A multiagent urban traffic simulation. Part II: dealing with the extraordinary

Abstract: In Probabilistic Risk Management, risk is characterized by two quantities: the magnitude (or severity) of the adverse consequences that can potentially result from the given activity or action, and by the likelihood of occurrence of the given adverse consequences. But a risk seldom exists in isolation: chain of consequences must be examined, as the outcome of one risk can increase the likelihood of other risks. Systemic theory must complement classic PRM. Indeed these chains are composed of many different elements, all of which may have a critical importance at many different levels. Furthermore, when urban catastrophes are envisioned, space and time constraints are key determinants of the workings and dynamics of these chains of catastrophes: models must include a correct spatial topology of the studied risk. Finally, literature insists on the importance small events can have on the risk on a greater scale: urban risks management models belong to self-organized criticality theory. We chose multiagent systems to incorporate this property in our model: the behavior of an agent can transform the dynamics of important groups of them.


Weak Kleene Algebra is Sound and (Possibly) Complete for Simulation

Abstract: We show that the axioms of Weak Kleene Algebra (WKA) are sound and complete for the theory of regular expressions modulo simulation equivalence, assuming their completeness for monodic trees (as conjectured by Takai and Furusawa).


Embedding into the rectilinear plane in optimal O*(n^2)

Abstract: We present an optimal O*(n^2) time algorithm for deciding if a metric space (X,d) on n points can be isometrically embedded into the plane endowed with the l_1-metric. It improves the O*(n^2 log^2 n) time algorithm of J. Edmonds (2008). Together with some ingredients introduced by J. Edmonds, our algorithm uses the concept of tight span and the injectivity of the l_1-plane. A different O*(n^2) time algorithm was recently proposed by D. Eppstein (2009).


Sorting from Noisy Information

Abstract: This paper studies problems of inferring order given noisy information. In these problems there is an unknown order (permutation) $\pi$ on $n$ elements denoted by $1,...,n$. We assume that information is generated in a way correlated with $\pi$. The goal is to find a maximum likelihood $\pi^*$ given the information observed. We will consider two different types of observations: noisy comparisons and noisy orders. The data in Noisy orders are permutations given from an exponential distribution correlated with \pi (this is also called the Mallow's model). The data in Noisy Comparisons is a signal given for each pair of elements which is correlated with their true ordering. In this paper we present polynomial time algorithms for solving both problems with high probability. As part of our proof we show that for both models the maximum likelihood solution $\pi^{\ast}$ is close to the original permutation $\pi$. Our results are of interest in applications to ranking, such as ranking in sports, or ranking of search items based on comparisons by experts.


A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract)

Abstract: Constrained Optimum Path (COP) problems appear in many real-life applications, especially on communication networks. Some of these problems have been considered and solved by specific techniques which are usually difficult to extend. In this paper, we introduce a novel local search modeling for solving some COPs by local search. The modeling features the compositionality, modularity, reuse and strengthens the benefits of Constrained-Based Local Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We show that side constraints can easily be added in the model. Computational results show the significance of the approach.


Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Abstract: Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance.


On Improving Local Search for Unsatisfiability

Abstract: Stochastic local search (SLS) has been an active field of research in the last few years, with new techniques and procedures being developed at an astonishing rate. SLS has been traditionally associated with satisfiability solving, that is, finding a solution for a given problem instance, as its intrinsic nature does not address unsatisfiable problems. Unsatisfiable instances were therefore commonly solved using backtrack search solvers. For this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge to use local search instead to prove unsatisfiability. More recently, two SLS solvers - Ranger and Gunsat - have been developed, which are able to prove unsatisfiability albeit being SLS solvers. In this paper, we first compare Ranger with Gunsat and then propose to improve Ranger performance using some of Gunsat's techniques, namely unit propagation look-ahead and extended resolution.


Integrating Conflict Driven Clause Learning to Local Search

Abstract: This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach for propositional satisfiability. It combines local search and conflict driven clause learning (CDCL) scheme. Each time the local search part reaches a local minimum, the CDCL is launched. For SAT problems it behaves like a tabu list, whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable sub-formula (MUS). Experimental results show good performances on many classes of SAT instances from the last SAT competitions.


A Constraint-directed Local Search Approach to Nurse Rostering Problems

Abstract: In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach.


Sonet Network Design Problems

Abstract: This paper presents a new method and a constraint-based objective function to solve two problems related to the design of optical telecommunication networks, namely the Synchronous Optical Network Ring Assignment Problem (SRAP) and the Intra-ring Synchronous Optical Network Design Problem (IDP). These network topology problems can be represented as a graph partitioning with capacity constraints as shown in previous works. We present here a new objective function and a new local search algorithm to solve these problems. Experiments conducted in Comet allow us to compare our method to previous ones and show that we obtain better results.


Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)

Abstract: We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical.


Toward an automaton Constraint for Local Search

Abstract: We explore the idea of using finite automata to implement new constraints for local search (this is already a successful technique in constraint-based global search). We show how it is possible to maintain incrementally the violations of a constraint and its decision variables from an automaton that describes a ground checker for that constraint. We establish the practicality of our approach idea on real-life personnel rostering problems, and show that it is competitive with the approach of [Pralong, 2007].


Adaboost with "Keypoint Presence Features" for Real-Time Vehicle Visual Detection

Abstract: We present promising results for real-time vehicle visual detection, obtained with adaBoost using new original ?keypoints presence features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (~ a SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt?) and thus have a ?semantic? meaning.


Introducing New AdaBoost Features for Real-Time Vehicle Detection

Abstract: This paper shows how to improve the real-time object detection in complex robotics applications, by exploring new visual features as AdaBoost weak classifiers. These new features are symmetric Haar filters (enforcing global horizontal and vertical symmetry) and N-connexity control points. Experimental evaluation on a car database show that the latter appear to provide the best results for the vehicle-detection problem.


Visual object categorization with new keypoint-based adaBoost features

Abstract: We present promising results for visual object categorization, obtained with adaBoost using new original ?keypoints-based features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Preliminary tests on a small subset of a pedestrians database also gives promising 97% recall with 92 % precision, which shows the generality of our new family of features. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt? in the case of lateral-cars) and thus have a ?semantic? meaning. We also made a first test on video for detecting vehicles from adaBoostselected keypoints filtered in real-time from all detected keypoints.


Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs

Abstract: We present a new modular traffic signs recognition system, successfully applied to both American and European speed limit signs. Our sign detection step is based only on shape-detection (rectangles or circles). This enables it to work on grayscale images, contrary to most European competitors, which eases robustness to illumination conditions (notably night operation). Speed sign candidates are classified (or rejected) by segmenting potential digits inside them (which is rather original and has several advantages), and then applying a neural digit recognition. The global detection rate is ~90% for both (standard) U.S. and E.U. speed signs, with a misclassification rate <1%, and no validated false alarm in >150 minutes of video. The system processes in real-time ~20 frames/s on a standard high-end laptop.


Simpler Proofs by Symbolic Perturbation

Abstract: In analyses of algorithms, a substantial amount of effort has often to be spent on the discussion of special cases. For example, when the analysis considers the cases X<Y and X>Y separately, one might have to be especially careful about what happens when X=Y. On the other hand, experience tells us that when a yet unregarded special case of this kind is discovered, one nearly always finds a way to handle it. This is typically done by modifying the analysis and/or the algorithm very slightly. In this article we substantiate this observation theoretically. We concentrate on deterministic algorithms for weighted combinatorial optimization problems. A problem instance of this kind is defined by its structure and a vector of weights. The concept of a null case is introduced as set of problem instances whose weight vectors constitute a nowhere open set (or null set) in the space of all possible weight configurations. An algorithm is called robust if any null case can be disregarded in the analysis of both its solution quality and resource requirements. We show that achieving robustness is only a matter of breaking ties the right way. More specifically, we show that the concept of symbolic perturbation known from the area of geometric algorithms guarantees that no surprises will happen in null cases. We argue that for a huge class of combinatorial optimization algorithms it is easy to verify that they implicitly use symbolic perturbation for breaking ties and thus can be analyzed under the assumption that some arbitrary null case never occurs. Finally, we prove that there exists a symbolic perturbation tie breaking policy for any algorithm.


Simple, efficient maxima-finding algorithms for multidimensional samples

Abstract: New algorithms are devised for finding the maxima of multidimensional point samples, one of the very first problems studied in computational geometry. The algorithms are very simple and easily coded and modified for practical needs. The expected complexity of some measures related to the performance of the algorithms is analyzed. We also compare the efficiency of the algorithms with a few major ones used in practice, and apply our algorithms to find the maximal layers and the longest common subsequences of multiple sequences.


Proceedings 6th International Workshop on Local Search Techniques in Constraint Satisfaction

Abstract: LSCS is a satellite workshop of the international conference on principles and practice of Constraint Programming (CP), since 2004. It is devoted to local search techniques in constraint satisfaction, and focuses on all aspects of local search techniques, including: design and implementation of new algorithms, hybrid stochastic-systematic search, reactive search optimization, adaptive search, modeling for local-search, global constraints, flexibility and robustness, learning methods, and specific applications.


Hybrid Semantics of Stochastic Programs with Dynamic Reconfiguration

Abstract: We begin by reviewing a technique to approximate the dynamics of stochastic programs --written in a stochastic process algebra-- by a hybrid system, suitable to capture a mixed discrete/continuous evolution. In a nutshell, the discrete dynamics is kept stochastic while the continuous evolution is given in terms of ODEs, and the overall technique, therefore, naturally associates a Piecewise Deterministic Markov Process with a stochastic program. The specific contribution in this work consists in an increase of the flexibility of the translation scheme, obtained by allowing a dynamic reconfiguration of the degree of discreteness/continuity of the semantics. We also discuss the relationships of this approach with other hybrid simulation strategies for biochemical systems.


Use of L-system mathematics for making new subfamily members of olfactory receptor full length genes, OR1D2, OR1D4 and OR1D5

Abstract: Ligands for only two human olfactory receptors are known. One of them, OR1D2, binds to Bourgeonal [Malnic B, Godfrey P-A, Buck L-B (2004) The human olfactory receptor gene family. Proc. Natl. Acad. Sci U. S. A. 101: 2584-2589 and Erratum in: Proc Natl Acad Sci U. S. A. (2004) 101: 7205]. OR1D2, OR1D4 and OR1D5 are three full length olfactory receptors present in an olfactory locus in human genome. These receptors are more than 80% identical in DNA sequences and have 108 base pair mismatches among them. We have used L-system mathematics and have been able to show a closely related subfamily of OR1D2, OR1D4 and OR1D5.


Balancing Bounded Treewidth Circuits

Abstract: Algorithmic tools for graphs of small treewidth are used to address questions in complexity theory. For both arithmetic and Boolean circuits, it is shown that any circuit of size $n^{O(1)}$ and treewidth $O(\log^i n)$ can be simulated by a circuit of width $O(\log^{i+1} n)$ and size $n^c$, where $c = O(1)$, if $i=0$, and $c=O(\log \log n)$ otherwise. For our main construction, we prove that multiplicatively disjoint arithmetic circuits of size $n^{O(1)}$ and treewidth $k$ can be simulated by bounded fan-in arithmetic formulas of depth $O(k^2\log n)$. From this we derive the analogous statement for syntactically multilinear arithmetic circuits, which strengthens a theorem of Mahajan and Rao. As another application, we derive that constant width arithmetic circuits of size $n^{O(1)}$ can be balanced to depth $O(\log n)$, provided certain restrictions are made on the use of iterated multiplication. Also from our main construction, we derive that Boolean bounded fan-in circuits of size $n^{O(1)}$ and treewidth $k$ can be simulated by bounded fan-in formulas of depth $O(k^2\log n)$. This strengthens in the non-uniform setting the known inclusion that $SC^0 \subseteq NC^1$. Finally, we apply our construction to show that {\sc reachability} for directed graphs of bounded treewidth is in $LogDCFL$.


Tracking object's type changes with fuzzy based fusion rule

Abstract: In this paper the behavior of three combinational rules for temporal/sequential attribute data fusion for target type estimation are analyzed. The comparative analysis is based on: Dempster's fusion rule proposed in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 (PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion rule, connecting the combination rules for information fusion with particular fuzzy operators, focusing on the t-norm based Conjunctive rule as an analog of the ordinary conjunctive rule and t-conorm based Disjunctive rule as an analog of the ordinary disjunctive rule. The way how different t-conorms and t-norms functions within TCN fusion rule influence over target type estimation performance is studied and estimated.


Weakening Assumptions for Deterministic Subexponential Time Non-Singular Matrix Completion

Abstract: In (Kabanets, Impagliazzo, 2004) it is shown how to decide the circuit polynomial identity testing problem (CPIT) in deterministic subexponential time, assuming hardness of some explicit multilinear polynomial family for arithmetical circuits. In this paper, a special case of CPIT is considered, namely low-degree non-singular matrix completion (NSMC). For this subclass of problems it is shown how to obtain the same deterministic time bound, using a weaker assumption in terms of determinantal complexity. Hardness-randomness tradeoffs will also be shown in the converse direction, in an effort to make progress on Valiant's VP versus VNP problem. To separate VP and VNP, it is known to be sufficient to prove that the determinantal complexity of the m-by-m permanent is $m^{\omega(\log m)}$. In this paper it is shown, for an appropriate notion of explicitness, that the existence of an explicit multilinear polynomial family with determinantal complexity m^{\omega(\log m)}$ is equivalent to the existence of an efficiently computable generator $G_n$ for multilinear NSMC with seed length $O(n^{1/\sqrt{\log n}})$. The latter is a combinatorial object that provides an efficient deterministic black-box algorithm for NSMC. ``Multilinear NSMC'' indicates that $G_n$ only has to work for matrices $M(x)$ of $poly(n)$ size in $n$ variables, for which $det(M(x))$ is a multilinear polynomial.


Prefetching of VoD Programs Based On ART1 Requesting Clustering

Abstract: In this paper, we propose a novel approach to group users according to the VoD user request pattern. We cluster the user requests based on ART1 neural network algorithm. The knowledge extracted from the cluster is used to prefetch the multimedia object from each cluster before the users request. We have developed an algorithm to cluster users according to the users request patterns based on ART1 neural network algorithm that offers an unsupervised clustering. This approach adapts to changes in user request patterns over period without losing previous information. Each cluster is represented as prototype vector by generalizing the most frequently used URLs that are accessed by all the cluster members. The simulation results of our proposed clustering and prefetching algorithm, shows enormous increase in the performance of streaming server. Our algorithm helps the servers agent to learn user preferences and discover the information about the corresponding sources and other similar interested individuals.


Prefix based Chaining Scheme for Streaming Popular Videos using Proxy servers in VoD

Abstract: Streaming high quality videos consumes significantly large amount of network resources. In this context request to service delay, network traffic, congestion and server overloading are the main parameters to be considered in video streaming over the internet that effect the quality of service (QoS). In this paper, we propose an efficient architecture as a cluster of proxy servers and clients that uses a peer to peer (P2P) approach to cooperatively stream the video using chaining technique. We consider the following two key issues in the proposed architecture (1) Prefix caching technique to accommodate more number of videos close to client (2) Cooperative client and proxy chaining to achieve the network efficiency. Our simulation results shows that the proposed approach yields a prefix caching close to the optimal solution minimizing WAN bandwidth usage on server-proxy path by utilizing the proxy-client and client-client path bandwidth, which is much cheaper than the expensive server proxy path bandwidth, server load, and client rejection ratio significantly using chaining.


Convergence Time Evaluation of Algorithms in MANETs

Abstract: Since the advent of wireless communication, the need for mobile ad hoc networks has been growing exponentially. This has opened up a Pandoras Box of algorithms for dealing with mobile ad hoc networks, or MANETs, as they are generally referred to. Most attempts made at evaluating these algorithms so far have focused on parameters such as throughput, packet delivery ratio, overhead etc. An analysis of the convergence times of these algorithms is still an open issue. The work carried out fills this gap by evaluating the algorithms on the basis of convergence time. Algorithms for MANETs can be classified into three categories: reactive, proactive, and hybrid protocols. In this project, we compare the convergence times of representative algorithms in each category, namely Ad hoc On Demand Distance Vector (AODV) reactive, Destination Sequence Distance Vector protocol (DSDV) proactive, and Temporally Ordered Routing Algorithm (TORA) hybrid. The algorithm performances are compared by simulating them in ns2. Tcl is used to conduct the simulations, while perl is used to extract data from the simulation output and calculate convergence time. The design of the experiments carried on is documented using Unified modeling Language. Also, a user interface is created using perl, which enables the user to either run a desired simulation and measure convergence time, or measure the convergence time of a simulation that has been run earlier.


Ludics and its Applications to natural Language Semantics

Abstract: Proofs, in Ludics, have an interpretation provided by their counter-proofs, that is the objects they interact with. We follow the same idea by proposing that sentence meanings are given by the counter-meanings they are opposed to in a dialectical interaction. The conception is at the intersection of a proof-theoretic and a game-theoretic accounts of semantics, but it enlarges them by allowing to deal with possibly infinite processes.


Some Thoughts on Hypercomputation

Abstract: Hypercomputation is a relatively new branch of computer science that emerged from the idea that the Church--Turing Thesis, which is supposed to describe what is computable and what is noncomputable, cannot possible be true. Because of its apparent validity, the Church--Turing Thesis has been used to investigate the possible limits of intelligence of any imaginable life form, and, consequently, the limits of information processing, since living beings are, among others, information processors. However, in the light of hypercomputation, which seems to be feasibly in our universe, one cannot impose arbitrary limits to what intelligence can achieve unless there are specific physical laws that prohibit the realization of something. In addition, hypercomputation allows us to ponder about aspects of communication between intelligent beings that have not been considered before


Estimating Entropy of Data Streams Using Compressed Counting

Abstract: The Shannon entropy is a widely used summary statistic, for example, network traffic measurement, anomaly detection, neural computations, spike trains, etc. This study focuses on estimating Shannon entropy of data streams. It is known that Shannon entropy can be approximated by Reenyi entropy or Tsallis entropy, which are both functions of the p-th frequency moments and approach Shannon entropy as p->1. Compressed Counting (CC) is a new method for approximating the p-th frequency moments of data streams. Our contributions include: 1) We prove that Renyi entropy is (much) better than Tsallis entropy for approximating Shannon entropy. 2) We propose the optimal quantile estimator for CC, which considerably improves the previous estimators. 3) Our experiments demonstrate that CC is indeed highly effective approximating the moments and entropies. We also demonstrate the crucial importance of utilizing the variance-bias trade-off.


Length of the Shortest Word in the Intersection of Regular Languages

Abstract: In this note, we give a construction that provides a tight lower bound of mn-1 for the length of the shortest word in the intersection of two regular languages with state complexities m and n.


Distributed Computing with Adaptive Heuristics

Abstract: We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best replying" to others' actions, and minimizing "regret", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.


Proceedings Second International Workshop on Computational Models for Cell Processes

Abstract: The second international workshop on Computational Models for Cell Processes (ComProc 2009) took place on November 3, 2009 at the Eindhoven University of Technology, in conjunction with Formal Methods 2009. The workshop was jointly organized with the EC-MOAN project. This volume contains the final versions of all contributions accepted for presentation at the workshop.


Covering Points by Disjoint Boxes with Outliers

Abstract: For a set of n points in the plane, we consider the axis--aligned (p,k)-Box Covering problem: Find p axis-aligned, pairwise-disjoint boxes that together contain n-k points. In this paper, we consider the boxes to be either squares or rectangles, and we want to minimize the area of the largest box. For general p we show that the problem is NP-hard for both squares and rectangles. For a small, fixed number p, we give algorithms that find the solution in the following running times: For squares we have O(n+k log k) time for p=1, and O(n log n+k^p log^p k time for p = 2,3. For rectangles we get O(n + k^3) for p = 1 and O(n log n+k^{2+p} log^{p-1} k) time for p = 2,3. In all cases, our algorithms use O(n) space.


Local and global approaches of affinity propagation clustering for large scale data

Abstract: Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two approaches are feasible and practicable.


Tool-Assisted Multi-Facet Analysis of Formal Specifications (Using Alelier-B and ProB)

Abstract: Tool-assisted analysis of software systems and convenient guides to practise the formal methods are still motivating challenges. This article addresses these challenges. We ex periment on analysing a formal specification from multiple aspects. The B method and the Atelier-B tool are used for formal specifications, for safety property analysis and for refinements. The ProB tool is used to supplement the study with model checking; it helps to discover errors and there fore to improve the former specifications.


High availability using virtualization

Abstract: High availability has always been one of the main problems for a data center. Till now high availability was achieved by host per host redundancy, a highly expensive method in terms of hardware and human costs. A new approach to the problem can be offered by virtualization. Using virtualization, it is possible to achieve a redundancy system for all the services running on a data center. This new approach to high availability allows to share the running virtual machines over the servers up and running, by exploiting the features of the virtualization layer: start, stop and move virtual machines between physical hosts. The system (3RC) is based on a finite state machine with hysteresis, providing the possibility to restart each virtual machine over any physical host, or reinstall it from scratch. A complete infrastructure has been developed to install operating system and middleware in a few minutes. To virtualize the main servers of a data center, a new procedure has been developed to migrate physical to virtual hosts. The whole Grid data center SNS-PISA is running at the moment in virtual environment under the high availability system. As extension of the 3RC architecture, several storage solutions have been tested to store and centralize all the virtual disks, from NAS to SAN, to grant data safety and access from everywhere. Exploiting virtualization and ability to automatically reinstall a host, we provide a sort of host on-demand, where the action on a virtual machine is performed only when a disaster occurs.


Decomposition of forging die for high speed machining

Abstract: Today's forging die manufacturing process must be adapted to several evolutions in machining process generation: CAD/CAM models, CAM software solutions and High Speed Machining (HSM). In this context, the adequacy between die shape and HSM process is in the core of machining preparation and process planning approaches. This paper deals with an original approach of machining preparation integrating this adequacy in the main tasks carried out. In this approach, the design of the machining process is based on two levels of decomposition of the geometrical model of a given die with respect to HSM cutting conditions (cutting speed and feed rate) and technological constrains (tool selection, features accessibility). This decomposition assists machining assistant to generate an HSM process. The result of this decomposition is the identification of machining features.


Circular tests for HSM machine tools: Bore machining application

Abstract: Today's High-Speed Machining (HSM) machine tool combines productivity and part quality. The difficulty inherent in HSM operations lies in understanding the impact of machine tool behaviour on machining time and part quality. Analysis of some of the relevant ISO standards (230-1998, 10791-1998) and a complementary protocol for better understanding HSM technology are presented in the first part of this paper. These ISO standards are devoted to the procedures implemented in order to study the behavior of machine tool. As these procedures do not integrate HSM technology, the need for HSM machine tool tests becomes critical to improving the trade-off between machining time and part quality. A new protocol for analysing the HSM technology impact during circular interpolation is presented in the second part of the paper. This protocol which allows evaluating kinematic machine tool behaviour during circular interpolation was designed from tests without machining. These tests are discussed and their results analysed in the paper. During the circular interpolation, axis capacities (such as acceleration or Jerk) related to certain setting parameters of the numerical control unit have a significant impact on the value of the feed rate. Consequently, a kinematic model for a circular-interpolated trajectory was developed on the basis of these parameters. Moreover, the link between part accuracy and kinematic machine tool behaviour was established. The kinematic model was ultimately validated on a bore machining simulation.


Machining strategy choice: performance VIEWER

Abstract: Nowadays high speed machining (HSM) machine tool combines productivity and part quality. So mould and die maker invested in HSM. Die and mould features are more and more complex shaped. Thus, it is difficult to choose the best machining strategy according to part shape. Geometrical analysis of machining features is not sufficient to make an optimal choice. Some research show that security, technical, functional and economical constrains must be taken into account to elaborate a machining strategy. During complex shape machining, production system limits induce feed rate decreases, thus loss of productivity, in some part areas. In this paper we propose to analyse these areas by estimating tool path quality. First we perform experiments on HSM machine tool to determine trajectory impact on machine tool behaviour. Then, we extract critical criteria and establish models of performance loss. Our work is focused on machine tool kinematical performance and numerical controller unit calculation capacity. We implement these models on Esprit CAM Software. During machining trajectory creation, critical part areas can be visualised and analysed. Parameters, such as, segment or arc lengths, nature of discontinuities encountered are used to analyse critical part areas. According to this visualisation, process development engineer should validate or modify the trajectory.


Decomposition of forging dies for machining planning

Abstract: This paper will provide a method to decompose forging dies for machining planning in the case of high speed machining finishing operations. This method lies on a machining feature approach model presented in the following paper. The two main decomposition phases, called Basic Machining Features Extraction and Process Planning Generation, are presented. These two decomposition phases integrates machining resources models and expert machining knowledge to provide an outstanding process planning.


D\'efinition d'une pi\`ece test pour la caract\'erisation d'une machine UGV

Abstract: In several fields like aeronautics, die and automotive, the machining of the parts is done more and more on high speed machines tools. Today, the offer for purchasing these machine tools is very wide. This situation poses the problem of the judicious and objective choice meeting industrial needs that must be necessary well expressed. The choice remains difficult insofar as the technical data provided to the customers by the manufacturers of machine tools are insufficient as well quantitatively as qualitatively. In this paper we present a protocol for the characterization of machines tools in order to direct the choice. The protocol is based on the one hand on no-load complementary tests to those recommended by the standards ISO 230 and ISO 10791 and on the other hand on the tests in load on a part test. In the first part, we present the industrial needs as well as an analysis of the technical data of machine tools. The second part is devoted to the study of the standards, the description of the protocol and the presentation of the results.


A zonotopic framework for functional abstractions

Abstract: This article formalizes an abstraction of input/output relations, based on parameterized zonotopes, which we call affine sets. We describe the abstract transfer functions and prove their correctness, which allows the generation of accurate numerical invariants. Other applications range from compositional reasoning to proofs of user-defined complex invariants and test case generation.


Sensitive White Space Detection with Spectral Covariance Sensing

Abstract: This paper proposes a novel, highly effective spectrum sensing algorithm for cognitive radio and whitespace applications. The proposed spectral covariance sensing (SCS) algorithm exploits the different statistical correlations of the received signal and noise in the frequency domain. Test statistics are computed from the covariance matrix of a partial spectrogram and compared with a decision threshold to determine whether a primary signal or arbitrary type is present or not. This detector is analyzed theoretically and verified through realistic open-source simulations using actual digital television signals captured in the US. Compared to the state of the art in the literature, SCS improves sensitivity by 3 dB for the same dwell time, which is a very significant improvement for this application. Further, it is shown that SCS is highly robust to noise uncertainty, whereas many other spectrum sensors are not.


Finding a sun in building-free graphs

Abstract: Deciding whether an arbitrary graph contains a sun was recently shown to be NP-complete. We show that whether a building-free graph contains a sun can be decided in O(min$\{m{n^3}, m^{1.5}n^2\}$) time and, if a sun exists, it can be found in the same time bound. The class of building-free graphs contains many interesting classes of perfect graphs such as Meyniel graphs which, in turn, contains classes such as hhd-free graphs, i-triangulated graphs, and parity graphs. Moreover, there are imperfect graphs that are building-free. The class of building-free graphs generalizes several classes of graphs for which an efficient test for the presence of a sun is known. We also present a vertex elimination scheme for the class of (building, gem)-free graphs. The class of (building, gem)-free graphs is a generalization of the class of distance hereditary graphs and a restriction of the class of (building, sun)-free graphs.


Password Based a Generalize Robust Security System Design Using Neural Network

Abstract: Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented.


3D/2D Registration of Mapping Catheter Images for Arrhythmia Interventional Assistance

Abstract: Radiofrequency (RF) catheter ablation has transformed treatment for tachyarrhythmias and has become first-line therapy for some tachycardias. The precise localization of the arrhythmogenic site and the positioning of the RF catheter over that site are problematic: they can impair the efficiency of the procedure and are time consuming (several hours). Electroanatomic mapping technologies are available that enable the display of the cardiac chambers and the relative position of ablation lesions. However, these are expensive and use custom-made catheters. The proposed methodology makes use of standard catheters and inexpensive technology in order to create a 3D volume of the heart chamber affected by the arrhythmia. Further, we propose a novel method that uses a priori 3D information of the mapping catheter in order to estimate the 3D locations of multiple electrodes across single view C-arm images. The monoplane algorithm is tested for feasibility on computer simulations and initial canine data.


Parallel Computation of Finite Element Navier-Stokes codes using MUMPS Solver

Abstract: The study deals with the parallelization of 2D and 3D finite element based Navier-Stokes codes using direct solvers. Development of sparse direct solvers using multifrontal solvers has significantly reduced the computational time of direct solution methods. Although limited by its stringent memory requirements, multifrontal solvers can be computationally efficient. First the performance of MUltifrontal Massively Parallel Solver (MUMPS) is evaluated for both 2D and 3D codes in terms of memory requirements and CPU times. The scalability of both Newton and modified Newton algorithms is tested.


Color Image Clustering using Block Truncation Algorithm

Abstract: With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters.


DAMQ-Based Schemes for Efficiently Using the Buffer Spaces of a NoC Router

Abstract: In this paper we present high performance dynamically allocated multi-queue (DAMQ) buffer schemes for fault tolerance systems on chip applications that require an interconnection network. Two or four virtual channels shared the same buffer space. On the message switching layer, we make improvement to boost system performance when there are faults involved in the components communication. The proposed schemes are when a node or a physical channel is deemed as faulty, the previous hop node will terminate the buffer occupancy of messages destined to the failed link. The buffer usage decisions are made at switching layer without interactions with higher abstract layer, thus buffer space will be released to messages destined to other healthy nodes quickly. Therefore, the buffer space will be efficiently used in case fault occurs at some nodes.


Abstract: Digital medical informatics and images are commonly used in hospitals today,. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM) to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients' data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common Object Request Broker Architecture (CORBA), Java Database Connectivity (JDBC), and Java language provide the capability to combine the DOMIM resources into an integrated, interoperable, and scalable system. The underneath technology, including IDL ORB, Event Service, IIOP JDBC/ODBC, legacy system wrapping and Java implementation are explored. This paper explores a distributed collaborative CORBA/JDBC based framework that will enhance medical information management requirements and development. It encompasses a new paradigm for the delivery of health services that requires process reengineering, cultural changes, as well as organizational changes


The intersection of two halfspaces has high threshold degree

Abstract: The threshold degree of a Boolean function f:{0,1}^n->{-1,+1} is the least degree of a real polynomial p such that f(x)=sgn p(x). We construct two halfspaces on {0,1}^n whose intersection has threshold degree Theta(sqrt n), an exponential improvement on previous lower bounds. This solves an open problem due to Klivans (2002) and rules out the use of perceptron-based techniques for PAC learning the intersection of two halfspaces, a central unresolved challenge in computational learning. We also prove that the intersection of two majority functions has threshold degree Omega(log n), which is tight and settles a conjecture of O'Donnell and Servedio (2003). Our proof consists of two parts. First, we show that for any nonconstant Boolean functions f and g, the intersection f(x)^g(y) has threshold degree O(d) if and only if ||f-F||_infty + ||g-G||_infty < 1 for some rational functions F, G of degree O(d). Second, we settle the least degree required for approximating a halfspace and a majority function to any given accuracy by rational functions. Our technique further allows us to make progress on Aaronson's challenge (2008) and contribute strong direct product theorems for polynomial representations of composed Boolean functions of the form F(f_1,...,f_n). In particular, we give an improved lower bound on the approximate degree of the AND-OR tree.


Towards Participatory Design of Multi-agent Approach to Transport Demands

Abstract: The design of multi-agent based simulations (MABS) is up to now mainly done in laboratories and based on designers' understanding of the activities to be simulated. Domain experts have little chance to directly validate agent behaviors. To fill this gap, we are investigating participatory methods of design, which allow users to participate in the design the pickup and delivery problem (PDP) in the taxi planning problem. In this paper, we present a participatory process for designing new socio-technical architectures to afford the taxi dispatch for this transportation system. The proposed dispatch architecture attempts to increase passenger satisfaction more globally, by concurrently dispatching multiple taxis to the same number of passengers in the same geographical region, and vis-avis human driver and dispatcher satisfaction.


Evaluation of Hindi to Punjabi Machine Translation System

Abstract: Machine Translation in India is relatively young. The earliest efforts date from the late 80s and early 90s. The success of every system is judged from its evaluation experimental results. Number of machine translation systems has been started for development but to the best of author knowledge, no high quality system has been completed which can be used in real applications. Recently, Punjabi University, Patiala, India has developed Punjabi to Hindi Machine translation system with high accuracy of about 92%. Both the systems i.e. system under question and developed system are between same closely related languages. Thus, this paper presents the evaluation results of Hindi to Punjabi machine translation system. It makes sense to use same evaluation criteria as that of Punjabi to Hindi Punjabi Machine Translation System. After evaluation, the accuracy of the system is found to be about 95%.


Management Of Volatile Information In Incremental Web Crawler

Abstract: Paper has been withdrawn.


Can Component/Service-Based Systems Be Proved Correct?

Abstract: Component-oriented and service-oriented approaches have gained a strong enthusiasm in industries and academia with a particular interest for service-oriented approaches. A component is a software entity with given functionalities, made available by a provider, and used to build other application within which it is integrated. The service concept and its use in web-based application development have a huge impact on reuse practices. Accordingly a considerable part of software architectures is influenced; these architectures are moving towards service-oriented architectures. Therefore applications (re)use services that are available elsewhere and many applications interact, without knowing each other, using services available via service servers and their published interfaces and functionalities. Industries propose, through various consortium, languages, technologies and standards. More academic works are also undertaken concerning semantics and formalisation of components and service-based systems. We consider here both streams of works in order to raise research concerns that will help in building quality software. Are there new challenging problems with respect to service-based software construction? Besides, what are the links and the advances compared to distributed systems?


A Branch and Cut Algorithm for the Halfspace Depth Problem

Abstract: The concept of \emph{data depth} in non-parametric multivariate descriptive statistics is the generalization of the univariate rank method to multivariate data. \emph{Halfspace depth} is a measure of data depth. Given a set $S$ of points and a point $p$, the halfspace depth (or rank) of $p$ is defined as the minimum number of points of $S$ contained in any closed halfspace with $p$ on its boundary. Computing halfspace depth is NP-hard, and it is equivalent to the Maximum Feasible Subsystem problem. In this paper a mixed integer program is formulated with the big-$M$ method for the halfspace depth problem. We suggest a branch and cut algorithm for these integer programs. In this algorithm, Chinneck's heuristic algorithm is used to find an upper bound and a related technique based on sensitivity analysis is used for branching. Irreducible Infeasible Subsystem (IIS) hitting set cuts are applied. We also suggest a binary search algorithm which may be more numerically stable. The algorithms are implemented with the BCP framework from the \textbf{COIN-OR} project.


Faster algorithms for the square root and reciprocal of power series

Abstract: We give new algorithms for the computation of square roots and reciprocals of power series in C[[x]]. If M(n) denotes the cost of multiplying polynomials of degree n, the square root to order n costs (1.333... + o(1)) M(n) and the reciprocal costs (1.444... + o(1)) M(n). These improve on the previous best results, respectively (1.8333... + o(1)) M(n) and (1.5 + o(1)) M(n).


Information Retrieval via Truncated Hilbert-Space Expansions

Abstract: In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents. In this paper, a new approach for representing term positions in documents is presented. The approach allows an efficient evaluation of term-positional information at query evaluation time. Three applications are investigated: a function-based ranking optimization representing a user-defined document region, a query expansion technique based on overlapping the term distributions in the top-ranked documents, and cluster analysis of terms in documents. Experimental results demonstrate the effectiveness of the proposed approach.


A Generalized Recursive Algorithm for Binary Multiplication based on Vedic Mathematics

Abstract: A generalized algorithm for multiplication is proposed through recursive application of the Nikhilam Sutra from Vedic Mathematics, operating in radix - 2 number system environment suitable for digital platforms. Statistical analysis has been carried out based on the number of recursions profile as a function of the smaller multiplicand. The proposed algorithm is efficient for smaller multiplicands as well, unlike most of the asymptotically fast algorithms. Further, a basic block schematic of Hardware Implementation of our algorithm is suggested to exploit parallelism and speed up the implementation of the algorithm in a multiprocessor environment.


Cloudbus Toolkit for Market-Oriented Cloud Computing

Abstract: This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.


High-Performance Cloud Computing: A View of Scientific Applications

Abstract: Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.


Engineering a Scalable High Quality Graph Partitioner

Abstract: We describe an approach to parallel graph partitioning that scales to hundreds of processors and produces a high solution quality. For example, for many instances from Walshaw's benchmark collection we improve the best known partitioning. We use the well known framework of multi-level graph partitioning. All components are implemented by scalable parallel algorithms. Quality improvements compared to previous systems are due to better prioritization of edges to be contracted, better approximation algorithms for identifying matchings, better local search heuristics, and perhaps most notably, a parallelization of the FM local search algorithm that works more locally than previous approaches.


Interference with Symbol-misalignment

Abstract: This paper studies the impact of interference asynchrony among different links in a wireless network. Without deliberate coordination and cooperation among the active links, there is a naturally occurring misalignment between the symbols of the targeted signal of a receiver and the symbols of the interfering signals. Interestingly, we show that the interference asynchrony can actually improve the BER performance, compared with the situation in which symbols of al signals ay aligned. In particular, we show that symbol misalignment can decrease the "effective interference power" and change the distribution of the interfering signals, in a way that results in lower BER. To ensure that symbol misalignment can be consistently attained, we propose two simple schemes that introduce time-varying symbol offsets to obtain an "average" performance of random symbol misalignment. Notably, our schemes do not change the simple receiver design structure; only the transmitters are modified in a minor way.


Congestion Control in the Internet by Employing a Ratio dependent Plant Herbivore Carnivorous Model

Abstract: The demand for Internet based services has exploded over the last decade. Many organizations use the Internet and particularly the World Wide Web as their primary medium for communication and business. This phenomenal growth has dramatically increased the performance requirements for the Internet. To have a high performance Internet, a good congestion control system is essential for it. The current work proposes that the congestion control in the Internet can be inspired from the population control tactics of the nature. Toward this idea, each flow (W) in the network is viewed as a species whose population size is congestion window size of the flow. By this assumption, congestion control problem is redefined as population control of flow species. This paper defines a three trophic food chain analogy in congestion control area, and gives a ratio dependent model to control population size of W species within this plant herbivore carnivorous food chain. Simulation results show that this model achieves fair bandwidth allocation, high utilization and small queue size. It does not maintain any per flow state in routers and have few computational loads per packet, which makes it scalable.


A Framework For Intelligent Multi Agent System Based Neural Network Classification Model

Abstract: TIntelligent multi agent systems have great potentials to use in different purposes and research areas. One of the important issues to apply intelligent multi agent systems in real world and virtual environment is to develop a framework that support machine learning model to reflect the whole complexity of the real world. In this paper, we proposed a framework of intelligent agent based neural network classification model to solve the problem of gap between two applicable flows of intelligent multi agent technology and learning model from real environment. We consider the new Supervised Multilayers Feed Forward Neural Network (SMFFNN) model as an intelligent classification for learning model in the framework. The framework earns the information from the respective environment and its behavior can be recognized by the weights. Therefore, the SMFFNN model that lies in the framework will give more benefits in finding the suitable information and the real weights from the environment which result for better recognition. The framework is applicable to different domains successfully and for the potential case study, the clinical organization and its domain is considered for the proposed framework


The Role of Spreadsheets in the Allied Irish Bank / Allfirst Currency Trading Fraud

Abstract: This brief paper outlines how spreadsheets were used as one of the vehicles for John Rusnak's fraud and the revenue control lessons this case gives us.


A real world network pricing game with less severe Braess' Paradox

Abstract: Internet and graphs are very much related. The graphical structure of internet has been studied extensively to provide efficient solutions to routing and other problems. But most of these studies assume a central authority which controls and manages the internet. In the recent years game theoretic models have been proposed which do not require a central authority and the users are assumed to be routing their flows selfishly. The existence of Nash Equilibria, congestion and the amount of inefficiency caused by this selfish routing is a major concern in this field. A type of paradox in the selfish routing networks, Braess' Paradox, first discovered by Braess, is a major contributor to inefficiency. Several pricing mechanisms have also been provided which give a game theoretical model between users(consumers) and ISPs ({Internet Service Providers} or sellers) for the internet. We propose a novel pricing mechanism, based on real world Internet network architecture, which reduces the severity of Braess' Paradox in selfish routing game theoretic networks. It's a pricing mechanism between combinatorial users and ISPs. We prove that Nash equilibria exists in this network and provide bounds on inefficiency . We use graphical properties of internet to prove our result. Several interesting extensions and future work have also been discussed.


Security Flaws in a Recent Ultralightweight RFID Protocol

Abstract: In 2006, Peris-Lopez et al. [1, 2, 3] initiated the design of ultralightweight RFID protocols -with the UMAP family of protocols- involving only simple bitwise logical or arithmetic operations such as bitwise XOR, OR, AND, and addition. This combination of operations was revealed later to be insufficient for security. Then, Chien et al. proposed the SASI protocol [4] with the aim of offering better security, by adding the bitwise rotation to the set of supported operations. The SASI protocol represented a milestone in the design of ultralightweight protocols, although certain attacks have been published against this scheme [5, 6, 7]. In 2008, a new protocol, named Gossamer [8], was proposed that can be considered a further development of both the UMAP family and SASI. Although no attacks have been published against Gossamer, Lee et al. [9] have recently published an alternative scheme that is highly reminiscent of SASI. In this paper, we show that Lee et al.'s scheme fails short of many of its security objectives, being vulnerable to several important attacks like traceability, full disclosure, cloning and desynchronization.


A critical look at power law modelling of the Internet

Abstract: This paper takes a critical look at the usefulness of power law models of the Internet. The twin focuses of the paper are Internet traffic and topology generation. The aim of the paper is twofold. Firstly it summarises the state of the art in power law modelling particularly giving attention to existing open research questions. Secondly it provides insight into the failings of such models and where progress needs to be made for power law research to feed through to actual improvements in network performance.


Improving Integral Cryptanalysis against Rijndael with Large Blocks

Abstract: This report presents new four-round integral properties against the Rijndael cipher with block sizes larger than 128 bits. Using higher-order multiset distinguishers and other well-known extensions of those properties, the deduced attacks reach up to 7 and 8 rounds of Rijndael variants with 160 up to 256-bit blocks. For example, a 7-rounds attack against Rijndael-224 has a time complexity equal to $2^{80}$.


Assessment of a percutaneous iliosacral screw insertion simulator

Abstract: BACKGROUND: Navigational simulator use for specialized training purposes is rather uncommon in orthopaedic and trauma surgery. However, it reveals providing a valuable tool to train orthopaedic surgeons and help them to plan complex surgical procedures. PURPOSE: This work's objective was to assess educational efficiency of a path simulator under fluoroscopic guidance applied to sacroiliac joint percutaneous screw fixation. MATERIALS AND METHODS: We evaluated 23 surgeons' accuracy inserting a guide-wire in a human cadaver experiment, following a pre-established procedure. These medical trainees were defined in three prospective respects: novice or skilled; with or without theoretical knowledge; with or without surgical procedure familiarity. Analysed criteria for each tested surgeon included the number of intraoperative X-rays taken in order to achieve the surgical procedure as well as an iatrogenic index reflecting the surgeon's ability to detect any hazardous trajectory at the time of performing said procedure. RESULTS: An average number of 13 X-rays was required for wire implantation by the G1 group. G2 group, assisted by the simulator use, required an average of 10 X-rays. A substantial difference was especially observed within the novice sub-group (N), with an average of 12.75 X-rays for the G1 category and an average of 8.5 X-rays for the G2 category. As far as the iatrogenic index is concerned, we were unable to observe any significant difference between the groups.


Self-optimized Coverage Coordination in Femtocell Networks

Abstract: This paper proposes a self-optimized coverage coordination scheme for two-tier femtocell networks, in which a femtocell base station adjusts the transmit power based on the statistics of the signal and the interference power that is measured at a femtocell downlink. Furthermore, an analytic expression is derived for the coverage leakage probability that a femtocell coverage area leaks into an outdoor macrocell. The coverage analysis is verified by simulation, which shows that the proposed scheme provides sufficient indoor femtocell coverage and that the femtocell coverage does not leak into an outdoor macrocell.


Finite element model selection using Particle Swarm Optimization

Abstract: This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has the least number of updated parameters and has the smallest parameter variable variation from the mean material properties. Two different objective functions are used to compare performance of the PSO algorithm.


Interference Mitigation Using Uplink Power Control for Two-Tier Femtocell Networks

Abstract: This paper proposes two interference mitigation strategies that adjust the maximum transmit power of femtocell users to suppress the cross-tier interference at a macrocell base station (BS). The open-loop and the closed-loop control suppress the cross-tier interference less than a fixed threshold and an adaptive threshold based on the noise and interference (NI) level at the macrocell BS, respectively. Simulation results show that both schemes effectively compensate the uplink throughput degradation of the macrocell BS due to the cross-tier interference and that the closed-loop control provides better femtocell throughput than the open-loop control at a minimal cost of macrocell throughput.


Improved Inapproximability Results for Maximum k-Colorable Subgraph

Abstract: We study the maximization version of the fundamental graph coloring problem. Here the goal is to color the vertices of a k-colorable graph with k colors so that a maximum fraction of edges are properly colored (i.e. their endpoints receive different colors). A random k-coloring properly colors an expected fraction 1-1/k of edges. We prove that given a graph promised to be k-colorable, it is NP-hard to find a k-coloring that properly colors more than a fraction ~1-O(1/k} of edges. Previously, only a hardness factor of 1-O(1/k^2) was known. Our result pins down the correct asymptotic dependence of the approximation factor on k. Along the way, we prove that approximating the Maximum 3-colorable subgraph problem within a factor greater than 32/33 is NP-hard. Using semidefinite programming, it is known that one can do better than a random coloring and properly color a fraction 1-1/k +2 ln k/k^2 of edges in polynomial time. We show that, assuming the 2-to-1 conjecture, it is hard to properly color (using k colors) more than a fraction 1-1/k + O(ln k/ k^2) of edges of a k-colorable graph.


State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation

Abstract: Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended


Positive Semidefinite Metric Learning with Boosting

Abstract: The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. \BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.


Enhancing network transmission capacity by efficiently allocating node capability

Abstract: A network's transmission capacity is the maximal rate of traffic inflow that the network can handle without causing congestion. Here we study how to enhance this quantity by redistributing the capability of individual nodes while preserving the total sum of node capability. We propose a practical and effective node-capability allocation scheme which allocates a node's capability based on the local knowledge of the node's connectivity. We show the scheme enhances the transmission capacity by two orders of magnitude for networks with heterogenous structures.


The Complexity of Translation Membership for Macro Tree Transducers

Abstract: Macro tree transducers (mtts) are a useful formal model for XML query and transformation languages. In this paper one of the fundamental decision problems on translations, namely the "translation membership problem" is studied for mtts. For a fixed translation, the translation membership problem asks whether a given input/output pair is element of the translation. For call-by-name mtts this problem is shown to be NP-complete. The main result is that translation membership for call-by-value mtts is in polynomial time. For several extensions, such as addition of regular look-ahead or the generalization to multi-return mtts, it is shown that translation membership still remains in PTIME.


An algorithm for computing cutpoints in finite metric spaces

Abstract: The theory of the tight span, a cell complex that can be associated to every metric $D$, offers a unifying view on existing approaches for analyzing distance data, in particular for decomposing a metric $D$ into a sum of simpler metrics as well as for representing it by certain specific edge-weighted graphs, often referred to as realizations of $D$. Many of these approaches involve the explicit or implicit computation of the so-called cutpoints of (the tight span of) $D$, such as the algorithm for computing the "building blocks" of optimal realizations of $D$ recently presented by A. Hertz and S. Varone. The main result of this paper is an algorithm for computing the set of these cutpoints for a metric $D$ on a finite set with $n$ elements in $O(n^3)$ time. As a direct consequence, this improves the run time of the aforementioned $O(n^6)$-algorithm by Hertz and Varone by ``three orders of magnitude''.


Accelerating the Execution of Matrix Languages on the Cell Broadband Engine Architecture

Abstract: Matrix languages, including MATLAB and Octave, are established standards for applications in science and engineering. They provide interactive programming environments that are easy to use due to their scripting languages with matrix data types. Current implementations of matrix languages do not fully utilise high-performance, special-purpose chip architectures such as the IBM PowerXCell processor (Cell), which is currently used in the fastest computer in the world. We present a new framework that extends Octave to harness the computational power of the Cell. With this framework the programmer is relieved of the burden of introducing explicit notions of parallelism. Instead the programmer uses a new matrix data-type to execute matrix operations in parallel on the synergistic processing elements (SPEs) of the Cell. We employ lazy evaluation semantics for our new matrix data-type to obtain execution traces of matrix operations. Traces are converted to data dependence graphs; operations in the data dependence graph are lowered (split into sub-matrices), scheduled and executed on the SPEs. Thereby we exploit (1) data parallelism, (2) instruction level parallelism, (3) pipeline parallelism and (4) task parallelism of matrix language programs. We conducted extensive experiments to show the validity of our approach. Our Cell-based implementation achieves speedups of up to a factor of 12 over code run on recent Intel Core2 Quad processors.


On the hardness of the noncommutative determinant

Abstract: In this paper we study the computational complexity of computing the noncommutative determinant. We first consider the arithmetic circuit complexity of computing the noncommutative determinant polynomial. Then, more generally, we also examine the complexity of computing the determinant (as a function) over noncommutative domains. Our hardness results are summarized below: 1. We show that if the noncommutative determinant polynomial has small noncommutative arithmetic circuits then so does the noncommutative permanent. Consequently, the commutative permanent polynomial has small commutative arithmetic circuits. 2. For any field F we show that computing the n X n permanent over F is polynomial-time reducible to computing the 2n X 2n (noncommutative) determinant whose entries are O(n^2) X O(n^2) matrices over the field F. 3. We also derive as a consequence that computing the n X n permanent over nonnegative rationals is polynomial-time reducible to computing the noncommutative determinant over Clifford algebras of n^{O(1)} dimension. Our techniques are elementary and use primarily the notion of the Hadamard Product of noncommutative polynomials.


Fractional differentiation based image processing

Abstract: There are many resources useful for processing images, most of them freely available and quite friendly to use. In spite of this abundance of tools, a study of the processing methods is still worthy of efforts. Here, we want to discuss the possibilities arising from the use of fractional differential calculus. This calculus evolved in the research field of pure mathematics until 1920, when applied science started to use it. Only recently, fractional calculus was involved in image processing methods. As we shall see, the fractional calculation is able to enhance the quality of images, with interesting possibilities in edge detection and image restoration. We suggest also the fractional differentiation as a tool to reveal faint objects in astronomical images.


Generating Concise and Readable Summaries of XML Documents

Abstract: XML has become the de-facto standard for data representation and exchange, resulting in large scale repositories and warehouses of XML data. In order for users to understand and explore these large collections, a summarized, bird's eye view of the available data is a necessity. In this paper, we are interested in semantic XML document summaries which present the "important" information available in an XML document to the user. In the best case, such a summary is a concise replacement for the original document itself. At the other extreme, it should at least help the user make an informed choice as to the relevance of the document to his needs. In this paper, we address the two main issues which arise in producing such meaningful and concise summaries: i) which tags or text units are important and should be included in the summary, ii) how to generate summaries of different sizes.%for different memory budgets. We conduct user studies with different real-life datasets and show that our methods are useful and effective in practice.


Towards Plugging Privacy Leaks in Domain Name System

Abstract: Privacy leaks are an unfortunate and an integral part of the current Internet domain name resolution. Each DNS query generated by a user reveals -- to one or more DNS servers -- the origin and target of that query. Over time, a user's browsing behavior might be exposed to entities with little or no trust. Current DNS privacy leaks stem from fundamental DNS features and are not easily fixable by simple patches. Moreover, privacy issues have been overlooked by DNS security efforts (i.e. DNSSEC) and are thus likely to propagate into future versions of DNS. In order to mitigate privacy issues in current DNS, this paper proposes a Privacy-Preserving Domain Name System (PPDNS), which maintains privacy during domain name resolution. PPDNS is based on distributed hash tables (DHTs), an alternative naming infrastructure, and computational private information retrieval (cPIR), an advanced cryptographic construct. PPDNS takes advantage of the DHT's index structure to improve name resolution query privacy, while leveraging cPIR to reduce communication overhead for bandwidth-sensitive clients. Our analysis shows that PPDNS is a viable approach for obtaining a higher degree of privacy for name resolution queries. PPDNS also serves as a demonstration of blending advanced systems techniques with their cryptographic counterparts.


Effectiveness and Limitations of Statistical Spam Filters

Abstract: In this paper we discuss the techniques involved in the design of the famous statistical spam filters that include Naive Bayes, Term Frequency-Inverse Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes Additive Regression Tree. We compare these techniques with each other in terms of accuracy, recall, precision, etc. Further, we discuss the effectiveness and limitations of statistical filters in filtering out various types of spam from legitimate e-mails.


Scalable Distributed-Memory External Sorting

Abstract: We engineer algorithms for sorting huge data sets on massively parallel machines. The algorithms are based on the multiway merging paradigm. We first outline an algorithm whose I/O requirement is close to a lower bound. Thus, in contrast to naive implementations of multiway merging and all other approaches known to us, the algorithm works with just two passes over the data even for the largest conceivable inputs. A second algorithm reduces communication overhead and uses more conventional specifications of the result at the cost of slightly increased I/O requirements. An implementation wins the well known sorting benchmark in several categories and by a large margin over its competitors.


A Component Based Heuristic Search Method with Evolutionary Eliminations

Abstract: Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.


Physical layer network coding with multiple antennas

Abstract: The two-phase MIMO NC (network coding) scheme can be used to boost the throughput in a two-way relay channel in which nodes are equipped with multiple antennas. The obvious strategy is for the relay node to extract the individual packets from the two end nodes and mix the two packets to form a network-coded packet. In this paper, we propose a new scheme called MIMO PNC (physical network coding), in which the relay extracts the summation and difference of the two end packets and then converts them to the network-coded form. MIMO PNC is a natural combination of the single-antenna PNC scheme and the linear MIMO detection scheme. The advantages of MIMO PNC are many. First, it removes the stringent carrier-phase requirement in single-antenna PNC. Second, it is linear in complexity with respect to the constellation size and the number of simultaneous data streams in MIMO. Simulation shows that MIMO PNC outperforms the straightforward MIMO NC significantly under random Rayleigh fading channel. Based on our analysis, we further conjecture that MIMO PNC outperforms MIMO NC under all possible realizations of the channel.


Applying physical layer network coding in wireless networks

Abstract: A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1-D regular linear networks with multiple random flows. The throughput improvements are even larger in 2-D regular networks: 200% and 100%, respectively.


On challenges and opportunities of designing integrated IT platforms for supporting knowledge works in organizations

Abstract: Designing and implementing comprehensive IT-based support environments for KM in organizations is fraught with many problems. Solving them requires intimate knowledge about the information usage in knowledge works and the scopes of technology intervention. In this paper, the Task-oriented Organizational Knowledge Management or TOKM, a design theory for building integrated IT platforms for supporting organizational KM, is proposed. TOKM brings together two apparently mutually exclusive practices of building KM systems, the task-based approach and the generic or universalistic approach. In developing the design, the information requirements of knowledge workers in light of an information usage model of knowledge works is studied. Then the model is extended to study possibilities of more advanced IT support and formulate them in form of a set of meta-requirements. Following the IS design theory paradigm, a set of artifacts are hypothesized to meet the requirements. Finally, a design method, as a possible approach of building an IT-based integrated platform, the Knowledge Work Support Platform (KWSP) to realize the artifacts in order to meet the requirements, is outlined. The KWSP is a powerful platform for building and maintaining a number of task-type specific Knowledge Work Support Systems (KWSS) on a common sharable platform. Each KWSS, for the task-type supported by it, can be easily designed to provide extensive and sophisticated support to individual as well as group of knowledge workers in performing their respective knowledge work instances


Communication scientifique : Pour le meilleur et pour le PEER

Abstract: This paper provides an overview (in French) of the European PEER project, focusing on its origins, the actual objectives and the technical deployment.


On building Information Warehouses

Abstract: One of the most important goals of information management (IM) is supporting the knowledge workers in performing their works. In this paper we examine issues of relevance, linkage and provenance of information, as accessed and used by the knowledge workers. These are usually not adequately addressed in most of the IT based solutions for IM. Here we propose a non-conventional approach for building information systems for supporting the knowledge workers which addresses these issues. The approach leads to the ideas of building Information Warehouses (IW) and Knowledge work Support Systems (KwSS). Such systems can open up potential for building innovative applications of significant impact, including those capable of helping organizations in implementing processes for double-loop learning.


Polynomially Correlated Knapsack is NP-complete

Abstract: 0-1 Knapsack is a fundamental NP-complete problem. In this article we prove that it remains NP-complete even when the weights of the objects in the packing constraints and their values in the objective function satisfy specific stringent conditions: the values are integral powers of the weights of the objects.


Type Safe Extensible Programming

Abstract: Software products evolve over time. Sometimes they evolve by adding new features, and sometimes by either fixing bugs or replacing outdated implementations with new ones. When software engineers fail to anticipate such evolution during development, they will eventually be forced to re-architect or re-build from scratch. Therefore, it has been common practice to prepare for changes so that software products are extensible over their lifetimes. However, making software extensible is challenging because it is difficult to anticipate successive changes and to provide adequate abstraction mechanisms over potential changes. Such extensibility mechanisms, furthermore, should not compromise any existing functionality during extension. Software engineers would benefit from a tool that provides a way to add extensions in a reliable way. It is natural to expect programming languages to serve this role. Extensible programming is one effort to address these issues. In this thesis, we present type safe extensible programming using the MLPolyR language. MLPolyR is an ML-like functional language whose type system provides type-safe extensibility mechanisms at several levels. After presenting the language, we will show how these extensibility mechanisms can be put to good use in the context of product line engineering. Product line engineering is an emerging software engineering paradigm that aims to manage variations, which originate from successive changes in software.


The Effect of Malice on the Social Optimum in Linear Load Balancing Games

Abstract: In this note we consider the following problem to study the effect of malicious players on the social optimum in load balancing games: Consider two players SOC and MAL controlling (1-f) and f fraction of the flow in a load balancing game. SOC tries to minimize the total cost faced by her players while MAL tries to maximize the same. If the latencies are linear, we show that this 2-player zero-sum game has a pure strategy Nash equilibrium. Moreover, we show that one of the optimal strategies for MAL is to play selfishly: let the f fraction of the flow be sent as when the flow was controlled by infinitesimal players playing selfishly and reaching a Nash equilibrium. This shows that a malicious player cannot cause more harm in this game than a set of selfish agents. We also introduce the notion of Cost of Malice - the ratio of the cost faced by SOC at equilibrium to (1-f)OPT, where OPT is the social optimum minimizing the cost of all the players. In linear load balancing games we bound the cost of malice by (1+f/2).


Multiple Cross-Layer Design Based Complete Architecture for Mobile Adhoc Networks

Abstract: Different cross layer design for mobile adhoc network focuses on different optimization purpose, different Quality of Service (QoS) metric and the functions like delay, priority handling, security, etc. Existing cross layer designs provide individual solution for congestion control, fault tolerance, power conservation, energy minimization and flow control and the major drawback is of high cost and overhead. In this paper, we propose to design multiple cross layer design based architecture to provide a combined solution for link failure management, power conservation, congestion control and admission control. By simulation results, we show that the average end to end delay, average energy consumption and the packet loss are considerably reduced with the increase in high throughput and good delivery ratio.


A unifying approach to picture grammars

Abstract: Several old and recent classes of picture grammars, that variously extend context-free string grammars in two dimensions, are based on rules that rewrite arrays of pixels. Such grammars can be unified and extended using a tiling based approach, whereby the right part of a rule is formalized by means of a finite set of permitted tiles. We focus on a simple type of tiling,named regional, and define the corresponding regional tile grammars. They include both Siromoney's (or Matz's) Kolam grammars and their generalization by Prusa, as well as Drewes's grid grammars. Regionally defined pictures can be recognized with polynomial-time complexity by an algorithm extending the CKY one for strings. Regional tile grammars and languages are strictly included into our previous tile grammars and languages, and are incomparable with Giammarresi-Restivo tiling systems (or Wang systems).


Decreasing Diagrams and Relative Termination

Abstract: In this paper we use the decreasing diagrams technique to show that a left-linear term rewrite system R is confluent if all its critical pairs are joinable and the critical pair steps are relatively terminating with respect to R. We further show how to encode the rule-labeling heuristic for decreasing diagrams as a satisfiability problem. Experimental data for both methods are presented.


Can we debug the Universe?

Abstract: Roughly, the Church-Turing thesis is a hypothesis that describes exactly what can be computed by any real or feasible conceptual computing device. Generally speaking, the computational metaphor is the idea that everything, including the universe itself, has a computational nature. However, if the Church-Turing thesis is not valid, then does it make sense to expect the construction of a computer program capable of simulating the whole Universe? In the lights of hypercomputation, the scientific discipline that is about computing beyond the Church-Turing barrier, the most natural answer to this question is: No. This note is a justification of this answer and its deeper meaning based on arguments from physics, the philosophy of the mind, and, of course, (hyper)computability theory.