarization algorithms and propose a methodologies for the validation of Binarization algorithms. In this work we have developed two novel algorithms to determine threshold values for the pixels value of the gray scale image. The performance estimation of the algorithm utilizes test images with, the evaluation metrics for Binarization of textual and synthetic images. We have achieved better resolution of the image by using the Binarization method of optimum thresholding techniques.


Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks

Abstract: It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.


Enhanced Antenna Position Implementation Over Vehicular Ad Hoc Network (VNET) In 3D Space

Abstract: The technology related to networking moves wired connection to wireless connection.The basic problem concern in the wireless domain, random packet loss for the end to end connection. In this paper we show the performance and the impact of the packet loss and delay, by the bit error rate throughput etc with respect to the real world scenario vehicular ad hoc network in 3-dimension space (VANET in 3D). Over the years software development has responded to the increasing growth of wireless connectivity in developing network enabled software. In this paper we consider the real world physical problem in three dimensional wireless domain and map the problem to analytical problem . In this paper we simulate that analytic problem with respect to real world scenario by using enhanced antenna position system (EAPS) mounted over the mobile node in 3D space. In this paper we convert the real world problem into lab oriented problem by using the EAPS -system and shown the performance in wireless domain in 3 dimensional space.


Chebyshev Polynomials in Distributed Consensus Applications

Abstract: In this paper we analyze the use of Chebyshev polynomials in distributed consensus applications. We study the properties of these polynomials to propose a distributed algorithm that reaches the consensus in a fast way. The algorithm is expressed in the form of a linear iteration and, at each step, the agents only require to transmit their current state to their neighbors. The difference with respect to previous approaches is that the update rule used by the network is based on the second order difference equation that describes the Chebyshev polynomials of first kind. As a consequence, we show that our algorithm achieves the consensus using far less iterations than other approaches. We characterize the main properties of the algorithm for both, fixed and switching communication topologies. The main contribution of the paper is the study of the properties of the Chebyshev polynomials in distributed consensus applications, proposing an algorithm that increases the convergence rate with respect to existing approaches. Theoretical results, as well as experiments with synthetic data, show the benefits using our algorithm.


Distributed Multi-view Matching in Networks with Limited Communications

Abstract: We address the problem of distributed matching of features in networks with vision systems. Every camera in the network has limited communication capabilities and can only exchange local matches with its neighbors. We propose a distributed algorithm that takes these local matches and computes global correspondences by a proper propagation in the network. When the algorithm finishes, each camera knows the global correspondences between its features and the features of all the cameras in the network. The presence of spurious introduced by the local matcher may produce inconsistent global correspondences, which are association paths between features from the same camera. The contributions of this work are the propagation of the local matches and the detection and resolution of these inconsistencies by deleting local matches. Our resolution algorithm considers the quality of each local match, when this information is provided by the local matcher. We formally prove that after executing the algorithm, the network finishes with a global data association free of inconsistencies. We provide a fully decentralized solution to the problem which does not rely on any particular communication topology. Simulations and experimental results with real images show the performance of the method considering different features, matching functions and scenarios.


Flipping the Winner of a Poset Game

Abstract: Partially-ordered set games, also called poset games, are a class of two-player combinatorial games. The playing field consists of a set of elements, some of which are greater than other elements. Two players take turns removing an element and all elements greater than it, and whoever takes the last element wins. Examples of poset games include Nim and Chomp. We investigate the complexity of computing which player of a poset game has a winning strategy. We give an inductive procedure that modifies poset games to change the nim- value which informally captures the winning strategies in the game. For a generic poset game G, we describe an efficient method for constructing a game not G such that the first player has a winning strategy if and only if the second player has a winning strategy on G. This solves the long-standing problem of whether this construction can be done efficiently. This construction also allows us to reduce the class of Boolean formulas to poset games, establishing a lower bound on the complexity of poset games.


Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Abstract: Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.


Renewal-Theoretical Dynamic Spectrum Access in Cognitive Radio Networks with Unknown Primary Behavior

Abstract: Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the PU's licensed channels. If the SUs can be synchronous with the PU's time slots, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's exact communication mechanism, additional interference may occur. In this paper, we propose a dynamic spectrum access protocol for the SUs confronting with unknown primary behavior and study the interference caused by their dynamic access. Through analyzing the SUs' dynamic behavior in the primary channel which is modeled as an ON-OFF process, we prove that the SUs' communication behavior is a renewal process. Based on the Renewal Theory, we quantify the interference caused by the SUs and derive the corresponding close-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS) and the secondary network's stability. Finally, simulation results are shown to verify the effectiveness of our analysis.


Drivers of the Cost of Spreadsheet Audit

Abstract: A review of 75 formal audit assignments shows that the effort taken to identify defects in financial models taken from the domain of limited recourse (project) finance is uncorrelated with common measures of the physical characteristics of the spreadsheets concerned.


Leveraging User Profile and Behaviour to Design Practical Spreadsheet Controls for the Finance Function

Abstract: Recognizing that the use of spreadsheets within finance will likely not subside in the near future, this paper discusses a major barrier that is preventing more organizations from adopting enterprise spreadsheet management programs. But even without a corporate mandated effort to improve spreadsheet controls, finance functions can still take simple yet effective steps to start managing the risk of errors in key spreadsheets by strategically selecting controls that complement existing user practice


Reaction Automata

Abstract: Reaction systems are a formal model that has been introduced to investigate the interactive behaviors of biochemical reactions. Based on the formal framework of reaction systems, we propose new computing models called reaction automata that feature (string) language acceptors with multiset manipulation as a computing mechanism, and show that reaction automata are computationally Turing universal. Further, some subclasses of reaction automata with space complexity are investigated and their language classes are compared to the ones in the Chomsky hierarchy.


EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Abstract: Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.


YouSense: Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing

Abstract: Collaborative spectrum sensing has been recognized as a promising approach to improve the sensing performance via exploiting the spatial diversity of the secondary users. In this study, a new selfishness issue is identified, that selfish users sense no spectrum in collaborative sensing. For easier presentation, it's denoted as entropy selfishness. This selfish behavior is difficult to distinguish, making existing detection based incentive schemes fail to work. To thwart entropy selfishness in distributed collaborative sensing, we propose YouSense, a One-Time Pad (OTP) based incentive design that could naturally isolate entropy selfish users from the honest users without selfish node detection. The basic idea of YouSense is to construct a trapdoor one-time pad for each sensing report by combining the original report and a random key. Such a one-time pad based encryption could prevent entropy selfish users from accessing the original sensing report while enabling the honest users to recover the report. Different from traditional cryptography based OTP which requires the key delivery, YouSense allows an honest user to recover the pad (or key) by exploiting a unique characteristic of collaborative sensing that different secondary users share some common observations on the same radio spectrum. We further extend YouSense to improve the recovery successful rate by reducing the cardinality of set of the possible pads. By extensive USRP based experiments, we show that YouSense can successfully thwart entropy selfishness with low system overhead.


A Theory for Optical flow-based Transport on Image Manifolds

Abstract: An image articulation manifold (IAM) is the collection of images formed when an object is articulated in front of a camera. IAMs arise in a variety of image processing and computer vision applications, where they provide a natural low-dimensional embedding of the collection of high-dimensional images. To date IAMs have been studied as embedded submanifolds of Euclidean spaces. Unfortunately, their promise has not been realized in practice, because real world imagery typically contains sharp edges that render an IAM non-differentiable and hence non-isometric to the low-dimensional parameter space under the Euclidean metric. As a result, the standard tools from differential geometry, in particular using linear tangent spaces to transport along the IAM, have limited utility. In this paper, we explore a nonlinear transport operator for IAMs based on the optical flow between images and develop new analytical tools reminiscent of those from differential geometry using the idea of optical flow manifolds (OFMs). We define a new metric for IAMs that satisfies certain local isometry conditions, and we show how to use this metric to develop a new tools such as flow fields on IAMs, parallel flow fields, parallel transport, as well as a intuitive notion of curvature. The space of optical flow fields along a path of constant curvature has a natural multi-scale structure via a monoid structure on the space of all flow fields along a path. We also develop lower bounds on approximation errors while approximating non-parallel flow fields by parallel flow fields.


Pretty Private Group Management

Abstract: Group management is a fundamental building block of today's Internet applications. Mailing lists, chat systems, collaborative document edition but also online social networks such as Facebook and Twitter use group management systems. In many cases, group security is required in the sense that access to data is restricted to group members only. Some applications also require privacy by keeping group members anonymous and unlinkable. Group management systems routinely rely on a central authority that manages and controls the infrastructure and data of the system. Personal user data related to groups then becomes de facto accessible to the central authority. In this paper, we propose a completely distributed approach for group management based on distributed hash tables. As there is no enrollment to a central authority, the created groups can be leveraged by various applications. Following this paradigm we describe a protocol for such a system. We consider security and privacy issues inherently introduced by removing the central authority and provide a formal validation of security properties of the system using AVISPA. We demonstrate the feasibility of this protocol by implementing a prototype running on top of Vuze's DHT.


Decentralised LTL Monitoring

Abstract: Users wanting to monitor distributed or component-based systems often perceive them as monolithic systems which, seen from the outside, exhibit a uniform behaviour as opposed to many components displaying many local behaviours that together constitute the system's global behaviour. This level of abstraction is often reasonable, hiding implementation details from users who may want to specify the system's global behaviour in terms of an LTL formula. However, the problem that arises then is how such a specification can actually be monitored in a distributed system that has no central data collection point, where all the components' local behaviours are observable. In this case, the LTL specification needs to be decomposed into sub-formulae which, in turn, need to be distributed amongst the components' locally attached monitors, each of which sees only a distinct part of the global behaviour. The main contribution of this paper is an algorithm for distributing and monitoring LTL formulae, such that satisfac- tion or violation of specifications can be detected by local monitors alone. We present an implementation and show that our algorithm introduces only a minimum delay in detecting satisfaction/violation of a specification. Moreover, our practical results show that the communication overhead introduced by the local monitors is considerably lower than the number of messages that would need to be sent to a central data collection point.


A New IRIS Normalization Process For Recognition System With Cryptographic Techniques

Abstract: Biometric technologies are the foundation of personal identification systems. It provides an identification based on a unique feature possessed by the individual. This paper provides a walkthrough for image acquisition, segmentation, normalization, feature extraction and matching based on the Human Iris imaging. A Canny Edge Detection scheme and a Circular Hough Transform, is used to detect the iris boundaries in the eye's digital image. The extracted IRIS region was normalized by using Image Registration technique. A phase correlation base method is used for this iris image registration purpose. The features of the iris region is encoded by convolving the normalized iris region with 2D Gabor filter. Hamming distance measurement is used to compare the quantized vectors and authenticate the users. To improve the security, Reed-Solomon technique is employed directly to encrypt and decrypt the data. Experimental results show that our system is quite effective and provides encouraging performance. Keywords: Biometric, Iris Recognition, Phase correlation, cryptography, Reed-Solomon


Concurrent Models for Function Execution

Abstract: We derive an abstract computational model from a sequential computational model that is generally used for function execution. This abstract computational model allows for the concurrent execution of functions. We discuss concurrent models for function execution as implementations from the abstract computational model. We give an example of a particular concurrent function construct that can be implemented on a concurrent machine model using multi-threading. The result is a framework of computational models at different levels of abstraction that can be used in further development of concurrent computational models that deal with the problems inherent with concurrency.


Communicating Concurrent Functions

Abstract: In this article we extend the framework of execution of concurrent functions on different abstract levels from previous work with communication between the concurrent functions. We classify the communications and identify problems that can occur with these communications. We present solutions for the problems based on encapsulation and abstraction to obtain correct behaviours. The result is that communication on a low level of abstraction in the form of shared memory and message passing is dealt with on an higher level of abstraction.


A Frame Rate Optimization Framework For Improving Continuity In Video Streaming

Abstract: This paper aims to reduce the prebuffering requirements, while maintaining continuity, for video streaming. Current approaches do this by making use of adaptive media playout (AMP) to reduce the playout rate. However, this introduces playout distortion to the viewers and increases the viewing latency. We approach this by proposing a frame rate optimization framework that adjusts both the encoder frame generation rate and the decoder playout frame rate. Firstly, we model this problem as the joint adjustment of the encoder frame generation interval and the decoder playout frame interval. This model is used with a discontinuity penalty virtual buffer to track the accumulated difference between the receiving frame interval and the playout frame interval. We then apply Lyapunov optimization to the model to systematically derive a pair of decoupled optimization policies. We show that the occupancy of the discontinuity penalty virtual buffer is correlated to the video discontinuity and that this framework produces a very low playout distortion in addition to a significant reduction in the prebuffering requirements compared to existing approaches. Secondly, we introduced a delay constraint into the framework by using a delay accumulator virtual buffer. Simulation results show that the the delay constrained framework provides a superior tradeoff between the video quality and the delay introduced compared to the existing approach. Finally, we analyzed the impact of delayed feedback between the receiver and the sender on the optimization policies. We show that the delayed feedbacks have a minimal impact on the optimization policies.


Wireless Capacity and Admission Control in Cognitive Radio

Abstract: We give algorithms with constant-factor performance guarantees for several capacity and throughput problems in the SINR model. The algorithms are all based on a novel LP formulation for capacity problems. First, we give a new constant-factor approximation algorithm for selecting the maximum subset of links that can be scheduled simultaneously, under any non-decreasing and sublinear power assignment. For the case of uniform power, we extend this to the case of variable QoS requirements and link-dependent noise terms. Second, we approximate a problem related to cognitive radio: find a maximum set of links that can be simultaneously scheduled without affecting a given set of previously assigned links. Finally, we obtain constant-factor approximation of weighted capacity under linear power assignment.


Robot Companions: Technology for Humans

Abstract: Creation of devices and mechanisms which help people has a long history. Their inventors always targeted practical goals such as irrigation, harvesting, devices for construction sites, measurement, and, last but not least, military tasks for different mechanical and later mechatronic systems. Development of such assisting mechanisms counts back to Greek engineering, came through Middle Ages and led finally in XIX and XX centuries to autonomous devices, which we call today "Robots". This chapter provides overview of several robotic technologies, introduces bio-/chemo- hybrid and collective systems and discuss their applications in service areas.


Awareness and Self-Awareness for Multi-Robot Organisms

Abstract: Awareness and self-awareness are two different notions related to knowing the environment and itself. In a general context, the mechanism of self-awareness belongs to a class of co-called "self-issues" (self-* or self-star): self-adaptation, self-repairing, self-replication, self-development or self-recovery. The self-* issues are connected in many ways to adaptability and evolvability, to the emergence of behavior and to the controllability of long-term developmental processes. Self-* are either natural properties of several systems, such as self-assembling of molecular networks, or may emerge as a result of homeostatic regulation. Different computational processes, leading to a global optimization, increasing scalability and reliability of collective systems, create such a homeostatic regulation. Moreover, conditions of ecological survival, imposed on such systems, lead to a discrimination between "self" and "non-self" as well as to the emergence of different self-phenomena. There are many profound challenges, such as understanding these mechanisms, or long-term predictability, which have a considerable impact on research in the area of artificial intelligence and intelligent systems.


Fast Compressed Tries through Path Decompositions

Abstract: Tries are popular data structures for storing a set of strings, where common prefixes are represented by common root-to-node paths. Over fifty years of usage have produced many variants and implementations to overcome some of their limitations. We explore new succinct representations of path-decomposed tries and experimentally evaluate the corresponding reduction in space usage and memory latency, comparing with the state of the art. We study two cases of applications: (1) a compressed dictionary for (compressed) strings, and (2) a monotone minimal perfect hash for strings that preserves their lexicographic order. For (1), we obtain data structures that outperform other state-of-the-art compressed dictionaries in space efficiency, while obtaining predictable query times that are competitive with data structures preferred by the practitioners. In (2), our tries perform several times faster than other trie-based monotone perfect hash functions, while occupying nearly the same space.


Distributed Signal Processing via Chebyshev Polynomial Approximation

Abstract: Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators. The proposed method features approximations of the graph multipliers by shifted Chebyshev polynomials, whose recurrence relations make them readily amenable to distributed computation. We demonstrate how the proposed method can be applied to distributed processing tasks such as smoothing, denoising, inverse filtering, and semi-supervised classification, and show that the communication requirements of the method scale gracefully with the size of the network.


Evolution of a Modular Software Network

Abstract: "Evolution behaves like a tinkerer" (Francois Jacob, Science, 1977). Software systems provide a unique opportunity to understand biological processes using concepts from network theory. The Debian GNU/Linux operating system allows us to explore the evolution of a complex network in a novel way. The modular design detected during its growth is based on the reuse of existing code in order to minimize costs during programming. The increase of modularity experienced by the system over time has not counterbalanced the increase in incompatibilities between software packages within modules. This negative effect is far from being a failure of design. A random process of package installation shows that the higher the modularity the larger the fraction of packages working properly in a local computer. The decrease in the relative number of conflicts between packages from different modules avoids a failure in the functionality of one package spreading throughout the entire system. Some potential analogies with the evolutionary and ecological processes determining the structure of ecological networks of interacting species are discussed.


Coverage Related Issues in Networks

Abstract: Wireless sensor networks consisting of great number of cheap and tiny sensor nodes which are used for military environment controlling, natural events recording, traffic monitoring, robot navigation, and etc. Such a networks encounter with various types of challenges like energy consumption, routing, coverage, reliability. The most significant types of these problems are coverage that originated from the nodes energy consumption constrained. In order to dominate this problem different kinds of methods has been presented where the majority of them based on theoretical methods and used unbalanced and calculated distributions. In all of the proposed methods a large numbers of nodes are used. In this paper our attempt is based on using a few numbers of sensors in order to cover the vast area of environment. We proposed an algorithm that divides the desired environment to several areas and in each of these areas by using the genetic algorithm improve the coverage. The proposed method is simulated in MATLAB software and the obtained results are compared with the existing algorithms. Results show that the presented algorithm has a substantial coverage in compare with its previous counterparts.


Using Scalp Electrical Biosignals to Control an Object by Concentration and Relaxation Tasks: Design and Evaluation

Abstract: In this paper we explore the use of electrical biosignals measured on scalp and corresponding to mental relaxation and concentration tasks in order to control an object in a video game. To evaluate the requirements of such a system in terms of sensors and signal processing we compare two designs. The first one uses only one scalp electroencephalographic (EEG) electrode and the power in the alpha frequency band. The second one uses sixteen scalp EEG electrodes and machine learning methods. The role of muscular activity is also evaluated using five electrodes positioned on the face and the neck. Results show that the first design enabled 70% of the participants to successfully control the game, whereas 100% of the participants managed to do it with the second design based on machine learning. Subjective questionnaires confirm these results: users globally felt to have control in both designs, with an increased feeling of control in the second one. Offline analysis of face and neck muscle activity shows that this activity could also be used to distinguish between relaxation and concentration tasks. Results suggest that the combination of muscular and brain activity could improve performance of this kind of system. They also suggest that muscular activity has probably been recorded by EEG electrodes.


A Novel Approach for Periodic Assessment of Business Process Interoperability

Abstract: Business collaboration networks provide collaborative organizations a favorable context for automated business process interoperability. This paper aims to present a novel approach for assessing interoperability of process driven services by considering the three main aspects of interoperation: potentiality, compatibility and operational performance. It presents also a software tool that supports the proposed assessment method. In addition to its capacity to track and control the evolution of interoperation degree in time, the proposed tool measures the required effort to reach a planned degree of interoperability. Public accounting of financial authority is given as an illustrative case study of interoperability monitoring in public collaboration network.


Rule based Part of speech Tagger for Homoeopathy Clinical realm

Abstract: A tagger is a mandatory segment of most text scrutiny systems, as it consigned a s yntax class (e.g., noun, verb, adjective, and adverb) to every word in a sentence. In this paper, we present a simple part of speech tagger for homoeopathy clinical language. This paper reports about the anticipated part of speech tagger for homoeopathy clinical language. It exploit standard pattern for evaluating sentences, untagged clinical corpus of 20085 words is used, from which we had selected 125 sentences (2322 tokens). The problem of tagging in natural language processing is to find a way to tag every word in a text as a meticulous part of speech. The basic idea is to apply a set of rules on clinical sentences and on each word, Accuracy is the leading factor in evaluating any POS tagger so the accuracy of proposed tagger is also conversed.


Analytical and Learning-Based Spectrum Sensing Time Optimization in Cognitive Radio Systems

Abstract: Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by optimizing its spectrum sensing time is formulated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. The energy consumed for finding a transmission opportunity is evaluated and a discussion on the impact of the number of the primary users on the secondary user throughput and consumed energy is presented. In order to avoid the challenges associated with the analytical method, as a second solution, a systematic neural network-based sensing time optimization approach is also proposed in this paper. The proposed adaptive scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance, and cooperation of the artificial neural networks used in the proposed method are disclosed in detail and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimization scheme.


On a Linear Program for Minimum-Weight Triangulation

Abstract: Minimum-weight triangulation (MWT) is NP-hard. It has a polynomial-time constant-factor approximation algorithm, and a variety of effective polynomial- time heuristics that, for many instances, can find the exact MWT. Linear programs (LPs) for MWT are well-studied, but previously no connection was known between any LP and any approximation algorithm or heuristic for MWT. Here we show the first such connections: for an LP formulation due to Dantzig et al. (1985): (i) the integrality gap is bounded by a constant; (ii) given any instance, if the aforementioned heuristics find the MWT, then so does the LP.


On the Expected Complexity of Random Convex Hulls

Abstract: In this paper we present several results on the expected complexity of a convex hull of $n$ points chosen uniformly and independently from a convex shape. (i) We show that the expected number of vertices of the convex hull of $n$ points, chosen uniformly and independently from a disk is $O(n^{1/3})$, and $O(k \log{n})$ for the case a convex polygon with $k$ sides. Those results are well known (see \cite{rs-udkhv-63,r-slcdn-70,ps-cgi-85}), but we believe that the elementary proof given here are simpler and more intuitive. (ii) Let $\D$ be a set of directions in the plane, we define a generalized notion of convexity induced by $\D$, which extends both rectilinear convexity and standard convexity. We prove that the expected complexity of the $\D$-convex hull of a set of $n$ points, chosen uniformly and independently from a disk, is $O(n^{1/3} + \sqrt{n\alpha(\D)})$, where $\alpha(\D)$ is the largest angle between two consecutive vectors in $\D$. This result extends the known bounds for the cases of rectilinear and standard convexity. (iii) Let $\B$ be an axis parallel hypercube in $\Re^d$. We prove that the expected number of points on the boundary of the quadrant hull of a set $S$ of $n$ points, chosen uniformly and independently from $\B$ is $O(\log^{d-1}n)$. Quadrant hull of a set of points is an extension of rectilinear convexity to higher dimensions. In particular, this number is larger than the number of maxima in $S$, and is also larger than the number of points of $S$ that are vertices of the convex hull of $S$. Those bounds are known \cite{bkst-anmsv-78}, but we believe the new proof is simpler.


A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average

Abstract: Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.


Digraph Complexity Measures and Applications in Formal Language Theory

Abstract: We investigate structural complexity measures on digraphs, in particular the cycle rank. This concept is intimately related to a classical topic in formal language theory, namely the star height of regular languages. We explore this connection, and obtain several new algorithmic insights regarding both cycle rank and star height. Among other results, we show that computing the cycle rank is NP-complete, even for sparse digraphs of maximum outdegree 2. Notwithstanding, we provide both a polynomial-time approximation algorithm and an exponential-time exact algorithm for this problem. The former algorithm yields an O((log n)^(3/2))- approximation in polynomial time, whereas the latter yields the optimum solution, and runs in time and space O*(1.9129^n) on digraphs of maximum outdegree at most two. Regarding the star height problem, we identify a subclass of the regular languages for which we can precisely determine the computational complexity of the star height problem. Namely, the star height problem for bideterministic languages is NP-complete, and this holds already for binary alphabets. Then we translate the algorithmic results concerning cycle rank to the bideterministic star height problem, thus giving a polynomial-time approximation as well as a reasonably fast exact exponential algorithm for bideterministic star height.


Contextually Guided Semantic Labeling and Search for 3D Point Clouds

Abstract: RGB-D cameras, which give an RGB image to- gether with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the 3D point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.


DECENT: A Decentralized Architecture for Enforcing Privacy in Online Social Networks

Abstract: A multitude of privacy breaches, both accidental and malicious, have prompted users to distrust centralized providers of online social networks (OSNs) and investigate decentralized solutions. We examine the design of a fully decentralized (peer-to-peer) OSN, with a special focus on privacy and security. In particular, we wish to protect the confidentiality, integrity, and availability of user content and the privacy of user relationships. We propose DECENT, an architecture for OSNs that uses a distributed hash table to store user data, and features cryptographic protections for confidentiality and integrity, as well as support for flexible attribute policies and fast revocation. DECENT ensures that neither data nor social relationships are visible to unauthorized users and provides availability through replication and authentication of updates. We evaluate DECENT through simulation and experiments on the PlanetLab network and show that DECENT is able to replicate the main functionality of current centralized OSNs with manageable overhead.


Edit Distance to Monotonicity in Sliding Windows

Abstract: Given a stream of items each associated with a numerical value, its edit distance to monotonicity is the minimum number of items to remove so that the remaining items are non-decreasing with respect to the numerical value. The space complexity of estimating the edit distance to monotonicity of a data stream is becoming well-understood over the past few years. Motivated by applications on network quality monitoring, we extend the study to estimating the edit distance to monotonicity of a sliding window covering the $w$ most recent items in the stream for any $w \ge 1$. We give a deterministic algorithm which can return an estimate within a factor of $(4+\eps)$ using $O(\frac{1}{\eps^2} \log^2(\eps w))$ space. We also extend the study in two directions. First, we consider a stream where each item is associated with a value from a partial ordered set. We give a randomized $(4+\epsilon)$-approximate algorithm using $O(\frac{1}{\epsilon^2} \log \epsilon^2 w \log w)$ space. Second, we consider an out-of-order stream where each item is associated with a creation time and a numerical value, and items may be out of order with respect to their creation times. The goal is to estimate the edit distance to monotonicity with respect to the numerical value of items arranged in the order of creation times. We show that any randomized constant-approximate algorithm requires linear space.


Hamiltonian Connectivity of Twisted Hypercube-Like Networks under the Large Fault Model

Abstract: Twisted hypercube-like networks (THLNs) are an important class of interconnection networks for parallel computing systems, which include most popular variants of the hypercubes, such as crossed cubes, M\"obius cubes, twisted cubes and locally twisted cubes. This paper deals with the fault-tolerant hamiltonian connectivity of THLNs under the large fault model. Let $G$ be an $n$-dimensional THLN and $F \subseteq V(G)\bigcup E(G)$, where $n \geq 7$ and $|F| \leq 2n - 10$. We prove that for any two nodes $u,v \in V(G - F)$ satisfying a simple necessary condition on neighbors of $u$ and $v$, there exists a hamiltonian or near-hamiltonian path between $u$ and $v$ in $G-F$. The result extends further the fault-tolerant graph embedding capability of THLNs.


Randomized Speedup of the Bellman-Ford Algorithm

Abstract: We describe a variant of the Bellman-Ford algorithm for single-source shortest paths in graphs with negative edges but no negative cycles that randomly permutes the vertices and uses this randomized order to process the vertices within each pass of the algorithm. The modification reduces the worst-case expected number of relaxation steps of the algorithm, compared to the previously-best variant by Yen (1970), by a factor of 2/3 with high probability. We also use our high probability bound to add negative cycle detection to the randomized algorithm.


Improved Lower Bounds for the Shortest Superstring and Related Problems

Abstract: We study the approximation hardness of the Shortest Superstring, the Maximal Compression and the Maximum Asymmetric Traveling Salesperson (MAX-ATSP) problem. We introduce a new reduction method that produces strongly restricted instances of the Shortest Superstring problem, in which the maximal orbit size is eight (with no character appearing more than eight times) and all given strings having length four. Based on this reduction method, we are able to improve the best up to now known approximation lower bound for the Shortest Superstring problem and the Maximal Compression problem by an order of magnitude. The results imply also an improved approximation lower bound for the MAX-ATSP problem.


The Management and Use of Social Network Sites in a Government Department

Abstract: In this paper we report findings from a study of social network site use in a UK Government department. We have investigated this from a managerial, organisational perspective. We found at the study site that there are already several social network technologies in use, and that these: misalign with and problematize organisational boundaries; blur boundaries between working and social lives; present differing opportunities for control; have different visibilities; have overlapping functionality with each other and with other information technologies; that they evolve and change over time; and that their uptake is conditioned by existing infrastructure and availability. We find the organisational complexity that social technologies are often hoped to cut across is, in reality, something that shapes their uptake and use. We argue the idea of a single, central social network site for supporting cooperative work within an organisation will hit the same problems as any effort of centralisation in organisations. We argue that while there is still plenty of scope for design and innovation in this area, an important challenge now is in supporting organisations in managing what can best be referred to as a social network site 'ecosystem'.


Independent sets of words and the synchronization problem

Abstract: The synchronization problem is investigated for the class of locally strongly transitive automata introduced in a previous work of the authors. Some extensions of this problem related to the notions of stable set and word of minimal rank of an automaton are studied. An application to synchronizing colorings of aperiodic graphs with a Hamiltonian path is also considered.


Truthful Mechanisms for Agents that Value Privacy

Abstract: Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from the truthfulness; it is not incorporated in players' utility functions (and doing so has been shown to lead to non-truthfulness in some cases). In this work, we propose a new, general way of modelling privacy in players' utility functions. Specifically, we only assume that if an outcome $o$ has the property that any report of player $i$ would have led to $o$ with approximately the same probability, then $o$ has small privacy cost to player $i$. We give three mechanisms that are truthful with respect to our modelling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number $n$ of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of $n$).


Directed Steiner Tree and the Lasserre Hierarchy

Abstract: The goal for the Directed Steiner Tree problem is to find a minimum cost tree in a directed graph G=(V,E) that connects all terminals X to a given root r. It is well known that modulo a logarithmic factor it suffices to consider acyclic graphs where the nodes are arranged in L <= log |X| levels. Unfortunately the natural LP formulation has a |X|^(1/2) integrality gap already for 5 levels. We show that for every L, the O(L)-round Lasserre Strengthening of this LP has integrality gap O(L log |X|). This provides a polynomial time |X|^{epsilon}-approximation and a O(log^3 |X|) approximation in O(n^{log |X|) time, matching the best known approximation guarantee obtained by a greedy algorithm of Charikar et al.


A Formalization of Social Requirements for Human Interactions with Service Protocols

Abstract: Collaboration models and tools aim at improving the efficiency and effectiveness of human interactions. Although social relations among collaborators have been identified as having a strong influence on collaboration, they are still insufficiently taken into account in current collaboration models and tools. In this paper, the concept of service protocols is proposed as a model for human interactions supporting social requirements, i.e., sets of constraints on the relations among interacting humans. Service protocols have been proposed as an answer to the need for models for human interactions in which not only the potential sequences of activities are specified-as in process models-but also the constraints on the relations among collaborators. Service protocols are based on two main ideas: first, service protocols are rooted in the service-oriented architecture (SOA): each service protocol contains a service-oriented summary which provides a representation of the activities of an associated process model in SOA terms. Second, a class-based graph-referred to as a service network schema-restricts the set of potential service elements that may participate in the service protocol by defining constraints on nodes and constraints on arcs, i.e., social requirements. Another major contribution to the modelling of human interactions is a unified approach organized around the concept of service, understood in a broad sense with services being not only Web services, but also provided by humans.


Understanding the Role of Cooperation in Emergency Plan Construction

Abstract: In this paper we describe a proposal for information organization for computer supported cooperative work, while working with spatial information. It is focused on emergency response plan construction, and the requirements extracted from that task. At the centre of our proposal is the analysis of the structure of the cooperative workspace. We argue that the internal information representation should follow a spatial approach, tying the structure used to manage users with the structure used to manage information, suggesting the use of different spaces to represent the information. The gain we expect from this approach is the improved capacity to extract information on how people are cooperating and their relationship with the information they are working with. The ideas are introduced while focusing on real life emergency planning activities, where we discuss the current shortcomings of the cooperation strategies in use and propose a solution.


Modelling Competences for Partner Selection in Service-Oriented Virtual Organization Breeding Environments

Abstract: In the context of globalization and dynamic markets, collaboration among organizations is a condition sine qua non for organizations, especially small and medium enterprises, to remain competitive. Virtual organizations have been proposed as an organizational structure adapted to collaboration among organizations. The concept of Virtual Organization Breeding Environment (VOBE) has been proposed as a means to support the creation and operation of virtual organizations. With the rise of the service-oriented architecture (SOA), the concept of service-oriented VOBE (SOVOBE) has been proposed as a VOBE systematically organized around the concept of services. In the context of SOVOBEs, novel competence models supporting both service orientation and collaboration among organizations have to be developed to support efficiently partner selection, a key aspect of VO creation. In this paper, such a competence model is presented. Our competence model consists of a competence description model, a competence verification method, and a competence search method. The competence description model is an information model to describe organizations, their competences, and services they provides. The competence verification method enables the verification of the reliance and relevance of competence descriptions. The competence search method allows a VO planner to select appropriate partners based on VO specifications, encompassing competence requirements. Finally, implementation concerns based on the development of the prototype ErGo system are presented.


Efficient Super-Peer-Based Queries Routing: Simulation and Evaluation

Abstract: Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of internet traffic. P2P systems have emerged as a popular way to share huge volumes of data. Requirements for widely distributed information systems supporting virtual organizations have given rise to a new category of P2P systems called schema- based. In such systems each peer is a database management system in itself, ex-posing its own schema. A fundamental problem that confronts peer-to-peer applications is the efficient location of the node that stores a desired data item. In such settings, the main objective is the efficient search across peer databases by processing each incoming query without overly consuming bandwidth. The usability of these systems depends on effective techniques to find and retrieve data; however, efficient and effective routing of content- based queries is an emerging problem in P2P networks. In this paper, we propose an architecture, based on super-peers, and we focus on query routing. Our approach considers that super-Peers having similar interests are grouped together for an efficient query routing method. In such groups, called Knowledge-Super-Peers (KSP), super-peers submit queries that are often processed by members of this group.


Energy-aware scheduling under reliability and makespan constraints

Abstract: We consider a task graph mapped on a set of homogeneous processors. We aim at minimizing the energy consumption while enforcing two constraints: a prescribed bound on the execution time (or makespan), and a reliability threshold. Dynamic voltage and frequency scaling (DVFS) is an approach frequently used to reduce the energy consumption of a schedule, but slowing down the execution of a task to save energy is decreasing the reliability of the execution. In this work, to improve the reliability of a schedule while reducing the energy consumption, we allow for the re-execution of some tasks. We assess the complexity of the tri-criteria scheduling problem (makespan, reliability, energy) of deciding which task to re-execute, and at which speed each execution of a task should be done, with two different speed models: either processors can have arbitrary speeds (continuous model), or a processor can run at a finite number of different speeds and change its speed during a computation (VDD model). We propose several novel tri-criteria scheduling heuristics under the continuous speed model, and we evaluate them through a set of simulations. The two best heuristics turn out to be very efficient and complementary.


Agile Professional Virtual Community Inheritance via Adaptation of Social Protocols

Abstract: Support for human-to-human interactions over a network is still insufficient, particularly for professional virtual communities (PVC). Among other limitations, adaptation and learning-by-experience capabilities of humans are not taken into account in existing models for collaboration processes in PVC. This paper presents a model for adaptive human collaboration. A key element of this model is the use of negotiation for adaptation of social protocols modelling processes. A second contribution is the proposition of various adaptation propagation strategies as means for continuous management of the PVC inheritance.


Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements

Abstract: This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or positioning of the vision sensors. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model in order to describe the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem to estimate the corresponding features in correlated images given by quantized linear measurements. The estimated features have to comply with the compressed information and to represent consistent transformation between images. The correlation model is given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that estimates the compressed images such that they stay consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multi-view datasets. In addition, the proposed algorithm provides effective decoding performance that compares advantageously to independent coding solutions as well as state-of-the-art distributed coding schemes based on disparity learning.


A New Technique to Backup and Restore DBMS using XML and .NET Technologies

Abstract: In this paper, we proposed a new technique for backing up and restoring different Database Management Systems (DBMS). The technique is enabling to backup and restore a part of or the whole database using a unified interface using ASP.NET and XML technologies. It presents a Web Solution allowing the administrators to do their jobs from everywhere, locally or remotely. To show the importance of our solution, we have taken two case studies, oracle 11g and SQL Server 2008.


A New Proposed Technique to Improve Software Regression Testing Cost

Abstract: In this article, we describe the regression test process to test and verify the changes made on software. A developed technique use the automation test based on decision tree and test selection process in order to reduce the testing cost is given. The developed technique is applied to a practical case and the result show its improvement.


An Improvement of RC4 Cipher Using Vigenere Cipher

Abstract: This paper develops a new algorithm to improve the security of RC4. Given that RC4 cipher is widely used in the wireless communication and has some weaknesses in the security of RC4 cipher, our idea is based on the combination of the RC4 and the poly alphabetic cipher Vigen\`ere to give a new and more secure algorithm which we called VRC4. In this technique the plain text is encrypted using the classic RC4 cipher then re-encrypt the resulted cipher text using Vigen\`ere cipher to be a more secure cipher text. For simplicity, we have implemented our new algorithm in Java Script taking into consideration two factors: improvement of the security and the time complexity. To show the performance of the new algorithm, we have used the well known network cracking software KisMac.


Ground interpolation for the theory of equality

Abstract: Theory interpolation has found several successful applications in model checking. We present a novel method for computing interpolants for ground formulas in the theory of equality. The method produces interpolants from colored congruence graphs representing derivations in that theory. These graphs can be produced by conventional congruence closure algorithms in a straightforward manner. By working with graphs, rather than at the level of individual proof steps, we are able to derive interpolants that are pleasingly simple (conjunctions of Horn clauses) and smaller than those generated by other tools. Our interpolation method can be seen as a theory-specific implementation of a cooperative interpolation game between two provers. We present a generic version of the interpolation game, parametrized by the theory T, and define a general method to extract runs of the game from proofs in T and then generate interpolants from these runs.


Estimation of the Embedding Capacity in Pixel-pair based Watermarking Schemes

Abstract: Estimation of the Embedding capacity is an important problem specifically in reversible multi-pass watermarking and is required for analysis before any image can be watermarked. In this paper, we propose an efficient method for estimating the embedding capacity of a given cover image under multi-pass embedding, without actually embedding the watermark. We demonstrate this for a class of reversible watermarking schemes which operate on a disjoint group of pixels, specifically for pixel pairs. The proposed algorithm iteratively updates the co-occurrence matrix at every stage, to estimate the multi-pass embedding capacity, and is much more efficient vis-a-vis actual watermarking. We also suggest an extremely efficient, pre-computable tree based implementation which is conceptually similar to the co-occurrence based method, but provides the estimates in a single iteration, requiring a complexity akin to that of single pass capacity estimation. We also provide bounds on the embedding capacity. We finally show how our method can be easily used on a number of watermarking algorithms and specifically evaluate the performance of our algorithms on the benchmark watermarking schemes of Tian [11] and Coltuc [6].


Serf and Turf: Crowdturfing for Fun and Profit

Abstract: Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses using crowd-sourcing systems. However, crowd-sourcing systems can also pose a real challenge to existing security mechanisms deployed to protect Internet services. Many of these techniques make the assumption that malicious activity is generated automatically by machines, and perform poorly or fail if users can be organized to perform malicious tasks using crowd-sourcing systems. Through measurements, we have found surprising evidence showing that not only do malicious crowd-sourcing systems exist, but they are rapidly growing in both user base and total revenue. In this paper, we describe a significant effort to study and understand these "crowdturfing" systems in today's Internet. We use detailed crawls to extract data about the size and operational structure of these crowdturfing systems. We analyze details of campaigns offered and performed in these sites, and evaluate their end-to-end effectiveness by running active, non-malicious campaigns of our own. Finally, we study and compare the source of workers on crowdturfing sites in different countries. Our results suggest that campaigns on these systems are highly effective at reaching users, and their continuing growth poses a concrete threat to online communities such as social networks, both in the US and elsewhere.


The Disclosure of University Research for Third Parties: A Non-Market Perspective on an Italian University

Abstract: Nations, universities, and regional governments commit resources to promote the dissemination of scientific and technical knowledge. One focuses on knowledge-based innovations and the economic function of the university in terms of technology transfer, intellectual property, university-industry-government relations, etc. Faculties other than engineering or applied sciences, however, may not be able to recognize opportunities in this "linear model" of technology transfer. We elaborate a non-market perspective on the third mission in terms of disclosure of the knowledge and areas of expertise available for disclosure to other audiences at a provincial university. The use of ICT can enhance communication between actors on the supply and demand sides. Using an idea originally developed in the context of the Dutch science shops, the university staff was questionnaired about keywords and areas of expertise with the specific purpose of disclosing this information to audiences other than academic colleagues. The results were brought online in a thesaurus-like structure that enables users to access the university at the level of individual email address. This model stimulates variation on both the supply and demand side of the innovation process, and strengthens the accessibility and embeddedness of the knowledge base in a regional economy.


Coron : Plate-forme d'extraction de connaissances dans les bases de donn\'ees

Abstract: Coron is a domain and platform independent, multi-purposed data mining toolkit, which incorporates not only a rich collection of data mining algorithms, but also allows a number of auxiliary operations. To the best of our knowledge, a data mining toolkit designed specifically for itemset extraction and association rule generation like Coron does not exist elsewhere. Coron also provides support for preparing and filtering data, and for interpreting the extracted units of knowledge.


Revisiting Numerical Pattern Mining with Formal Concept Analysis

Abstract: In this paper, we investigate the problem of mining numerical data in the framework of Formal Concept Analysis. The usual way is to use a scaling procedure --transforming numerical attributes into binary ones-- leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and efficient way, and we prove it. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two original algorithms are proposed and used in an evaluation involving real-world data, showing the predominance of the present approach.


The Coron System