A Theory of Sampling for Continuous-time Metric Temporal Logic

Abstract: This paper revisits the classical notion of sampling in the setting of real-time temporal logics for the modeling and analysis of systems. The relationship between the satisfiability of Metric Temporal Logic (MTL) formulas over continuous-time models and over discrete-time models is studied. It is shown to what extent discrete-time sequences obtained by sampling continuous-time signals capture the semantics of MTL formulas over the two time domains. The main results apply to "flat" formulas that do not nest temporal operators and can be applied to the problem of reducing the verification problem for MTL over continuous-time models to the same problem over discrete-time, resulting in an automated partial practically-efficient discretization technique.


Modeling Human Interaction to Design a Human-Computer Dialog System

Abstract: This article presents the Cogni-CISMeF project, which aims at improving the health information search engine CISMeF, by including a conversational agent that interacts with the user in natural language. To study the cognitive processes involved during information search, a bottom-up methodology was adopted. An experiment has been set up to obtain human dialogs related to such searches. The analysis of these dialogs underlines the establishment of a common ground and accommodation effects to the user. A model of artificial agent is proposed, that guides the user by proposing examples, assistance and choices.


Faster and simpler approximation of stable matchings

Abstract: We give a 3/2-approximation algorithm for stable matchings that runs in $O(m)$ time. The previously best known algorithm by McDermid has the same approximation ratio but runs in $O(n^{3/2}m)$ time, where $n$ denotes the number of people and $m$ is the total length of the preference lists in a given instance. Also the algorithm and the analysis are much simpler. We also give the extension of the algorithm for the many-to-many setting. (This is the version of the paper from March 2011)


Hierarchies in Dictionary Definition Space

Abstract: A dictionary defines words in terms of other words. Definitions can tell you the meanings of words you don't know, but only if you know the meanings of the defining words. How many words do you need to know (and which ones) in order to be able to learn all the rest from definitions? We reduced dictionaries to their "grounding kernels" (GKs), about 10% of the dictionary, from which all the other words could be defined. The GK words turned out to have psycholinguistic correlates: they were learned at an earlier age and more concrete than the rest of the dictionary. But one can compress still more: the GK turns out to have internal structure, with a strongly connected "kernel core" (KC) and a surrounding layer, from which a hierarchy of definitional distances can be derived, all the way out to the periphery of the full dictionary. These definitional distances, too, are correlated with psycholinguistic variables (age of acquisition, concreteness, imageability, oral and written frequency) and hence perhaps with the "mental lexicon" in each of our heads.


Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Abstract: Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for statistical query processing. In this paper we study this trade-off in private Support Vector Machine (SVM) learning. We present two efficient mechanisms, one for the case of finite-dimensional feature mappings and one for potentially infinite-dimensional feature mappings with translation-invariant kernels. For the case of translation-invariant kernels, the proposed mechanism minimizes regularized empirical risk in a random Reproducing Kernel Hilbert Space whose kernel uniformly approximates the desired kernel with high probability. This technique, borrowed from large-scale learning, allows the mechanism to respond with a finite encoding of the classifier, even when the function class is of infinite VC dimension. Differential privacy is established using a proof technique from algorithmic stability. Utility--the mechanism's response function is pointwise epsilon-close to non-private SVM with probability 1-delta--is proven by appealing to the smoothness of regularized empirical risk minimization with respect to small perturbations to the feature mapping. We conclude with a lower bound on the optimal differential privacy of the SVM. This negative result states that for any delta, no mechanism can be simultaneously (epsilon,delta)-useful and beta-differentially private for small epsilon and small beta.


Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D

Abstract: We investigate the power of the Wang tile self-assembly model at temperature 1, a threshold value that permits attachment between any two tiles that share even a single bond. When restricted to deterministic assembly in the plane, no temperature 1 assembly system has been shown to build a shape with a tile complexity smaller than the diameter of the shape. In contrast, we show that temperature 1 self-assembly in 3 dimensions, even when growth is restricted to at most 1 step into the third dimension, is capable of simulating a large class of temperature 2 systems, in turn permitting the simulation of arbitrary Turing machines and the assembly of $n\times n$ squares in near optimal $O(\log n)$ tile complexity. Further, we consider temperature 1 probabilistic assembly in 2D, and show that with a logarithmic scale up of tile complexity and shape scale, the same general class of temperature $\tau=2$ systems can be simulated with high probability, yielding Turing machine simulation and $O(\log^2 n)$ assembly of $n\times n$ squares with high probability. Our results show a sharp contrast in achievable tile complexity at temperature 1 if either growth into the third dimension or a small probability of error are permitted. Motivated by applications in nanotechnology and molecular computing, and the plausibility of implementing 3 dimensional self-assembly systems, our techniques may provide the needed power of temperature 2 systems, while at the same time avoiding the experimental challenges faced by those systems.


Proceedings Third Workshop on Membrane Computing and Biologically Inspired Process Calculi 2009

Abstract: This volume contains the accepted papers at the third Workshop on Membrane Computing and Biologically Inspired Process Calculi, held in Bologna on 5th September 2009. The papers are devoted to both membrane computing and biologically inspired process calculi, as well as to other related formalisms. The papers of this volume are selected by the programme committee due to their quality and relevance; they have defined an exciting programme highlighting interesting problems and stimulating the search for novel ways of describing related biological phenomena. In addition, we had an invited talk given by Luca Cardelli on a spatial process algebra for developmental biology. Membrane systems were introduced as a class of distributed parallel computing devices inspired by the observation that any biological system is a complex hierarchical structure, with a flow of materials and information that underlies their functioning. The emphasis is on the computational properties of the model, and it makes use of automata, languages, and complexity theoretic tools. On the other hand, certain calculi such as mobile ambients and brane calculi work with similar notions (compartments, membranes). These calculi are used to model and analyze the various biological systems. The workshop on Membrane Computing and Biologically Inspired Process Calculi brings together researchers working in these fields to present their recent work and discuss new ideas concerning the formalisms, their properties and relationships.


Differentially Private Empirical Risk Minimization

Abstract: Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the $\epsilon$-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.


Learning Mixtures of Gaussians using the k-means Algorithm

Abstract: One of the most popular algorithms for clustering in Euclidean space is the $k$-means algorithm; $k$-means is difficult to analyze mathematically, and few theoretical guarantees are known about it, particularly when the data is {\em well-clustered}. In this paper, we attempt to fill this gap in the literature by analyzing the behavior of $k$-means on well-clustered data. In particular, we study the case when each cluster is distributed as a different Gaussian -- or, in other words, when the input comes from a mixture of Gaussians. We analyze three aspects of the $k$-means algorithm under this assumption. First, we show that when the input comes from a mixture of two spherical Gaussians, a variant of the 2-means algorithm successfully isolates the subspace containing the means of the mixture components. Second, we show an exact expression for the convergence of our variant of the 2-means algorithm, when the input is a very large number of samples from a mixture of spherical Gaussians. Our analysis does not require any lower bound on the separation between the mixture components. Finally, we study the sample requirement of $k$-means; for a mixture of 2 spherical Gaussians, we show an upper bound on the number of samples required by a variant of 2-means to get close to the true solution. The sample requirement grows with increasing dimensionality of the data, and decreasing separation between the means of the Gaussians. To match our upper bound, we show an information-theoretic lower bound on any algorithm that learns mixtures of two spherical Gaussians; our lower bound indicates that in the case when the overlap between the probability masses of the two distributions is small, the sample requirement of $k$-means is {\em near-optimal}.


Opportunistic Adaptation Knowledge Discovery

Abstract: Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (CA) acquisition task: CA is learned from the case base by the means of knowledge discovery techniques, and the CA acquisition sessions are opportunistically triggered, i.e., at problem-solving time.


Self-Stabilizing Byzantine Asynchronous Unison

Abstract: We explore asynchronous unison in the presence of systemic transient and permanent Byzantine faults in shared memory. We observe that the problem is not solvable under less than strongly fair scheduler or for system topologies with maximum node degree greater than two. We present a self-stabilizing Byzantine-tolerant solution to asynchronous unison for chain and ring topologies. Our algorithm has minimum possible containment radius and optimal stabilization time.


Robust Cooperative Spectrum Sensing for Disaster Relief Networks in Correlated Environments

Abstract: Disaster relief networks are designed to be adaptable and resilient so to encompass the demands of the emergency service. Cognitive Radio enhanced ad-hoc architecture has been put forward as a candidate to enable such networks. Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the focus of intensive research, from which the main conclusion was that its performance can be greatly enhanced through the use of cooperative sensing schemes. To apply the Cognitive Radio paradigm to Ad-hoc disaster relief networks, the design of effective cooperative spectrum sensing schemes is essential. In this paper we propose a cluster based orchestration cooperative sensing scheme, which adapts to the cluster nodes surrounding radio environment state as well as to the degree of correlation observed between those nodes. The proposed scheme is given both in a centralized as well as in a decentralized approach. In the centralized approach, the cluster head controls and adapts the distribution of the cluster sensing nodes according to the monitored spectrum state. While in the decentralized approach, each of the cluster nodes decides which spectrum it should monitor, according to the past local sensing decisions of the cluster nodes. The centralized and decentralized schemes can be combined to achieve a more robust cooperative spectrum sensing scheme. The proposed scheme performance is evaluated through a framework, which allows measuring the accuracy of the spectrum sensing cooperative scheme by measuring the error in the estimation of the monitored spectrum state. Through this evaluation it is shown that the proposed scheme outperforms the case where the choice of which spectrum to sense is done without using the knowledge obtained in previous sensing iterations, i.e. a implementation of a blind Round Robin scheme.


On the indefinite Helmholtz equation: complex stretched absorbing boundary layers, iterative analysis, and preconditioning

Abstract: This paper studies and analyzes a preconditioned Krylov solver for Helmholtz problems that are formulated with absorbing boundary layers based on complex coordinate stretching. The preconditioner problem is a Helmholtz problem where not only the coordinates in the absorbing layer have an imaginary part, but also the coordinates in the interior region. This results into a preconditioner problem that is invertible with a multigrid cycle. We give a numerical analysis based on the eigenvalues and evaluate the performance with several numerical experiments. The method is an alternative to the complex shifted Laplacian and it gives a comparable performance for the studied model problems.


A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning

Abstract: Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be efficient in solving high dimensional problems. Even though several RRT variants have been proposed for dynamic replanning, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs for initial planning and informed local search for navigation. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.


Max-Leaves Spanning Tree is APX-hard for Cubic Graphs

Abstract: We consider the problem of finding a spanning tree with maximum number of leaves (MaxLeaf). A 2-approximation algorithm is known for this problem, and a 3/2-approximation algorithm when restricted to graphs where every vertex has degree 3 (cubic graphs). MaxLeaf is known to be APX-hard in general, and NP-hard for cubic graphs. We show that the problem is also APX-hard for cubic graphs. The APX-hardness of the related problem Minimum Connected Dominating Set for cubic graphs follows.


Answering Hilbert's 1st Problem

Abstract: Hilbert's first problem is of importance in relation to work being done in computational systems. It is the question of equipollence of natural and real numbers. By construction equipollence is established for real numbers in open interval (0, 1) and natural numbers and, from such to all real numbers. Construction stands in contradiction of the generally accepted diagonal argument of Cantor. Mathematics being irrefutable, in absence rejection of all theory of mathematics and logic, the problem exists in acceptance; that itself arises of more fundamental a problem in science generally. The problem within Hilbert's problem is of Schopenhauer's, et al, "will and representation" born.


Optimal lower bounds for locality sensitive hashing (except when q is tiny)

Abstract: We study lower bounds for Locality Sensitive Hashing (LSH) in the strongest setting: point sets in {0,1}^d under the Hamming distance. Recall that here H is said to be an (r, cr, p, q)-sensitive hash family if all pairs x, y in {0,1}^d with dist(x,y) at most r have probability at least p of collision under a randomly chosen h in H, whereas all pairs x, y in {0,1}^d with dist(x,y) at least cr have probability at most q of collision. Typically, one considers d tending to infinity, with c fixed and q bounded away from 0. For its applications to approximate nearest neighbor search in high dimensions, the quality of an LSH family H is governed by how small its "rho parameter" rho = ln(1/p)/ln(1/q) is as a function of the parameter c. The seminal paper of Indyk and Motwani showed that for each c, the extremely simple family H = {x -> x_i : i in d} achieves rho at most 1/c. The only known lower bound, due to Motwani, Naor, and Panigrahy, is that rho must be at least .46/c (minus o_d(1)). In this paper we show an optimal lower bound: rho must be at least 1/c (minus o_d(1)). This lower bound for Hamming space yields a lower bound of 1/c^2 for Euclidean space (or the unit sphere) and 1/c for the Jaccard distance on sets; both of these match known upper bounds. Our proof is simple; the essence is that the noise stability of a boolean function at e^{-t} is a log-convex function of t.


Combining a Probabilistic Sampling Technique and Simple Heuristics to solve the Dynamic Path Planning Problem

Abstract: Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.


Single-Agent On-line Path Planning in Continuous, Unpredictable and Highly Dynamic Environments

Abstract: This document is a thesis on the subject of single-agent on-line path planning in continuous,unpredictable and highly dynamic environments. The problem is finding and traversing a collision-free path for a holonomic robot, without kinodynamic restrictions, moving in an environment with several unpredictably moving obstacles or adversaries. The availability of perfect information of the environment at all times is assumed. Several static and dynamic variants of the Rapidly Exploring Random Trees (RRT) algorithm are explored, as well as an evolutionary algorithm for planning in dynamic environments called the Evolutionary Planner/Navigator. A combination of both kinds of algorithms is proposed to overcome shortcomings in both, and then a combination of a RRT variant for initial planning and informed local search for navigation, plus a simple greedy heuristic for optimization. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.


Tight Thresholds for Cuckoo Hashing via XORSAT

Abstract: We settle the question of tight thresholds for offline cuckoo hashing. The problem can be stated as follows: we have n keys to be hashed into m buckets each capable of holding a single key. Each key has k >= 3 (distinct) associated buckets chosen uniformly at random and independently of the choices of other keys. A hash table can be constructed successfully if each key can be placed into one of its buckets. We seek thresholds alpha_k such that, as n goes to infinity, if n/m <= alpha for some alpha < alpha_k then a hash table can be constructed successfully with high probability, and if n/m >= alpha for some alpha > alpha_k a hash table cannot be constructed successfully with high probability. Here we are considering the offline version of the problem, where all keys and hash values are given, so the problem is equivalent to previous models of multiple-choice hashing. We find the thresholds for all values of k > 2 by showing that they are in fact the same as the previously known thresholds for the random k-XORSAT problem. We then extend these results to the setting where keys can have differing number of choices, and provide evidence in the form of an algorithm for a conjecture extending this result to cuckoo hash tables that store multiple keys in a bucket.


Hardness Results for the Gapped Consecutive-Ones Property

Abstract: Motivated by problems of comparative genomics and paleogenomics, in [Chauve et al., 2009], the authors introduced the Gapped Consecutive-Ones Property Problem (k,delta)-C1P: given a binary matrix M and two integers k and delta, can the columns of M be permuted such that each row contains at most k blocks of ones and no two consecutive blocks of ones are separated by a gap of more than delta zeros. The classical C1P problem, which is known to be polynomial is equivalent to the (1,0)-C1P problem. They showed that the (2,delta)-C1P Problem is NP-complete for all delta >= 2 and that the (3,1)-C1P problem is NP-complete. They also conjectured that the (k,delta)-C1P Problem is NP-complete for k >= 2, delta >= 1 and (k,delta) =/= (2,1). Here, we prove that this conjecture is true. The only remaining case is the (2,1)-C1P Problem, which could be polynomial-time solvable.


Submodular Functions: Extensions, Distributions, and Algorithms. A Survey

Abstract: Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization, and many more. Therefore, a lot of work has been concerned with maximizing or minimizing a submodular function, often subject to combinatorial constraints. Many of these algorithmic results exhibit a common structure. Namely, the function is extended to a continuous, usually non-linear, function on a convex domain. Then, this relaxation is solved, and the fractional solution rounded to yield an integral solution. Often, the continuous extension has a natural interpretation in terms of distributions on subsets of the ground set. This interpretation is often crucial to the results and their analysis. The purpose of this survey is to highlight this connection between extensions, distributions, relaxations, and optimization in the context of submodular functions. We also present the first constant factor approximation algorithm for minimizing symmetric submodular functions subject to a cardinality constraint.


Variants of Constrained Longest Common Subsequence

Abstract: In this work, we consider a variant of the classical Longest Common Subsequence problem called Doubly-Constrained Longest Common Subsequence (DC-LCS). Given two strings s1 and s2 over an alphabet A, a set C_s of strings, and a function Co from A to N, the DC-LCS problem consists in finding the longest subsequence s of s1 and s2 such that s is a supersequence of all the strings in Cs and such that the number of occurrences in s of each symbol a in A is upper bounded by Co(a). The DC-LCS problem provides a clear mathematical formulation of a sequence comparison problem in Computational Biology and generalizes two other constrained variants of the LCS problem: the Constrained LCS and the Repetition-Free LCS. We present two results for the DC-LCS problem. First, we illustrate a fixed-parameter algorithm where the parameter is the length of the solution. Secondly, we prove a parameterized hardness result for the Constrained LCS problem when the parameter is the number of the constraint strings and the size of the alphabet A. This hardness result also implies the parameterized hardness of the DC-LCS problem (with the same parameters) and its NP-hardness when the size of the alphabet is constant.


An affine-intuitionistic system of types and effects: confluence and termination

Abstract: We present an affine-intuitionistic system of types and effects which can be regarded as an extension of Barber-Plotkin Dual Intuitionistic Linear Logic to multi-threaded programs with effects. In the system, dynamically generated values such as references or channels are abstracted into a finite set of regions. We introduce a discipline of region usage that entails the confluence (and hence determinacy) of the typable programs. Further, we show that a discipline of region stratification guarantees termination.


On the issues of building Information Warehouses

Abstract: While performing knowledge-intensive tasks of professional nature, the knowledge workers need to access and process large volume of information. Apart from the quantity, they also require that the information received is of high quality in terms of authenticity and details. This, in turn, requires that the information delivered should also include argumentative support, exhibiting the reasoning process behind their development and provenance to indicate their lineage. In conventional document-centric practices for information management, such details are difficult to capture, represent/archive and retrieve/deliver. To achieve such capability we need to re-think some core issues of information management from the above requirements perspective. In this paper we develop a framework for comprehensive representation of information in archive, capturing informational contents along with their context. We shall call it the "Information Warehouse (IW)" framework of information archival. The IW is a significant yet technologically realizable conceptual advancement which can support efficiently some interesting classes of applications which can be very useful to the knowledge workers.


NP-completeness of Certain Sub-classes of the Syndrome Decoding Problem

Abstract: The problem of Syndrome Decoding was proven to be NP-complete in 1978 and, since then, quite a few cryptographic applications have had their security rely on the (provable) difficulty of solving some instances of it. However, in most cases, the instances to be solved follow some specific constraint: the target weight is a function of the dimension and length of the code. In these cases, is the Syndrome Decoding problem still NP-complete? This is the question that this article intends to answer.


Inferring Information from Feature Diagrams to Product Line Economic Models

Abstract: Existing economic models support the estimation of the costs and benefits of developing and evolving a Software Product Line (SPL) as compared to undertaking traditional software development approaches. In addition, Feature Diagrams (FDs) are a valuable tool to scope the domain of a SPL. This paper proposes an algorithm to calculate, from a FD, the following information for economic models: the total number of products of a SPL, the SPL homogeneity and the commonality of the SPL requirements. The algorithm running time belongs to the complexity class $O(f^42^c)$. In contrast to related work, the algorithm is free of dependencies on off-the-self tools and is generally specified for an abstract FD notation, that works as a pivot language for most of the available notations for feature modeling.


Modular Workflow Engine for Distributed Services using Lightweight Java Clients

Abstract: In this article we introduce the concept and the first implementation of a lightweight client-server-framework as middleware for distributed computing. On the client side an installation without administrative rights or privileged ports can turn any computer into a worker node. Only a Java runtime environment and the JAR files comprising the workflow client are needed. To connect all clients to the engine one open server port is sufficient. The engine submits data to the clients and orchestrates their work by workflow descriptions from a central database. Clients request new task descriptions periodically, thus the system is robust against network failures. In the basic set-up, data up- and downloads are handled via HTTP communication with the server. The performance of the modular system could additionally be improved using dedicated file servers or distributed network file systems. We demonstrate the design features of the proposed engine in real-world applications from mechanical engineering. We have used this system on a compute cluster in design-of-experiment studies, parameter optimisations and robustness validations of finite element structures.


A non-interleaving process calculus for multi-party synchronisation

Abstract: We introduce the wire calculus. Its dynamic features are inspired by Milner's CCS: a unary prefix operation, binary choice and a standard recursion construct. Instead of an interleaving parallel composition operator there are operators for synchronisation along a common boundary and non-communicating parallel composition. The (operational) semantics is a labelled transition system obtained with SOS rules. Bisimilarity is a congruence with respect to the operators of the language. Quotienting terms by bisimilarity results in a compact closed category.


Hardness Amplification in Proof Complexity

Abstract: We present a general method for converting any family of unsatisfiable CNF formulas that is hard for one of the simplest proof systems, tree resolution, into formulas that require large rank in any proof system that manipulates polynomials or polynomial threshold functions of degree at most k (known as Th(k) proofs). Such systems include Lovasz-Schrijver and Cutting Planes proof systems as well as their high degree analogues. These are based on analyzing two new proof systems, denoted by T^cc(k) and R^cc(k). The proof lines of T^cc(k) are arbitrary Boolean functions, each of which can be evaluated by an efficient k-party randomized communication protocol. They include Th{k-1} proofs as a special case. R^cc(k) proofs are stronger and only require that each inference be locally checkable by an efficient k-party randomized communication protocol. Our main results are the following: (1) When k is O(loglogn), for any unsatisfiable CNF formula F requiring resolution rank r, there is a related CNF formula G=Lift_k(F) requiring refutation rank r^Omega(1/k) log^O(1) n in all R^cc(k) systems. (2) There are strict hierarchies for T^cc(k) and R^cc(k) systems with respect to k when k is O(loglogn in that there are unsatisfiable CNF formulas requiring large rank R^cc(k) refutations but having log^O(1) n rank Th(k) refutations. (3) When k is O(loglogn) there are 2^(n^Omega(1/k)) lower bounds on the size of tree-like T^cc(k) refutations for large classes of lifted CNF formulas. (4) A general method for producing integrality gaps for low rank R^cc(2) inference (and hence Cutting Planes and Th(1) inference) based on related gaps for low rank resolution. These gaps are optimal for MAX-2t-SAT.


Isometric Multi-Manifolds Learning

Abstract: Isometric feature mapping (Isomap) is a promising manifold learning method. However, Isomap fails to work on data which distribute on clusters in a single manifold or manifolds. Many works have been done on extending Isomap to multi-manifolds learning. In this paper, we first proposed a new multi-manifolds learning algorithm (M-Isomap) with help of a general procedure. The new algorithm preserves intra-manifold geodesics and multiple inter-manifolds edges precisely. Compared with previous methods, this algorithm can isometrically learn data distributed on several manifolds. Secondly, the original multi-cluster manifold learning algorithm first proposed in \cite{DCIsomap} and called D-C Isomap has been revised so that the revised D-C Isomap can learn multi-manifolds data. Finally, the features and effectiveness of the proposed multi-manifolds learning algorithms are demonstrated and compared through experiments.


Making Palm Print Matching Mobile

Abstract: With the growing importance of personal identification and authentication in todays highly advanced world where most business and personal tasks are being replaced by electronic means, the need for a technology that is able to uniquely identify an individual and has high fraud resistance see the rise of biometric technologies. Making biometric based solution mobile is a promising trend. A new RST invariant square based palm print ROI extraction method was successfully implemented and integrated into the current application suite. A new set of palm print image database captured using embedded cameras in mobile phone was created to test its robustness. Comparing to those extraction methods that are based on boundary tracking of the overall hand shape that has limitation of being unable to process palm print images that has one or more fingers closed, the system can now effectively handle the segmentation of palm print images with varying finger positioning. The high flexibility makes palm print matching mobile possible.


A Multidatabase System as 4-Tiered Client-Server Distributed Heterogeneous Database System

Abstract: In this paper, we describe a multidatabase system as 4tiered Client-Server DBMS architectures. We discuss their functional components and provide an overview of their performance characteristics. The first component of this proposed system is a web based interface or Graphical User Interface, which resides on top of the Client Application Program, the second component of the system is a client Application program running in an application server, which resides on top of the Global Database Management System, the third component of the system is a Global Database Management System and global schema of the multidatabase system server, which resides on top of the distributed heterogeneous local component database system servers, and the fourth component is remote heterogeneous local component database system servers. Transaction submitted from client interface to a multidatabase system server through an application server will be decomposed into a set of sub queries and will be executed at various remote heterogeneous local component database servers and also in case of information retrieval all sub queries will be composed and will get back results to the end users.


A New Efficient Key Management Protocol for Wireless Sensor and Actor Networks

Abstract: Research on sensor networks has become much more active and is currently being applied to many different fields. However since sensor networks are limited to only collecting and reporting information regarding a certain event, and requires human intervention with that given information, it is often difficult to react to an event or situation immediately and proactively. To overcome this kind of limitation, Wireless Sensor and Actor Networks (WSANs) with immediate-response actor nodes have been proposed which adds greater mobility and activity to the existing sensor networks. Although WSANs share many common grounds with sensor networks, it is difficult to apply existing security technologies due to the fact that WSANs contain actor nodes that are resource-independent and mobile. Therefore, this research seeks to demonstrate ways to provide security, integrity, and authentication services for WSANs secure operation, by separating networks into hierarchical structure by each node's abilities and provides different encryption key based secure protocols for each level of hierarchy, Pairwise key, node key, and region key for sensor levels, and public key for actor


Sequential Clustering based Facial Feature Extraction Method for Automatic Creation of Facial Models from Orthogonal Views

Abstract: Multiview 3D face modeling has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent nonperfect orthogonal condition and noncoherent luminance. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the coordinates of the features in the hidden profile view based on the visible features extracted in the two orthogonal views. Finally, based on the coordinates of the extracted features, we deform a 3D generic model to perform the desired 3D clone modeling. Present study proves the scope of resulted facial models for practical applications like face recognition and facial animation.


A Reliable and Fault Tolerant Routing for Optical WDM Networks

Abstract: In optical WDM networks, since each lightpath can carry a huge mount of traffic, failures may seriously damage the end user applications. Hence fault tolerance becomes an important issue on these networks. The light path which carries traffic during normal operation is called as primary path. The traffic is rerouted on a backup path in case of a failure. In this paper we propose to design a reliable and fault tolerant routing algorithm for establishing primary and backup paths. In order to establish the primary path, this algorithm uses load balancing in which link cost metrics are estimated based on the current load of the links. In backup path setup, the source calculates the blocking probability through the received feedback from the destination by sending a small fraction of probe packets along the existing paths. It then selects the optimal light path with the lowest blocking probability. Based on the simulation results, we show that the reliable and fault tolerant routing algorithm reduces the blocking probability and latency while increasing the throughput and channel utilization.


Object Oriented Approach for Integration of Heterogeneous Databases in a Multidatabase System and Local Schemas Modifications Propagation

Abstract: One of the challenging problems in the multidatabase systems is to find the most viable solution to the problem of interoperability of distributed heterogeneous autonomous local component databases. This has resulted in the creation of a global schema over set of these local component database schemas to provide a uniform representation of local schemas. The aim of this paper is to use object oriented approach to integrate schemas of distributed heterogeneous autonomous local component database schemas into a global schema. The resulting global schema provides a uniform interface and high level of location transparency for retrieval of data from the local component databases. A set of integration operators are defined to integrate local schemas based on the semantic relevance of their classes and to provide a model independent representation of virtual classes of the global schema. The schematic representation and heterogeneity is also taken into account in the integration process. Justifications about Object Oriented Modal are also discussed. Bottom up local schema modifications propagation in Global schema is also considered to maintain Global schema as local schemas are autonomous and evolve over time. An example illustrates the applicability of the integration operator defined.


A New Scheduling Algorithms For Real Time Tasks

Abstract: The main objective of this paper is to develop the two different ways in which round robin architecture is modified and made suitable to be implemented in real time and embedded systems. The scheduling algorithm plays a significant role in the design of real time embedded systems. Simple round robin architecture is not efficient to be implemented in embedded systems because of higher context switch rate, larger waiting time and larger response time. Missing of deadlines will degrade the system performance in soft real time systems. The main objective of this paper is to develop the scheduling algorithm which removes the drawbacks in simple round robin architecture. A comparison with round robin architecture to the proposed architectures has been made. It is observed that the proposed architectures solves the problems encountered in round robin architecture in soft real time by decreasing the number of context switches waiting time and response time thereby increasing the system throughput.


Reversible Image Authentication with Tamper Localization Based on Integer Wavelet Transform

Abstract: In this paper, a new reversible image authentication technique with tamper localization based on watermarking in integer wavelet transform is proposed. If the image authenticity is verified, then the distortion due to embedding the watermark can be completely removed from the watermarked image. If the image is tampered, then the tampering positions can also be localized. Two layers of watermarking are used. The first layer embedded in spatial domain verifies authenticity and the second layer embedded in transform domain provides reversibility. This technique utilizes selective LSB embedding and histogram characteristics of the difference images of the wavelet coefficients and modifies pixel values slightly to embed the watermark. Experimental results demonstrate that the proposed scheme can detect any modifications of the watermarked image.


Unraveling BitTorrent's File Unavailability: Measurements, Analysis and Solution Exploration

Abstract: BitTorrent suffers from one fundamental problem: the long-term availability of content. This occurs on a massive-scale with 38% of torrents becoming unavailable within the first month. In this paper we explore this problem by performing two large-scale measurement studies including 46K torrents and 29M users. The studies go significantly beyond any previous work by combining per-node, per-torrent and system-wide observations to ascertain the causes, characteristics and repercussions of file unavailability. The study confirms the conclusion from previous works that seeders have a significant impact on both performance and availability. However, we also present some crucial new findings: (i) the presence of seeders is not the sole factor involved in file availability, (ii) 23.5% of nodes that operate in seedless torrents can finish their downloads, and (iii) BitTorrent availability is discontinuous, operating in cycles of temporary unavailability. Due to our new findings, we consider it is important to revisit the solution space; to this end, we perform large-scale trace-based simulations to explore the potential of two abstract approaches.


A Local Spectral Method for Graphs: with Applications to Improving Graph Partitions and Exploring Data Graphs Locally

Abstract: The second eigenvalue of the Laplacian matrix and its associated eigenvector are fundamental features of an undirected graph, and as such they have found widespread use in scientific computing, machine learning, and data analysis. In many applications, however, graphs that arise have several \emph{local} regions of interest, and the second eigenvector will typically fail to provide information fine-tuned to each local region. In this paper, we introduce a locally-biased analogue of the second eigenvector, and we demonstrate its usefulness at highlighting local properties of data graphs in a semi-supervised manner. To do so, we first view the second eigenvector as the solution to a constrained optimization problem, and we incorporate the local information as an additional constraint; we then characterize the optimal solution to this new problem and show that it can be interpreted as a generalization of a Personalized PageRank vector; and finally, as a consequence, we show that the solution can be computed in nearly-linear time. In addition, we show that this locally-biased vector can be used to compute an approximation to the best partition \emph{near} an input seed set in a manner analogous to the way in which the second eigenvector of the Laplacian can be used to obtain an approximation to the best partition in the entire input graph. Such a primitive is useful for identifying and refining clusters locally, as it allows us to focus on a local region of interest in a semi-supervised manner. Finally, we provide a detailed empirical evaluation of our method by showing how it can applied to finding locally-biased sparse cuts around an input vertex seed set in social and information networks.


Uniform sampling of undirected and directed graphs with a fixed degree sequence

Abstract: Many applications in network analysis require algorithms to sample uniformly at random from the set of all graphs with a prescribed degree sequence. We present a Markov chain based approach which converges to the uniform distribution of all realizations for both the directed and undirected case. It remains an open challenge whether these Markov chains are rapidly mixing. For the case of directed graphs, we also explain in this paper that a popular switching algorithm fails in general to sample uniformly at random because the state graph of the Markov chain decomposes into different isomorphic components. We call degree sequences for which the state graph is strongly connected arc swap sequences. To handle arbitrary degree sequences, we develop two different solutions. The first uses an additional operation (a reorientation of induced directed 3-cycles) which makes the state graph strongly connected, the second selects randomly one of the isomorphic components and samples inside it. Our main contribution is a precise characterization of arc swap sequences, leading to an efficient recognition algorithm. Finally, we point out some interesting consequences for network analysis.


Behavior and performance of the deep belief networks on image classification

Abstract: We apply deep belief networks of restricted Boltzmann machines to bags of words of sift features obtained from databases of 13 Scenes, 15 Scenes and Caltech 256 and study experimentally their behavior and performance. We find that the final performance in the supervised phase is reached much faster if the system is pre-trained. Pre-training the system on a larger dataset keeping the supervised dataset fixed improves the performance (for the 13 Scenes case). After the unsupervised pre-training, neurons arise that form approximate explicit representations for several categories (meaning they are mostly active for this category). The last three facts suggest that unsupervised training really discovers structure in these data. Pre-training can be done on a completely different dataset (we use Corel dataset) and we find that the supervised phase performs just as good (on the 15 Scenes dataset). This leads us to conjecture that one can pre-train the system once (e.g. in a factory) and subsequently apply it to many supervised problems which then learn much faster. The best performance is obtained with single hidden layer system suggesting that the histogram of sift features doesn't have much high level structure. The overall performance is almost equal, but slightly worse then that of the support vector machine and the spatial pyramidal matching.


A boundary between universality and non-universality in spiking neural P systems

Abstract: In this work we offer a significant improvement on the previous smallest spiking neural P systems and solve the problem of finding the smallest possible extended spiking neural P system. Paun and Paun gave a universal spiking neural P system with 84 neurons and another that has extended rules with 49 neurons. Subsequently, Zhang et al. reduced the number of neurons used to give universality to 67 for spiking neural P systems and to 41 for the extended model. Here we give a small universal spiking neural P system that has only 17 neurons and another that has extended rules with 5 neurons. All of the above mentioned spiking neural P systems suffer from an exponential slow down when simulating Turing machines. Using a more relaxed encoding technique we get a universal spiking neural P system that has extended rules with only 4 neurons. This latter spiking neural P system simulates 2-counter machines in linear time and thus suffer from a double exponential time overhead when simulating Turing machines. We show that extended spiking neural P systems with 3 neurons are simulated by log-space bounded Turing machines, and so there exists no such universal system with 3 neurons. It immediately follows that our 4-neuron system is the smallest possible extended spiking neural P system that is universal. Finally, we show that if we generalise the output technique we can give a universal spiking neural P system with extended rules that has only 3 neurons. This system is also the smallest of its kind as a universal spiking neural P system with extended rules and generalised output is not possible with 2 neurons.


A Digital Guitar Tuner

Abstract: The objective of this paper is to understand the critical parameters that need to be addressed while designing a guitar tuner. The focus of the design lies in developing a suitable algorithm to accurately detect the fundamental frequency of a plucked guitar string from its frequency spectrum. A userfriendly graphical interface is developed using Matlab to allow any user to easily tune his guitar using the developed program.


Proceedings 2nd Interaction and Concurrency Experience: Structured Interactions

Abstract: This volume contains the proceedings of the 2nd Workshop on Interaction and Concurrency Experience (ICE'09). The workshop was held in Bologna, Italy on 31th of August 2009, as a satellite workshop of CONCUR'09. The previous edition of ICE has been organized in Reykjavik (2008). The ICE workshop is intended as a series of international scientific meetings oriented to researchers in various fields of theoretical computer science and, each year, the workshop focuses on a specific topic: ICE 2009 focused on structured interactions meant as the class of synchronisations that go beyond the "simple" point-to-point synchronisations (e.g., multicast or broadcast synchronisations, even-notification based interactions, time dependent interactions, distributed transactions,...).


New Algorithmic Approaches for Computing Optimal Network Paths with Several Types of QoS Constraints

Abstract: The problem of efficiently delivering data within networks is very important nowadays, especially in the context of the large volumes of data which are being produced each year and of the increased data access needs of the users. Efficient data delivery strategies must satisfy several types of Quality of Service (QoS) constraints which are imposed by the data consumers. One possibility of achieving this goal (particularly in the case of in-order data transfers) is to choose a satisfactory network delivery path. In this paper we present novel algorithmic approaches for computing optimal network paths which satisfy several types of (QoS) constraints.


Practical Algorithmic Techniques for Several String Processing Problems

Abstract: The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for specific patterns within texts, occur frequently in this context. In this paper we present several novel and practical algorithmic techniques for processing textual data (strings) in order to efficiently solve multiple problems. Our techniques make use of efficient string algorithms and data structures, like KMP, suffix arrays, tries and deterministic finite automata.


Applying an XML Warehouse to Social Network Analysis, Lessons from the WebStand Project

Abstract: In this paper we present the state of advancement of the French ANR WebStand project. The objective of this project is to construct a customizable XML based warehouse platform to acquire, transform, analyze, store, query and export data from the web, in particular mailing lists, with the final intension of using this data to perform sociological studies focused on social groups of World Wide Web, with a specific emphasis on the temporal aspects of this data. We are currently using this system to analyze the standardization process of the W3C, through its social network of standard setters.


Grammar-Based Compression in a Streaming Model

Abstract: We show that, given a string $s$ of length $n$, with constant memory and logarithmic passes over a constant number of streams we can build a context-free grammar that generates $s$ and only $s$ and whose size is within an $\Oh{\min (g \log g, \sqrt{n \log g})}$-factor of the minimum $g$. This stands in contrast to our previous result that, with polylogarithmic memory and polylogarithmic passes over a single stream, we cannot build such a grammar whose size is within any polynomial of $g$.


Gouverner la standardisation par les changements d'arene. Le cas du XML

Abstract: In this paper, we discuss the available approches of the new governance structures of standardization, in order to propose new hypothesis on the way computer sciences languages are dealt with. We consider the example of the XML language and its applications in order to propose a dynamic analysis of this governance, focusing on the coordination that is done by companies, and the strategic usage they have of these arenas to further their goals. We advocate the development of more of such empirical analysis in order to cover all the perspectives of possible international policies in this area.


Flow Splitting with Fate Sharing in a Next Generation Transport Services Architecture

Abstract: The challenges of optimizing end-to-end performance over diverse Internet paths has driven widespread adoption of in-path optimizers, which can destructively interfere with TCP's end-to-end semantics and with each other, and are incompatible with end-to-end IPsec. We identify the architectural cause of these conflicts and resolve them in Tng, an experimental next-generation transport services architecture, by factoring congestion control from end-to-end semantic functions. Through a technique we call "queue sharing", Tng enables in-path devices to interpose on, split, and optimize congestion controlled flows without affecting or seeing the end-to-end content riding these flows. Simulations show that Tng's decoupling cleanly addresses several common performance problems, such as communication over lossy wireless links and reduction of buffering-induced latency on residential links. A working prototype and several incremental deployment paths suggest Tng's practicality.


Deterministic Consistency: A Programming Model for Shared Memory Parallelism

Abstract: The difficulty of developing reliable parallel software is generating interest in deterministic environments, where a given program and input can yield only one possible result. Languages or type systems can enforce determinism in new code, and runtime systems can impose synthetic schedules on legacy parallel code. To parallelize existing serial code, however, we would like a programming model that is naturally deterministic without language restrictions or artificial scheduling. We propose "deterministic consistency", a parallel programming model as easy to understand as the "parallel assignment" construct in sequential languages such as Perl and JavaScript, where concurrent threads always read their inputs before writing shared outputs. DC supports common data- and task-parallel synchronization abstractions such as fork/join and barriers, as well as non-hierarchical structures such as producer/consumer pipelines and futures. A preliminary prototype suggests that software-only implementations of DC can run applications written for popular parallel environments such as OpenMP with low (<10%) overhead for some applications.


Modified Opportunistic Deficit Round Robin Scheduling for improved QOS in IEEE 802.16 WBA networks

Abstract: Packet and flow scheduling algorithms for WiMAX has been a topic of interest for a long time since the very inception of WiMAX networks. WiMAX offers advantages particularly in terms of Quality of service it offers over a longer range at the MAC level. In our work, we propose two credit based scheduling schemes one in which completed flows distributes the left over credits equally to all higher priority uncompleted flows(ODRREDC) and another in which completed flows give away all the excess credits to the highest priority uncompleted flow(ODRRSDC). Both the schemes are compatible with 802.16 MAC protocol and can efficiently serve real time bursty traffic with reduced latency and hence improved QOS for real time flows. We compare the two proposed schemes for their latency, bandwidth utilization and throughput for real time burst flows with the opportunity based Deficit Round Robin scheduling scheme. While the ODRR scheduler focuses on reducing the credits for the flows with errors, our approach also distributes these remaining credits together with the credits from completed flows equally among the higher priority uncompleted flows or totally to the highest priority uncompleted flow.


Orthomodular lattices, Foulis Semigroups and Dagger Kernel Categories

Abstract: This paper is a sequel to arXiv:0902.2355 and continues the study of quantum logic via dagger kernel categories. It develops the relation between these categories and both orthomodular lattices and Foulis semigroups. The relation between the latter two notions has been uncovered in the 1960s. The current categorical perspective gives a broader context and reconstructs this relationship between orthomodular lattices and Foulis semigroups as special instance.


Educational Objectives Of Different Laboratory Types: A Comparative Study

Abstract: Laboratory based courses play a critical role in scientific education. Automation is changing the nature of the laboratories, and there is a long running debate about the value of hands on versus simulated and remote laboratories. The remote lab technology has brought a significant improvement in communication within the Academic community and has improved students learning experiences. There are different educational objectives as criteria for judging the laboratories: Hands on advocates emphasize design skills, while remote lab advocates focus on conceptual understanding. Remote laboratories offer all the advantages of the new technology, but are often a poor replacement for real laboratory work. Remote laboratories are similar to simulation techniques in that they require minimal space and time, because the experiments can be rapidly configured and run over the Internet [Web]. But unlike simulations, they provide real data. This paper presents a comparative analysis for the educational objectives of the three laboratory techniques, hands on, simulated, and remote laboratories. In addition, it proposes enhancements for the remote lab activities leading to improving its performance.


Neural-estimator for the surface emission rate of atmospheric gases

Abstract: The emission rate of minority atmospheric gases is inferred by a new approach based on neural networks. The neural network applied is the multi-layer perceptron with backpropagation algorithm for learning. The identification of these surface fluxes is an inverse problem. A comparison between the new neural-inversion and regularized inverse solution id performed. The results obtained from the neural networks are significantly better. In addition, the inversion with the neural netwroks is fster than regularized approaches, after training.


A Survey on Recognition Based Graphical User Authentication Algorithms

Abstract: Nowadays, user authentication is one of the important topics in information security. Strong textbased password schemes could provide with certain degree of security. However, the fact that strong passwords are difficult to memorize often leads their owners to write them down on papers or even save them in a computer file. Graphical authentication has been proposed as a possible alternative solution to textbased authentication, motivated particularly by the fact that humans can remember images better than text. In recent years, many networks, computer systems and Internet based environments try used graphical authentication technique for their users authentication. All of graphical passwords have two different aspects which are usability and security. Unfortunately none of these algorithms were being able to cover both of these aspects at the same time. In this paper, we described eight recognition based authentication algorithms in terms of their drawbacks and attacks. In the next section, the usability standards from ISO and the related attributes for graphical user authentication usability are discussed. The related attack patterns for graphical user authentication security part are also discussed. Finally, a comparison table of all recognition based algorithms is presented based on ISO and attack patterns standards.


Comparative Study of Different Guard Time Intervals to Improve the BER Performance of Wimax Systems to Minimize the Effects of ISI and ICI under Adaptive Modulation Techniques over SUI1 and AWGN Communication Channels

Abstract: The WIMAX technology based on air interface standard 802.16 wireless MAN is configured in the same way as a traditional cellular network with base stations using point to multipoint architecture to drive a service over a radius up to several kilometers. The range and the Non Line of Sight (NLOS) ability of WIMAX make the system very attractive for users, but there will be slightly higher BER at low SNR. The aim of this paper is the comparative study of different guard time intervals effect for improving BER at different SNR under digital modulation (QPSK, 16QAM and 64QAM) techniques and different communication channels AWGN and fading channels Stanford University Interim (SUI 1) of an WIMAX system. The comparison between these effects with Reed-Solomon (RS) encoder with Convolutional encoder (half) rated codes in FEC channel coding will be investigated. The simulation results of estimated Bit Error Rate (BER) displays that the implementation of interleaved RS code (255,239,8) with (half) rated Convolutional code of 0.25 guard time intervals under QPSK modulation technique over AWGN channel is highly effective to combat in the Wimax communication system. To complete this performance analysis in Wimax based systems, a segment of audio signal is used for analysis. The transmitted audio message is found to have retrieved effectively under noisy situation.


A novel approach for implementing Steganography with computing power obtained by combining Cuda and Matlab

Abstract: With the current development of multiprocessor systems, strive for computing data on such processor have also increased exponentially. If the multi core processors are not fully utilized, then even though we have the computing power the speed is not available to the end users for their respective applications. In accordance to this, the users or application designers also have to design newer applications taking care of the computing infrastructure available within. Our approach is to use the CUDA (Compute Unified Device Architecture) as backend and MATLAB as the front end to design an application for implementing steganography. Steganography is the term used for hiding information in the cover object like Image, Audio or Video data. As the computing required for multimedia data is much more than the text information, we have been successful in implementing image Steganography with the help of technology for the next generation.


Abstract: Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper we propose Fingerprint Verification based on Gabor Filter Enhancement (FVGFE) algorithm for minutiae feature extraction and post processing based on 9 pixel neighborhood. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms.


Shoulder Surfing attack in graphical password authentication

Abstract: Information and computer security is supported largely by passwords which are the principle part of the authentication process. The most common computer authentication method is to use alphanumerical username and password which has significant drawbacks. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text based scheme. A potential drawback of graphical password schemes is that they are more vulnerable to shoulder surfing than conventional alphanumeric text passwords. When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individuals authentication session. This is referred to as shouldersurfing and is a known risk, of special concern when authenticating in public places. In this paper we will present a survey on graphical password schemes from 2005 till 2009 which are proposed to be resistant against shoulder surfing attacks.


An approach to secure highly confidential documents of any size in the corporate or institutes having unsecured networks

Abstract: With the tremendous amount of computing because of the wide usage of internet it is observed that some user(s) are not able to manage their desktop with antivirus software properly installed. It is happening few times, that we allow our friends, students and colleagues to sit on our networked PC. Sometimes the user is unaware of the situation that there workstations are unsecured and so some one else could also be monitoring your flow of information and your most important data could go haywire, resulting into leakage of most confidential data to unwanted or malicious user(s). Example of some such documents could be question papers designed by the faculty member by various universities. Now a day most of the universities are having the biggest threat about the question papers and many other confidential documents designed by their faculty members. We in this paper present the solution to over come such a situation using the concept of Steganography. Steganography is a technique through which one can hide information into some cover object. This technique, if used, in positive direction could be of great help to solve such a problem and even other.


Robust Multi biometric Recognition Using Face and Ear Images

Abstract: This study investigates the use of ear as a biometric for authentication and shows experimental results obtained on a newly created dataset of 420 images. Images are passed to a quality module in order to reduce False Rejection Rate. The Principal Component Analysis (eigen ear) approach was used, obtaining 90.7 percent recognition rate. Improvement in recognition results is obtained when ear biometric is fused with face biometric. The fusion is done at decision level, achieving a recognition rate of 96 percent.


Privacy Preserving k Secure Sum Protocol

Abstract: Secure Multiparty Computation (SMC) allows parties to know the result of cooperative computation while preserving privacy of individual data. Secure sum computation is an important application of SMC. In our proposed protocols parties are allowed to compute the sum while keeping their individual data secret with increased computation complexity for hacking individual data. In this paper the data of individual party is broken into a fixed number of segments. For increasing the complexity we have used the randomization technique with segmentation


An expected-case sub-cubic solution to the all-pairs shortest path problem in R

Abstract: It has been shown by Alon et al. that the so-called 'all-pairs shortest-path' problem can be solved in O((MV)^2.688 * log^3(V)) for graphs with V vertices, with integer distances bounded by M. We solve the more general problem for graphs in R (assuming no negative cycles), with expected-case running time O(V^2.5 * log(V)). While our result appears to violate the Omega(V^3) requirement of "Funny Matrix Multiplication" (due to Kerr), we find that it has a sub-cubic expected time solution subject to reasonable conditions on the data distribution. The expected time solution arises when certain sub-problems are uncorrelated, though we can do better/worse than the expected-case under positive/negative correlation (respectively). Whether we observe positive/negative correlation depends on the statistics of the graph in question. In practice, our algorithm is significantly faster than Floyd-Warshall, even for dense graphs.


Ethics Understanding of Software Professional In Risk Reducing Reusability Coding Using Inclusion Set Theory

Abstract: The technical skill or ability of an individual is different to person in software developments of projects. So, it is necessary to identify the talent and attitude of an individual contribution can be uniformly distributed to the different phases of software development cycle. The line of code analysis metrics to understanding the various skills of the programmers in code development. By using the inclusion set theory of n (AUB) refer to strength and risk free code developed from union of software professionals and system must comprise of achievement of the system goal, effective memory utilization and intime delivery of the product.


Architectural Design Activities for JAS

Abstract: The critical part for building any software system is its architecture. Architectural design is a design at a higher level of abstraction. A good architecture ensures that software will satisfy its requirement. This paper defines the most important activities of architectural design that used through building any software; also it applies these activities on one type of Electronic Commerce (EC) applications that is Job Agency System(JAS) to show how these activities can work through these types of applications.


Ant Based Adaptive Multicast Routing Protocol (AAMRP) for Mobile Ad Hoc Networks

Abstract: Multicasting is effective when its group members are sparse and the speed is low. On the other hand, broadcasting is effective when the group members dense and the speed are high. Since mobile ad hoc networks are highly dynamic in nature, either of the above two strategies can be adopted at different scenarios. In this paper, we propose an ant agent based adaptive, multicast protocol that exploits group members desire to simplify multicast routing and invoke broadcast operations in appropriate localized regimes. By reducing the number of group members that participate in the construction of the multicast structure and by providing robustness to mobility by performing broadcasts in densely clustered local regions, the proposed protocol achieves packet delivery statistics that are comparable to that with a pure multicast protocol but with significantly lower overheads. By our simulation results, we show that our proposed protocol achieves increased Packet Delivery Fraction (PDF) with reduced overhead and routing load.


A New Approach to Cold Start in Peer to Peer File Sharing Networks

Abstract: Solving free riding and selecting a reliable service provider in P2P networks has been separately investigated in last few years. Using trust has shown to be one of the best ways of solving these problems. But using this approach to simultaneously deal with both problems makes it impossible for newcomers to join the network and the expansion of network is prevented. In this paper we used the game theory to model the behavior of peers and developed a mechanism in which free riding and providing bad service are dominated strategies for peers. At the same time newcomers can participate and are encouraged to be active in the network. The proposed model has been simulated and the results showed that the trust value of free riders and bad service providers converge to a finite value and trust of peers who provide good service is monotonically increased despite the time they join the network.


Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree

Abstract: We presents in this paper a novel fish classification methodology based on a combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task and do not make the combination between the feature selection, image segmentation and geometrical parameter, we propose a general set of features extraction using robust feature selection, image segmentation and geometrical parameter and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a black box and focus our research in the determination of which input information must bring a robust fish discrimination.The main contribution of this paper is enhancement recognize and classify fishes based on digital image and To develop and implement a novel fish recognition prototype using global feature extraction, image segmentation and geometrical parameters, it have the ability to Categorize the given fish into its cluster and Categorize the clustered fish into poison or non-poison fish, and categorizes the non-poison fish into its family .


Comparative Study Of Congestion Control Techniques In High Speed Networks

Abstract: Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially to handle bursty traffic of todays very high speed networks. Since late 90s numerous schemes i.e. [1]...[10] etc. have been proposed. This paper concentrates on comparative study of the different congestion control schemes based on some key performance metrics. An effort has been made to judge the performance of Maximum Entropy (ME) based solution for a steady state GE/GE/1/N censored queues with partial buffer sharing scheme against these key performance metrics.


Performance analysis of Non Linear Filtering Algorithms for underwater images

Abstract: Image filtering algorithms are applied on images to remove the different types of noise that are either present in the image during capturing or injected in to the image during transmission. Underwater images when captured usually have Gaussian noise, speckle noise and salt and pepper noise. In this work, five different image filtering algorithms are compared for the three different noise types. The performances of the filters are compared using the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The modified spatial median filter gives desirable results in terms of the above two parameters for the three different noise. Forty underwater images are taken for study.


Designing Kernel Scheme for Classifiers Fusion

Abstract: In this paper, we propose a special fusion method for combining ensembles of base classifiers utilizing new neural networks in order to improve overall efficiency of classification. While ensembles are designed such that each classifier is trained independently while the decision fusion is performed as a final procedure, in this method, we would be interested in making the fusion process more adaptive and efficient. This new combiner, called Neural Network Kernel Least Mean Square1, attempts to fuse outputs of the ensembles of classifiers. The proposed Neural Network has some special properties such as Kernel abilities,Least Mean Square features, easy learning over variants of patterns and traditional neuron capabilities. Neural Network Kernel Least Mean Square is a special neuron which is trained with Kernel Least Mean Square properties. This new neuron is used as a classifiers combiner to fuse outputs of base neural network classifiers. Performance of this method is analyzed and compared with other fusion methods. The analysis represents higher performance of our new method as opposed to others.


Approaches for user profile Investigation in Orkut Social Network

Abstract: Internet becomes a large and rich repository of information about us as individually. Any thing form user profile information to friends links the user subscribes to are reflection of social interactions as user has in real worlds. Social networking has created new ways to communicate and share information. Social networking websites are being used regularly by millions of people, and it now seems that social networking will be an enduring part of everyday life. Social networks such as Orkut, Bebo, MySpace, Flickr, Facebook, Friendster and LinkedIn, have attracted millions of internet user who are involved in bogging, participatory book reviewing, personal networking and photo sharing. Social network services are increasingly being used in legal and criminal investigations. Information posted on sites such as Orkut and Facebook has been used by police, probation, and university officials to prosecute users of said sites. In some situations, content posted on web social network has been used in court. In the proposed work degree of closeness is identified by link weight approaches and information matrices are generated and matched on the basis of similarity in user profile information. The proposed technique is useful to investigate a user profile and calculate closeness or interaction between users.


Biogeography based Satellite Image Classification

Abstract: Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible areas. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. This paper is focused on classification of the satellite image of a particular land cover using the theory of Biogeography based Optimization. The original BBO algorithm does not have the inbuilt property of clustering which is required during image classification. Hence modifications have been proposed to the original algorithm and the modified algorithm is used to classify the satellite image of a given region. The results indicate that highly accurate land cover features can be extracted effectively when the proposed algorithm is used.


Web Document Analysis for Companies Listed in Bursa Malaysia

Abstract: This paper discusses a research on web document analysis for companies listed on Bursa Malaysia which is the forerunner of financial and investment center in Malaysia. Data set used in this research are from the company web documents listed in the Main Board and Second Board on Bursa Malaysia. This research has used the Web Resources Extraction System which was developed by the research group mainly to extract information for the web documents involved. Our research findings have shown that the level of website usage among the companies on Bursa Malaysia is still minimal. Furthermore, research has also found that 60.02 percent of the image files are utilized making it the most used type of file in creating websites.


A Reliable Replication Strategy for VoD System using Markov Chain

Abstract: In this paper we have investigated on the reliability of streams for a VoD system. The objective of the paper is to maximize the availability of streams for the peers in the VoD system. We have achieved this by using data replication technique in the peers. Hence, we proposed a new data replication technique to optimally store the videos in the peers. The new data replication technique generates more number of replicas than the existing techniques such as random, minimum request and maximize hit. We have also investigated by applying the CTMC model for the reliability of replications during the peer failures. Our result shows that the mean lifetime of replicas are more under various circumstances. We have addressed the practical issues of efficient utilization of overall bandwidth and buffer in the VoD system. We achieved greater success playback probability of videos than the existing techniques.


An Efficient Admission Control Algorithm for Load Balancing In Hierarchical Mobile IPv6 Networks

Abstract: In hierarchical Mobile IPv6 networks, Mobility Anchor Point (MAP) may become a single point of bottleneck as it handles more and more mobile nodes (MNs). A number of schemes have been proposed to achieve load balancing among different MAPs. However, signaling reduction is still imperfect because these schemes also avoid the effect of the number of CNs. Also only the balancing of MN is performed, but not the balancing of the actual traffic load, since CN of each MN may be different. This paper proposes an efficient admission control algorithm along with a replacement mechanism for HMIPv6 networks. The admission control algorithm is based on the number of serving CNs and achieves actual load balancing among MAPs. Moreover, a replacement mechanism is introduced to decrease the new MN blocking probability and the handoff MN dropping probability. By simulation results, we show that, the handoff delay and packet loss are reduced in our scheme, when compared with the standard HMIPv6 based handoff.


An ensemble approach for feature selection of Cyber Attack Dataset

Abstract: Feature selection is an indispensable preprocessing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper phase whose output the final feature subset. The final feature subsets are passed through the Knearest neighbor classifier for classification of attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99 cyber attack dataset.


Short Term Load Forecasting Using Multi Parameter Regression

Abstract: Short Term Load forecasting in this paper uses input data dependent on parameters such as load for current hour and previous two hours, temperature for current hour and previous two hours, wind for current hour and previous two hours, cloud for current hour and previous two hours. Forecasting will be of load demand for coming hour based on input parameters at that hour. In this paper we are using multiparameter regression method for forecasting which has error within tolerable range. Algorithms implementing these forecasting techniques have been programmed using MATLAB and applied to the case study. Other methodologies in this area are ANN, Fuzzy and Evolutionary Algorithms for which investigations are under process. Adaptive multiparameter regression for load forecasting, in near future will be possible.


Refactoring of a Database

Abstract: The technique of database refactoring is all about applying disciplined and controlled techniques to change an existing database schema. The problem is to successfully create a Database Refactoring Framework for databases. This paper concentrates on the feasibility of adapting this concept to work as a generic template. To retain the constraints regardless of the modifications to the metadata, the paper proposes a MetaData Manipulation Tool to facilitate change. The tool adopts a Template Design Pattern to make it database independent. The paper presents a drawback of using java for constraint extraction and proposes an alternative.


Genetic Programming Framework for Fingerprint Matching

Abstract: A fingerprint matching is a very difficult problem. Minutiae based matching is the most popular and widely used technique for fingerprint matching. The minutiae points considered in automatic identification systems are based normally on termination and bifurcation points. In this paper we propose a new technique for fingerprint matching using minutiae points and genetic programming. The goal of this paper is extracting the mathematical formula that defines the minutiae points.


Location Update Accuracy in Human Tracking system using Zigbee modules

Abstract: A location and tracking system becomes very important to our future world of pervasive computing. An algorithm for accurate location information is being incorporated in the human walking model and in the blind human walking model. We want to implement an accurate location tracking mechanism using Zigbee along with GPS, we have incorporated Markov chain algorithm for establishing accuracy. Normal Human and blind human walking steps were actually taken in the known environment within our campus and the Markov chain algorithm was used for smoothening the stepwise variation in location updates. A comparison module is also implemented to show the difference between normal human and blind human walking step variations. This accuracy is used for designing a blind tracking device so that the device can be used by the blind for finding the path without obstacles. We present a system level approach to localizing and tracking Human and blind users on a basis of different sources of location information [GPS plus Zigbee]. The system can be applied outdoors especially for avoiding accidents, GPS as the source of location data. Performance evaluation shows that the system is accurate and it is a future path finding device with service for the blind.


Performance Evaluation of WiMAX (802.16) Using Different Encoding Schemes

Abstract: This paper deals with the performance of Worldwide Interoperability for Microwave Access (WiMAX), when we enhance its physical layer attributes with help of different encoding techniques. For this evaluation Space Time Block Codes (STBC) and Turbo codes are separately introduced into the architecture of WiMAX that works on adaptive modulation technique.


Quotient Complexity of Closed Languages

Abstract: A language L is prefix-closed if, whenever a word w is in L, then every prefix of w is also in L. We define suffix-, factor-, and subword-closed languages in the same way, where by subword we mean subsequence. We study the quotient complexity (usually called state complexity) of operations on prefix-, suffix-, factor-, and subword-closed languages. We find tight upper bounds on the complexity of the prefix-, suffix-, factor-, and subword-closure of arbitrary languages, and on the complexity of boolean operations, concatenation, star and reversal in each of the four classes of closed languages. We show that repeated application of positive closure and complement to a closed language results in at most four distinct languages, while Kleene closure and complement gives at most eight languages.


Thresholded Covering Algorithms for Robust and Max-Min Optimization

Abstract: The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worst-case cost (summed over both days) is minimized? Feige et al. and Khandekar et al. considered the k-robust model where the possible outcomes tomorrow are given by all demand-subsets of size k, and gave algorithms for the set cover problem, and the Steiner tree and facility location problems in this model, respectively. In this paper, we give the following simple and intuitive template for k-robust problems: "having built some anticipatory solution, if there exists a single demand whose augmentation cost is larger than some threshold, augment the anticipatory solution to cover this demand as well, and repeat". In this paper we show that this template gives us improved approximation algorithms for k-robust Steiner tree and set cover, and the first approximation algorithms for k-robust Steiner forest, minimum-cut and multicut. All our approximation ratios (except for multicut) are almost best possible. As a by-product of our techniques, we also get algorithms for max-min problems of the form: "given a covering problem instance, which k of the elements are costliest to cover?".


Abstract Milling with Turn Costs

Abstract: The Abstract Milling problem is a natural and quite general graph-theoretic model for geometric milling problems. Given a graph, one asks for a walk that covers all its vertices with a minimum number of turns, as specified in the graph model by a 0/1 turncost function fx at each vertex x giving, for each ordered pair of edges (e,f) incident at x, the turn cost at x of a walk that enters the vertex on edge e and departs on edge f. We describe an initial study of the parameterized complexity of the problem. Our main positive result shows that Abstract Milling, parameterized by: number of turns, treewidth and maximum degree, is fixed-parameter tractable, We also show that Abstract Milling parameterized by (only) the number of turns and the pathwidth, is hard for W[1] -- one of the few parameterized intractability results for bounded pathwidth.


Modern Symmetric Cryptography methodologies and its applications

Abstract: Nowadays, using cryptographic systems play an effective role in security and safety technologies. One of the most applied kind of cryptography is Symmetric Cryptography and its applications. New aspects of symmetric Cryptography methodologies and applications has been presented by this paper. Security-based networks and some complex technologies such as RFID and parallel security settings has been intro-duced by using Symmetric Cryptography is the main base of discussion in this paper. Designing an unique protocol for Symmetric Cryptography in security networks elements is our focus. Reviewing benefits of using these methodologies has been pre-sented and discussed in this paper.


XML Multidimensional Modelling and Querying

Abstract: As XML becomes ubiquitous and XML storage and processing becomes more efficient, the range of use cases for these technologies widens daily. One promising area is the integration of XML and data warehouses, where an XML-native database stores multidimensional data and processes OLAP queries written in the XQuery interrogation language. This paper explores issues arising in the implementation of such a data warehouse. We first compare approaches for multidimensional data modelling in XML, then describe how typical OLAP queries on these models can be expressed in XQuery. We then show how, regardless of the model, the grouping features of XQuery 1.1 improve performance and readability of these queries. Finally, we evaluate the performance of query evaluation in each modelling choice using the eXist database, which we extended with a grouping clause implementation.


A fast randomized algorithm for orthogonal projection

Abstract: We describe an algorithm that, given any full-rank matrix A having fewer rows than columns, can rapidly compute the orthogonal projection of any vector onto the null space of A, as well as the orthogonal projection onto the row space of A, provided that both A and its adjoint can be applied rapidly to arbitrary vectors. As an intermediate step, the algorithm solves the overdetermined linear least-squares regression involving the adjoint of A (and so can be used for this, too). The basis of the algorithm is an obvious but numerically unstable scheme; suitable use of a preconditioner yields numerical stability. We generate the preconditioner rapidly via a randomized procedure that succeeds with extremely high probability. In many circumstances, the method can accelerate interior-point methods for convex optimization, such as linear programming (Ming Gu, personal communication).


Euclidean Prize-collecting Steiner Forest

Abstract: In this paper, we consider Steiner forest and its generalizations, prize-collecting Steiner forest and k-Steiner forest, when the vertices of the input graph are points in the Euclidean plane and the lengths are Euclidean distances. First, we present a simpler analysis of the polynomial-time approximation scheme (PTAS) of Borradaile et al. [12] for the Euclidean Steiner forest problem. This is done by proving a new structural property and modifying the dynamic programming by adding a new piece of information to each dynamic programming state. Next we develop a PTAS for a well-motivated case, i.e., the multiplicative case, of prize-collecting and budgeted Steiner forest. The ideas used in the algorithm may have applications in design of a broad class of bicriteria PTASs. At the end, we demonstrate why PTASs for these problems can be hard in the general Euclidean case (and thus for PTASs we cannot go beyond the multiplicative case).


A Learning-Based Approach to Reactive Security

Abstract: Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender's strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker's incentives and knowledge.


Delay-Optimal Power and Subcarrier Allocation for OFDMA Systems via Stochastic Approximation

Abstract: In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with $N_F$ subcarriers, $K$ mobiles and one base station. There are $K$ queues at the base station for the downlink traffic to the $K$ mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a $K$-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). This problem is challenging because it corresponds to a stochastic Network Utility Maximization (NUM) problem where general solution is still unknown. We propose an {\em online stochastic value iteration} solution using {\em stochastic approximation}. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1 (One is the stepsize condition. The other is the condition on accessibility of the Markov Chain, which can be easily satisfied in most of the cases we are interested.), the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear $\mathcal{O}(KN_F)$ complexity and $\mathcal{O}(K)$ memory requirement.


Extending Karger's randomized min-cut Algorithm for a Synchronous Distributed setting

Abstract: A min-cut that seperates vertices s and t in a network is an edge set of minimum weight whose removal will disconnect s and t. This problem is the dual of the well known s-t max-flow problem. Several algorithms for the min-cut problem are based on max-flow computation although the fastest known min-cut algorithms are not flow based. The well known Karger's randomized algorithm for min-cut is a non-flow based method for solving the (global) min-cut problem of finding the min s-t cut over all pair of vertices s,t in a weighted undirected graph. This paper presents an adaptation of Karger's algorithm for a synchronous distributed setting where each node is allowed to perform only local computations. The paper essentially addresses the technicalities involved in circumventing the limitations imposed by a distributed setting to the working of Karger's algorithm. While the correctness proof follows directly from Karger's algorithm, the complexity analysis differs significantly. The algorithm achieves the same probability of success as the original algorithm with O(mn^{2}) message complexity and O(n^{2}) time complexity, where n and m denote the number of vertices and edges in the graph.