Swarm-Based Spatial Sorting

Abstract: Purpose: To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach: A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. Findings: The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications: Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications: The algorithm may find applications in distributed robotics. Originality/value: The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.


Detecting Errors in Spreadsheets

Abstract: The paper presents two complementary strategies for identifying errors in spreadsheet programs. The strategies presented are grounded on the assumption that spreadsheets are software, albeit of a different nature than conventional procedural software. Correspondingly, strategies for identifying errors have to take into account the inherent properties of spreadsheets as much as they have to recognize that the conceptual models of 'spreadsheet programmers' differ from the conceptual models of conventional programmers. Nevertheless, nobody can and will write a spreadsheet, without having such a conceptual model in mind, be it of numeric nature or be it of geometrical nature focused on some layout.


A Spreadsheet Auditing Tool Evaluated in an Industrial Context

Abstract: Amongst the large number of write-and-throw-away spreadsheets developed for one-time use there is a rather neglected proportion of spreadsheets that are huge, periodically used, and submitted to regular update-cycles like any conventionally evolving valuable legacy application software. However, due to the very nature of spreadsheets, their evolution is particularly tricky and therefore error-prone. In our strive to develop tools and methodologies to improve spreadsheet quality, we analysed consolidation spreadsheets of an internationally operating company for the errors they contain. The paper presents the results of the field audit, involving 78 spreadsheets with 60,446 non-empty cells. As a by-product, the study performed was also to validate our analysis tools in an industrial context. The evaluated auditing tool offers the auditor a new view on the formula structure of the spreadsheet by grouping similar formulas into equivalence classes. Our auditing approach defines three similarity criteria between formulae, namely copy, logical and structural equivalence. To improve the visualization of large spreadsheets, equivalences and data dependencies are displayed in separated windows that are interlinked with the spreadsheet. The auditing approach helps to find irregularities in the geometrical pattern of similar formulas.


Algorithmic Methods for Sponsored Search Advertising

Abstract: Modern commercial Internet search engines display advertisements along side the search results in response to user queries. Such sponsored search relies on market mechanisms to elicit prices for these advertisements, making use of an auction among advertisers who bid in order to have their ads shown for specific keywords. We present an overview of the current systems for such auctions and also describe the underlying game-theoretic aspects. The game involves three parties--advertisers, the search engine, and search users--and we present example research directions that emphasize the role of each. The algorithms for bidding and pricing in these games use techniques from three mathematical areas: mechanism design, optimization, and statistical estimation. Finally, we present some challenges in sponsored search advertising.


Efficiently Testing Sparse GF(2) Polynomials

Abstract: We give the first algorithm that is both query-efficient and time-efficient for testing whether an unknown function $f: \{0,1\}^n \to \{0,1\}$ is an $s$-sparse GF(2) polynomial versus $\eps$-far from every such polynomial. Our algorithm makes $\poly(s,1/\eps)$ black-box queries to $f$ and runs in time $n \cdot \poly(s,1/\eps)$. The only previous algorithm for this testing problem \cite{DLM+:07} used poly$(s,1/\eps)$ queries, but had running time exponential in $s$ and super-polynomial in $1/\eps$. Our approach significantly extends the ``testing by implicit learning'' methodology of \cite{DLM+:07}. The learning component of that earlier work was a brute-force exhaustive search over a concept class to find a hypothesis consistent with a sample of random examples. In this work, the learning component is a sophisticated exact learning algorithm for sparse GF(2) polynomials due to Schapire and Sellie \cite{SchapireSellie:96}. A crucial element of this work, which enables us to simulate the membership queries required by \cite{SchapireSellie:96}, is an analysis establishing new properties of how sparse GF(2) polynomials simplify under certain restrictions of ``low-influence'' sets of variables.


Distributed Self Management for Distributed Security Systems

Abstract: Distributed system as e.g. artificial immune systems, complex adaptive systems, or multi-agent systems are widely used in Computer Science, e.g. for network security, optimisations, or simulations. In these systems, small entities move through the network and perform certain tasks. At some time, the entities move to another place and require therefore information where to move is most profitable. Common used systems do not provide any information or use a centralised approach where a center delegates the entities. This article discusses whether small information about the neighbours enhances the performance of the overall system or not. Therefore, two information-protocols are introduced and analysed. In addition, the protocols are implemented and tested using the artificial immune system SANA that protects a network against intrusions.


Next Challenges in Bringing Artificial Immune Systems to Production in Network Security

Abstract: The human immune system protects the human body against various pathogens like e.g. biological viruses and bacteria. Artificial immune systems reuse the architecture, organization, and workflows of the human immune system for various problems in computer science. In the network security, the artificial immune system is used to secure a network and its nodes against intrusions like viruses, worms, and trojans. However, these approaches are far away from production where they are academic proof-of-concept implementations or use only a small part to protect against a certain intrusion. This article discusses the required steps to bring artificial immune systems into production in the network security domain. It furthermore figures out the challenges and provides the description and results of the prototype of an artificial immune system, which is SANA called.


A Network Protection Framework through Artificial Immunity

Abstract: Current network protection systems use a collection of intelligent components - e.g. classifiers or rule-based firewall systems to detect intrusions and anomalies and to secure a network against viruses, worms, or trojans. However, these network systems rely on individuality and support an architecture with less collaborative work of the protection components. They give less administration support for maintenance, but offer a large number of individual single points of failures - an ideal situation for network attacks to succeed. In this work, we discuss the required features, the performance, and the problems of a distributed protection system called {\it SANA}. It consists of a cooperative architecture, it is motivated by the human immune system, where the components correspond to artificial immune cells that are connected for their collaborative work. SANA promises a better protection against intruders than common known protection systems through an adaptive self-management while keeping the resources efficiently by an intelligent reduction of redundancies. We introduce a library of several novel and common used protection components and evaluate the performance of SANA by a proof-of-concept implementation.


(Mechanical) Reasoning on Infinite Extensive Games

Abstract: In order to better understand reasoning involved in analyzing infinite games in extensive form, we performed experiments in the proof assistant Coq that are reported here.


Tuplix Calculus Specifications of Financial Transfer Networks

Abstract: We study the application of Tuplix Calculus in modular financial budget design. We formalize organizational structure using financial transfer networks. We consider the notion of flux of money over a network, and a way to enforce the matching of influx and outflux for parts of a network. We exploit so-called signed attribute notation to make internal streams visible through encapsulations. Finally, we propose a Tuplix Calculus construct for the definition of data functions.


Parallel Pricing Algorithms for Multi--Dimensional Bermudan/American Options using Monte Carlo methods

Abstract: In this paper we present two parallel Monte Carlo based algorithms for pricing multi--dimensional Bermudan/American options. First approach relies on computation of the optimal exercise boundary while the second relies on classification of continuation and exercise values. We also evaluate the performance of both the algorithms in a desktop grid environment. We show the effectiveness of the proposed approaches in a heterogeneous computing environment, and identify scalability constraints due to the algorithmic structure.


A New Algorithm for Interactive Structural Image Segmentation

Abstract: This paper proposes a novel algorithm for the problem of structural image segmentation through an interactive model-based approach. Interaction is expressed in the model creation, which is done according to user traces drawn over a given input image. Both model and input are then represented by means of attributed relational graphs derived on the fly. Appearance features are taken into account as object attributes and structural properties are expressed as relational attributes. To cope with possible topological differences between both graphs, a new structure called the deformation graph is introduced. The segmentation process corresponds to finding a labelling of the input graph that minimizes the deformations introduced in the model when it is updated with input information. This approach has shown to be faster than other segmentation methods, with competitive output quality. Therefore, the method solves the problem of multiple label segmentation in an efficient way. Encouraging results on both natural and target-specific color images, as well as examples showing the reusability of the model, are presented and discussed.


Perfect tag identification protocol in RFID networks

Abstract: Radio Frequency IDentification (RFID) systems are becoming more and more popular in the field of ubiquitous computing, in particular for objects identification. An RFID system is composed by one or more readers and a number of tags. One of the main issues in an RFID network is the fast and reliable identification of all tags in the reader range. The reader issues some queries, and tags properly answer. Then, the reader must identify the tags from such answers. This is crucial for most applications. Since the transmission medium is shared, the typical problem to be faced is a MAC-like one, i.e. to avoid or limit the number of tags transmission collisions. We propose a protocol which, under some assumptions about transmission techniques, always achieves a 100% perfomance. It is based on a proper recursive splitting of the concurrent tags sets, until all tags have been identified. The other approaches present in literature have performances of about 42% in the average at most. The counterpart is a more sophisticated hardware to be deployed in the manufacture of low cost tags.


Platform-Independent Firewall Policy Representation

Abstract: In this paper we will discuss the design of abstract firewall model along with platform-independent policy definition language. We will also discuss the main design challenges and solutions to these challenges, as well as examine several differences in policy semantics between vendors and how it could be mapped to our platform-independent language. We will also touch upon a processing model, describing the mechanism by which an abstract policy could be compiled into a concrete firewall policy syntax. We will discuss briefly some future research directions, such as policy optimization and validation


Heavy-Tailed Limits for Medium Size Jobs and Comparison Scheduling

Abstract: We study the conditional sojourn time distributions of processor sharing (PS), foreground background processor sharing (FBPS) and shortest remaining processing time first (SRPT) scheduling disciplines on an event where the job size of a customer arriving in stationarity is smaller than exactly k>=0 out of the preceding m>=k arrivals. Then, conditioning on the preceding event, the sojourn time distribution of this newly arriving customer behaves asymptotically the same as if the customer were served in isolation with a server of rate (1-\rho)/(k+1) for PS/FBPS, and (1-\rho) for SRPT, respectively, where \rho is the traffic intensity. Hence, the introduced notion of conditional limits allows us to distinguish the asymptotic performance of the studied schedulers by showing that SRPT exhibits considerably better asymptotic behavior for relatively smaller jobs than PS/FBPS. Inspired by the preceding results, we propose an approximation to the SRPT discipline based on a novel adaptive job grouping mechanism that uses relative size comparison of a newly arriving job to the preceding m arrivals. Specifically, if the newly arriving job is smaller than k and larger than m-k of the previous m jobs, it is routed into class k. Then, the classes of smaller jobs are served with higher priorities using the static priority scheduling. The good performance of this mechanism, even for a small number of classes m+1, is demonstrated using the asymptotic queueing analysis under the heavy-tailed job requirements. We also discuss refinements of the comparison grouping mechanism that improve the accuracy of job classification at the expense of a small additional complexity.


Lower Bound for the Communication Complexity of the Russian Cards Problem

Abstract: In this paper it is shown that no public announcement scheme that can be modeled in Dynamic Epistemic Logic (DEL) can solve the Russian Cards Problem (RCP) in one announcement. Since DEL is a general model for any public announcement scheme we conclude that there exist no single announcement solution to the RCP. The proof demonstrates the utility of DEL in proving lower bounds for communication protocols. It is also shown that a general version of RCP has no two announcement solution when the adversary has sufficiently large number of cards.


P&P protocol: local coordination of mobile sensors for self-deployment

Abstract: The use of mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. In these networks, each sensor adapts its position on the basis of a local evaluation of the coverage efficiency, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over the area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the PUSH&PULL algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations, irregularly shaped target areas, and node failures.


Rollout Sampling Approximate Policy Iteration

Abstract: Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.


Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems

Abstract: Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.


Replication via Invalidating the Applicability of the Fixed Point Theorem

Abstract: We present a construction of a certain infinite complete partial order (CPO) that differs from the standard construction used in Scott's denotational semantics. In addition, we construct several other infinite CPO's. For some of those, we apply the usual Fixed Point Theorem (FPT) to yield a fixed point for every continuous function $\mu:2\to 2$ (where 2 denotes the set $\{0,1\}$), while for the other CPO's we cannot invoke that theorem to yield such fixed points. Every element of each of these CPO's is a binary string in the monotypic form and we show that invalidation of the applicability of the FPT to the CPO that Scott's constructed yields the concept of replication.


Fork Sequential Consistency is Blocking

Abstract: We consider an untrusted server storing shared data on behalf of clients. We show that no storage access protocol can on the one hand preserve sequential consistency and wait-freedom when the server is correct, and on the other hand always preserve fork sequential consistency.


Least change in the Determinant or Permanent of a matrix under perturbation of a single element: continuous and discrete cases

Abstract: We formulate the problem of finding the probability that the determinant of a matrix undergoes the least change upon perturbation of one of its elements, provided that most or all of the elements of the matrix are chosen at random and that the randomly chosen elements have a fixed probability of being non-zero. Also, we show that the procedure for finding the probability that the determinant undergoes the least change depends on whether the random variables for the matrix elements are continuous or discrete.


Approximate formulation of the probability that the Determinant or Permanent of a matrix undergoes the least change under perturbation of a single element

Abstract: In an earlier paper, we discussed the probability that the determinant of a matrix undergoes the least change upon perturbation of one of its elements, provided that most or all of the elements of the matrix are chosen at random and that the randomly chosen elements have a fixed probability of being non-zero. In this paper, we derive approximate formulas for that probability by assuming that the terms in the permanent of a matrix are independent of one another, and we apply that assumption to several classes of matrices. In the course of deriving those formulas, we identified several integer sequences that are not listed on Sloane's Web site.


Communication Lower Bounds Using Dual Polynomials

Abstract: Representations of Boolean functions by real polynomials play an important role in complexity theory. Typically, one is interested in the least degree of a polynomial p(x_1,...,x_n) that approximates or sign-represents a given Boolean function f(x_1,...,x_n). This article surveys a new and growing body of work in communication complexity that centers around the dual objects, i.e., polynomials that certify the difficulty of approximating or sign-representing a given function. We provide a unified guide to the following results, complete with all the key proofs: (1) Sherstov's Degree/Discrepancy Theorem, which translates lower bounds on the threshold degree of a Boolean function into upper bounds on the discrepancy of a related function; (2) Two different methods for proving lower bounds on bounded-error communication based on the approximate degree: Sherstov's pattern matrix method and Shi and Zhu's block composition method; (3) Extension of the pattern matrix method to the multiparty model, obtained by Lee and Shraibman and by Chattopadhyay and Ada, and the resulting improved lower bounds for DISJOINTNESS; (4) David and Pitassi's separation of NP and BPP in multiparty communication complexity for k=(1-eps)log n players.


Independence of P vs. NP in regards to oracle relativizations

Abstract: This is the third article in a series of four articles dealing with the P vs. NP question. The purpose of this work is to demonstrate that the methods used in the first two articles of this series are not affected by oracle relativizations. Furthermore, the solution to the P vs. NP problem is actually independent of oracle relativizations.


Mnesors for databases

Abstract: We add commutativity to axioms defining mnesors and substitute a bitrop for the lattice. We show that it can be applied to relational database querying: set union, intersection and selection are redifined only from the mnesor addition and the granular multiplication. Union-compatibility is not required.


Visual Checking of Spreadsheets

Abstract: The difference between surface and deep structures of a spreadsheet is a major cause of difficulty in checking spreadsheets. After a brief survey of current methods of checking (or debugging) spreadsheets, new visual methods of showing the deep structures are presented. Illustrations are given on how these visual methods can be employed in various interactive local and global debugging strategies.


Secure Network Coding Against the Contamination and Eavesdropping Adversaries

Abstract: In this paper, we propose an algorithm that targets contamination and eavesdropping adversaries. We consider the case when the number of independent packets available to the eavesdropper is less than the multicast capacity of the network. By means of our algorithm every node can verify the integrity of the received packets easily and an eavesdropper is unable to get any meaningful information about the source. We call it practical security if an eavesdropper is unable to get any meaningful information about the source.We show that, by giving up a small amount of overall capacity, our algorithm achieves achieves the practically secure condition at a probability of one. Furthermore, the communication overhead of our algorithm are negligible compared with previous works, since the transmission of the hash values and the code coefficients are both avoided.


Graph Algorithms for Improving Type-Logical Proof Search

Abstract: Proof nets are a graph theoretical representation of proofs in various fragments of type-logical grammar. In spite of this basis in graph theory, there has been relatively little attention to the use of graph theoretic algorithms for type-logical proof search. In this paper we will look at several ways in which standard graph theoretic algorithms can be used to restrict the search space. In particular, we will provide an O(n4) algorithm for selecting an optimal axiom link at any stage in the proof search as well as a O(kn3) algorithm for selecting the k best proof candidates.


Toward Fuzzy block theory

Abstract: This study, fundamentals of fuzzy block theory, and its application in assessment of stability in underground openings, has surveyed. Using fuzzy topics and inserting them in to key block theory, in two ways, fundamentals of fuzzy block theory has been presented. In indirect combining, by coupling of adaptive Neuro Fuzzy Inference System (NFIS) and classic block theory, we could extract possible damage parts around a tunnel. In direct solution, some principles of block theory, by means of different fuzzy facets theory, were rewritten.


A multilateral filtering method applied to airplane runway image

Abstract: By considering the features of the airport runway image filtering, an improved bilateral filtering method was proposed which can remove noise with edge preserving. Firstly the steerable filtering decomposition is used to calculate the sub-band parameters of 4 orients, and the texture feature matrix is then obtained from the sub-band local median energy. The texture similar, the spatial closer and the color similar functions are used to filter the image.The effect of the weighting function parameters is qualitatively analyzed also. In contrast with the standard bilateral filter and the simulation results for the real airport runway image show that the multilateral filtering is more effective than the standard bilateral filtering.


An optimization problem on the sphere

Abstract: We prove existence and uniqueness of the minimizer for the average geodesic distance to the points of a geodesically convex set on the sphere. This implies a corresponding existence and uniqueness result for an optimal algorithm for halfspace learning, when data and target functions are drawn from the uniform distribution.


A Kernel Method for the Two-Sample Problem

Abstract: We propose a framework for analyzing and comparing distributions, allowing us to design statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS). We present two tests based on large deviation bounds for the test statistic, while a third is based on the asymptotic distribution of this statistic. The test statistic can be computed in quadratic time, although efficient linear time approximations are available. Several classical metrics on distributions are recovered when the function space used to compute the difference in expectations is allowed to be more general (eg. a Banach space). We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.


Malicious Bayesian Congestion Games

Abstract: In this paper, we introduce malicious Bayesian congestion games as an extension to congestion games where players might act in a malicious way. In such a game each player has two types. Either the player is a rational player seeking to minimize her own delay, or - with a certain probability - the player is malicious in which case her only goal is to disturb the other players as much as possible. We show that such games do in general not possess a Bayesian Nash equilibrium in pure strategies (i.e. a pure Bayesian Nash equilibrium). Moreover, given a game, we show that it is NP-complete to decide whether it admits a pure Bayesian Nash equilibrium. This result even holds when resource latency functions are linear, each player is malicious with the same probability, and all strategy sets consist of singleton sets. For a slightly more restricted class of malicious Bayesian congestion games, we provide easy checkable properties that are necessary and sufficient for the existence of a pure Bayesian Nash equilibrium. In the second part of the paper we study the impact of the malicious types on the overall performance of the system (i.e. the social cost). To measure this impact, we use the Price of Malice. We provide (tight) bounds on the Price of Malice for an interesting class of malicious Bayesian congestion games. Moreover, we show that for certain congestion games the advent of malicious types can also be beneficial to the system in the sense that the social cost of the worst case equilibrium decreases. We provide a tight bound on the maximum factor by which this happens.


Certified Exact Transcendental Real Number Computation in Coq

Abstract: Reasoning about real number expressions in a proof assistant is challenging. Several problems in theorem proving can be solved by using exact real number computation. I have implemented a library for reasoning and computing with complete metric spaces in the Coq proof assistant and used this library to build a constructive real number implementation including elementary real number functions and proofs of correctness. Using this library, I have created a tactic that automatically proves strict inequalities over closed elementary real number expressions by computation.


Analysis of hydrocyclone performance based on information granulation theory

Abstract: This paper describes application of information granulation theory, on the analysis of hydrocyclone perforamance. In this manner, using a combining of Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are obtained(briefly called SONFIS). Balancing of crisp granules and sub fuzzy granules, within non fuzzy information (initial granulation), is rendered in an open-close iteration. Using two criteria, "simplicity of rules "and "adaptive threoshold error level", stability of algorithm is guaranteed. Validation of the proposed method, on the data set of the hydrocyclone is rendered.


A toolkit for a generative lexicon

Abstract: In this paper we describe the conception of a software toolkit designed for the construction, maintenance and collaborative use of a Generative Lexicon. In order to ease its portability and spreading use, this tool was built with free and open source products. We eventually tested the toolkit and showed it filters the adequate form of anaphoric reference to the modifier in endocentric compounds.


Algorithms for B\"uchi Games

Abstract: The classical algorithm for solving B\"uchi games requires time $O(n\cdot m)$ for game graphs with $n$ states and $m$ edges. For game graphs with constant outdegree, the best known algorithm has running time $O(n^2/\log n)$. We present two new algorithms for B\"uchi games. First, we give an algorithm that performs at most $O(m)$ more work than the classical algorithm, but runs in time O(n) on infinitely many graphs of constant outdegree on which the classical algorithm requires time $O(n^2)$. Second, we give an algorithm with running time $O(n\cdot m\cdot\log\delta(n)/\log n)$, where $1\le\delta(n)\le n$ is the outdegree of the game graph. Note that this algorithm performs asymptotically better than the classical algorithm if $\delta(n)=O(\log n)$.


Stochastic Limit-Average Games are in EXPTIME

Abstract: The value of a finite-state two-player zero-sum stochastic game with limit-average payoff can be approximated to within $\epsilon$ in time exponential in a polynomial in the size of the game times polynomial in logarithmic in $\frac{1}{\epsilon}$, for all $\epsilon>0$.


Fast Monte Carlo Estimation of Timing Yield: Importance Sampling with Stochastic Logical Effort (ISLE)

Abstract: In the nano era in integrated circuit fabrication technologies, the performance variability due to statistical process and circuit parameter variations is becoming more and more significant. Considerable effort has been expended in the EDA community during the past several years in trying to cope with the so-called statistical timing problem. Most of this effort has been aimed at generalizing the static timing analyzers to the statistical case. In this paper, we take a pragmatic approach in pursuit of making the Monte Carlo method for timing yield estimation practically feasible. The Monte Carlo method is widely used as a golden reference in assessing the accuracy of other timing yield estimation techniques. However, it is generally believed that it can not be used in practice for estimating timing yield as it requires too many costly full circuit simulations for acceptable accuracy. In this paper, we present a novel approach to constructing an improvedMonte Carlo estimator for timing yield which provides the same accuracy as the standard Monte Carlo estimator, but at a cost of much fewer full circuit simulations. This improved estimator is based on a novel combination of a variance reduction technique, importance sampling, and a stochastic generalization of the logical effort formalism for cheap but approximate delay estimation. The results we present demonstrate that our improved yield estimator achieves the same accuracy as the standard Monte Carlo estimator at a cost reduction reaching several orders of magnitude.


Sequential Design of Experiments via Linear Programming

Abstract: The celebrated multi-armed bandit problem in decision theory models the basic trade-off between exploration, or learning about the state of a system, and exploitation, or utilizing the system. In this paper we study the variant of the multi-armed bandit problem where the exploration phase involves costly experiments and occurs before the exploitation phase; and where each play of an arm during the exploration phase updates a prior belief about the arm. The problem of finding an inexpensive exploration strategy to optimize a certain exploitation objective is NP-Hard even when a single play reveals all information about an arm, and all exploration steps cost the same. We provide the first polynomial time constant-factor approximation algorithm for this class of problems. We show that this framework also generalizes several problems of interest studied in the context of data acquisition in sensor networks. Our analyses also extends to switching and setup costs, and to concave utility objectives. Our solution approach is via a novel linear program rounding technique based on stochastic packing. In addition to yielding exploration policies whose performance is within a small constant factor of the adaptive optimal policy, a nice feature of this approach is that the resulting policies explore the arms sequentially without revisiting any arm. Sequentiality is a well-studied concept in decision theory, and is very desirable in domains where multiple explorations can be conducted in parallel, for instance, in the sensor network context.


Small Approximate Pareto Sets for Bi-objective Shortest Paths and Other Problems

Abstract: We investigate the problem of computing a minimum set of solutions that approximates within a specified accuracy $\epsilon$ the Pareto curve of a multiobjective optimization problem. We show that for a broad class of bi-objective problems (containing many important widely studied problems such as shortest paths, spanning tree, and many others), we can compute in polynomial time an $\epsilon$-Pareto set that contains at most twice as many solutions as the minimum such set. Furthermore we show that the factor of 2 is tight for these problems, i.e., it is NP-hard to do better. We present upper and lower bounds for three or more objectives, as well as for the dual problem of computing a specified number $k$ of solutions which provide a good approximation to the Pareto curve.


Finger Indexed Sets: New Approaches

Abstract: In the particular case we have insertions/deletions at the tail of a given set S of $n$ one-dimensional elements, we present a simpler and more concrete algorithm than that presented in [Anderson, 2007] achieving the same (but also amortized) upper bound of $O(\sqrt{logd/loglogd})$ for finger searching queries, where $d$ is the number of sorted keys between the finger element and the target element we are looking for. Furthermore, in general case we have insertions/deletions anywhere we present a new randomized algorithm achieving the same expected time bounds. Even the new solutions achieve the optimal bounds in amortized or expected case, the advantage of simplicity is of great importance due to practical merits we gain.


MAPEL: Achieving Global Optimality for a Non-convex Wireless Power Control Problem

Abstract: Achieving weighted throughput maximization (WTM) through power control has been a long standing open problem in interference-limited wireless networks. The complicated coupling between the mutual interferences of links gives rise to a non-convex optimization problem. Previous work has considered the WTM problem in the high signal to interference-and-noise ratio (SINR) regime, where the problem can be approximated and transformed into a convex optimization problem through proper change of variables. In the general SINR regime, however, the approximation and transformation approach does not work. This paper proposes an algorithm, MAPEL, which globally converges to a global optimal solution of the WTM problem in the general SINR regime. The MAPEL algorithm is designed based on three key observations of the WTM problem: (1) the objective function is monotonically increasing in SINR, (2) the objective function can be transformed into a product of exponentiated linear fraction functions, and (3) the feasible set of the equivalent transformed problem is always normal although not necessarily convex. The MAPLE algorithm finds the desired optimal power control solution by constructing a series of polyblocks that approximate the feasible SINR region in increasing precision. Furthermore, by tuning the approximation factor in MAPEL, we could engineer a desirable tradeoff between optimality and convergence time. MAPEL provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. With the help of MAPEL, we evaluate the performance of several respective algorithms through extensive simulations.


Canonical polygon Queries on the plane: a New Approach

Abstract: The polygon retrieval problem on points is the problem of preprocessing a set of $n$ points on the plane, so that given a polygon query, the subset of points lying inside it can be reported efficiently. It is of great interest in areas such as Computer Graphics, CAD applications, Spatial Databases and GIS developing tasks. In this paper we study the problem of canonical $k$-vertex polygon queries on the plane. A canonical $k$-vertex polygon query always meets the following specific property: a point retrieval query can be transformed into a linear number (with respect to the number of vertices) of point retrievals for orthogonal objects such as rectangles and triangles (throughout this work we call a triangle orthogonal iff two of its edges are axis-parallel). We present two new algorithms for this problem. The first one requires $O(n\log^2{n})$ space and $O(k\frac{log^3n}{loglogn}+A)$ query time. A simple modification scheme on first algorithm lead us to a second solution, which consumes $O(n^2)$ space and $O(k \frac{logn}{loglogn}+A)$ query time, where $A$ denotes the size of the answer and $k$ is the number of vertices. The best previous solution for the general polygon retrieval problem uses $O(n^2)$ space and answers a query in $O(k\log{n}+A)$ time, where $k$ is the number of vertices. It is also very complicated and difficult to be implemented in a standard imperative programming language such as C or C++.


Increasing Linear Dynamic Range of Commercial Digital Photocamera Used in Imaging Systems with Optical Coding

Abstract: Methods of increasing linear optical dynamic range of commercial photocamera for optical-digital imaging systems are described. Use of such methods allows to use commercial photocameras for optical measurements. Experimental results are reported.


Innovation in Scholarly Communication: Vision and Projects from High-Energy Physics

Abstract: Having always been at the forefront of information management and open access, High-Energy Physics (HEP) proves to be an ideal test-bed for innovations in scholarly communication including new information and communication technologies. Three selected topics of scholarly communication in High-Energy Physics are presented here: A new open access business model, SCOAP3, a world-wide sponsoring consortium for peer-reviewed HEP literature; the design, development and deployment of an e-infrastructure for information management; and the emerging debate on long-term preservation, re-use and (open) access to HEP data.


State and history in operating systems

Abstract: A method of using recursive functions to describe state change is applied to process switching in UNIX-like operating systems.


The Margitron: A Generalised Perceptron with Margin

Abstract: We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. Experiments comparing the Margitron with decomposition SVMs on tasks involving linear kernels and 2-norm soft margin are also reported.


Sample Selection Bias Correction Theory

Abstract: This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. This relies on weights derived by various estimation techniques based on finite samples. We analyze the effect of an error in that estimation on the accuracy of the hypothesis returned by the learning algorithm for two estimation techniques: a cluster-based estimation technique and kernel mean matching. We also report the results of sample bias correction experiments with several data sets using these techniques. Our analysis is based on the novel concept of distributional stability which generalizes the existing concept of point-based stability. Much of our work and proof techniques can be used to analyze other importance weighting techniques and their effect on accuracy when using a distributionally stable algorithm.


Proof Search Specifications of Bisimulation and Modal Logics for the pi-Calculus

Abstract: We specify the operational semantics and bisimulation relations for the finite pi-calculus within a logic that contains the nabla quantifier for encoding generic judgments and definitions for encoding fixed points. Since we restrict to the finite case, the ability of the logic to unfold fixed points allows this logic to be complete for both the inductive nature of operational semantics and the coinductive nature of bisimulation. The nabla quantifier helps with the delicate issues surrounding the scope of variables within pi-calculus expressions and their executions (proofs). We illustrate several merits of the logical specifications permitted by this logic: they are natural and declarative; they contain no side-conditions concerning names of variables while maintaining a completely formal treatment of such variables; differences between late and open bisimulation relations arise from familar logic distinctions; the interplay between the three quantifiers (for all, exists, and nabla) and their scopes can explain the differences between early and late bisimulation and between various modal operators based on bound input and output actions; and proof search involving the application of inference rules, unification, and backtracking can provide complete proof systems for one-step transitions, bisimulation, and satisfaction in modal logic. We also illustrate how one can encode the pi-calculus with replications, in an extended logic with induction and co-induction.


Young's axiomatization of the Shapley value - a new proof

Abstract: We consider Young (1985)'s characterization of the Shapley value, and give a new proof of this axiomatization. Moreover, as applications of the new proof, we show that Young (1985)'s axiomatization of the Shapley value works on various well-known subclasses of TU games.


Network QoS Management in Cyber-Physical Systems

Abstract: Technical advances in ubiquitous sensing, embedded computing, and wireless communication are leading to a new generation of engineered systems called cyber-physical systems (CPS). CPS promises to transform the way we interact with the physical world just as the Internet transformed how we interact with one another. Before this vision becomes a reality, however, a large number of challenges have to be addressed. Network quality of service (QoS) management in this new realm is among those issues that deserve extensive research efforts. It is envisioned that wireless sensor/actuator networks (WSANs) will play an essential role in CPS. This paper examines the main characteristics of WSANs and the requirements of QoS provisioning in the context of cyber-physical computing. Several research topics and challenges are identified. As a sample solution, a feedback scheduling framework is proposed to tackle some of the identified challenges. A simple example is also presented that illustrates the effectiveness of the proposed solution.


LCSH, SKOS and Linked Data

Abstract: A technique for converting Library of Congress Subject Headings MARCXML to Simple Knowledge Organization System (SKOS) RDF is described. Strengths of the SKOS vocabulary are highlighted, as well as possible points for extension, and the integration of other semantic web vocabularies such as Dublin Core. An application for making the vocabulary available as linked-data on the Web is also described.


Fusion d'images: application au contr\^ole de la distribution des biopsies prostatiques

Abstract: This paper is about the application of a 3D ultrasound data fusion technique to the 3D reconstruction of prostate biopies in a reference volume. The method is introduced and its evaluation on a series of data coming from 15 patients is described.


Learning Low-Density Separators

Abstract: We define a novel, basic, unsupervised learning problem - learning the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task is relevant to several problems in machine learning, such as semi-supervised learning and clustering stability. We investigate the question of existence of a universally consistent algorithm for this problem. We propose two natural learning paradigms and prove that, on input unlabeled random samples generated by any member of a rich family of distributions, they are guaranteed to converge to the optimal separator for that distribution. We complement this result by showing that no learning algorithm for our task can achieve uniform learning rates (that are independent of the data generating distribution).


Steganography of VoIP Streams

Abstract: The paper concerns available steganographic techniques that can be used for creating covert channels for VoIP (Voice over Internet Protocol) streams. Apart from characterizing existing steganographic methods we provide new insights by presenting two new techniques. The first one is network steganography solution which exploits free/unused protocols' fields and is known for IP, UDP or TCP protocols but has never been applied to RTP (Real-Time Transport Protocol) and RTCP (Real-Time Control Protocol) which are characteristic for VoIP. The second method, called LACK (Lost Audio Packets Steganography), provides hybrid storage-timing covert channel by utilizing delayed audio packets. The results of the experiment, that was performed to estimate a total amount of data that can be covertly transferred during typical VoIP conversation phase, regardless of steganalysis, are also included in this paper.


Performability Aspects of the Atlas Vo; Using Lmbench Suite

Abstract: The ATLAS Virtual Organization is grid's largest Virtual Organization which is currently in full production stage. Hereby a case is being made that a user working within that VO is going to face a wide spectrum of different systems, whose heterogeneity is enough to count as "orders of magnitude" according to a number of metrics; including integer/float operations, memory throughput (STREAM) and communication latencies. Furthermore, the spread of performance does not appear to follow any known distribution pattern, which is demonstrated in graphs produced during May 2007 measurements. It is implied that the current practice where either "all-WNs-are-equal" or, the alternative of SPEC-based rating used by LCG/EGEE is an oversimplification which is inappropriate and expensive from an operational point of view, therefore new techniques are needed for optimal grid resources allocation.


A New Structural Property of SAT

Abstract: We review a minimum set of notions from our previous paper on structural properties of SAT at arXiv:0802.1790 that will allow us to define and discuss the "complete internal independence" of a decision problem. This property is strictly stronger than the independence property that was called "strong internal independence" in cited paper. We show that SAT exhibits this property. We argue that this form of independence of a decision problem is the strongest possible for a problem. By relying upon this maximally strong form of internal independence, we reformulate in more strict terms the informal remarks on possible exponentiality of SAT that concluded our previous paper. The net result of that reformulation is a hint for a proof for SAT being exponential. We conjecture that a complete proof of that proposition can be obtained by strictly following the line of given hint of proof.


Fuzzy Feedback Scheduling of Resource-Constrained Embedded Control Systems

Abstract: The quality of control (QoC) of a resource-constrained embedded control system may be jeopardized in dynamic environments with variable workload. This gives rise to the increasing demand of co-design of control and scheduling. To deal with uncertainties in resource availability, a fuzzy feedback scheduling (FFS) scheme is proposed in this paper. Within the framework of feedback scheduling, the sampling periods of control loops are dynamically adjusted using the fuzzy control technique. The feedback scheduler provides QoC guarantees in dynamic environments through maintaining the CPU utilization at a desired level. The framework and design methodology of the proposed FFS scheme are described in detail. A simplified mobile robot target tracking system is investigated as a case study to demonstrate the effectiveness of the proposed FFS scheme. The scheme is independent of task execution times, robust to measurement noises, and easy to implement, while incurring only a small overhead.


Neural Feedback Scheduling of Real-Time Control Tasks

Abstract: Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.


Cognitive Architecture for Direction of Attention Founded on Subliminal Memory Searches, Pseudorandom and Nonstop

Abstract: By way of explaining how a brain works logically, human associative memory is modeled with logical and memory neurons, corresponding to standard digital circuits. The resulting cognitive architecture incorporates basic psychological elements such as short term and long term memory. Novel to the architecture are memory searches using cues chosen pseudorandomly from short term memory. Recalls alternated with sensory images, many tens per second, are analyzed subliminally as an ongoing process, to determine a direction of attention in short term memory.


Marketing in Random Networks

Abstract: Viral marketing takes advantage of preexisting social networks among customers to achieve large changes in behaviour. Models of influence spread have been studied in a number of domains, including the effect of "word of mouth" in the promotion of new products or the diffusion of technologies. A social network can be represented by a graph where the nodes are individuals and the edges indicate a form of social relationship. The flow of influence through this network can be thought of as an increasing process of active nodes: as individuals become aware of new technologies, they have the potential to pass them on to their neighbours. The goal of marketing is to trigger a large cascade of adoptions. In this paper, we develop a mathematical model that allows to analyze the dynamics of the cascading sequence of nodes switching to the new technology. To this end we describe a continuous-time and a discrete-time models and analyse the proportion of nodes that adopt the new technology over time.


Coupling Component Systems towards Systems of Systems

Abstract: Systems of systems (SoS) are a hot topic in our "fully connected global world". Our aim is not to provide another definition of what SoS are, but rather to focus on the adequacy of reusing standard system architecting techniques within this approach in order to improve performance, fault detection and safety issues in large-scale coupled systems that definitely qualify as SoS, whatever the definition is. A key issue will be to secure the availability of the services provided by the SoS despite the evolution of the various systems composing the SoS. We will also tackle contracting issues and responsibility transfers, as they should be addressed to ensure the expected behavior of the SoS whilst the various independently contracted systems evolve asynchronously.


Statistical region-based active contours with exponential family observations

Abstract: In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain) and derive the corresponding evolution speed. A general result is stated within the framework of multi-parameter exponential family. More particularly, when using Maximum Likelihood estimators, the evolution speed has a closed-form expression that depends simply on the probability density function, while complicating additive terms appear when using other estimators, e.g. moments method. Experimental results on both synthesized and real images demonstrate the applicability of our approach.


Region-based active contour with noise and shape priors

Abstract: In this paper, we propose to combine formally noise and shape priors in region-based active contours. On the one hand, we use the general framework of exponential family as a prior model for noise. On the other hand, translation and scale invariant Legendre moments are considered to incorporate the shape prior (e.g. fidelity to a reference shape). The combination of the two prior terms in the active contour functional yields the final evolution equation whose evolution speed is rigorously derived using shape derivative tools. Experimental results on both synthetic images and real life cardiac echography data clearly demonstrate the robustness to initialization and noise, flexibility and large potential applicability of our segmentation algorithm.


Integrating Job Parallelism in Real-Time Scheduling Theory

Abstract: We investigate the global scheduling of sporadic, implicit deadline, real-time task systems on multiprocessor platforms. We provide a task model which integrates job parallelism. We prove that the time-complexity of the feasibility problem of these systems is linear relatively to the number of (sporadic) tasks for a fixed number of processors. We propose a scheduling algorithm theoretically optimal (i.e., preemptions and migrations neglected). Moreover, we provide an exact feasibility utilization bound. Lastly, we propose a technique to limit the number of migrations and preemptions.


Model Checking Event-B by Encoding into Alloy

Abstract: As systems become ever more complex, verification becomes more main stream. Event-B and Alloy are two formal specification languages based on fairly different methodologies. While Event-B uses theorem provers to prove that invariants hold for a given specification, Alloy uses a SAT-based model finder. In some settings, Event-B invariants may not be proved automatically, and so the often difficult step of interactive proof is required. One solution for this problem is to validate invariants with model checking. This work studies the encoding of Event-B machines and contexts to Alloy in order to perform temporal model checking with Alloy's SAT-based engine.


k-Hyperarc Consistency for Soft Constraints over Divisible Residuated Lattices

Abstract: We investigate the applicability of divisible residuated lattices (DRLs) as a general evaluation framework for soft constraint satisfaction problems (soft CSPs). DRLs are in fact natural candidates for this role, since they form the algebraic semantics of a large family of substructural and fuzzy logics. We present the following results. (i) We show that DRLs subsume important valuation structures for soft constraints, such as commutative idempotent semirings and fair valuation structures, in the sense that the last two are members of certain subvarieties of DRLs (namely, Heyting algebras and BL-algebras respectively). (ii) In the spirit of previous work of J. Larrosa and T. Schiex [2004], and S. Bistarelli and F. Gadducci [2006] we describe a polynomial-time algorithm that enforces k-hyperarc consistency on soft CSPs evaluated over DRLs. Observed that, in general, DRLs are neither idempotent nor totally ordered, this algorithm amounts to a generalization of the available algorithms that enforce k-hyperarc consistency.


Compressing Binary Decision Diagrams

Abstract: The paper introduces a new technique for compressing Binary Decision Diagrams in those cases where random access is not required. Using this technique, compression and decompression can be done in linear time in the size of the BDD and compression will in many cases reduce the size of the BDD to 1-2 bits per node. Empirical results for our compression technique are presented, including comparisons with previously introduced techniques, showing that the new technique dominate on all tested instances.


Tri de la table de faits et compression des index bitmaps avec alignement sur les mots

Abstract: Bitmap indexes are frequently used to index multidimensional data. They rely mostly on sequential input/output. Bitmaps can be compressed to reduce input/output costs and minimize CPU usage. The most efficient compression techniques are based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. This type of compression accelerates logical operations (AND, OR) over the bitmaps. However, run-length encoding is sensitive to the order of the facts. Thus, we propose to sort the fact tables. We review lexicographic, Gray-code, and block-wise sorting. We found that a lexicographic sort improves compression--sometimes generating indexes twice as small--and make indexes several times faster. While sorting takes time, this is partially offset by the fact that it is faster to index a sorted table. Column order is significant: it is generally preferable to put the columns having more distinct values at the beginning. A block-wise sort is much less efficient than a full sort. Moreover, we found that Gray-code sorting is not better than lexicographic sorting when using word-aligned compression.


Computational Representation of Linguistic Structures using Domain-Specific Languages

Abstract: We describe a modular system for generating sentences from formal definitions of underlying linguistic structures using domain-specific languages. The system uses Java in general, Prolog for lexical entries and custom domain-specific languages based on Functional Grammar and Functional Discourse Grammar notation, implemented using the ANTLR parser generator. We show how linguistic and technological parts can be brought together in a natural language processing system and how domain-specific languages can be used as a tool for consistent formal notation in linguistic description.


Design of Attitude Stability System for Prolate Dual-spin Satellite in Its Inclined Elliptical Orbit

Abstract: In general, most of communication satellites were designed to be operated in geostationary orbit. And many of them were designed in prolate dual-spin configuration. As a prolate dual-spin vehicle, they have to be stabilized against their internal energy dissipation effect. Several countries that located in southern hemisphere, has shown interest to use communication satellite. Because of those countries southern latitude, an idea emerged to incline the communication satellite (due to its prolate dualspin configuration) in elliptical orbit. This work is focused on designing Attitude Stability System for prolate dual-spin satellite in the effect of perturbed field of gravity due to the inclination of its elliptical orbit. DANDE (De-spin Active Nutation Damping Electronics) provides primary stabilization method for the satellite in its orbit. Classical Control Approach is used for the iteration of DANDE parameters. The control performance is evaluated based on time response analysis.


Exploring a type-theoretic approach to accessibility constraint modelling

Abstract: The type-theoretic modelling of DRT that [degroote06] proposed features continuations for the management of the context in which a clause has to be interpreted. This approach, while keeping the standard definitions of quantifier scope, translates the rules of the accessibility constraints of discourse referents inside the semantic recipes. In this paper, we deal with additional rules for these accessibility constraints. In particular in the case of discourse referents introduced by proper nouns, that negation does not block, and in the case of rhetorical relations that structure discourses. We show how this continuation-based approach applies to those accessibility constraints and how we can consider the parallel management of various principles.


Enriched MU-Calculi Module Checking

Abstract: The model checking problem for open systems has been intensively studied in the literature, for both finite-state (module checking) and infinite-state (pushdown module checking) systems, with respect to Ctl and Ctl*. In this paper, we further investigate this problem with respect to the \mu-calculus enriched with nominals and graded modalities (hybrid graded Mu-calculus), in both the finite-state and infinite-state settings. Using an automata-theoretic approach, we show that hybrid graded \mu-calculus module checking is solvable in exponential time, while hybrid graded \mu-calculus pushdown module checking is solvable in double-exponential time. These results are also tight since they match the known lower bounds for Ctl. We also investigate the module checking problem with respect to the hybrid graded \mu-calculus enriched with inverse programs (Fully enriched \mu-calculus): by showing a reduction from the domino problem, we show its undecidability. We conclude with a short overview of the model checking problem for the Fully enriched Mu-calculus and the fragments obtained by dropping at least one of the additional constructs.


Logic programming with social features

Abstract: In everyday life it happens that a person has to reason about what other people think and how they behave, in order to achieve his goals. In other words, an individual may be required to adapt his behaviour by reasoning about the others' mental state. In this paper we focus on a knowledge representation language derived from logic programming which both supports the representation of mental states of individual communities and provides each with the capability of reasoning about others' mental states and acting accordingly. The proposed semantics is shown to be translatable into stable model semantics of logic programs with aggregates.


Public Discourse in the Web Does Not Exhibit Group Polarization

Abstract: We performed a massive study of the dynamics of group deliberation among several websites containing millions of opinions on topics ranging from books to media. Contrary to the common phenomenon of group polarization observed offline, we measured a strong tendency towards moderate views in the course of time. This phenomenon possibly operates through a self-selection bias whereby previous comments and ratings elicit contrarian views that soften the previous opinions.


Covert Channels in SIP for VoIP signalling

Abstract: In this paper, we evaluate available steganographic techniques for SIP (Session Initiation Protocol) that can be used for creating covert channels during signaling phase of VoIP (Voice over IP) call. Apart from characterizing existing steganographic methods we provide new insights by introducing new techniques. We also estimate amount of data that can be transferred in signalling messages for typical IP telephony call.


Constructing Folksonomies from User-specified Relations on Flickr

Abstract: Many social Web sites allow users to publish content and annotate with descriptive metadata. In addition to flat tags, some social Web sites have recently began to allow users to organize their content and metadata hierarchically. The social photosharing site Flickr, for example, allows users to group related photos in sets, and related sets in collections. The social bookmarking site Del.icio.us similarly lets users group related tags into bundles. Although the sites themselves don't impose any constraints on how these hierarchies are used, individuals generally use them to capture relationships between concepts, most commonly the broader/narrower relations. Collective annotation of content with hierarchical relations may lead to an emergent classification system, called a folksonomy. While some researchers have explored using tags as evidence for learning folksonomies, we believe that hierarchical relations described above offer a high-quality source of evidence for this task. We propose a simple approach to aggregate shallow hierarchies created by many distinct Flickr users into a common folksonomy. Our approach uses statistics to determine if a particular relation should be retained or discarded. The relations are then woven together into larger hierarchies. Although we have not carried out a detailed quantitative evaluation of the approach, it looks very promising since it generates very reasonable, non-trivial hierarchies.


The Structure of Narrative: the Case of Film Scripts

Abstract: We analyze the style and structure of story narrative using the case of film scripts. The practical importance of this is noted, especially the need to have support tools for television movie writing. We use the Casablanca film script, and scripts from six episodes of CSI (Crime Scene Investigation). For analysis of style and structure, we quantify various central perspectives discussed in McKee's book, "Story: Substance, Structure, Style, and the Principles of Screenwriting". Film scripts offer a useful point of departure for exploration of the analysis of more general narratives. Our methodology, using Correspondence Analysis, and hierarchical clustering, is innovative in a range of areas that we discuss. In particular this work is groundbreaking in taking the qualitative analysis of McKee and grounding this analysis in a quantitative and algorithmic framework.


An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

Abstract: The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.


Feature Selection for Bayesian Evaluation of Trauma Death Risk

Abstract: In the last year more than 70,000 people have been brought to the UK hospitals with serious injuries. Each time a clinician has to urgently take a patient through a screening procedure to make a reliable decision on the trauma treatment. Typically, such procedure comprises around 20 tests; however the condition of a trauma patient remains very difficult to be tested properly. What happens if these tests are ambiguously interpreted, and information about the severity of the injury will come misleading? The mistake in a decision can be fatal: using a mild treatment can put a patient at risk of dying from posttraumatic shock, while using an overtreatment can also cause death. How can we reduce the risk of the death caused by unreliable decisions? It has been shown that probabilistic reasoning, based on the Bayesian methodology of averaging over decision models, allows clinicians to evaluate the uncertainty in decision making. Based on this methodology, in this paper we aim at selecting the most important screening tests, keeping a high performance. We assume that the probabilistic reasoning within the Bayesian methodology allows us to discover new relationships between the screening tests and uncertainty in decisions. In practice, selection of the most informative tests can also reduce the cost of a screening procedure in trauma care centers. In our experiments we use the UK Trauma data to compare the efficiency of the proposed technique in terms of the performance. We also compare the uncertainty in decisions in terms of entropy.


A New Type of Cipher: DICING_csb

Abstract: In this paper, we will propose a new type of cipher named DICING_csb, which is derived from our previous stream cipher DICING. It has applied a stream of subkey and an encryption form of block ciphers, so it may be viewed as a combinative of stream cipher and block cipher. Hence, the new type of cipher has fast rate like a stream cipher and need no MAC.


SPARK00: A Benchmark Package for the Compiler Evaluation of Irregular/Sparse Codes

Abstract: We propose a set of benchmarks that specifically targets a major cause of performance degradation in high performance computing platforms: irregular access patterns. These benchmarks are meant to be used to asses the performance of optimizing compilers on codes with a varying degree of irregular access. The irregularity caused by the use of pointers and indirection arrays are a major challenge for optimizing compilers. Codes containing such patterns are notoriously hard to optimize but they have a huge impact on the performance of modern architectures, which are under-utilized when encountering irregular memory accesses. In this paper, a set of benchmarks is described that explicitly measures the performance of kernels containing a variety of different access patterns found in real world applications. By offering a varying degree of complexity, we provide a platform for measuring the effectiveness of transformations. The difference in complexity stems from a difference in traversal patterns, the use of multiple indirections and control flow statements. The kernels used cover a variety of different access patterns, namely pointer traversals, indirection arrays, dynamic loop bounds and run-time dependent if-conditions. The kernels are small enough to be fully understood which makes this benchmark set very suitable for the evaluation of restructuring transformations.


Properly Coloured Cycles and Paths: Results and Open Problems

Abstract: In this paper, we consider a number of results and seven conjectures on properly edge-coloured (PC) paths and cycles in edge-coloured multigraphs. We overview some known results and prove new ones. In particular, we consider a family of transformations of an edge-coloured multigraph $G$ into an ordinary graph that allow us to check the existence PC cycles and PC $(s,t)$-paths in $G$ and, if they exist, to find shortest ones among them. We raise a problem of finding the optimal transformation and consider a possible solution to the problem.


Fusion for Evaluation of Image Classification in Uncertain Environments

Abstract: We present in this article a new evaluation method for classification and segmentation of textured images in uncertain environments. In uncertain environments, real classes and boundaries are known with only a partial certainty given by the experts. Most of the time, in many presented papers, only classification or only segmentation are considered and evaluated. Here, we propose to take into account both the classification and segmentation results according to the certainty given by the experts. We present the results of this method on a fusion of classifiers of sonar images for a seabed characterization.


DimReduction - Interactive Graphic Environment for Dimensionality Reduction

Abstract: Feature selection is a pattern recognition approach to choose important variables according to some criteria to distinguish or explain certain phenomena. There are many genomic and proteomic applications which rely on feature selection to answer questions such as: selecting signature genes which are informative about some biological state, e.g. normal tissues and several types of cancer; or defining a network of prediction or inference among elements such as genes, proteins, external stimuli and other elements of interest. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions are proposed, although it is difficult to point the best solution in general. The intent of this work is to provide an open-source multiplataform graphical environment to apply, test and compare many feature selection approaches suitable to be used in bioinformatics problems.


Intuitive visualization of the intelligence for the run-down of terrorist wire-pullers

Abstract: The investigation of the terrorist attack is a time-critical task. The investigators have a limited time window to diagnose the organizational background of the terrorists, to run down and arrest the wire-pullers, and to take an action to prevent or eradicate the terrorist attack. The intuitive interface to visualize the intelligence data set stimulates the investigators' experience and knowledge, and aids them in decision-making for an immediately effective action. This paper presents a computational method to analyze the intelligence data set on the collective actions of the perpetrators of the attack, and to visualize it into the form of a social network diagram which predicts the positions where the wire-pullers conceals themselves.


Every hierarchy of beliefs is a type

Abstract: When modeling game situations of incomplete information one usually considers the players' hierarchies of beliefs, a source of all sorts of complications. Hars\'anyi (1967-68)'s idea henceforth referred to as the "Hars\'anyi program" is that hierarchies of beliefs can be replaced by "types". The types constitute the "type space". In the purely measurable framework Heifetz and Samet (1998) formalize the concept of type spaces and prove the existence and the uniqueness of a universal type space. Meier (2001) shows that the purely measurable universal type space is complete, i.e., it is a consistent object. With the aim of adding the finishing touch to these results, we will prove in this paper that in the purely measurable framework every hierarchy of beliefs can be represented by a unique element of the complete universal type space.


Event Synchronization by Lightweight Message Passing

Abstract: Concurrent ML's events and event combinators facilitate modular concurrent programming with first-class synchronization abstractions. A standard implementation of these abstractions relies on fairly complex manipulations of first-class continuations in the underlying language. In this paper, we present a lightweight implementation of these abstractions in Concurrent Haskell, a language that already provides first-order message passing. At the heart of our implementation is a new distributed synchronization protocol. In contrast with several previous translations of event abstractions in concurrent languages, we remain faithful to the standard semantics for events and event combinators; for example, we retain the symmetry of $\mathtt{choose}$ for expressing selective communication.


An NP-hardness Result on the Monoid Frobenius Problem

Abstract: The following problem is NP-hard: given a regular expression $E$, decide if $E^*$ is not co-finite.


Extensional Uniformity for Boolean Circuits

Abstract: Imposing an extensional uniformity condition on a non-uniform circuit complexity class C means simply intersecting C with a uniform class L. By contrast, the usual intensional uniformity conditions require that a resource-bounded machine be able to exhibit the circuits in the circuit family defining C. We say that (C,L) has the "Uniformity Duality Property" if the extensionally uniform class C \cap L can be captured intensionally by means of adding so-called "L-numerical predicates" to the first-order descriptive complexity apparatus describing the connection language of the circuit family defining C. This paper exhibits positive instances and negative instances of the Uniformity Duality Property.


Goal-oriented Dialog as a Collaborative Subordinated Activity involving Collective Acceptance

Abstract: Modeling dialog as a collaborative activity consists notably in specifying the content of the Conversational Common Ground and the kind of social mental state involved. In previous work (Saget, 2006), we claim that Collective Acceptance is the proper social attitude for modeling Conversational Common Ground in the particular case of goal-oriented dialog. In this paper, a formalization of Collective Acceptance is shown, besides elements in order to integrate this attitude in a rational model of dialog are provided; and finally, a model of referential acts as being part of a collaborative activity is presented. The particular case of reference has been chosen in order to exemplify our claims.


Spiral Walk on Triangular Meshes : Adaptive Replication in Data P2P Networks

Abstract: We introduce a decentralized replication strategy for peer-to-peer file exchange based on exhaustive exploration of the neighborhood of any node in the network. The replication scheme lets the replicas evenly populate the network mesh, while regulating the total number of replicas at the same time. This is achieved by self adaptation to entering or leaving of nodes. Exhaustive exploration is achieved by a spiral walk algorithm that generates a number of messages linearly proportional to the number of visited nodes. It requires a dedicated topology (a triangular mesh on a closed surface). We introduce protocols for node connection and departure that maintain the triangular mesh at low computational and bandwidth cost. Search efficiency is increased using a mechanism based on dynamically allocated super peers. We conclude with a discussion on experimental validation results.


Design and Implementation Aspects of a novel Java P2P Simulator with GUI

Abstract: Peer-to-peer networks consist of thousands or millions of nodes that might join and leave arbitrarily. The evaluation of new protocols in real environments is many times practically impossible, especially at design and testing stages. The purpose of this paper is to describe the implementation aspects of a new Java based P2P simulator that has been developed to support scalability in the evaluation of such P2P dynamic environments. Evolving the functionality presented by previous solutions, we provide a friendly graphical user interface through which the high-level theoretic researcher/designer of a P2P system can easily construct an overlay with the desirable number of nodes and evaluate its operations using a number of key distributions. Furthermore, the simulator has built-in ability to produce statistics about the distributed structure. Emphasis was given to the parametrical configuration of the simulator. As a result the developed tool can be utilized in the simulation and evaluation procedures of a variety of different protocols, with only few changes in the Java code.


Succinct Geometric Indexes Supporting Point Location Queries

Abstract: We propose to design data structures called succinct geometric indexes of negligible space (more precisely, o(n) bits) that, by taking advantage of the n points in the data set permuted and stored elsewhere as a sequence, to support geometric queries in optimal time. Our first and main result is a succinct geometric index that can answer point location queries, a fundamental problem in computational geometry, on planar triangulations in O(lg n) time. We also design three variants of this index. The first supports point location using $\lg n + 2\sqrt{\lg n} + O(\lg^{1/4} n)$ point-line comparisons. The second supports point location in o(lg n) time when the coordinates are integers bounded by U. The last variant can answer point location in O(H+1) expected time, where H is the entropy of the query distribution. These results match the query efficiency of previous point location structures that use O(n) words or O(n lg n) bits, while saving drastic amounts of space. We then generalize our succinct geometric index to planar subdivisions, and design indexes for other types of queries. Finally, we apply our techniques to design the first implicit data structures that support point location in $O(\lg^2 n)$ time.


Environment Assumptions for Synthesis

Abstract: The synthesis problem asks to construct a reactive finite-state system from an $\omega$-regular specification. Initial specifications are often unrealizable, which means that there is no system that implements the specification. A common reason for unrealizability is that assumptions on the environment of the system are incomplete. We study the problem of correcting an unrealizable specification $\phi$ by computing an environment assumption $\psi$ such that the new specification $\psi\to\phi$ is realizable. Our aim is to construct an assumption $\psi$ that constrains only the environment and is as weak as possible. We present a two-step algorithm for computing assumptions. The algorithm operates on the game graph that is used to answer the realizability question. First, we compute a safety assumption that removes a minimal set of environment edges from the graph. Second, we compute a liveness assumption that puts fairness conditions on some of the remaining environment edges. We show that the problem of finding a minimal set of fair edges is computationally hard, and we use probabilistic games to compute a locally minimal fairness assumption.


Managing Critical Spreadsheets in a Compliant Environment

Abstract: The use of uncontrolled financial spreadsheets can expose organizations to unacceptable business and compliance risks, including errors in the financial reporting process, spreadsheet misuse and fraud, or even significant operational errors. These risks have been well documented and thoroughly researched. With the advent of regulatory mandates such as SOX 404 and FDICIA in the U.S., and MiFID, Basel II and Combined Code in the UK and Europe, leading tax and audit firms are now recommending that organizations automate their internal controls over critical spreadsheets and other end-user computing applications, including Microsoft Access databases. At a minimum, auditors mandate version control, change control and access control for operational spreadsheets, with more advanced controls for critical financial spreadsheets. This paper summarises the key issues regarding the establishment and maintenance of control of Business Critical spreadsheets.


A Structured Methodology for Spreadsheet Modelling

Abstract: In this paper, we discuss the problem of the software engineering of a class of business spreadsheet models. A methodology for structured software development is proposed, which is based on structured analysis of data, represented as Jackson diagrams. It is shown that this analysis allows a straightforward modularisation, and that individual modules may be represented with indentation in the block-structured form of structured programs. The benefits of structured format are discussed, in terms of comprehensibility, ease of maintenance, and reduction in errors. The capability of the methodology to provide a modular overview in the model is described, and examples are given. The potential for a reverse-engineering tool, to transform existing spreadsheet models is discussed.


Building Financial Accuracy into Spreadsheets

Abstract: Students learning how to apply spreadsheets to accounting problems are not always well served by the built-in financial functions. Problems can arise because of differences between UK and US practice, through anomalies in the functions themselves, and because the promptings of Wizards' engender an attitude of filling in the blanks on the screen, and hoping for the best. Some examples of these problems are described, and suggestions are presented for ways of improving the situation. Principally, it is suggested that spreadsheet prompts and 'Help' screens should offer integrated guidance, covering some aspects of financial practice, as well as matters of spreadsheet technique.


Classification of Spreadsheet Errors

Abstract: This paper describes a framework for a systematic classification of spreadsheet errors. This classification or taxonomy of errors is aimed at facilitating analysis and comprehension of the different types of spreadsheet errors. The taxonomy is an outcome of an investigation of the widespread problem of spreadsheet errors and an analysis of specific types of these errors. This paper contains a description of the various elements and categories of the classification and is supported by appropriate examples.


Risk Assessment For Spreadsheet Developments: Choosing Which Models to Audit

Abstract: Errors in spreadsheet applications and models are alarmingly common (some authorities, with justification cite spreadsheets containing errors as the norm rather than the exception). Faced with this body of evidence, the auditor can be faced with a huge task - the temptation may be to launch code inspections for every spreadsheet in an organisation. This can be very expensive and time-consuming. This paper describes risk assessment based on the "SpACE" audit methodology used by H M Customs & Excise's tax inspectors. This allows the auditor to target resources on the spreadsheets posing the highest risk of error, and justify the deployment of those resources to managers and clients. Since the opposite of audit risk is audit assurance the paper also offers an overview of some elements of good practice in the use of spreadsheets in business.


Neural network learning of optimal Kalman prediction and control

Abstract: Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw inferences for biological function. It has been suggested that the local cortical circuit (LCC) architecture may perform core functions (as yet unknown) that underlie sensory, motor,and other cortical processing. It is reasonable to conjecture that such functions may include prediction, the estimation or inference of missing or noisy sensory data, and the goal-driven generation of control signals. The resemblances found between the KPC NN architecture and that of the LCC are consistent with this conjecture.


From Data Topology to a Modular Classifier

Abstract: This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given.


Balanced Families of Perfect Hash Functions and Their Applications

Abstract: The construction of perfect hash functions is a well-studied topic. In this paper, this concept is generalized with the following definition. We say that a family of functions from $[n]$ to $[k]$ is a $\delta$-balanced $(n,k)$-family of perfect hash functions if for every $S \subseteq [n]$, $|S|=k$, the number of functions that are 1-1 on $S$ is between $T/\delta$ and $\delta T$ for some constant $T>0$. The standard definition of a family of perfect hash functions requires that there will be at least one function that is 1-1 on $S$, for each $S$ of size $k$. In the new notion of balanced families, we require the number of 1-1 functions to be almost the same (taking $\delta$ to be close to 1) for every such $S$. Our main result is that for any constant $\delta > 1$, a $\delta$-balanced $(n,k)$-family of perfect hash functions of size $2^{O(k \log \log k)} \log n$ can be constructed in time $2^{O(k \log \log k)} n \log n$. Using the technique of color-coding we can apply our explicit constructions to devise approximation algorithms for various counting problems in graphs. In particular, we exhibit a deterministic polynomial time algorithm for approximating both the number of simple paths of length $k$ and the number of simple cycles of size $k$ for any $k \leq O(\frac{\log n}{\log \log \log n})$ in a graph with $n$ vertices. The approximation is up to any fixed desirable relative error.


Network Connection Games with Disconnected Equilibria

Abstract: In this paper we extend a popular non-cooperative network creation game (NCG) to allow for disconnected equilibrium networks. There are n players, each is a vertex in a graph, and a strategy is a subset of players to build edges to. For each edge a player must pay a cost \alpha, and the individual cost for a player represents a trade-off between edge costs and shortest path lengths to all other players. We extend the model to a penalized game (PCG), for which we reduce the penalty counted towards the individual cost for a pair of disconnected players to a finite value \beta. Our analysis concentrates on existence, structure, and cost of disconnected Nash and strong equilibria. Although the PCG is not a potential game, pure Nash equilibria always and pure strong equilibria very often exist. We provide tight conditions under which disconnected Nash (strong) equilibria can evolve. Components of these equilibria must be Nash (strong) equilibria of a smaller NCG. However, in contrast to the NCG, for almost all parameter values no tree is a stable component. Finally, we present a detailed characterization of the price of anarchy that reveals cases in which the price of anarchy is \Theta(n) and thus several orders of magnitude larger than in the NCG. Perhaps surprisingly, the strong price of anarchy increases to at most 4. This indicates that global communication and coordination can be extremely valuable to overcome socially inferior topologies in distributed selfish network design.


A semantic space for modeling children's semantic memory

Abstract: The goal of this paper is to present a model of children's semantic memory, which is based on a corpus reproducing the kinds of texts children are exposed to. After presenting the literature in the development of the semantic memory, a preliminary French corpus of 3.2 million words is described. Similarities in the resulting semantic space are compared to human data on four tests: association norms, vocabulary test, semantic judgments and memory tasks. A second corpus is described, which is composed of subcorpora corresponding to various ages. This stratified corpus is intended as a basis for developmental studies. Finally, two applications of these models of semantic memory are presented: the first one aims at tracing the development of semantic similarities paragraph by paragraph; the second one describes an implementation of a model of text comprehension derived from the Construction-integration model (Kintsch, 1988, 1998) and based on such models of semantic memory.


Confidentiality, Integrity and High Availability with Open Source IT green

Abstract: This paper presents elements that form the structure of a network of data using secure stable and mature technologies that meet the requirement of having code free. The principle would be conflicting code open Tuesday where he wants to keep maximum control over the data but is already evidence that open source does not hide the famous backdoor possible in closed systems code. Basearemos this work experience gained in a real environment and using paravirtualization to show a situation more critical and now real in most companies, the virtualization of servers.


Modeling Loosely Annotated Images with Imagined Annotations

Abstract: In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning topic models. In particular, some imagined keywords are poured into the incomplete annotation through measuring similarity between keywords. Then, both given and imagined annotations are used to learning probabilistic topic models for automatically annotating new images. We conduct experiments on a typical Corel dataset of images and loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods (using a set of discrete blobs to represent an image). The proposed method improves word-driven probability Latent Semantic Analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.


Textual Entailment Recognizing by Theorem Proving Approach

Abstract: In this paper we present two original methods for recognizing textual inference. First one is a modified resolution method such that some linguistic considerations are introduced in the unification of two atoms. The approach is possible due to the recent methods of transforming texts in logic formulas. Second one is based on semantic relations in text, as presented in WordNet. Some similarities between these two methods are remarked.