Efficient Generation of Craig Interpolants in Satisfiability Modulo Theories

Abstract: The problem of computing Craig Interpolants has recently received a lot of interest. In this paper, we address the problem of efficient generation of interpolants for some important fragments of first order logic, which are amenable for effective decision procedures, called Satisfiability Modulo Theory solvers. We make the following contributions. First, we provide interpolation procedures for several basic theories of interest: the theories of linear arithmetic over the rationals, difference logic over rationals and integers, and UTVPI over rationals and integers. Second, we define a novel approach to interpolate combinations of theories, that applies to the Delayed Theory Combination approach. Efficiency is ensured by the fact that the proposed interpolation algorithms extend state of the art algorithms for Satisfiability Modulo Theories. Our experimental evaluation shows that the MathSAT SMT solver can produce interpolants with minor overhead in search, and much more efficiently than other competitor solvers.


Learning with Spectral Kernels and Heavy-Tailed Data

Abstract: Two ubiquitous aspects of large-scale data analysis are that the data often have heavy-tailed properties and that diffusion-based or spectral-based methods are often used to identify and extract structure of interest. Perhaps surprisingly, popular distribution-independent methods such as those based on the VC dimension fail to provide nontrivial results for even simple learning problems such as binary classification in these two settings. In this paper, we develop distribution-dependent learning methods that can be used to provide dimension-independent sample complexity bounds for the binary classification problem in these two popular settings. In particular, we provide bounds on the sample complexity of maximum margin classifiers when the magnitude of the entries in the feature vector decays according to a power law and also when learning is performed with the so-called Diffusion Maps kernel. Both of these results rely on bounding the annealed entropy of gap-tolerant classifiers in a Hilbert space. We provide such a bound, and we demonstrate that our proof technique generalizes to the case when the margin is measured with respect to more general Banach space norms. The latter result is of potential interest in cases where modeling the relationship between data elements as a dot product in a Hilbert space is too restrictive.


Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments

Abstract: Many data sources are naturally modeled by multiple weight assignments over a set of keys: snapshots of an evolving database at multiple points in time, measurements collected over multiple time periods, requests for resources served at multiple locations, and records with multiple numeric attributes. Over such vector-weighted data we are interested in aggregates with respect to one set of weights, such as weighted sums, and aggregates over multiple sets of weights such as the $L_1$ difference. Sample-based summarization is highly effective for data sets that are too large to be stored or manipulated. The summary facilitates approximate processing queries that may be specified after the summary was generated. Current designs, however, are geared for data sets where a single {\em scalar} weight is associated with each key. We develop a sampling framework based on {\em coordinated weighted samples} that is suited for multiple weight assignments and obtain estimators that are {\em orders of magnitude tighter} than previously possible. We demonstrate the power of our methods through an extensive empirical evaluation on diverse data sets ranging from IP network to stock quotes data.


Verifying the Interplay of Authorization Policies and Workflow in Service-Oriented Architectures (Full version)

Abstract: A widespread design approach in distributed applications based on the service-oriented paradigm, such as web-services, consists of clearly separating the enforcement of authorization policies and the workflow of the applications, so that the interplay between the policy level and the workflow level is abstracted away. While such an approach is attractive because it is quite simple and permits one to reason about crucial properties of the policies under consideration, it does not provide the right level of abstraction to specify and reason about the way the workflow may interfere with the policies, and vice versa. For example, the creation of a certificate as a side effect of a workflow operation may enable a policy rule to fire and grant access to a certain resource; without executing the operation, the policy rule should remain inactive. Similarly, policy queries may be used as guards for workflow transitions. In this paper, we present a two-level formal verification framework to overcome these problems and formally reason about the interplay of authorization policies and workflow in service-oriented architectures. This allows us to define and investigate some verification problems for SO applications and give sufficient conditions for their decidability.


A Bandwidth Characterization Tool For MPEG-2 File

Abstract: This paper proposes the design and development of MPEG 2 Video Decoder to offer flexible and effective utilization of bandwidth services. The decoder is capable of decoding the MPEG 2 bit stream on a single host machine. The present decoder is designed to be simple, but yet effectively reconstruct the video from MPEG 2 bit stream.


Proceedings International Workshop on The Complexity of Simple Programs

Abstract: This is the first volume of Electronic Proceedings in Theoretical Computer Science (EPTCS), a free international refereed open access venue for the rapid electronic publication of the proceedings of workshops and conferences, and of festschriften, etc, in the general area of theoretical computer science, broadly construed. It contains the proceedings of the International Workshop on The Complexity of Simple Programs, which was hosted at University College Cork on the 6th and 7th of December, 2008. All speakers were invited and all of the papers went through a thorough peer-review process.


Shedding Light on RFID Distance Bounding Protocols and Terrorist Fraud Attacks

Abstract: The vast majority of RFID authentication protocols assume the proximity between readers and tags due to the limited range of the radio channel. However, in real scenarios an intruder can be located between the prover (tag) and the verifier (reader) and trick this last one into thinking that the prover is in close proximity. This attack is generally known as a relay attack in which scope distance fraud, mafia fraud and terrorist attacks are included. Distance bounding protocols represent a promising countermeasure to hinder relay attacks. Several protocols have been proposed during the last years but vulnerabilities of major or minor relevance have been identified in most of them. In 2008, Kim et al. [1] proposed a new distance bounding protocol with the objective of being the best in terms of security, privacy, tag computational overhead and fault tolerance. In this paper, we analyze this protocol and we present a passive full disclosure attack, which allows an adversary to discover the long-term secret key of the tag. The presented attack is very relevant, since no security objectives are met in Kim et al.'s protocol. Then, design guidelines are introduced with the aim of facilitating protocol designers the stimulating task of designing secure and efficient schemes against relay attacks. Finally a new protocol, named Hitomi and inspired by [1], is designed conforming the guidelines proposed previously.


Acquiring Knowledge for Evaluation of Teachers Performance in Higher Education using a Questionnaire

Abstract: In this paper, we present the step by step knowledge acquisition process by choosing a structured method through using a questionnaire as a knowledge acquisition tool. Here we want to depict the problem domain as, how to evaluate teachers performance in higher education through the use of expert system technology. The problem is how to acquire the specific knowledge for a selected problem efficiently and effectively from human experts and encode it in the suitable computer format. Acquiring knowledge from human experts in the process of expert systems development is one of the most common problems cited till yet. This questionnaire was sent to 87 domain experts within all public and private universities in Pakistani. Among them 25 domain experts sent their valuable opinions. Most of the domain experts were highly qualified, well experienced and highly responsible persons. The whole questionnaire was divided into 15 main groups of factors, which were further divided into 99 individual questions. These facts were analyzed further to give a final shape to the questionnaire. This knowledge acquisition technique may be used as a learning tool for further research work.


Client-Server Password Recovery (Extended Abstract)

Abstract: Human memory is not perfect - people constantly memorize new facts and forget old ones. One example is forgetting a password, a common problem raised at IT help desks. We present several protocols that allow a user to automatically recover a password from a server using partial knowledge of the password. These protocols can be easily adapted to the personal entropy setting, where a user can recover a password only if he can answer a large enough subset of personal questions. We introduce client-server password recovery methods, in which the recovery data are stored at the server, and the recovery procedures are integrated into the login procedures. These methods apply to two of the most common types of password based authentication systems. The security of these solutions is significantly better than the security of presently proposed password recovery schemes. Our protocols are based on a variation of threshold encryption that may be of independent interest.


Reconfiguration of Distributed Information Fusion System ? A case study

Abstract: Information Fusion Systems are now widely used in different fusion contexts, like scientific processing, sensor networks, video and image processing. One of the current trends in this area is to cope with distributed systems. In this context, we have defined and implemented a Dynamic Distributed Information Fusion System runtime model. It allows us to cope with dynamic execution supports while trying to maintain the functionalities of a given Dynamic Distributed Information Fusion System. The paper presents our system, the reconfiguration problems we are faced with and our solutions.


Fuzzy Logic Based Method for Improving Text Summarization

Abstract: Text summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentence selection. One of the methods to obtain the suitable sentences is to assign some numerical measure of a sentence for the summary called sentence weighting and then select the best ones. The first step in summarization by extraction is the identification of important features. In our experiment, we used 125 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 8 important features and calculate their score for each sentence. We propose text summarization based on fuzzy logic to improve the quality of the summary created by the general statistic method. We compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the best average precision, recall, and f-measure for the summaries were obtained by fuzzy method.


Clarksons Algorithm for Violator Spaces

Abstract: Clarksons algorithm is a two-staged randomized algorithm for solving linear programs. This algorithm has been simplified and adapted to fit the framework of LP-type problems. In this framework we can tackle a number of non-linear problems such as computing the smallest enclosing ball of a set of points in R^d . In 2006, it has been shown that the algorithm in its original form works for violator spaces too, which are a proper general- ization of LP-type problems. It was not clear, however, whether previous simplifications of the algorithm carry over to the new setting. In this paper we show the following theoretical results: (a) It is shown, for the first time, that Clarksons second stage can be simplified. (b) The previous simplifications of Clarksons first stage carry over to the violator space setting. (c) Furthermore, we show the equivalence of violator spaces and partitions of the hypercube by hypercubes.


On Relaxing Metric Information in Linear Temporal Logic

Abstract: Metric LTL formulas rely on the next operator to encode time distances, whereas qualitative LTL formulas use only the until operator. This paper shows how to transform any metric LTL formula M into a qualitative formula Q, such that Q is satisfiable if and only if M is satisfiable over words with variability bounded with respect to the largest distances used in M (i.e., occurrences of next), but the size of Q is independent of such distances. Besides the theoretical interest, this result can help simplify the verification of systems with time-granularity heterogeneity, where large distances are required to express the coarse-grain dynamics in terms of fine-grain time units.


(Non)existence of Pleated Folds: How Paper Folds Between Creases

Abstract: We prove that the pleated hyperbolic paraboloid, a familiar origami model known since 1927, in fact cannot be folded with the standard crease pattern in the standard mathematical model of zero-thickness paper. In contrast, we show that the model can be folded with additional creases, suggesting that real paper "folds" into this model via small such creases. We conjecture that the circular version of this model, consisting simply of concentric circular creases, also folds without extra creases. At the heart of our results is a new structural theorem characterizing uncreased intrinsically flat surfaces--the portions of paper between the creases. Differential geometry has much to say about the local behavior of such surfaces when they are sufficiently smooth, e.g., that they are torsal ruled. But this classic result is simply false in the context of the whole surface. Our structural characterization tells the whole story, and even applies to surfaces with discontinuities in the second derivative. We use our theorem to prove fundamental properties about how paper folds, for example, that straight creases on the piece of paper must remain piecewise-straight (polygonal) by folding.


On maximal repetitions of arbitrary exponent

Abstract: The first two authors have shown [KK99,KK00] that the sum the exponent (and thus the number) of maximal repetitions of exponent at least 2 (also called runs) is linear in the length of the word. The exponent 2 in the definition of a run may seem arbitrary. In this paper, we consider maximal repetitions of exponent strictly greater than 1.


Design and Implementation of a High Quality and High Throughput TRNG in FPGA

Abstract: This paper focuses on the design and implementation of a high-quality and high-throughput true-random number generator (TRNG) in FPGA. Various practical issues which we encountered are highlighted and the influence of the various parameters on the functioning of the TRNG are discussed. We also propose a few values for the parameters which use the minimum amount of the resources but still pass common random number generator test batteries such as DieHard and TestU01.


A Novel Bid Optimizer for Sponsored Search Auctions based on Cooperative Game Theory

Abstract: In this paper, we propose a bid optimizer for sponsored keyword search auctions which leads to better retention of advertisers by yielding attractive utilities to the advertisers without decreasing the revenue to the search engine. The bid optimizer is positioned as a key value added tool the search engine provides to the advertisers. The proposed bid optimizer algorithm transforms the reported values of the advertisers for a keyword into a correlated bid profile using many ideas from cooperative game theory. The algorithm is based on a characteristic form game involving the search engine and the advertisers. Ideas from Nash bargaining theory are used in formulating the characteristic form game to provide for a fair share of surplus among the players involved. The algorithm then computes the nucleolus of the characteristic form game since we find that the nucleolus is an apt way of allocating the gains of cooperation among the search engine and the advertisers. The algorithm next transforms the nucleolus into a correlated bid profile using a linear programming formulation. This bid profile is input to a standard generalized second price mechanism (GSP) for determining the allocation of sponsored slots and the prices to be be paid by the winners. The correlated bid profile that we determine is a locally envy-free equilibrium and also a correlated equilibrium of the underlying game. Through detailed simulation experiments, we show that the proposed bid optimizer retains more customers than a plain GSP mechanism and also yields better long-run utilities to the search engine and the advertisers.


Efficient IRIS Recognition through Improvement of Feature Extraction and subset Selection

Abstract: The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. The deterministic feature sequence is extracted from the iris image by using the contourlet transform technique. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. And finally we use SVM (Support Vector Machine) classifier for approximating the amount of people identification in our proposed system. Experimental result show that most proposed method reduces processing time and increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.


Sharp kernel clustering algorithms and their associated Grothendieck inequalities

Abstract: In the kernel clustering problem we are given a (large) $n\times n$ symmetric positive semidefinite matrix $A=(a_{ij})$ with $\sum_{i=1}^n\sum_{j=1}^n a_{ij}=0$ and a (small) $k\times k$ symmetric positive semidefinite matrix $B=(b_{ij})$. The goal is to find a partition $\{S_1,...,S_k\}$ of $\{1,... n\}$ which maximizes $ \sum_{i=1}^k\sum_{j=1}^k (\sum_{(p,q)\in S_i\times S_j}a_{pq})b_{ij}$. We design a polynomial time approximation algorithm that achieves an approximation ratio of $\frac{R(B)^2}{C(B)}$, where $R(B)$ and $C(B)$ are geometric parameters that depend only on the matrix $B$, defined as follows: if $b_{ij} = < v_i, v_j>$ is the Gram matrix representation of $B$ for some $v_1,...,v_k\in \R^k$ then $R(B)$ is the minimum radius of a Euclidean ball containing the points $\{v_1, ..., v_k\}$. The parameter $C(B)$ is defined as the maximum over all measurable partitions $\{A_1,...,A_k\}$ of $\R^{k-1}$ of the quantity $\sum_{i=1}^k\sum_{j=1}^k b_{ij}< z_i,z_j>$, where for $i\in \{1,...,k\}$ the vector $z_i\in \R^{k-1}$ is the Gaussian moment of $A_i$, i.e., $z_i=\frac{1}{(2\pi)^{(k-1)/2}}\int_{A_i}xe^{-\|x\|_2^2/2}dx$. We also show that for every $\eps > 0$, achieving an approximation guarantee of $(1-\e)\frac{R(B)^2}{C(B)}$ is Unique Games hard.


Global Stability Analysis for an Internet Congestion Control Model with a Time-Varying Link Capacity

Abstract: In this paper, a global stability analysis is given for a rate-based congestion control system modeled by a nonlinear delayed differential equation. The model determines the dynamics of a single-source single-link network, with a time-varying capacity of link and a fixed communication delay. We obtain a sufficient delay-independent conditions on system parameters under which global asymptotic stability of the system is guarantied. The proof is based on an extension of Lyapunov-Krasovskii theorem for a class of nonlinear time-delay systems. The numerical simulations for a typical scenario justify the theoretical results.


Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment

Abstract: We present advances in the software engineering design and implementation of the multi-tier run-time system for the General Intensional Programming System (GIPSY) by further unifying the distributed technologies used to implement the Demand Migration Framework (DMF) in order to streamline distributed execution of hybrid intensional-imperative programs using Java.


A genetic algorithm for structure-activity relationships: software implementation

Abstract: The design and the implementation of a genetic algorithm are described. The applicability domain is on structure-activity relationships expressed as multiple linear regressions and predictor variables are from families of structure-based molecular descriptors. An experiment to compare different selection and survival strategies was designed and realized. The genetic algorithm was run using the designed experiment on a set of 206 polychlorinated biphenyls searching on structure-activity relationships having known the measured octanol-water partition coefficients and a family of molecular descriptors. The experiment shows that different selection and survival strategies create different partitions on the entire population of all possible genotypes.


Proceedings Fourth European Young Researchers Workshop on Service Oriented Computing

Abstract: Service-Oriented Computing (SOC) is an emerging new paradigm for distributed and object-oriented computing by allowing autonomous, platform-independent computational entities (called services) to be built (described, discovered, composed, orchestrated) within and across organizational boundaries. Like no other computing paradigm before, SOC is destined to exert a lasting influence on the business domain, among others (e-commerce, e-government, e-business, e-learning, e-health, etc.). The Young Researchers workshop series on Service-Oriented Computing is meant to be a platform for junior researchers from industry and academics alike. Its core objectives are to exchange information regarding advancements in the state of the art and practice of SOC, as well as to identify emerging research topics and the future trends in this domain. Following the success of the previous three workshops, the 4th European Young Researchers Workshop on Service-Oriented Computing (YR-SOC 2009) introduced two novelties: it was organised outside of the UK and it saw the introduction of a number of tutorials, thus making the workshop a 3-day event. YR-SOC 2009 took place at the CNR Institute of Information Science and Technologies in Pisa, Italy, and was organised by Maurice ter Beek, Barry Norton, Stephan Reiff-Marganiec and Monika Solanki. The contributions in this volume cover aspects such as automated service composition, context-aware SOC, service-oriented programming, QoS-aware SOC, service-oriented architectures, SOC modelling and analysis, process management, web services, ontologies and the semantic web.


Simultaneous Integer Relation Detection and Its an Application

Abstract: Let $\mathbf{x_1}, ..., \mathbf{x_t} \in \mathbb{R}^{n}$. A simultaneous integer relation (SIR) for $\mathbf{x_1}, ..., \mathbf{x_t}$ is a vector $\mathbf{m} \in \mathbb{Z}^{n}\setminus\{\textbf{0}\}$ such that $\mathbf{x_i}^T\mathbf{m} = 0$ for $i = 1, ..., t$. In this paper, we propose an algorithm SIRD to detect an SIR for real vectors, which constructs an SIR within $\mathcal {O}(n^4 + n^3 \log \lambda(X))$ arithmetic operations, where $\lambda(X)$ is the least Euclidean norm of SIRs for $\mathbf{x_1}, >..., \mathbf{x_t}$. One can easily generalize SIRD to complex number field. Experimental results show that SIRD is practical and better than another detecting algorithm in the literature. In its application, we present a new algorithm for finding the minimal polynomial of an arbitrary complex algebraic number from its an approximation, which is not based on LLL. We also provide a sufficient condition on the precision of the approximate value, which depends only on the height and the degree of the algebraic number.


Fast Probabilistic Ranking under x-Relation Model

Abstract: The probabilistic top-k queries based on the interplay of score and probability, under the possible worlds semantic, become an important research issue that considers both score and uncertainty on the same basis. In the literature, many different probabilistic top-k queries are proposed. Almost all of them need to compute the probability of a tuple t_i to be ranked at the j-th position across the entire set of possible worlds. The cost of such computing is the dominant cost and is known as O(kn^2), where n is the size of dataset. In this paper, we propose a new novel algorithm that computes such probability in O(kn).


A New Approach to Manage QoS in Distributed Multimedia Systems

Abstract: Dealing with network congestion is a criterion used to enhance quality of service (QoS) in distributed multimedia systems. The existing solutions for the problem of network congestion ignore scalability considerations because they maintain a separate classification for each video stream. In this paper, we propose a new method allowing to control QoS provided to clients according to the network congestion, by discarding some frames when needed. The technique proposed, called (m,k)-frame, is scalable with little degradation in application performances. (m,k)-frame method is issued from the notion of (m,k)-firm realtime constraints which means that among k invocations of a task, m invocations must meet their deadline. Our simulation studies show the usefulness of (m,k)-frame method to adapt the QoS to the real conditions in a multimedia application, according to the current system load. Notably, the system must adjust the QoS provided to active clients1 when their number varies, i.e. dynamic arrival of clients.


Vision Based Navigation for a Mobile Robot with Different Field of Views

Abstract: The basic idea behind evolutionary robotics is to evolve a set of neural controllers for a particular task at hand. It involves use of various input parameters such as infrared sensors, light sensors and vision based methods. This paper aims to explore the evolution of vision based navigation in a mobile robot. It discusses in detail the effect of different field of views for a mobile robot. The individuals have been evolved using different FOV values and the results have been recorded and analyzed.The optimum values for FOV have been proposed after evaluating more than 100 different values. It has been observed that the optimum FOV value requires lesser number of generations for evolution and the mobile robot trained with that particular value is able to navigate well in the environment.


Dispositif de supervision pour les tuteurs impliqu\'es dans un apprentissage \`a la gestion de projets

Abstract: This article presents results of the observation of a project management teaching-by-project course. It describe return of experience not only of student but also of tutors one years after the course. Many problems are identified like individual student evaluation, tutor distance, lake of coordination, lake of communication. We ask tutor about the pertinence of an instrumental solution, based upon dashboard, to solve problems of coordination and evaluation identified. We made the hypothese that a cognitive tool will improve too the knowledge construction and the learning process.


Testing cycle-freeness: Finding a certificate

Abstract: We deal with the problem of designing one-sided error property testers for cycle-freeness in bounded degree graphs. Such a property tester always accepts forests. Furthermore, when it rejects an input, it provides a short cycle as a certificate. The problem of testing cycle-freeness in this model was first considered by Goldreich and Ron \cite{GR97}. They give a constant time tester with two-sided error (it does not provide certificates for rejection) and prove a $\Omega(\sqrt{n})$ lower bound for testers with one-sided error. We design a property tester with one-sided error whose running time matches this lower bound (upto polylogarithmic factors). Interestingly, this has connections to a recent conjecture of Benjamini, Schramm, and Shapira \cite{BSS08}. The property of cycle-freeness is closed under the operation of taking minors. This is the first example of such a property that has an almost optimal $\otilde(\sqrt{n})$-time one-sided error tester, but has a constant time two-sided error tester. It was conjectured in \cite{BSS08} that this happens for a vast class of minor-closed properties, and this result can seen as the first indication towards that.


Collaborative filtering with diffusion-based similarity on tripartite graphs

Abstract: Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.


Intrusion Detection System Using Advanced Honeypots

Abstract: The exponential growth of Internet traffic has made public servers increasingly vulnerable to unauthorized accesses and intrusions. In addition to maintaining low latency for the client, filtering unauthorized accesses has become one of the major concerns of a server maintainer. This implementation of an Intrusion Detection System distinguishes between the traffic coming from clients and the traffic originated from the attackers, in an attempt to simultaneously mitigate the problems of both latency and security. We then present the results of a series of stress and scalability tests, and suggest a number of potential uses for such a system. As computer attacks are becoming more and more difficult to identify the need for better and more efficient intrusion detection systems increases. The main problem with current intrusion detection systems is high rate of false alarms. Using honeypots provides effective solution to increase the security.


Effective Focused Crawling Based on Content and Link Structure Analysis

Abstract: A focused crawler traverses the web selecting out relevant pages to a predefined topic and neglecting those out of concern. While surfing the internet it is difficult to deal with irrelevant pages and to predict which links lead to quality pages. In this paper a technique of effective focused crawling is implemented to improve the quality of web navigation. To check the similarity of web pages w.r.t. topic keywords a similarity function is used and the priorities of extracted out links are also calculated based on meta data and resultant pages generated from focused crawler. The proposed work also uses a method for traversing the irrelevant pages that met during crawling to improve the coverage of a specific topic.


A Novel Two-Stage Dynamic Decision Support based Optimal Threat Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne threats

Abstract: This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (TEWA) into three broad categories (Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution for a range of scenarios. Analysis part of this paper presents the strengths and weaknesses of the proposed algorithm over an alternative greedy algorithm as applied to different offline scenarios.


A new approach for digit recognition based on hand gesture analysis

Abstract: We present in this paper a new approach for hand gesture analysis that allows digit recognition. The analysis is based on extracting a set of features from a hand image and then combining them by using an induction graph. The most important features we extract from each image are the fingers locations, their heights and the distance between each pair of fingers. Our approach consists of three steps: (i) Hand detection and localization, (ii) fingers extraction and (iii) features identification and combination to digit recognition. Each input image is assumed to contain only one person, thus we apply a fuzzy classifier to identify the skin pixels. In the finger extraction step, we attempt to remove all the hand components except the fingers, this process is based on the hand anatomy properties. The final step consists on representing histogram of the detected fingers in order to extract features that will be used for digit recognition. The approach is invariant to scale, rotation and translation of the hand. Some experiments have been undertaken to show the effectiveness of the proposed approach.


Towards the Patterns of Hard CSPs with Association Rule Mining

Abstract: The hardness of finite domain Constraint Satisfaction Problems (CSPs) is a very important research area in Constraint Programming (CP) community. However, this problem has not yet attracted much attention from the researchers in the association rule mining community. As a popular data mining technique, association rule mining has an extremely wide application area and it has already been successfully applied to many interdisciplines. In this paper, we study the association rule mining techniques and propose a cascaded approach to extract the interesting patterns of the hard CSPs. As far as we know, this problem is investigated with the data mining techniques for the first time. Specifically, we generate the random CSPs and collect their characteristics by solving all the CSP instances, and then apply the data mining techniques on the data set and further to discover the interesting patterns of the hardness of the randomly generated CSPs


AFPTAS results for common variants of bin packing: A new method to handle the small items

Abstract: We consider two well-known natural variants of bin packing, and show that these packing problems admit asymptotic fully polynomial time approximation schemes (AFPTAS). In bin packing problems, a set of one-dimensional items of size at most 1 is to be assigned (packed) to subsets of sum at most 1 (bins). It has been known for a while that the most basic problem admits an AFPTAS. In this paper, we develop methods that allow to extend this result to other variants of bin packing. Specifically, the problems which we study in this paper, for which we design asymptotic fully polynomial time approximation schemes, are the following. The first problem is "Bin packing with cardinality constraints", where a parameter k is given, such that a bin may contain up to k items. The goal is to minimize the number of bins used. The second problem is "Bin packing with rejection", where every item has a rejection penalty associated with it. An item needs to be either packed to a bin or rejected, and the goal is to minimize the number of used bins plus the total rejection penalty of unpacked items. This resolves the complexity of two important variants of the bin packing problem. Our approximation schemes use a novel method for packing the small items. This new method is the core of the improved running times of our schemes over the running times of the previous results, which are only asymptotic polynomial time approximation schemes (APTAS).


Bin packing with general cost structures

Abstract: Following the work of Anily et al., we consider a variant of bin packing, called "bin packing with general cost structures" (GCBP) and design an asymptotic fully polynomial time approximation scheme (AFPTAS) for this problem. In the classic bin packing problem, a set of one-dimensional items is to be assigned to subsets of total size at most 1, that is, to be packed into unit sized bins. However, in GCBP, the cost of a bin is not 1 as in classic bin packing, but it is a non-decreasing and concave function of the number of items packed in it, where the cost of an empty bin is zero. The construction of the AFPTAS requires novel techniques for dealing with small items, which are developed in this work. In addition, we develop a fast approximation algorithm which acts identically for all non-decreasing and concave functions, and has an asymptotic approximation ratio of 1.5 for all functions simultaneously.


Incidence Handling and Response System

Abstract: A computer network can be attacked in a number of ways. The security-related threats have become not only numerous but also diverse and they may also come in the form of blended attacks. It becomes difficult for any security system to block all types of attacks. This gives rise to the need of an incidence handling capability which is necessary for rapidly detecting incidents, minimizing loss and destruction, mitigating the weaknesses that were exploited and restoring the computing services. Incidence response has always been an important aspect of information security but it is often overlooked by security administrators. in this paper, we propose an automated system which will handle the security threats and make the computer network capable enough to withstand any kind of attack. we also present the state-of-the-art technology in computer, network and software which is required to build such a system.


Geometrical Interpretation of the Master Theorem for Divide-and-conquer Recurrences

Abstract: We provide geometrical interpretation of the Master Theorem to solve divide-and-conquer recurrences. We show how different cases of the recurrences correspond to different kinds of fractal images. Fractal dimension and Hausdorff measure are shown to be closely related to the solution of such recurrences.


Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling

Abstract: This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-complete problem, but it is difficult to find the optimal solution. This paper applies Genetic Algorithms and Tabu Search for Job Shop Scheduling problem and compares the results obtained by each. With the implementation of our approach the JSS problems reaches optimal solution and minimize the makespan.


TTSS Packet Classification Algorithm to enhance Multimedia Applications in Network Processor based Router

Abstract: The objective of this paper is to implement the Trie based Tuple Space Search(TTSS) packet classification algorithm for Network Processor(NP) based router to enhance multimedia applications. The performance is evaluated using Intel IXP2400 NP Simulator. The results demonstrate that, TTSS has better performance than Tuple Space Search algorithm and is well suited to achieve high speed packet classification to support multimedia applications.


Comparing and Aggregating Partially Resolved Trees

Abstract: We define, analyze, and give efficient algorithms for two kinds of distance measures for rooted and unrooted phylogenies. For rooted trees, our measures are based on the topologies the input trees induce on triplets; that is, on three-element subsets of the set of species. For unrooted trees, the measures are based on quartets (four-element subsets). Triplet and quartet-based distances provide a robust and fine-grained measure of the similarities between trees. The distinguishing feature of our distance measures relative to traditional quartet and triplet distances is their ability to deal cleanly with the presence of unresolved nodes, also called polytomies. For rooted trees, these are nodes with more than two children; for unrooted trees, they are nodes of degree greater than three. Our first class of measures are parametric distances, where there is a parameter that weighs the difference between an unresolved triplet/quartet topology and a resolved one. Our second class of measures are based on Hausdorff distance. Each tree is viewed as a set of all possible ways in which the tree could be refined to eliminate unresolved nodes. The distance between the original (unresolved) trees is then taken to be the Hausdorff distance between the associated sets of fully resolved trees, where the distance between trees in the sets is the triplet or quartet distance, as appropriate.


Statistical Analysis of Privacy and Anonymity Guarantees in Randomized Security Protocol Implementations

Abstract: Security protocols often use randomization to achieve probabilistic non-determinism. This non-determinism, in turn, is used in obfuscating the dependence of observable values on secret data. Since the correctness of security protocols is very important, formal analysis of security protocols has been widely studied in literature. Randomized security protocols have also been analyzed using formal techniques such as process-calculi and probabilistic model checking. In this paper, we consider the problem of validating implementations of randomized protocols. Unlike previous approaches which treat the protocol as a white-box, our approach tries to verify an implementation provided as a black box. Our goal is to infer the secrecy guarantees provided by a security protocol through statistical techniques. We learn the probabilistic dependency of the observable outputs on secret inputs using Bayesian network. This is then used to approximate the leakage of secret. In order to evaluate the accuracy of our statistical approach, we compare our technique with the probabilistic model checking technique on two examples: crowds protocol and dining crypotgrapher's protocol.


Response to Refutation of Aslam's Proof that NP = P

Abstract: This paper provides a further refinement to the previous response by introducing new structures and algorithms for counting VMPs of common \emph{Edge Requirement} (ER) and hence for counting the perfect matchings.


Non-Parametric Bayesian Areal Linguistics

Abstract: We describe a statistical model over linguistic areas and phylogeny. Our model recovers known areas and identifies a plausible hierarchy of areal features. The use of areas improves genetic reconstruction of languages both qualitatively and quantitatively according to a variety of metrics. We model linguistic areas by a Pitman-Yor process and linguistic phylogeny by Kingman's coalescent.


General combination rules for qualitative and quantitative beliefs

Abstract: Martin and Osswald \cite{Martin07} have recently proposed many generalizations of combination rules on quantitative beliefs in order to manage the conflict and to consider the specificity of the responses of the experts. Since the experts express themselves usually in natural language with linguistic labels, Smarandache and Dezert \cite{Li07} have introduced a mathematical framework for dealing directly also with qualitative beliefs. In this paper we recall some element of our previous works and propose the new combination rules, developed for the fusion of both qualitative or quantitative beliefs.


Comments on "A new combination of evidence based on compromise" by K. Yamada

Abstract: Comments on ``A new combination of evidence based on compromise'' by K. Yamada


Cryptanalysis of SDES via evolutionary computation techniques

Abstract: The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. The goal of this paper is two fold. First we want to make a study about how evolutionary computation techniques can efficiently solve the NP-Hard combinatorial problem. For achieving this goal we test several evolutionary computation techniques like memetic algorithm, genetic algorithm and simulated annealing for the cryptanalysis of simplified data encryption standard problem (SDES). And second was a comparison between memetic algorithm, genetic algorithm and simulated annealing were made in order to investigate the performance for the cryptanalysis on SDES. The methods were tested and extensive computational results show that memetic algorithm performs better than genetic algorithms and simulated annealing for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient memetic algorithm for the cryptanalysis of SDES.


Unsupervised Search-based Structured Prediction

Abstract: We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems. We show that it is possible to reduce unsupervised learning to supervised learning and demonstrate a high-quality unsupervised shift-reduce parsing model. We additionally show a close connection between unsupervised Searn and expectation maximization. Finally, we demonstrate the efficacy of a semi-supervised extension. The key idea that enables this is an application of the predict-self idea for unsupervised learning.


Reasoning About a Simulated Printer Case Investigation with Forensic Lucid

Abstract: In this work we model the ACME (a fictitious company name) "printer case incident" and make its specification in Forensic Lucid, a Lucid- and intensional-logic-based programming language for cyberforensic analysis and event reconstruction specification. The printer case involves a dispute between two parties that was previously solved using the finite-state automata (FSA) approach, and is now re-done in a more usable way in Forensic Lucid. Our simulation is based on the said case modeling by encoding concepts like evidence and the related witness accounts as an evidential statement context in a Forensic Lucid program, which is an input to the transition function that models the possible deductions in the case. We then invoke the transition function (actually its reverse) with the evidential statement context to see if the evidence we encoded agrees with one's claims and then attempt to reconstruct the sequence of events that may explain the claim or disprove it.


Restricted Global Grammar Constraints

Abstract: We investigate the global GRAMMAR constraint over restricted classes of context free grammars like deterministic and unambiguous context-free grammars. We show that detecting disentailment for the GRAMMAR constraint in these cases is as hard as parsing an unrestricted context free grammar.We also consider the class of linear grammars and give a propagator that runs in quadratic time. Finally, to demonstrate the use of linear grammars, we show that a weighted linear GRAMMAR constraint can efficiently encode the EDITDISTANCE constraint, and a conjunction of the EDITDISTANCE constraint and the REGULAR constraint


Putting Recommendations on the Map -- Visualizing Clusters and Relations

Abstract: For users, recommendations can sometimes seem odd or counterintuitive. Visualizing recommendations can remove some of this mystery, showing how a recommendation is grouped with other choices. A drawing can also lead a user's eye to other options. Traditional 2D-embeddings of points can be used to create a basic layout, but these methods, by themselves, do not illustrate clusters and neighborhoods very well. In this paper, we propose the use of geographic maps to enhance the definition of clusters and neighborhoods, and consider the effectiveness of this approach in visualizing similarities and recommendations arising from TV shows and music selections. All the maps referenced in this paper can be found in http://www.research.att.com/~volinsky/maps


Online Reinforcement Learning for Dynamic Multimedia Systems

Abstract: In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.


Measurable & Scalable NFRs using Fuzzy Logic and Likert Scale

Abstract: Most of the research related to Non Functional Requirements (NFRs) have presented NFRs frameworks by integrating non functional requirements with functional requirements while we proposed that measurement of NFRs is possible e.g. cost and performance and NFR like usability can be scaled. Our novel hybrid approach integrates three things rather than two i.e. Functional Requirements (FRs), Measurable NFRs (M-NFRs) and Scalable NFRs (S-NFRs). We have also found the use of Fuzzy Logic and Likert Scale effective for handling of discretely measurable as well as scalable NFRs as these techniques can provide a simple way to arrive at a discrete or scalable NFR in contrast to vague, ambiguous, imprecise, noisy or missing NFR. Our approach can act as baseline for new NFR and aspect oriented frameworks by using all types of UML diagrams.


Citing and Reading Behaviours in High-Energy Physics. How a Community Stopped Worrying about Journals and Learned to Love Repositories

Abstract: Contemporary scholarly discourse follows many alternative routes in addition to the three-century old tradition of publication in peer-reviewed journals. The field of High- Energy Physics (HEP) has explored alternative communication strategies for decades, initially via the mass mailing of paper copies of preliminary manuscripts, then via the inception of the first online repositories and digital libraries. This field is uniquely placed to answer recurrent questions raised by the current trends in scholarly communication: is there an advantage for scientists to make their work available through repositories, often in preliminary form? Is there an advantage to publishing in Open Access journals? Do scientists still read journals or do they use digital repositories? The analysis of citation data demonstrates that free and immediate online dissemination of preprints creates an immense citation advantage in HEP, whereas publication in Open Access journals presents no discernible advantage. In addition, the analysis of clickstreams in the leading digital library of the field shows that HEP scientists seldom read journals, preferring preprints instead.


Encapsulation and Dynamic Modularity in the Pi-Calculus

Abstract: We describe a process calculus featuring high level constructs for component-oriented programming in a distributed setting. We propose an extension of the higher-order pi-calculus intended to capture several important mechanisms related to component-based programming, such as dynamic update, reconfiguration and code migration. In this paper, we are primarily concerned with the possibility to build a distributed implementation of our calculus. Accordingly, we define a low-level calculus, that describes how the high-level constructs are implemented, as well as details of the data structures manipulated at runtime. We also discuss current and future directions of research in relation to our analysis of component-based programming.


Theory of Rule 6 and its Application to Round Robin Tournament

Abstract: In this paper we have used one 2 variable Boolean function called Rule 6 to define another beautiful transformation named as Extended Rule-6. Using this function we have explored the algebraic beauties and its application to an efficient Round Robin Tournament (RRT) routine for 2k (k is any natural number) number of teams. At the end, we have thrown some light towards any number of teams of the form nk where n, k are natural numbers.


A Note on Mathematical Modelling of Practical Multicampaign Assignment and Its Computational Complexity

Abstract: Within personalized marketing, a recommendation issue known as multicampaign assignment is to overcome a critical problem, known as the multiple recommendation problem which occurs when running several personalized campaigns simultaneously. This paper mainly deals with the hardness of multicampaign assignment, which is treated as a very challenging problem in marketing. The objective in this problem is to find a customer-campaign matrix which maximizes the effectiveness of multiple campaigns under some constraints. We present a realistic response suppression function, which is designed to be more practical, and explain how this can be learned from historical data. Moreover, we provide a proof that this more realistic version of the problem is NP-hard, thus justifying to use of heuristics presented in previous work.


Query Significance in Databases via Randomizations

Abstract: Many sorts of structured data are commonly stored in a multi-relational format of interrelated tables. Under this relational model, exploratory data analysis can be done by using relational queries. As an example, in the Internet Movie Database (IMDb) a query can be used to check whether the average rank of action movies is higher than the average rank of drama movies. We consider the problem of assessing whether the results returned by such a query are statistically significant or just a random artifact of the structure in the data. Our approach is based on randomizing the tables occurring in the queries and repeating the original query on the randomized tables. It turns out that there is no unique way of randomizing in multi-relational data. We propose several randomization techniques, study their properties, and show how to find out which queries or hypotheses about our data result in statistically significant information. We give results on real and generated data and show how the significance of some queries vary between different randomizations.


Relational Parametricity for Computational Effects

Abstract: According to Strachey, a polymorphic program is parametric if it applies a uniform algorithm independently of the type instantiations at which it is applied. The notion of relational parametricity, introduced by Reynolds, is one possible mathematical formulation of this idea. Relational parametricity provides a powerful tool for establishing data abstraction properties, proving equivalences of datatypes, and establishing equalities of programs. Such properties have been well studied in a pure functional setting. Many programs, however, exhibit computational effects, and are not accounted for by the standard theory of relational parametricity. In this paper, we develop a foundational framework for extending the notion of relational parametricity to programming languages with effects.


An Improved Algorithm based on Shannon-Happ Formula for Calculating Transfer Function from Signal Flow Graph and Its Visualization

Abstract: A new method based on Shannon-Happ formula to calculate transfer function from Signal Flow Graph (SFG) is presented. The algorithm provides an explicit approach to get the transfer function in a format with both numerical and symbolic expressions. The adoption of the symbolic variable in SFG, which could represent the nonlinear item or the independent sub-system, is achieved by variable separation approach. An investigation is given for the solutions of several special conditions of SFG. To improve the efficiency of the algorithm, a new technique combined with Johnson method for generating the combinations of the non-touching loops is developed. It uses the previous combinations in lower order to get the ones in higher order. There is an introduction about the visualization of SFG and the subroutines for system performance analysis in the software, AVANT.


Teaching Quality Assurance and Project Management to Undergraduate Computing Students in Pakistan

Abstract: Software Project Management (SPM) and Software Quality Assurance (SQA) are key components of undergraduate Computing programmes at educational establishments in Pakistan. Because of the nature of these subjects, there are a number of issues that need to be discussed and resolved so that the teaching becomes more effective, students learning experience is more enjoyable and their ability to be actively involved in SPM and SQA, after the completion of their studies, becomes further improved. In this paper, we discuss experience of teaching SPM and SQA at one particular institution in Islamabad Pakistan. Using this as a case study, we underline the students perspective, highlight the inherent issues and suggest ways to improve the delivery of these subjects. Since, the issues are mainly generic, the aim is to provide discussion and recommendations to benefit a wider computing community in academia.


Loading Arbitrary Knowledge Bases in Matrix Browser

Abstract: This paper describes the work done on Matrix Browser, which is a recently developed graphical user interface to explore and navigate complex networked information spaces. This approach presents a new way of navigating information nets in windows explorer like widget. The problem on hand was how to export arbitrary knowledge bases in Matrix Browser. This was achieved by identifying the relationships present in knowledge bases and then by forming the hierarchies from this data and these hierarchies are being exported to matrix browser. This paper gives solution to this problem and informs about implementation work.


A Novel Two-Staged Decision Support based Threat Evaluation and Weapon Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial Intelligence Techinques

Abstract: Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment algorithm for multi-target air-borne threats.


XDANNG: XML based Distributed Artificial Neural Network with Globus Toolkit

Abstract: Artificial Neural Network is one of the most common AI application fields. This field has direct and indirect usages most sciences. The main goal of ANN is to imitate biological neural networks for solving scientific problems. But the level of parallelism is the main problem of ANN systems in comparison with biological systems. To solve this problem, we have offered a XML-based framework for implementing ANN on the Globus Toolkit Platform. Globus Toolkit is well known management software for multipurpose Grids. Using the Grid for simulating the neuron network will lead to a high degree of parallelism in the implementation of ANN. We have used the XML for improving flexibility and scalability in our framework.


On Unique Independence Weighted Graphs

Abstract: An independent set in a graph G is a set of vertices no two of which are joined by an edge. A vertex-weighted graph associates a weight with every vertex in the graph. A vertex-weighted graph G is called a unique independence vertex-weighted graph if it has a unique independent set with maximum sum of weights. Although, in this paper we observe that the problem of recognizing unique independence vertex-weighted graphs is NP-hard in general and therefore no efficient characterization can be expected in general; we give, however, some combinatorial characterizations of unique independence vertex-weighted graphs. This paper introduces a motivating application of this problem in the area of combinatorial auctions, as well.


The computational complexity of universality problems for prefixes, suffixes, factors, and subwords of regular languages

Abstract: In this paper we consider the computational complexity of the following problems: given a DFA or NFA representing a regular language L over a finite alphabet Sigma is the set of all prefixes (resp., suffixes, factors, subwords) of all words of L equal to Sigma*? In the case of testing universality for factors of languages represented by DFA's, we find an interesting connection to Cerny's conjecture on synchronizing words.


Multi-Label MRF Optimization via Least Squares s-t Cuts

Abstract: There are many applications of graph cuts in computer vision, e.g. segmentation. We present a novel method to reformulate the NP-hard, k-way graph partitioning problem as an approximate minimal s-t graph cut problem, for which a globally optimal solution is found in polynomial time. Each non-terminal vertex in the original graph is replaced by a set of ceil(log_2(k)) new vertices. The original graph edges are replaced by new edges connecting the new vertices to each other and to only two, source s and sink t, terminal nodes. The weights of the new edges are obtained using a novel least squares solution approximating the constraints of the initial k-way setup. The minimal s-t cut labels each new vertex with a binary (s vs t) "Gray" encoding, which is then decoded into a decimal label number that assigns each of the original vertices to one of k classes. We analyze the properties of the approximation and present quantitative as well as qualitative segmentation results.


A new model of artificial neuron: cyberneuron and its use

Abstract: This article describes a new type of artificial neuron, called the authors "cyberneuron". Unlike classical models of artificial neurons, this type of neuron used table substitution instead of the operation of multiplication of input values for the weights. This allowed to significantly increase the information capacity of a single neuron, but also greatly simplify the process of learning. Considered an example of the use of "cyberneuron" with the task of detecting computer viruses.


An Iterative Fingerprint Enhancement Algorithm Based on Accurate Determination of Orientation Flow

Abstract: We describe an algorithm to enhance and binarize a fingerprint image. The algorithm is based on accurate determination of orientation flow of the ridges of the fingerprint image by computing variance of the neighborhood pixels around a pixel in different directions. We show that an iterative algorithm which captures the mutual interdependence of orientation flow computation, enhancement and binarization gives very good results on poor quality images.


Generating functions of Chebyshev-like polynomials

Abstract: In this short note, we give simple proofs of several results and conjectures formulated by Stolarsky and Tran concerning generating functions of some families of Chebyshev-like polynomials.


Improved approximation guarantees for weighted matching in the semi-streaming model

Abstract: We study the maximum weight matching problem in the semi-streaming model, and improve on the currently best one-pass algorithm due to Zelke (Proc. of STACS2008, pages 669-680) by devising a deterministic approach whose performance guarantee is 4.91+epsilon. In addition, we study preemptive online algorithms, a sub-class of one-pass algorithms where we are only allowed to maintain a feasible matching in memory at any point in time. All known results prior to Zelke's belong to this sub-class. We provide a lower bound of 4.967 on the competitive ratio of any such deterministic algorithm, and hence show that future improvements will have to store in memory a set of edges which is not necessarily a feasible matching.


Steganography in Handling Oversized IP Packets

Abstract: This paper identifies new class of network steganography methods that utilize mechanisms to handle oversized packets in IP networks: IP fragmentation, PMTUD (Path MTU Discovery) and PLPMTUD (Packetization Layer Path MTU Discovery). In particular, we propose two new steganographic methods and two extensions of existing ones. We show how IP fragmentation simplifies utilizing steganographic methods which requires transmitter-receiver synchronization. We present how mentioned mechanisms can be used to enable hidden communication for both versions of IP protocol: 4 and 6. Also the detection of the proposed methods is enclosed in this paper.


Degenerate neutrality creates evolvable fitness landscapes

Abstract: Understanding how systems can be designed to be evolvable is fundamental to research in optimization, evolution, and complex systems science. Many researchers have thus recognized the importance of evolvability, i.e. the ability to find new variants of higher fitness, in the fields of biological evolution and evolutionary computation. Recent studies by Ciliberti et al (Proc. Nat. Acad. Sci., 2007) and Wagner (Proc. R. Soc. B., 2008) propose a potentially important link between the robustness and the evolvability of a system. In particular, it has been suggested that robustness may actually lead to the emergence of evolvability. Here we study two design principles, redundancy and degeneracy, for achieving robustness and we show that they have a dramatically different impact on the evolvability of the system. In particular, purely redundant systems are found to have very little evolvability while systems with degeneracy, i.e. distributed robustness, can be orders of magnitude more evolvable. These results offer insights into the general principles for achieving evolvability and may prove to be an important step forward in the pursuit of evolvable representations in evolutionary computation.


Evidence of coevolution in multi-objective evolutionary algorithms

Abstract: This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution.


Survival of the flexible: explaining the recent dominance of nature-inspired optimization within a rapidly evolving world

Abstract: Although researchers often comment on the rising popularity of nature-inspired meta-heuristics (NIM), there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. This study presents evidence that the use of NIM is not only growing, but indeed appears to have surpassed mathematical optimization techniques (MOT) in several important metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article discusses some of the possible origins of this growing popularity. I review different explanations for NIM popularity and discuss why some of these arguments remain unsatisfying. I argue that a compelling and comprehensive explanation should directly account for the manner in which most NIM success has actually been achieved, e.g. through hybridization and customization to different problem environments. By taking a problem lifecycle perspective, this paper offers a fresh look at the hypothesis that nature-inspired meta-heuristics derive much of their utility from being flexible. I discuss global trends within the business environments where optimization algorithms are applied and I speculate that highly flexible algorithm frameworks could become increasingly popular within our diverse and rapidly changing world.


The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Abstract: Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system.


Strategic Positioning in Tactical Scenario Planning

Abstract: Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain.


Common Knowledge in Interaction Structures

Abstract: We consider two simple variants of a framework for reasoning about knowledge amongst communicating groups of players. Our goal is to clarify the resulting epistemic issues. In particular, we investigate what is the impact of common knowledge of the underlying hypergraph connecting the players, and under what conditions common knowledge distributes over disjunction. We also obtain two versions of the classic result that common knowledge cannot be achieved in the absence of a simultaneous event (here a message sent to the whole group).


Agent based Model for providing optimized, synchronized and failure free execution of workflow process

Abstract: The main objective of this paper is to provide an optimized solution and algorithm for the execution of a workflow process by ensuring the data consistency, correctness, completeness among various tasks involved. The solution proposed provides a synchronized and failure free flow of execution among various tasks involved in a workflow process. A synchronizing agent is bound at a very low level, i.e. with the workflow activity or task to get the desired goals to be done and an algorithm is provided to show the execution of workflow process completely.


Bounding the Probability of Error for High Precision Recognition

Abstract: We consider models for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low rates of recall. If some variables can be identified with near certainty, then they can be conditioned upon, allowing further inference to be done efficiently. Specifically, we consider optical character recognition (OCR) systems that can be bootstrapped by identifying a subset of correctly translated document words with very high precision. This "clean set" is subsequently used as document-specific training data. While many current OCR systems produce measures of confidence for the identity of each letter or word, thresholding these confidence values, even at very high values, still produces some errors. We introduce a novel technique for identifying a set of correct words with very high precision. Rather than estimating posterior probabilities, we bound the probability that any given word is incorrect under very general assumptions, using an approximate worst case analysis. As a result, the parameters of the model are nearly irrelevant, and we are able to identify a subset of words, even in noisy documents, of which we are highly confident. On our set of 10 documents, we are able to identify about 6% of the words on average without making a single error. This ability to produce word lists with very high precision allows us to use a family of models which depends upon such clean word lists.


Random DFAs are Efficiently PAC Learnable

Abstract: This paper has been withdrawn due to an error found by Dana Angluin and Lev Reyzin.


Agent-Oriented Approach for Detecting and Managing Risks in Emergency Situations

Abstract: This paper presents an agent-oriented approach to build a decision support system aimed at helping emergency managers to detect and to manage risks. We stress the flexibility and the adaptivity characteristics that are crucial to build a robust and efficient system, able to resolve complex problems. The system should be independent as much as possible from the subject of study. Thereby, an original approach based on a mechanism of perception, representation, characterisation and assessment is proposed. The work described here is applied on the RoboCupRescue application. Experimentations and results are provided.


Spontaneous organization leads to robustness in evolutionary algorithms

Abstract: The interaction networks of biological systems are known to take on several non-random structural properties, some of which are believed to positively influence system robustness. Researchers are only starting to understand how these structural properties emerge, however suggested roles for component fitness and community development (modularity) have attracted interest from the scientific community. In this study, we apply some of these concepts to an evolutionary algorithm and spontaneously organize its population using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based driving forces for guiding network structural dynamics, which in turn are controlled by the population dynamics of an evolutionary algorithm. To evaluate the effect this has on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and 16 other evolutionary algorithm designs. Our results indicate that a self-organizing topology evolutionary algorithm exhibits surprisingly robust search behavior with promising performance observed over short and long time scales. After a careful analysis of these results, we conclude that the coevolution between a population and its topology represents a powerful new paradigm for designing robust search heuristics.


Adaptation and Self-Organization in Evolutionary Algorithms

Abstract: Abbreviated Abstract: The objective of Evolutionary Computation is to solve practical problems (e.g. optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems.


Computational Scenario-based Capability Planning

Abstract: Scenarios are pen-pictures of plausible futures, used for strategic planning. The aim of this investigation is to expand the horizon of scenario-based planning through computational models that are able to aid the analyst in the planning process. The investigation builds upon the advances of Information and Communication Technology (ICT) to create a novel, flexible and customizable computational capability-based planning methodology that is practical and theoretically sound. We will show how evolutionary computation, in particular evolutionary multi-objective optimization, can play a central role - both as an optimizer and as a source for innovation.


D\'emarche d'\'evaluation de l'usage et des r\'epercussions psychosociales d'un environnement STIC sur une population de personnes \^ag\'ees en r\'esidence m\'edicalis\'ee

Abstract: The MNESIS Project aims to see whether the use of computerized environment by elderly people in medicalized residences stimulates their cognitive capacities and contributes to a better integration, recognition or acceptance within their social environment (friends, family, medical staff). In this paper we present the protocol of evaluation that is defined to check this assumption. This protocol is between users' centred traditional protocols (built on investigations and indirect observation) and studies of Web Usage Mining (where knowledge databases about the uses are built from traces of use). It allows collecting direct and indirect information on a large scale and over long periods.


Generalized Collective Inference with Symmetric Clique Potentials

Abstract: Collective graphical models exploit inter-instance associative dependence to output more accurate labelings. However existing models support very limited kind of associativity which restricts accuracy gains. This paper makes two major contributions. First, we propose a general collective inference framework that biases data instances to agree on a set of {\em properties} of their labelings. Agreement is encouraged through symmetric clique potentials. We show that rich properties leads to bigger gains, and present a systematic inference procedure for a large class of such properties. The procedure performs message passing on the cluster graph, where property-aware messages are computed with cluster specific algorithms. This provides an inference-only solution for domain adaptation. Our experiments on bibliographic information extraction illustrate significant test error reduction over unseen domains. Our second major contribution consists of algorithms for computing outgoing messages from clique clusters with symmetric clique potentials. Our algorithms are exact for arbitrary symmetric potentials on binary labels and for max-like and majority-like potentials on multiple labels. For majority potentials, we also provide an efficient Lagrangian Relaxation based algorithm that compares favorably with the exact algorithm. We present a 13/15-approximation algorithm for the NP-hard Potts potential, with runtime sub-quadratic in the clique size. In contrast, the best known previous guarantee for graphs with Potts potentials is only 1/2. We empirically show that our method for Potts potentials is an order of magnitude faster than the best alternatives, and our Lagrangian Relaxation based algorithm for majority potentials beats the best applicable heuristic -- ICM.


Credit Assignment in Adaptive Evolutionary Algorithms

Abstract: In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.


Use of statistical outlier detection method in adaptive evolutionary algorithms

Abstract: In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case.


Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem

Abstract: In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles that are tasked with the supply and re-supply of military units dispersed in an area of operation. The particular focus of this paper is to gain a better understanding of how the logistics environment changes when current Army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task-specifically. The experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re-supply missions and a multi-objective meta-heuristic search algorithm (i.e. Evolutionary Algorithm) for solving the particular scheduling and routing problems. The results presented in this paper allow for a better understanding of how (and under what conditions) a modularised vehicle fleet can provide advantages over the currently implemented system.


Robustness and Adaptiveness Analysis of Future Fleets

Abstract: Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future might hold. It is uncertain what future events might be encountered; how fleet design decisions will influence and shape the future; and how present and future decision makers will act based on available information, their personal biases regarding the importance of different objectives, and their economic preferences. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different classes of uncertainty are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present clear risks to the fleet. We call this the analysis of a fleet's strategic positioning. This paper introduces how strategic positioning can be evaluated using computer simulations. Our main aim is to introduce a framework for capturing information that can be useful to a decision maker and for defining the concepts of robustness and adaptiveness in the context of future fleet design. We demonstrate our conceptual framework using simulation studies of an air transportation fleet. We capture uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions, different model assumptions, and different economic preferences. Proposed changes to a fleet are then analysed based on their influence on the fleet's robustness, adaptiveness, and risk to different scenarios.


A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing

Abstract: Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and production cost reduction. Die manufacturing consists first of die design and process planning in order to make a die for extruding the customer's requirement products. The efficiency of die design and process planning are based on the knowledge and experience of die design and die manufacturer experts. This knowledge has been formulated into a computer system called the knowledge-based system. It can be reused to support a new die design and process planning. Such knowledge can be extracted directly from die geometry which is composed of die features. These features are stored in die feature library to be prepared for producing a new die manufacturing. Die geometry is defined according to the characteristics of the profile so we can reuse die features from the previous similar profile design cases. This paper presents the CaseXpert Process Planning System for die manufacturing based on feature based neural network technique. Die manufacturing cases in the case library would be retrieved with searching and learning method by neural network for reusing or revising it to build a die design and process planning when a new case is similar with the previous die manufacturing cases. The results of the system are dies design and machining process. The system has been successfully tested, it has been proved that the system can reduce planning time and respond high consistent plans.


Multidimensional Generalized Automatic Sequences and Shape-symmetric Morphic Words

Abstract: An infinite word is S-automatic if, for all n>=0, its (n + 1)st letter is the output of a deterministic automaton fed with the representation of n in the considered numeration system S. In this extended abstract, we consider an analogous definition in a multidimensional setting and present the connection to the shape-symmetric infinite words introduced by Arnaud Maes. More precisely, for d>=2, we state that a multidimensional infinite word x : N^d \to \Sigma over a finite alphabet \Sigma is S-automatic for some abstract numeration system S built on a regular language containing the empty word if and only if x is the image by a coding of a shape-symmetric infinite word.


Structure Theorem and Strict Alternation Hierarchy for FO^2 on Words

Abstract: It is well-known that every first-order property on words is expressible using at most three variables. The subclass of properties expressible with only two variables is also quite interesting and well-studied. We prove precise structure theorems that characterize the exact expressive power of first-order logic with two variables on words. Our results apply to both the case with and without a successor relation. For both languages, our structure theorems show exactly what is expressible using a given quantifier depth, n, and using m blocks of alternating quantifiers, for any m \leq n. Using these characterizations, we prove, among other results, that there is a strict hierarchy of alternating quantifiers for both languages. The question whether there was such a hierarchy had been completely open. As another consequence of our structural results, we show that satisfiability for first-order logic with two variables without successor, which is NEXP-complete in general, becomes NP-complete once we only consider alphabets of a bounded size.


A Decision Problem for Ultimately Periodic Sets in Non-standard Numeration Systems

Abstract: Consider a non-standard numeration system like the one built over the Fibonacci sequence where nonnegative integers are represented by words over $\{0,1\}$ without two consecutive 1. Given a set $X$ of integers such that the language of their greedy representations in this system is accepted by a finite automaton, we consider the problem of deciding whether or not $X$ is a finite union of arithmetic progressions. We obtain a decision procedure for this problem, under some hypothesis about the considered numeration system. In a second part, we obtain an analogous decision result for a particular class of abstract numeration systems built on an infinite regular language.


A note on syndeticity, recognizable sets and Cobham's theorem

Abstract: In this note, we give an alternative proof of the following result. Let p, q >= 2 be two multiplicatively independent integers. If an infinite set of integers is both p- and q-recognizable, then it is syndetic. Notice that this result is needed in the classical proof of the celebrated Cobham?s theorem. Therefore the aim of this paper is to complete [13] and [1] to obtain an accessible proof of Cobham?s theorem.


A Divide-and-Conquer Scheme for Assigning Roles in Multi-Channel Wireless Mesh Networks

Abstract: A multi-channel MAC seems to be an interesting approach for improving network throughput by multiplexing transmissions over orthogonal channels. In particular, Molecular MAC has recently proposed to modify the standard IEEE 802.11 DCF access method to use dynamic channel switching for efficient packet forwarding over multiple hops. However, this MAC layer requires role and channel assignment to nodes: some of them use a static channel, while others dynamically switch to neighbor channels on-demand. To assign roles and channels, we extend the notion of the Weakly Connected Dominating Set, the structure already used in clustering. More precisely, we adapt the WCDS structure and introduce new constraints to define what we call a reversible WCDS (r-WCDS), which is particularly suitable for wireless mesh networks operating under Molecular MAC. We propose a divide-and-conquer scheme that partitions the network into clusters with one leader per cluster solving a MILP formulation to assign roles in its cluster. By appropriately defining the roles at the border of clusters, we maintain global connectivity in the r-wcds. Finally, our simulations show that the performance of the propose scheme is close to a centralized algorithm.


Design and Analysis of an Attack Resilient and Adaptive Medium access Control Protocol for Computer Networks

Abstract: The challenge of designing an efficient Medium Access Control (MAC) protocol and analyzing it has been an important research topic for over 30 years. This paper focuses on the performance analysis (through simulation) and modification of a well known MAC protocol CSMA/CD. The existing protocol does not consider the wastage of bandwidth due to unutilized periods of the channel. By considering this fact, performance of MAC protocol can be enhanced. The purpose of this work is to modify the existing protocol by enabling it to adapt according to state of the network. The modified protocol takes appropriate action whenever unutilized periods detected. In this way, to increase the effective bandwidth utilization and determine how it behaves under increasing load, and varying packet sizes. It will also include effects of attacks i.e. Denial of service attacks, Replay Attack, Continuous Channel Access or Exhaustion attack, Flooding attack, Jamming (Radio interference) attack, Selective forwarding attack which degrade performance of MAC protocol. In Continuous Channel Access or Exhaustion attack, a malicious node disrupts the MAC protocol, by continuously requesting or transmitting over the channel. This eventually leads a starvation for other nodes in the network w.r.t channel access. remedy may be the network ignores excessive requests without sending expensive radio transmissions. This limit however cannot drop below the expected maximum data rate the network has to support. This limit is usually coded into the protocol during the design phase and requires additional logic also. Repeated application of these exhaustion or collision based MAC layer attacks can lead into unfairness.


On the complexity of deciding whether the distinguishing chromatic number of a graph is at most two

Abstract: In an article [3] published recently in this journal, it was shown that when k >= 3, the problem of deciding whether the distinguishing chromatic number of a graph is at most k is NP-hard. We consider the problem when k = 2. In regards to the issue of solvability in polynomial time, we show that the problem is at least as hard as graph automorphism but no harder than graph isomorphism.


A Randomized Sublinear Time Parallel GCD Algorithm for the EREW PRAM

Abstract: We present a randomized parallel algorithm that computes the greatest common divisor of two integers of n bits in length with probability 1-o(1) that takes O(n loglog n / log n) expected time using n^{6+\epsilon} processors on the EREW PRAM parallel model of computation. We believe this to be the first randomized sublinear time algorithm on the EREW PRAM for this problem.


Asymmetric Traveling Salesman Path and Directed Latency Problems

Abstract: We study integrality gaps and approximability of two closely related problems on directed graphs. Given a set V of n nodes in an underlying asymmetric metric and two specified nodes s and t, both problems ask to find an s-t path visiting all other nodes. In the asymmetric traveling salesman path problem (ATSPP), the objective is to minimize the total cost of this path. In the directed latency problem, the objective is to minimize the sum of distances on this path from s to each node. Both of these problems are NP-hard. The best known approximation algorithms for ATSPP had ratio O(log n) until the very recent result that improves it to O(log n/ log log n). However, only a bound of O(sqrt(n)) for the integrality gap of its linear programming relaxation has been known. For directed latency, the best previously known approximation algorithm has a guarantee of O(n^(1/2+eps)), for any constant eps > 0. We present a new algorithm for the ATSPP problem that has an approximation ratio of O(log n), but whose analysis also bounds the integrality gap of the standard LP relaxation of ATSPP by the same factor. This solves an open problem posed by Chekuri and Pal [2007]. We then pursue a deeper study of this linear program and its variations, which leads to an algorithm for the k-person ATSPP (where k s-t paths of minimum total length are sought) and an O(log n)-approximation for the directed latency problem.