Intelligent location of simultaneously active acoustic emission sources: Part I

Abstract: The intelligent acoustic emission locator is described in Part I, while Part II discusses blind source separation, time delay estimation and location of two simultaneously active continuous acoustic emission sources. The location of acoustic emission on complicated aircraft frame structures is a difficult problem of non-destructive testing. This article describes an intelligent acoustic emission source locator. The intelligent locator comprises a sensor antenna and a general regression neural network, which solves the location problem based on learning from examples. Locator performance was tested on different test specimens. Tests have shown that the accuracy of location depends on sound velocity and attenuation in the specimen, the dimensions of the tested area, and the properties of stored data. The location accuracy achieved by the intelligent locator is comparable to that obtained by the conventional triangulation method, while the applicability of the intelligent locator is more general since analysis of sonic ray paths is avoided. This is a promising method for non-destructive testing of aircraft frame structures by the acoustic emission method.


Intelligent location of simultaneously active acoustic emission sources: Part II

Abstract: Part I describes an intelligent acoustic emission locator, while Part II discusses blind source separation, time delay estimation and location of two continuous acoustic emission sources. Acoustic emission (AE) analysis is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. A difficult problem of AE analysis is separation and characterization of signal components when the signals from various sources and the mode of mixing are unknown. Recently, blind source separation (BSS) by independent component analysis (ICA) has been used to solve these problems. The purpose of this paper is to demonstrate the applicability of ICA to locate two independent simultaneously active acoustic emission sources on an aluminum band specimen. The method is promising for non-destructive testing of aircraft frame structures by acoustic emission analysis.


On-line Viterbi Algorithm and Its Relationship to Random Walks

Abstract: In this paper, we introduce the on-line Viterbi algorithm for decoding hidden Markov models (HMMs) in much smaller than linear space. Our analysis on two-state HMMs suggests that the expected maximum memory used to decode sequence of length $n$ with $m$-state HMM can be as low as $\Theta(m\log n)$, without a significant slow-down compared to the classical Viterbi algorithm. Classical Viterbi algorithm requires $O(mn)$ space, which is impractical for analysis of long DNA sequences (such as complete human genome chromosomes) and for continuous data streams. We also experimentally demonstrate the performance of the on-line Viterbi algorithm on a simple HMM for gene finding on both simulated and real DNA sequences.


Reducing SAT to 2-SAT

Abstract: Description of a polynomial time reduction of SAT to 2-SAT of polynomial size.


Geometric Complexity Theory V: On deciding nonvanishing of a generalized Littlewood-Richardson coefficient

Abstract: This article has been withdrawn because it has been merged with the earlier article GCT3 (arXiv: CS/0501076 [cs.CC]) in the series. The merged article is now available as: Geometric Complexity Theory III: on deciding nonvanishing of a Littlewood-Richardson Coefficient, Journal of Algebraic Combinatorics, vol. 36, issue 1, 2012, pp. 103-110. (Authors: Ketan Mulmuley, Hari Narayanan and Milind Sohoni) The new article in this GCT5 slot in the series is: Geometric Complexity Theory V: Equivalence between blackbox derandomization of polynomial identity testing and derandomization of Noether's Normalization Lemma, in the Proceedings of FOCS 2012 (abstract), arXiv:1209.5993 [cs.CC] (full version) (Author: Ketan Mulmuley)


On Almost Periodicity Criteria for Morphic Sequences in Some Particular Cases

Abstract: In some particular cases we give criteria for morphic sequences to be almost periodic (=uniformly recurrent). Namely, we deal with fixed points of non-erasing morphisms and with automatic sequences. In both cases a polynomial-time algorithm solving the problem is found. A result more or less supporting the conjecture of decidability of the general problem is given.


Geometric Complexity Theory VI: the flip via saturated and positive integer programming in representation theory and algebraic geometry

Abstract: This article belongs to a series on geometric complexity theory (GCT), an approach to the P vs. NP and related problems through algebraic geometry and representation theory. The basic principle behind this approach is called the flip. In essence, it reduces the negative hypothesis in complexity theory (the lower bound problems), such as the P vs. NP problem in characteristic zero, to the positive hypothesis in complexity theory (the upper bound problems): specifically, to showing that the problems of deciding nonvanishing of the fundamental structural constants in representation theory and algebraic geometry, such as the well known plethysm constants--or rather certain relaxed forms of these decision probelms--belong to the complexity class P. In this article, we suggest a plan for implementing the flip, i.e., for showing that these relaxed decision problems belong to P. This is based on the reduction of the preceding complexity-theoretic positive hypotheses to mathematical positivity hypotheses: specifically, to showing that there exist positive formulae--i.e. formulae with nonnegative coefficients--for the structural constants under consideration and certain functions associated with them. These turn out be intimately related to the similar positivity properties of the Kazhdan-Lusztig polynomials and the multiplicative structural constants of the canonical (global crystal) bases in the theory of Drinfeld-Jimbo quantum groups. The known proofs of these positivity properties depend on the Riemann hypothesis over finite fields and the related results. Thus the reduction here, in conjunction with the flip, in essence, says that the validity of the P vs. NP conjecture in characteristic zero is intimately linked to the Riemann hypothesis over finite fields and related problems.


Differential Recursion and Differentially Algebraic Functions

Abstract: Moore introduced a class of real-valued "recursive" functions by analogy with Kleene's formulation of the standard recursive functions. While his concise definition inspired a new line of research on analog computation, it contains some technical inaccuracies. Focusing on his "primitive recursive" functions, we pin down what is problematic and discuss possible attempts to remove the ambiguity regarding the behavior of the differential recursion operator on partial functions. It turns out that in any case the purported relation to differentially algebraic functions, and hence to Shannon's model of analog computation, fails.


The Complexity of HCP in Digraps with Degree Bound Two

Abstract: The Hamiltonian cycle problem (HCP) in digraphs D with degree bound two is solved by two mappings in this paper. The first bijection is between an incidence matrix C_{nm} of simple digraph and an incidence matrix F of balanced bipartite undirected graph G; The second mapping is from a perfect matching of G to a cycle of D. It proves that the complexity of HCP in D is polynomial, and finding a second non-isomorphism Hamiltonian cycle from a given Hamiltonian digraph with degree bound two is also polynomial. Lastly it deduces P=NP base on the results.


Inapproximability of Maximum Weighted Edge Biclique and Its Applications

Abstract: Given a bipartite graph $G = (V_1,V_2,E)$ where edges take on {\it both} positive and negative weights from set $\mathcal{S}$, the {\it maximum weighted edge biclique} problem, or $\mathcal{S}$-MWEB for short, asks to find a bipartite subgraph whose sum of edge weights is maximized. This problem has various applications in bioinformatics, machine learning and databases and its (in)approximability remains open. In this paper, we show that for a wide range of choices of $\mathcal{S}$, specifically when $| \frac{\min\mathcal{S}} {\max \mathcal{S}} | \in \Omega(\eta^{\delta-1/2}) \cap O(\eta^{1/2-\delta})$ (where $\eta = \max\{|V_1|, |V_2|\}$, and $\delta \in (0,1/2]$), no polynomial time algorithm can approximate $\mathcal{S}$-MWEB within a factor of $n^{\epsilon}$ for some $\epsilon > 0$ unless $\mathsf{RP = NP}$. This hardness result gives justification of the heuristic approaches adopted for various applied problems in the aforementioned areas, and indicates that good approximation algorithms are unlikely to exist. Specifically, we give two applications by showing that: 1) finding statistically significant biclusters in the SAMBA model, proposed in \cite{Tan02} for the analysis of microarray data, is $n^{\epsilon}$-inapproximable; and 2) no polynomial time algorithm exists for the Minimum Description Length with Holes problem \cite{Bu05} unless $\mathsf{RP=NP}$.


Refuting the Pseudo Attack on the REESSE1+ Cryptosystem

Abstract: We illustrate through example 1 and 2 that the condition at theorem 1 in [8] dissatisfies necessity, and the converse proposition of fact 1.1 in [8] does not hold, namely the condition Z/M - L/Ak < 1/(2 Ak^2) is not sufficient for f(i) + f(j) = f(k). Illuminate through an analysis and ex.3 that there is a logic error during deduction of fact 1.2, which causes each of fact 1.2, 1.3, 4 to be invalid. Demonstrate through ex.4 and 5 that each or the combination of qu+1 > qu * D at fact 4 and table 1 at fact 2.2 is not sufficient for f(i) + f(j) = f(k), property 1, 2, 3, 4, 5 each are invalid, and alg.1 based on fact 4 and alg.2 based on table 1 are disordered and wrong logically. Further, manifest through a repeated experiment and ex.5 that the data at table 2 is falsified, and the example in [8] is woven elaborately. We explain why Cx = Ax * W^f(x) (% M) is changed to Cx = (Ax * W^f(x))^d (% M) in REESSE1+ v2.1. To the signature fraud, we point out that [8] misunderstands the existence of T^-1 and Q^-1 % (M-1), and forging of Q can be easily avoided through moving H. Therefore, the conclusion of [8] that REESSE1+ is not secure at all (which connotes that [8] can extract a related private key from any public key in REESSE1+) is fully incorrect, and as long as the parameter Omega is fitly selected, REESSE1+ with Cx = Ax * W^f(x) (% M) is secure.


Revisiting the Issues On Netflow Sample and Export Performance

Abstract: The high volume of packets and packet rates of traffic on some router links makes it exceedingly difficult for routers to examine every packet in order to keep detailed statistics about the traffic which is traversing the router. Sampling is commonly applied on routers in order to limit the load incurred by the collection of information that the router has to undertake when evaluating flow information for monitoring purposes. The sampling process in nearly all cases is a deterministic process of choosing 1 in every N packets on a per-interface basis, and then forming the flow statistics based on the collected sampled statistics. Even though this sampling may not be significant for some statistics, such as packet rate, others can be severely distorted. However, it is important to consider the sampling techniques and their relative accuracy when applied to different traffic patterns. The main disadvantage of sampling is the loss of accuracy in the collected trace when compared to the original traffic stream. To date there has not been a detailed analysis of the impact of sampling at a router in various traffic profiles and flow criteria. In this paper, we assess the performance of the sampling process as used in NetFlow in detail, and we discuss some techniques for the compensation of loss of monitoring detail.


Optimal Synthesis of Multiple Algorithms

Abstract: In this paper we give a definition of "algorithm," "finite algorithm," "equivalent algorithms," and what it means for a single algorithm to dominate a set of algorithms. We define a derived algorithm which may have a smaller mean execution time than any of its component algorithms. We give an explicit expression for the mean execution time (when it exists) of the derived algorithm. We give several illustrative examples of derived algorithms with two component algorithms. We include mean execution time solutions for two-algorithm processors whose joint density of execution times are of several general forms. For the case in which the joint density for a two-algorithm processor is a step function, we give a maximum-likelihood estimation scheme with which to analyze empirical processing time data.


P-adic arithmetic coding

Abstract: A new incremental algorithm for data compression is presented. For a sequence of input symbols algorithm incrementally constructs a p-adic integer number as an output. Decoding process starts with less significant part of a p-adic integer and incrementally reconstructs a sequence of input symbols. Algorithm is based on certain features of p-adic numbers and p-adic norm. p-adic coding algorithm may be considered as of generalization a popular compression technique - arithmetic coding algorithms. It is shown that for p = 2 the algorithm works as integer variant of arithmetic coding; for a special class of models it gives exactly the same codes as Huffman's algorithm, for another special model and a specific alphabet it gives Golomb-Rice codes.


Lessons Learned from the deployment of a high-interaction honeypot

Abstract: This paper presents an experimental study and the lessons learned from the observation of the attackers when logged on a compromised machine. The results are based on a six months period during which a controlled experiment has been run with a high interaction honeypot. We correlate our findings with those obtained with a worldwide distributed system of lowinteraction honeypots.


Availability assessment of SunOS/Solaris Unix Systems based on Syslogd and wtmpx logfiles : a case study

Abstract: This paper presents a measurement-based availability assessment study using field data collected during a 4-year period from 373 SunOS/Solaris Unix workstations and servers interconnected through a local area network. We focus on the estimation of machine uptimes, downtimes and availability based on the identification of failures that caused total service loss. Data corresponds to syslogd event logs that contain a large amount of information about the normal activity of the studied systems as well as their behavior in the presence of failures. It is widely recognized that the information contained in such event logs might be incomplete or imperfect. The solution investigated in this paper to address this problem is based on the use of auxiliary sources of data obtained from wtmpx files maintained by the SunOS/Solaris Unix operating system. The results obtained suggest that the combined use of wtmpx and syslogd log files provides more complete information on the state of the target systems that is useful to provide availability estimations that better reflect reality.


Empirical analysis and statistical modeling of attack processes based on honeypots

Abstract: Honeypots are more and more used to collect data on malicious activities on the Internet and to better understand the strategies and techniques used by attackers to compromise target systems. Analysis and modeling methodologies are needed to support the characterization of attack processes based on the data collected from the honeypots. This paper presents some empirical analyses based on the data collected from the Leurr{\'e}.com honeypot platforms deployed on the Internet and presents some preliminary modeling studies aimed at fulfilling such objectives.


An architecture-based dependability modeling framework using AADL

Abstract: For efficiency reasons, the software system designers' will is to use an integrated set of methods and tools to describe specifications and designs, and also to perform analyses such as dependability, schedulability and performance. AADL (Architecture Analysis and Design Language) has proved to be efficient for software architecture modeling. In addition, AADL was designed to accommodate several types of analyses. This paper presents an iterative dependency-driven approach for dependability modeling using AADL. It is illustrated on a small example. This approach is part of a complete framework that allows the generation of dependability analysis and evaluation models from AADL models to support the analysis of software and system architectures, in critical application domains.


A Hierarchical Approach for Dependability Analysis of a Commercial Cache-Based RAID Storage Architecture

Abstract: We present a hierarchical simulation approach for the dependability analysis and evaluation of a highly available commercial cache-based RAID storage system. The archi-tecture is complex and includes several layers of overlap-ping error detection and recovery mechanisms. Three ab-straction levels have been developed to model the cache architecture, cache operations, and error detection and recovery mechanism. The impact of faults and errors oc-curring in the cache and in the disks is analyzed at each level of the hierarchy. A simulation submodel is associated with each abstraction level. The models have been devel-oped using DEPEND, a simulation-based environment for system-level dependability analysis, which provides facili-ties to inject faults into a functional behavior model, to simulate error detection and recovery mechanisms, and to evaluate quantitative measures. Several fault models are defined for each submodel to simulate cache component failures, disk failures, transmission errors, and data errors in the cache memory and in the disks. Some of the parame-ters characterizing fault injection in a given submodel cor-respond to probabilities evaluated from the simulation of the lower-level submodel. Based on the proposed method-ology, we evaluate and analyze 1) the system behavior un-der a real workload and high error rate (focusing on error bursts), 2) the coverage of the error detection mechanisms implemented in the system and the error latency distribu-tions, and 3) the accumulation of errors in the cache and in the disks.


Architecture for Pseudo Acausal Evolvable Embedded Systems

Abstract: Advances in semiconductor technology are contributing to the increasing complexity in the design of embedded systems. Architectures with novel techniques such as evolvable nature and autonomous behavior have engrossed lot of attention. This paper demonstrates conceptually evolvable embedded systems can be characterized basing on acausal nature. It is noted that in acausal systems, future input needs to be known, here we make a mechanism such that the system predicts the future inputs and exhibits pseudo acausal nature. An embedded system that uses theoretical framework of acausality is proposed. Our method aims at a novel architecture that features the hardware evolability and autonomous behavior alongside pseudo acausality. Various aspects of this architecture are discussed in detail along with the limitations.


The on-line shortest path problem under partial monitoring

Abstract: The on-line shortest path problem is considered under various models of partial monitoring. Given a weighted directed acyclic graph whose edge weights can change in an arbitrary (adversarial) way, a decision maker has to choose in each round of a game a path between two distinguished vertices such that the loss of the chosen path (defined as the sum of the weights of its composing edges) be as small as possible. In a setting generalizing the multi-armed bandit problem, after choosing a path, the decision maker learns only the weights of those edges that belong to the chosen path. For this problem, an algorithm is given whose average cumulative loss in n rounds exceeds that of the best path, matched off-line to the entire sequence of the edge weights, by a quantity that is proportional to 1/\sqrt{n} and depends only polynomially on the number of edges of the graph. The algorithm can be implemented with linear complexity in the number of rounds n and in the number of edges. An extension to the so-called label efficient setting is also given, in which the decision maker is informed about the weights of the edges corresponding to the chosen path at a total of m << n time instances. Another extension is shown where the decision maker competes against a time-varying path, a generalization of the problem of tracking the best expert. A version of the multi-armed bandit setting for shortest path is also discussed where the decision maker learns only the total weight of the chosen path but not the weights of the individual edges on the path. Applications to routing in packet switched networks along with simulation results are also presented.


A neural network approach to ordinal regression

Abstract: Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics.


Fast paths in large-scale dynamic road networks

Abstract: Efficiently computing fast paths in large scale dynamic road networks (where dynamic traffic information is known over a part of the network) is a practical problem faced by several traffic information service providers who wish to offer a realistic fast path computation to GPS terminal enabled vehicles. The heuristic solution method we propose is based on a highway hierarchy-based shortest path algorithm for static large-scale networks; we maintain a static highway hierarchy and perform each query on the dynamically evaluated network.


Novel algorithm to calculate hypervolume indicator of Pareto approximation set

Abstract: Hypervolume indicator is a commonly accepted quality measure for comparing Pareto approximation set generated by multi-objective optimizers. The best known algorithm to calculate it for $n$ points in $d$-dimensional space has a run time of $O(n^{d/2})$ with special data structures. This paper presents a recursive, vertex-splitting algorithm for calculating the hypervolume indicator of a set of $n$ non-comparable points in $d>2$ dimensions. It splits out multiple child hyper-cuboids which can not be dominated by a splitting reference point. In special, the splitting reference point is carefully chosen to minimize the number of points in the child hyper-cuboids. The complexity analysis shows that the proposed algorithm achieves $O((\frac{d}{2})^n)$ time and $O(dn^2)$ space complexity in the worst case.


A Doubly Distributed Genetic Algorithm for Network Coding

Abstract: We present a genetic algorithm which is distributed in two novel ways: along genotype and temporal axes. Our algorithm first distributes, for every member of the population, a subset of the genotype to each network node, rather than a subset of the population to each. This genotype distribution is shown to offer a significant gain in running time. Then, for efficient use of the computational resources in the network, our algorithm divides the candidate solutions into pipelined sets and thus the distribution is in the temporal domain, rather that in the spatial domain. This temporal distribution may lead to temporal inconsistency in selection and replacement, however our experiments yield better efficiency in terms of the time to convergence without incurring significant penalties.


Text Line Segmentation of Historical Documents: a Survey

Abstract: There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.


Parametric Learning and Monte Carlo Optimization

Abstract: This paper uncovers and explores the close relationship between Monte Carlo Optimization of a parametrized integral (MCO), Parametric machine-Learning (PL), and `blackbox' or `oracle'-based optimization (BO). We make four contributions. First, we prove that MCO is mathematically identical to a broad class of PL problems. This identity potentially provides a new application domain for all broadly applicable PL techniques: MCO. Second, we introduce immediate sampling, a new version of the Probability Collectives (PC) algorithm for blackbox optimization. Immediate sampling transforms the original BO problem into an MCO problem. Accordingly, by combining these first two contributions, we can apply all PL techniques to BO. In our third contribution we validate this way of improving BO by demonstrating that cross-validation and bagging improve immediate sampling. Finally, conventional MC and MCO procedures ignore the relationship between the sample point locations and the associated values of the integrand; only the values of the integrand at those locations are considered. We demonstrate that one can exploit the sample location information using PL techniques, for example by forming a fit of the sample locations to the associated values of the integrand. This provides an additional way to apply PL techniques to improve MCO.


A Disciplined Approach to Adopting Agile Practices: The Agile Adoption Framework

Abstract: Many organizations aspire to adopt agile processes to take advantage of the numerous benefits that it offers to an organization. Those benefits include, but are not limited to, quicker return on investment, better software quality, and higher customer satisfaction. To date however, there is no structured process (at least in the public domain) that guides organizations in adopting agile practices. To address this problem we present the Agile Adoption Framework. The framework consists of two components: an agile measurement index, and a 4-Stage process, that together guide and assist the agile adoption efforts of organizations. More specifically, the agile measurement index is used to identify the agile potential of projects and organizations. The 4-Stage process, on the other hand, helps determine (a) whether or not organizations are ready for agile adoption, and (b) guided by their potential, what set of agile practices can and should be introduced.


Supporting Knowledge and Expertise Finding within Australia's Defence Science and Technology Organisation

Abstract: This paper reports on work aimed at supporting knowledge and expertise finding within a large Research and Development (R&D) organisation. The paper first discusses the nature of knowledge important to R&D organisations and presents a prototype information system developed to support knowledge and expertise finding. The paper then discusses a trial of the system within an R&D organisation, the implications and limitations of the trial, and discusses future research questions.


A Language-Based Approach for Improving the Robustness of Network Application Protocol Implementations

Abstract: The secure and robust functioning of a network relies on the defect-free implementation of network applications. As network protocols have become increasingly complex, however, hand-writing network message processing code has become increasingly error-prone. In this paper, we present a domain-specific language, Zebu, for describing protocol message formats and related processing constraints. From a Zebu specification, a compiler automatically generates stubs to be used by an application to parse network messages. Zebu is easy to use, as it builds on notations used in RFCs to describe protocol grammars. Zebu is also efficient, as the memory usage is tailored to application needs and message fragments can be specified to be processed on demand. Finally, Zebu-based applications are robust, as the Zebu compiler automatically checks specification consistency and generates parsing stubs that include validation of the message structure. Using a mutation analysis in the context of SIP and RTSP, we show that Zebu significantly improves application robustness.


Calculating Valid Domains for BDD-Based Interactive Configuration

Abstract: In these notes we formally describe the functionality of Calculating Valid Domains from the BDD representing the solution space of valid configurations. The formalization is largely based on the CLab configuration framework.


Preconditioned Temporal Difference Learning

Abstract: This paper has been withdrawn by the author. This draft is withdrawn for its poor quality in english, unfortunately produced by the author when he was just starting his science route. Look at the ICML version instead: http://icml2008.cs.helsinki.fi/papers/111.pdf


Exploiting Social Annotation for Automatic Resource Discovery

Abstract: Information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery. In this paper, we describe a probabilistic model of the user annotation process in a social bookmarking system del.icio.us. We then use the model to automatically find resources relevant to a particular information domain. Our experimental results on data obtained from \emph{del.icio.us} show this approach as a promising method for helping automate the resource discovery task.


Personalizing Image Search Results on Flickr

Abstract: The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it. One option is tag search, which returns all images tagged with a specific keyword. If the keyword is ambiguous, e.g., ``beetle'' could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query. We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations. We show how to exploit this metadata to personalize search results for the user, thereby improving search performance. First, we show that we can significantly improve search precision by filtering tag search results by user's contacts or a larger social network that includes those contact's contacts. Secondly, we describe a probabilistic model that takes advantage of tag information to discover latent topics contained in the search results. The users' interests can similarly be described by the tags they used for annotating their images. The latent topics found by the model are then used to personalize search results by finding images on topics that are of interest to the user.


Settling the Complexity of Computing Two-Player Nash Equilibria

Abstract: We settle a long-standing open question in algorithmic game theory. We prove that Bimatrix, the problem of finding a Nash equilibrium in a two-player game, is complete for the complexity class PPAD Polynomial Parity Argument, Directed version) introduced by Papadimitriou in 1991. This is the first of a series of results concerning the complexity of Nash equilibria. In particular, we prove the following theorems: Bimatrix does not have a fully polynomial-time approximation scheme unless every problem in PPAD is solvable in polynomial time. The smoothed complexity of the classic Lemke-Howson algorithm and, in fact, of any algorithm for Bimatrix is not polynomial unless every problem in PPAD is solvable in randomized polynomial time. Our results demonstrate that, even in the simplest form of non-cooperative games, equilibrium computation and approximation are polynomial-time equivalent to fixed point computation. Our results also have two broad complexity implications in mathematical economics and operations research: Arrow-Debreu market equilibria are PPAD-hard to compute. The P-Matrix Linear Complementary Problem is computationally harder than convex programming unless every problem in PPAD is solvable in polynomial time.


A Cut-free Sequent Calculus for Bi-Intuitionistic Logic: Extended Version

Abstract: Bi-intuitionistic logic is the extension of intuitionistic logic with a connective dual to implication. Bi-intuitionistic logic was introduced by Rauszer as a Hilbert calculus with algebraic and Kripke semantics. But her subsequent ``cut-free'' sequent calculus for BiInt has recently been shown by Uustalu to fail cut-elimination. We present a new cut-free sequent calculus for BiInt, and prove it sound and complete with respect to its Kripke semantics. Ensuring completeness is complicated by the interaction between implication and its dual, similarly to future and past modalities in tense logic. Our calculus handles this interaction using extended sequents which pass information from premises to conclusions using variables instantiated at the leaves of failed derivation trees. Our simple termination argument allows our calculus to be used for automated deduction, although this is not its main purpose.


Assessment and Propagation of Input Uncertainty in Tree-based Option Pricing Models

Abstract: This paper aims to provide a practical example on the assessment and propagation of input uncertainty for option pricing when using tree-based methods. Input uncertainty is propagated into output uncertainty, reflecting that option prices are as unknown as the inputs they are based on. Option pricing formulas are tools whose validity is conditional not only on how close the model represents reality, but also on the quality of the inputs they use, and those inputs are usually not observable. We provide three alternative frameworks to calibrate option pricing tree models, propagating parameter uncertainty into the resulting option prices. We finally compare our methods with classical calibration-based results assuming that there is no options market established. These methods can be applied to pricing of instruments for which there is not an options market, as well as a methodological tool to account for parameter and model uncertainty in theoretical option pricing.


Unicast and Multicast Qos Routing with Soft Constraint Logic Programming

Abstract: We present a formal model to represent and solve the unicast/multicast routing problem in networks with Quality of Service (QoS) requirements. To attain this, first we translate the network adapting it to a weighted graph (unicast) or and-or graph (multicast), where the weight on a connector corresponds to the multidimensional cost of sending a packet on the related network link: each component of the weights vector represents a different QoS metric value (e.g. bandwidth, cost, delay, packet loss). The second step consists in writing this graph as a program in Soft Constraint Logic Programming (SCLP): the engine of this framework is then able to find the best paths/trees by optimizing their costs and solving the constraints imposed on them (e.g. delay < 40msec), thus finding a solution to QoS routing problems. Moreover, c-semiring structures are a convenient tool to model QoS metrics. At last, we provide an implementation of the framework over scale-free networks and we suggest how the performance can be improved.


Transaction-Oriented Simulation In Ad Hoc Grids

Abstract: This paper analyses the possibilities of performing parallel transaction-oriented simulations with a special focus on the space-parallel approach and discrete event simulation synchronisation algorithms that are suitable for transaction-oriented simulation and the target environment of Ad Hoc Grids. To demonstrate the findings a Java-based parallel transaction-oriented simulator for the simulation language GPSS/H is implemented on the basis of the promising Shock Resistant Time Warp synchronisation algorithm and using the Grid framework ProActive. The validation of this parallel simulator shows that the Shock Resistant Time Warp algorithm can successfully reduce the number of rolled back Transaction moves but it also reveals circumstances in which the Shock Resistant Time Warp algorithm can be outperformed by the normal Time Warp algorithm. The conclusion of this paper suggests possible improvements to the Shock Resistant Time Warp algorithm to avoid such problems.


On-line Chain Partitions of Up-growing Semi-orders

Abstract: On-line chain partition is a two-player game between Spoiler and Algorithm. Spoiler presents a partially ordered set, point by point. Algorithm assigns incoming points (immediately and irrevocably) to the chains which constitute a chain partition of the order. The value of the game for orders of width $w$ is a minimum number $\fVal(w)$ such that Algorithm has a strategy using at most $\fVal(w)$ chains on orders of width at most $w$. We analyze the chain partition game for up-growing semi-orders. Surprisingly, the golden ratio comes into play and the value of the game is $\lfloor\frac{1+\sqrt{5}}{2}\; w \rfloor$.


Analysis of the 802.11e Enhanced Distributed Channel Access Function

Abstract: The IEEE 802.11e standard revises the Medium Access Control (MAC) layer of the former IEEE 802.11 standard for Quality-of-Service (QoS) provision in the Wireless Local Area Networks (WLANs). The Enhanced Distributed Channel Access (EDCA) function of 802.11e defines multiple Access Categories (AC) with AC-specific Contention Window (CW) sizes, Arbitration Interframe Space (AIFS) values, and Transmit Opportunity (TXOP) limits to support MAC-level QoS and prioritization. We propose an analytical model for the EDCA function which incorporates an accurate CW, AIFS, and TXOP differentiation at any traffic load. The proposed model is also shown to capture the effect of MAC layer buffer size on the performance. Analytical and simulation results are compared to demonstrate the accuracy of the proposed approach for varying traffic loads, EDCA parameters, and MAC layer buffer space.


Performance Analysis of the IEEE 802.11e Enhanced Distributed Coordination Function using Cycle Time Approach

Abstract: The recently ratified IEEE 802.11e standard defines the Enhanced Distributed Channel Access (EDCA) function for Quality-of-Service (QoS) provisioning in the Wireless Local Area Networks (WLANs). The EDCA uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and slotted Binary Exponential Backoff (BEB) mechanism. We present a simple mathematical analysis framework for the EDCA function. Our analysis considers the fact that the distributed random access systems exhibit cyclic behavior where each station successfully transmits a packet in a cycle. Our analysis shows that an AC-specific cycle time exists for the EDCA function. Validating the theoretical results via simulations, we show that the proposed analysis accurately captures EDCA saturation performance in terms of average throughput, medium access delay, and packet loss ratio. The cycle time analysis is a simple and insightful substitute for previously proposed more complex EDCA models.


Fairness Provision in the IEEE 802.11e Infrastructure Basic Service Set

Abstract: Most of the deployed IEEE 802.11e Wireless Local Area Networks (WLANs) use infrastructure Basic Service Set (BSS) in which an Access Point (AP) serves as a gateway between wired and wireless domains. We present the unfairness problem between the uplink and the downlink flows of any Access Category (AC) in the 802.11e Enhanced Distributed Channel Access (EDCA) when the default settings of the EDCA parameters are used. We propose a simple analytical model to calculate the EDCA parameter settings that achieve weighted fair resource allocation for all uplink and downlink flows. We also propose a simple model-assisted measurement-based dynamic EDCA parameter adaptation algorithm. Moreover, our dynamic solution addresses the differences in the transport layer and the Medium Access Control (MAC) layer interactions of User Datagram Protocol (UDP) and Transmission Control Protocol (TCP). We show that proposed Contention Window (CW) and Transmit Opportunity (TXOP) limit adaptation at the AP provides fair UDP and TCP access between uplink and downlink flows of the same AC while preserving prioritization among ACs.


A study of structural properties on profiles HMMs

Abstract: Motivation: Profile hidden Markov Models (pHMMs) are a popular and very useful tool in the detection of the remote homologue protein families. Unfortunately, their performance is not always satisfactory when proteins are in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm and tool that tries to improve pHMM performance by using structural information while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs. Each pHMM is constructed by weighting each residue in an aligned protein according to a specific structural property of the residue. Properties used were primary, secondary and tertiary structures, accessibility and packing. HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP database to perform our experiments. Throughout, we apply leave-one-family-out cross-validation over protein superfamilies. First, we used the MAMMOTH-mult structural aligner to align the training set proteins. Then, we performed two sets of experiments. In a first experiment, we compared structure weighted models against standard pHMMs and against each other. In a second experiment, we compared the voting model against individual pHMMs. We compare method performance through ROC curves and through Precision/Recall curves, and assess significance through the paired two tailed t-test. Our results show significant performance improvements of all structurally weighted models over default HMMER, and a significant improvement in sensitivity of the combined models over both the original model and the structurally weighted models.


Extensive Games with Possibly Unaware Players

Abstract: Standard game theory assumes that the structure of the game is common knowledge among players. We relax this assumption by considering extensive games where agents may be unaware of the complete structure of the game. In particular, they may not be aware of moves that they and other agents can make. We show how such games can be represented; the key idea is to describe the game from the point of view of every agent at every node of the game tree. We provide a generalization of Nash equilibrium and show that every game with awareness has a generalized Nash equilibrium. Finally, we extend these results to games with awareness of unawareness, where a player i may be aware that a player j can make moves that i is not aware of, and to subjective games, where payers may have no common knowledge regarding the actual game and their beliefs are incompatible with a common prior.


Introduction to Arabic Speech Recognition Using CMUSphinx System

Abstract: In this paper Arabic was investigated from the speech recognition problem point of view. We propose a novel approach to build an Arabic Automated Speech Recognition System (ASR). This system is based on the open source CMU Sphinx-4, from the Carnegie Mellon University. CMU Sphinx is a large-vocabulary; speaker-independent, continuous speech recognition system based on discrete Hidden Markov Models (HMMs). We build a model using utilities from the OpenSource CMU Sphinx. We will demonstrate the possible adaptability of this system to Arabic voice recognition.


A Note on the Inapproximability of Correlation Clustering

Abstract: We consider inapproximability of the correlation clustering problem defined as follows: Given a graph $G = (V,E)$ where each edge is labeled either "+" (similar) or "-" (dissimilar), correlation clustering seeks to partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified with respect to the labels is maximized (resp. minimized). The two complementary problems are called MaxAgree and MinDisagree, respectively, and have been studied on complete graphs, where every edge is labeled, and general graphs, where some edge might not have been labeled. Natural edge-weighted versions of both problems have been studied as well. Let S-MaxAgree denote the weighted problem where all weights are taken from set S, we show that S-MaxAgree with weights bounded by $O(|V|^{1/2-\delta})$ essentially belongs to the same hardness class in the following sense: if there is a polynomial time algorithm that approximates S-MaxAgree within a factor of $\lambda = O(\log{|V|})$ with high probability, then for any choice of S', S'-MaxAgree can be approximated in polynomial time within a factor of $(\lambda + \epsilon)$, where $\epsilon > 0$ can be arbitrarily small, with high probability. A similar statement also holds for $S-MinDisagree. This result implies it is hard (assuming $NP \neq RP$) to approximate unweighted MaxAgree within a factor of $80/79-\epsilon$, improving upon a previous known factor of $116/115-\epsilon$ by Charikar et. al. \cite{Chari05}.


Arabic Speech Recognition System using CMU-Sphinx4

Abstract: In this paper we present the creation of an Arabic version of Automated Speech Recognition System (ASR). This system is based on the open source Sphinx-4, from the Carnegie Mellon University. Which is a speech recognition system based on discrete hidden Markov models (HMMs). We investigate the changes that must be made to the model to adapt Arabic voice recognition. Keywords: Speech recognition, Acoustic model, Arabic language, HMMs, CMUSphinx-4, Artificial intelligence.


Kekul\'e Cells for Molecular Computation

Abstract: The configurations of single and double bonds in polycyclic hydrocarbons are abstracted as Kekul\'e states of graphs. Sending a so-called soliton over an open channel between ports (external nodes) of the graph changes the Kekul\'e state and therewith the set of open channels in the graph. This switching behaviour is proposed as a basis for molecular computation. The proposal is highly speculative but may have tremendous impact. Kekul\'e states with the same boundary behaviour (port assignment) can be regarded as equivalent. This gives rise to the abstraction of Kekul\'e cells. The basic theory of Kekul\'e states and Kekul\'e cells is developed here, up to the classification of Kekul\'e cells with $\leq 4$ ports. To put the theory in context, we generalize Kekul\'e states to semi-Kekul\'e states, which form the solutions of a linear system of equations over the field of the bits 0 and 1. We briefly study so-called omniconjugated graphs, in which every port assignment of the right signature has a Kekul\'e state. Omniconjugated graphs may be useful as connectors between computational elements. We finally investigate some examples with potentially useful switching behaviour.


Using Image Attributes for Human Identification Protocols

Abstract: A secure human identification protocol aims at authenticating human users to a remote server when even the users' inputs are not hidden from an adversary. Recently, the authors proposed a human identification protocol in the RSA Conference 2007, which is loosely based on the ability of humans to efficiently process an image. The advantage being that an automated adversary is not effective in attacking the protocol without human assistance. This paper extends that work by trying to solve some of the open problems. First, we analyze the complexity of defeating the proposed protocols by quantifying the workload of a human adversary. Secondly, we propose a new construction based on textual CAPTCHAs (Reverse Turing Tests) in order to make the generation of automated challenges easier. We also present a brief experiment involving real human users to find out the number of possible attributes in a given image and give some guidelines for the selection of challenge questions based on the results. Finally, we analyze the previously proposed protocol in detail for the relationship between the secrets. Our results show that we can construct human identification protocols based on image evaluation with reasonably ``quantified'' security guarantees based on our model.


Parallel computing for the finite element method

Abstract: A finite element method is presented to compute time harmonic microwave fields in three dimensional configurations. Nodal-based finite elements have been coupled with an absorbing boundary condition to solve open boundary problems. This paper describes how the modeling of large devices has been made possible using parallel computation, New algorithms are then proposed to implement this formulation on a cluster of workstations (10 DEC ALPHA 300X) and on a CRAY C98. Analysis of the computation efficiency is performed using simple problems. The electromagnetic scattering of a plane wave by a perfect electric conducting airplane is finally given as example.


A Nice Labelling for Tree-Like Event Structures of Degree 3

Abstract: We address the problem of ﬁnding nice labellings for event structures of degree 3. We develop a minimum theory by which we prove that the labelling number of an event structure of degree 3 is bounded by a linear function of the height. The main theorem we present in this paper states that event structures of degree 3 whose causality order is a tree have a nice labelling with 3 colors. Finally, we exemplify how to use this theorem to construct upper bounds for the labelling number of other event structures of degree 3.


Light Logics and Optimal Reduction: Completeness and Complexity

Abstract: Typing of lambda-terms in Elementary and Light Affine Logic (EAL, LAL, resp.) has been studied for two different reasons: on the one hand the evaluation of typed terms using LAL (EAL, resp.) proof-nets admits a guaranteed polynomial (elementary, resp.) bound; on the other hand these terms can also be evaluated by optimal reduction using the abstract version of Lamping's algorithm. The first reduction is global while the second one is local and asynchronous. We prove that for LAL (EAL, resp.) typed terms, Lamping's abstract algorithm also admits a polynomial (elementary, resp.) bound. We also show its soundness and completeness (for EAL and LAL with type fixpoints), by using a simple geometry of interaction model (context semantics).


Narratives within immersive technologies

Abstract: The main goal of this project is to research technical advances in order to enhance the possibility to develop narratives within immersive mediated environments. An important part of the research is concerned with the question of how a script can be written, annotated and realized for an immersive context. A first description of the main theoretical framework and the ongoing work and a first script example is provided. This project is part of the program for presence research, and it will exploit physiological feedback and Computational Intelligence within virtual reality.


Supervised Feature Selection via Dependence Estimation

Abstract: We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.


Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A Case Study with the UCI Thyroid Disease Database

Abstract: The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modeling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.


The Complexity of Simple Stochastic Games

Abstract: In this paper we survey the computational time complexity of assorted simple stochastic game problems, and we give an overview of the best known algorithms associated with each problem.


Higher-order theories

Abstract: We extend our approach to abstract syntax (with binding constructions) through modules and linearity. First we give a new general definition of arity, yielding the companion notion of signature. Then we obtain a modularity result as requested by Ghani and Uustalu (2003): in our setting, merging two extensions of syntax corresponds to building an amalgamated sum. Finally we define a natural notion of equation concerning a signature and prove the existence of an initial semantics for a so-called representable signature equipped with a set of equations.


Recommending Related Papers Based on Digital Library Access Records

Abstract: An important goal for digital libraries is to enable researchers to more easily explore related work. While citation data is often used as an indicator of relatedness, in this paper we demonstrate that digital access records (e.g. http-server logs) can be used as indicators as well. In particular, we show that measures based on co-access provide better coverage than co-citation, that they are available much sooner, and that they are more accurate for recent papers.


Using Access Data for Paper Recommendations on ArXiv.org

Abstract: This thesis investigates in the use of access log data as a source of information for identifying related scientific papers. This is done for arXiv.org, the authority for publication of e-prints in several fields of physics. Compared to citation information, access logs have the advantage of being immediately available, without manual or automatic extraction of the citation graph. Because of that, a main focus is on the question, how far user behavior can serve as a replacement for explicit meta-data, which potentially might be expensive or completely unavailable. Therefore, we compare access, content, and citation-based measures of relatedness on different recommendation tasks. As a final result, an online recommendation system has been built that can help scientists to find further relevant literature, without having to search for them actively.


Algorithm for Evaluation of the Interval Power Function of Unconstrained Arguments

Abstract: We describe an algorithm for evaluation of the interval extension of the power function of variables x and y given by the expression x^y. Our algorithm reduces the general case to the case of non-negative bases.


Experimenting with recursive queries in database and logic programming systems

Abstract: This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: {\em (i)} the quantity of data that can be handled contemporarily is limited, due to the fact that reasoning is generally carried out in main-memory; {\em (ii)} the interaction with external (and independent) DBMSs is not trivial and, in several cases, not allowed at all; {\em (iii)} the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This paper provides a contribution in this setting; it presents a new system, called DLV$^{DB}$, which aims to solve these problems. Moreover, the paper reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are: LDL++, XSB, Smodels and three top-level commercial DBMSs. DLV$^{DB}$ significantly outperforms even the commercial Database Systems on recursive queries. To appear in Theory and Practice of Logic Programming (TPLP)


Euclidean Shortest Paths in Simple Cube Curves at a Glance

Abstract: This paper reports about the development of two provably correct approximate algorithms which calculate the Euclidean shortest path (ESP) within a given cube-curve with arbitrary accuracy, defined by $\epsilon >0$, and in time complexity $\kappa(\epsilon) \cdot {\cal O}(n)$, where $\kappa(\epsilon)$ is the length difference between the path used for initialization and the minimum-length path, divided by $\epsilon$. A run-time diagram also illustrates this linear-time behavior of the implemented ESP algorithm.


Characterization of P2P IPTV Traffic: Scaling Analysis

Abstract: P2P IPTV applications arise on the Internet and will be massively used in the future. It is expected that P2P IPTV will contribute to increase the overall Internet traffic. In this context, it is important to measure the impact of P2P IPTV on the networks and to characterize this traffic. Dur- ing the 2006 FIFA World Cup, we performed an extensive measurement campaign. We measured network traffic generated by broadcasting soc- cer games by the most popular P2P IPTV applications, namely PPLive, PPStream, SOPCast and TVAnts. From the collected data, we charac- terized the P2P IPTV traffic structure at different time scales by using wavelet based transform method. To the best of our knowledge, this is the first work, which presents a complete multiscale analysis of the P2P IPTV traffic. Our results show that the scaling properties of the TCP traffic present periodic behavior whereas the UDP traffic is stationary and lead to long- range depedency characteristics. For all the applications, the download traffic has different characteristics than the upload traffic. The signaling traffic has a significant impact on the download traffic but it has negligible impact on the upload. Both sides of the traffic and its granularity has to be taken into account to design accurate P2P IPTV traffic models.


Alternative axiomatics and complexity of deliberative STIT theories

Abstract: We propose two alternatives to Xu's axiomatization of the Chellas STIT. The first one also provides an alternative axiomatization of the deliberative STIT. The second one starts from the idea that the historic necessity operator can be defined as an abbreviation of operators of agency, and can thus be eliminated from the logic of the Chellas STIT. The second axiomatization also allows us to establish that the problem of deciding the satisfiability of a STIT formula without temporal operators is NP-complete in the single-agent case, and is NEXPTIME-complete in the multiagent case, both for the deliberative and the Chellas' STIT.


2D Path Solutions from a Single Layer Excitable CNN Model

Abstract: An easily implementable path solution algorithm for 2D spatial problems, based on excitable/programmable characteristics of a specific cellular nonlinear network (CNN) model is presented and numerically investigated. The network is a single layer bioinspired model which was also implemented in CMOS technology. It exhibits excitable characteristics with regionally bistable cells. The related response realizes propagations of trigger autowaves, where the excitable mode can be globally preset and reset. It is shown that, obstacle distributions in 2D space can also be directly mapped onto the coupled cell array in the network. Combining these two features, the network model can serve as the main block in a 2D path computing processor. The related algorithm and configurations are numerically experimented with circuit level parameters and performance estimations are also presented. The simplicity of the model also allows alternative technology and device level implementation, which may become critical in autonomous processor design of related micro or nanoscale robotic applications.


Straggler Identification in Round-Trip Data Streams via Newton's Identities and Invertible Bloom Filters

Abstract: We introduce the straggler identification problem, in which an algorithm must determine the identities of the remaining members of a set after it has had a large number of insertion and deletion operations performed on it, and now has relatively few remaining members. The goal is to do this in o(n) space, where n is the total number of identities. The straggler identification problem has applications, for example, in determining the set of unacknowledged packets in a high-bandwidth multicast data stream. We provide a deterministic solution to the straggler identification problem that uses only O(d log n) bits and is based on a novel application of Newton's identities for symmetric polynomials. This solution can identify any subset of d stragglers from a set of n O(log n)-bit identifiers, assuming that there are no false deletions of identities not already in the set. Indeed, we give a lower bound argument that shows that any small-space deterministic solution to the straggler identification problem cannot be guaranteed to handle false deletions. Nevertheless, we show that there is a simple randomized solution using O(d log n log(1/epsilon)) bits that can maintain a multiset and solve the straggler identification problem, tolerating false deletions, where epsilon>0 is a user-defined parameter bounding the probability of an incorrect response. This randomized solution is based on a new type of Bloom filter, which we call the invertible Bloom filter.


Direct Optimization of Ranking Measures

Abstract: Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.


General-Purpose Computing on a Semantic Network Substrate

Abstract: This article presents a model of general-purpose computing on a semantic network substrate. The concepts presented are applicable to any semantic network representation. However, due to the standards and technological infrastructure devoted to the Semantic Web effort, this article is presented from this point of view. In the proposed model of computing, the application programming interface, the run-time program, and the state of the computing virtual machine are all represented in the Resource Description Framework (RDF). The implementation of the concepts presented provides a practical computing paradigm that leverages the highly-distributed and standardized representational-layer of the Semantic Web.


Bayesian approach to rough set

Abstract: This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.


Polynomial algorithms for protein similarity search for restricted mRNA structures

Abstract: In this paper we consider the problem of computing an mRNA sequence of maximal similarity for a given mRNA of secondary structure constraints, introduced by Backofen et al. in [BNS02] denoted as the MRSO problem. The problem is known to be NP-complete for planar associated implied structure graphs of vertex degree at most 3. In [BFHV05] a first polynomial dynamic programming algorithms for MRSO on implied structure graphs with maximum vertex degree 3 of bounded cut-width is shown. We give a simple but more general polynomial dynamic programming solution for the MRSO problem for associated implied structure graphs of bounded clique-width. Our result implies that MRSO is polynomial for graphs of bounded tree-width, co-graphs, $P_4$-sparse graphs, and distance hereditary graphs. Further we conclude that the problem of comparing two solutions for MRSO is hard for the class of problems which can be solved in polynomial time with a number of parallel queries to an oracle in NP.


Une plate-forme dynamique pour l'\'evaluation des performances des bases de donn\'ees \`a objets

Abstract: In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques such as clustering have been evaluated up to now. This paper opens up research regarding this issue by proposing a dynamic object evaluation framework (DOEF). DOEF accomplishes access pattern change by defining configurable styles of change. It is a preliminary prototype that has been designed to be open and fully extensible. Though originally designed for the object-oriented model, it can also be used within the object-relational model with few adaptations. Furthermore, new access pattern change models can be added too. To illustrate the capabilities of DOEF, we conducted two different sets of experiments. In the first set of experiments, we used DOEF to compare the performances of four state of the art dynamic clustering algorithms. The results show that DOEF is effective at determining the adaptability of each dynamic clustering algorithm to changes in access pattern. They also led us to conclude that dynamic clustering algorithms can cope with moderate levels of access pattern change, but that performance rapidly degrades to be worse than no clustering when vigorous styles of access pattern change are applied. In the second set of experiments, we used DOEF to compare the performance of two different object stores: Platypus and SHORE. The use of DOEF exposed the poor swapping performance of Platypus.


Conception d'un banc d'essais d\'ecisionnel

Abstract: We present in this paper a new benchmark for evaluating the performances of data warehouses. Benchmarking is useful either to system users for comparing the performances of different systems, or to system engineers for testing the effect of various design choices. While the TPC (Transaction Processing Performance Council) standard benchmarks address the first point, they are not tuneable enough to address the second one. Our Data Warehouse Engineering Benchmark (DWEB) allows to generate various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized. However, two levels of parameterization keep it easy to tune. Since DWEB mainly meets engineering benchmarking needs, it is complimentary to the TPC standard benchmarks, and not a competitor. Finally, DWEB is implemented as a Java free software that can be interfaced with most existing relational database management systems.


Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition

Abstract: Noise, corruptions and variations in face images can seriously hurt the performance of face recognition systems. To make such systems robust, multiclass neuralnetwork classifiers capable of learning from noisy data have been suggested. However on large face data sets such systems cannot provide the robustness at a high level. In this paper we explore a pairwise neural-network system as an alternative approach to improving the robustness of face recognition. In our experiments this approach is shown to outperform the multiclass neural-network system in terms of the predictive accuracy on the face images corrupted by noise.


Vers l'auto-administration des entrep\^ots de donn\'ees

Abstract: With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has existed for some years. However, little research has been achieved. The aim of this study is to search for a way of extracting useful knowledge from stored data to automatically apply performance optimization techniques, and more particularly indexing techniques. We have designed a tool that extracts frequent itemsets from a given workload to compute an index configuration that helps optimizing data access time. The experiments we performed showed that the index configurations generated by our tool allowed performance gains of 15% to 25% on a test database and a test data warehouse.


Rough Sets Computations to Impute Missing Data

Abstract: Many techniques for handling missing data have been proposed in the literature. Most of these techniques are overly complex. This paper explores an imputation technique based on rough set computations. In this paper, characteristic relations are introduced to describe incompletely specified decision tables.It is shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable and promising insight to the problem of missing data. Missing data were predicted with an accuracy of up to 99%.


Sabbath Day Home Automation: "It's Like Mixing Technology and Religion"

Abstract: We present a qualitative study of 20 American Orthodox Jewish families' use of home automation for religious purposes. These lead users offer insight into real-life, long-term experience with home automation technologies. We discuss how automation was seen by participants to contribute to spiritual experience and how participants oriented to the use of automation as a religious custom. We also discuss the relationship of home automation to family life. We draw design implications for the broader population, including surrender of control as a design resource, home technologies that support long-term goals and lifestyle choices, and respite from technology.


Lower Bounds on Implementing Robust and Resilient Mediators

Abstract: We consider games that have (k,t)-robust equilibria when played with a mediator, where an equilibrium is (k,t)-robust if it tolerates deviations by coalitions of size up to k and deviations by up to $t$ players with unknown utilities. We prove lower bounds that match upper bounds on the ability to implement such mediators using cheap talk (that is, just allowing communication among the players). The bounds depend on (a) the relationship between k, t, and n, the total number of players in the system; (b) whether players know the exact utilities of other players; (c) whether there are broadcast channels or just point-to-point channels; (d) whether cryptography is available; and (e) whether the game has a $k+t)-punishment strategy; that is, a strategy that, if used by all but at most $k+t$ players, guarantees that every player gets a worse outcome than they do with the equilibrium strategy.


Evaluating Personal Archiving Strategies for Internet-based Information

Abstract: Internet-based personal digital belongings present different vulnerabilities than locally stored materials. We use responses to a survey of people who have recovered lost websites, in combination with supplementary interviews, to paint a fuller picture of current curatorial strategies and practices. We examine the types of personal, topical, and commercial websites that respondents have lost and the reasons they have lost this potentially valuable material. We further explore what they have tried to recover and how the loss influences their subsequent practices. We found that curation of personal digital materials in online stores bears some striking similarities to the curation of similar materials stored locally in that study participants continue to archive personal assets by relying on a combination of benign neglect, sporadic backups, and unsystematic file replication. However, we have also identified issues specific to Internet-based material: how risk is spread by distributing the files among multiple servers and services; the circular reasoning participants use when they discuss the safety of their digital assets; and the types of online material that are particularly vulnerable to loss. The study reveals ways in which expectations of permanence and notification are violated and situations in which benign neglect has far greater consequences for the long-term fate of important digital assets.


The Long Term Fate of Our Digital Belongings: Toward a Service Model for Personal Archives

Abstract: We conducted a preliminary field study to understand the current state of personal digital archiving in practice. Our aim is to design a service for the long-term storage, preservation, and access of digital belongings by examining how personal archiving needs intersect with existing and emerging archiving technologies, best practices, and policies. Our findings not only confirmed that experienced home computer users are creating, receiving, and finding an increasing number of digital belongings, but also that they have already lost irreplaceable digital artifacts such as photos, creative efforts, and records. Although participants reported strategies such as backup and file replication for digital safekeeping, they were seldom able to implement them consistently. Four central archiving themes emerged from the data: (1) people find it difficult to evaluate the worth of accumulated materials; (2) personal storage is highly distributed both on- and offline; (3) people are experiencing magnified curatorial problems associated with managing files in the aggregate, creating appropriate metadata, and migrating materials to maintainable formats; and (4) facilities for long-term access are not supported by the current desktop metaphor. Four environmental factors further complicate archiving in consumer settings: the pervasive influence of malware; consumer reliance on ad hoc IT providers; an accretion of minor system and registry inconsistencies; and strong consumer beliefs about the incorruptibility of digital forms, the reliability of digital technologies, and the social vulnerability of networked storage.


An Automated Evaluation Metric for Chinese Text Entry

Abstract: In this paper, we propose an automated evaluation metric for text entry. We also consider possible improvements to existing text entry evaluation metrics, such as the minimum string distance error rate, keystrokes per character, cost per correction, and a unified approach proposed by MacKenzie, so they can accommodate the special characteristics of Chinese text. Current methods lack an integrated concern about both typing speed and accuracy for Chinese text entry evaluation. Our goal is to remove the bias that arises due to human factors. First, we propose a new metric, called the correction penalty (P), based on Fitts' law and Hick's law. Next, we transform it into the approximate amortized cost (AAC) of information theory. An analysis of the AAC of Chinese text input methods with different context lengths is also presented.


On the Development of Text Input Method - Lessons Learned

Abstract: Intelligent Input Methods (IM) are essential for making text entries in many East Asian scripts, but their application to other languages has not been fully explored. This paper discusses how such tools can contribute to the development of computer processing of other oriental languages. We propose a design philosophy that regards IM as a text service platform, and treats the study of IM as a cross disciplinary subject from the perspectives of software engineering, human-computer interaction (HCI), and natural language processing (NLP). We discuss these three perspectives and indicate a number of possible future research directions.


Network statistics on early English Syntax: Structural criteria

Abstract: This paper includes a reflection on the role of networks in the study of English language acquisition, as well as a collection of practical criteria to annotate free-speech corpora from children utterances. At the theoretical level, the main claim of this paper is that syntactic networks should be interpreted as the outcome of the use of the syntactic machinery. Thus, the intrinsic features of such machinery are not accessible directly from (known) network properties. Rather, what one can see are the global patterns of its use and, thus, a global view of the power and organization of the underlying grammar. Taking a look into more practical issues, the paper examines how to build a net from the projection of syntactic relations. Recall that, as opposed to adult grammars, early-child language has not a well-defined concept of structure. To overcome such difficulty, we develop a set of systematic criteria assuming constituency hierarchy and a grammar based on lexico-thematic relations. At the end, what we obtain is a well defined corpora annotation that enables us i) to perform statistics on the size of structures and ii) to build a network from syntactic relations over which we can perform the standard measures of complexity. We also provide a detailed example.


Distributed Algorithms for Spectrum Allocation, Power Control, Routing, and Congestion Control in Wireless Networks

Abstract: We develop distributed algorithms to allocate resources in multi-hop wireless networks with the aim of minimizing total cost. In order to observe the fundamental duplexing constraint that co-located transmitters and receivers cannot operate simultaneously on the same frequency band, we first devise a spectrum allocation scheme that divides the whole spectrum into multiple sub-bands and activates conflict-free links on each sub-band. We show that the minimum number of required sub-bands grows asymptotically at a logarithmic rate with the chromatic number of network connectivity graph. A simple distributed and asynchronous algorithm is developed to feasibly activate links on the available sub-bands. Given a feasible spectrum allocation, we then design node-based distributed algorithms for optimally controlling the transmission powers on active links for each sub-band, jointly with traffic routes and user input rates in response to channel states and traffic demands. We show that under specified conditions, the algorithms asymptotically converge to the optimal operating point.


Avoiding Rotated Bitboards with Direct Lookup

Abstract: This paper describes an approach for obtaining direct access to the attacked squares of sliding pieces without resorting to rotated bitboards. The technique involves creating four hash tables using the built in hash arrays from an interpreted, high level language. The rank, file, and diagonal occupancy are first isolated by masking the desired portion of the board. The attacked squares are then directly retrieved from the hash tables. Maintaining incrementally updated rotated bitboards becomes unnecessary as does all the updating, mapping and shifting required to access the attacked squares. Finally, rotated bitboard move generation speed is compared with that of the direct hash table lookup method.


Stochastic Optimization Algorithms

Abstract: When looking for a solution, deterministic methods have the enormous advantage that they do find global optima. Unfortunately, they are very CPU-intensive, and are useless on untractable NP-hard problems that would require thousands of years for cutting-edge computers to explore. In order to get a result, one needs to revert to stochastic algorithms, that sample the search space without exploring it thoroughly. Such algorithms can find very good results, without any guarantee that the global optimum has been reached; but there is often no other choice than using them. This chapter is a short introduction to the main methods used in stochastic optimization.


A Note on Ontology and Ordinary Language

Abstract: We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial.


An algorithm for clock synchronization with the gradient property in sensor networks

Abstract: We introduce a distributed algorithm for clock synchronization in sensor networks. Our algorithm assumes that nodes in the network only know their immediate neighborhoods and an upper bound on the network's diameter. Clock-synchronization messages are only sent as part of the communication, assumed reasonably frequent, that already takes place among nodes. The algorithm has the gradient property of [2], achieving an O(1) worst-case skew between the logical clocks of neighbors. As in the case of [3,8], the algorithm's actions are such that no constant lower bound exists on the rate at which logical clocks progress in time, and for this reason the lower bound of [2,5] that forbids constant skew between neighbors does not apply.


Acyclic Preference Systems in P2P Networks

Abstract: In this work we study preference systems natural for the Peer-to-Peer paradigm. Most of them fall in three categories: global, symmetric and complementary. All these systems share an acyclicity property. As a consequence, they admit a stable (or Pareto efficient) configuration, where no participant can collaborate with better partners than their current ones. We analyze the representation of the such preference systems and show that any acyclic system can be represented with a symmetric mark matrix. This gives a method to merge acyclic preference systems and retain the acyclicity. We also consider such properties of the corresponding collaboration graph, as clustering coefficient and diameter. In particular, studying the example of preferences based on real latency measurements, we observe that its stable configuration is a small-world graph.


Ensemble Learning for Free with Evolutionary Algorithms ?

Abstract: Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.


The Complexity of Model Checking Higher-Order Fixpoint Logic

Abstract: Higher-Order Fixpoint Logic (HFL) is a hybrid of the simply typed \lambda-calculus and the modal \lambda-calculus. This makes it a highly expressive temporal logic that is capable of expressing various interesting correctness properties of programs that are not expressible in the modal \lambda-calculus. This paper provides complexity results for its model checking problem. In particular we consider those fragments of HFL built by using only types of bounded order k and arity m. We establish k-fold exponential time completeness for model checking each such fragment. For the upper bound we use fixpoint elimination to obtain reachability games that are singly-exponential in the size of the formula and k-fold exponential in the size of the underlying transition system. These games can be solved in deterministic linear time. As a simple consequence, we obtain an exponential time upper bound on the expression complexity of each such fragment. The lower bound is established by a reduction from the word problem for alternating (k-1)-fold exponential space bounded Turing Machines. Since there are fixed machines of that type whose word problems are already hard with respect to k-fold exponential time, we obtain, as a corollary, k-fold exponential time completeness for the data complexity of our fragments of HFL, provided m exceeds 3. This also yields a hierarchy result in expressive power.


Can the Internet cope with stress?

Abstract: When will the Internet become aware of itself? In this note the problem is approached by asking an alternative question: Can the Internet cope with stress? By extrapolating the psychological difference between coping and defense mechanisms a distributed software experiment is outlined which could reject the hypothesis that the Internet is not a conscious entity.


About the domino problem in the hyperbolic plane, a new solution: complement

Abstract: In this paper, we complete the construction of paper arXiv:cs.CG/0701096v2. Together with the proof contained in arXiv:cs.CG/0701096v2, this paper definitely proves that the general problem of tiling the hyperbolic plane with {\it \`a la} Wang tiles is undecidable.


Comparison of Discrete and Continuous Wavelet Transforms

Abstract: In this paper we outline several points of view on the interplay between discrete and continuous wavelet transforms; stressing both pure and applied aspects of both. We outline some new links between the two transform technologies based on the theory of representations of generators and relations. By this we mean a finite system of generators which are represented by operators in Hilbert space. We further outline how these representations yield sub-band filter banks for signal and image processing algorithms.


Oblivious Transfer based on Key Exchange

Abstract: Key-exchange protocols have been overlooked as a possible means for implementing oblivious transfer (OT). In this paper we present a protocol for mutual exchange of secrets, 1-out-of-2 OT and coin flipping similar to Diffie-Hellman protocol using the idea of obliviously exchanging encryption keys. Since, Diffie-Hellman scheme is widely used, our protocol may provide a useful alternative to the conventional methods for implementation of oblivious transfer and a useful primitive in building larger cryptographic schemes.


Fault Classification in Cylinders Using Multilayer Perceptrons, Support Vector Machines and Guassian Mixture Models

Abstract: Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM produces 94% classification accuracy while the MLP produces 88% classification rates.


The Parameter-Less Self-Organizing Map algorithm

Abstract: The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighbourhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.


Using Images to create a Hierarchical Grid Spatial Index

Abstract: This paper presents a hybrid approach to spatial indexing of two dimensional data. It sheds new light on the age old problem by thinking of the traditional algorithms as working with images. Inspiration is drawn from an analogous situation that is found in machine and human vision. Image processing techniques are used to assist in the spatial indexing of the data. A fixed grid approach is used and bins with too many records are sub-divided hierarchically. Search queries are pre-computed for bins that do not contain any data records. This has the effect of dividing the search space up into non rectangular regions which are based on the spatial properties of the data. The bucketing quad tree can be considered as an image with a resolution of two by two for each layer. The results show that this method performs better than the quad tree if there are more divisions per layer. This confirms our suspicions that the algorithm works better if it gets to look at the data with higher resolution images. An elegant class structure is developed where the implementation of concrete spatial indexes for a particular data type merely relies on rendering the data onto an image.


Riemannian level-set methods for tensor-valued data

Abstract: We present a novel approach for the derivation of PDE modeling curvature-driven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of Symmetric Positive Definite Matrices Pos(n).


The Complexity of Games on Higher Order Pushdown Automata

Abstract: We prove an n-EXPTIME lower bound for the problem of deciding the winner in a reachability game on Higher Order Pushdown Automata (HPDA) of level n. This bound matches the known upper bound for parity games on HPDA. As a consequence the mu-calculus model checking over graphs given by n-HPDA is n-EXPTIME complete.


Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity

Abstract: We present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance.


Edges and Switches, Tunnels and Bridges

Abstract: Edge casing is a well-known method to improve the readability of drawings of non-planar graphs. A cased drawing orders the edges of each edge crossing and interrupts the lower edge in an appropriate neighborhood of the crossing. Certain orders will lead to a more readable drawing than others. We formulate several optimization criteria that try to capture the concept of a "good" cased drawing. Further, we address the algorithmic question of how to turn a given drawing into an optimal cased drawing. For many of the resulting optimization problems, we either find polynomial time algorithms or NP-hardness results.


Undirected Graphs of Entanglement Two

Abstract: Entanglement is a complexity measure of directed graphs that origins in fixed point theory. This measure has shown its use in designing efficient algorithms to verify logical properties of transition systems. We are interested in the problem of deciding whether a graph has entanglement at most k. As this measure is defined by means of games, game theoretic ideas naturally lead to design polynomial algorithms that, for fixed k, decide the problem. Known characterizations of directed graphs of entanglement at most 1 lead, for k = 1, to design even faster algorithms. In this paper we present an explicit characterization of undirected graphs of entanglement at most 2. With such a characterization at hand, we devise a linear time algorithm to decide whether an undirected graph has this property.


Frugal Colouring of Graphs

Abstract: A $k$-frugal colouring of a graph $G$ is a proper colouring of the vertices of $G$ such that no colour appears more than $k$ times in the neighbourhood of a vertex. This type of colouring was introduced by Hind, Molloy and Reed in 1997. In this paper, we study the frugal chromatic number of planar graphs, planar graphs with large girth, and outerplanar graphs, and relate this parameter with several well-studied colourings, such as colouring of the square, cyclic colouring, and $L(p,q)$-labelling. We also study frugal edge-colourings of multigraphs.


Batch Processor Sharing with Hyper-Exponential Service Time

Abstract: We study Batch Processor-Sharing (BPS) queuing model with hyper-exponential service time distribution and Poisson batch arrival process. One of the main goals to study BPS is the possibility of its application in size-based scheduling, which is used in differentiation between Short and Long flows in the Internet. In the case of hyper-exponential service time distribution we find an analytical expression of the expected conditional response time for the BPS queue. We show, that the expected conditional response time is a concave function of the service time. We apply the received results to the Two Level Processor-Sharing (TLPS) model with hyper-exponential service time distribution and find the expression of the expected response time for the TLPS model. TLPS scheduling discipline can be applied to size-based differentiation in TCP/IP networks and Web server request handling.


Multiresolution Approximation of Polygonal Curves in Linear Complexity

Abstract: We propose a new algorithm to the problem of polygonal curve approximation based on a multiresolution approach. This algorithm is suboptimal but still maintains some optimality between successive levels of resolution using dynamic programming. We show theoretically and experimentally that this algorithm has a linear complexity in time and space. We experimentally compare the outcomes of our algorithm to the optimal "full search" dynamic programming solution and finally to classical merge and split approaches. The experimental evaluations confirm the theoretical derivations and show that the proposed approach evaluated on 2D coastal maps either show a lower time complexity or provide polygonal approximations closer to the input discrete curves.


VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs

Abstract: Performance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by simulation for a given experiment have been compared to the results obtained by benchmarking the real systems in the same conditions. Benchmarking and simulation performance evaluations have been observed to be consistent, so it appears that simulation can be a reliable approach to evaluate the performances of OODBs.