On Approximability of Block Sorting

Abstract: Block Sorting is a well studied problem, motivated by its applications in Optical Character Recognition (OCR), and Computational Biology. Block Sorting has been shown to be NP-Hard, and two separate polynomial time 2-approximation algorithms have been designed for the problem. But questions like whether a better approximation algorithm can be designed, and whether the problem is APX-Hard have been open for quite a while now. In this work we answer the latter question by proving Block Sorting to be Max-SNP-Hard (APX-Hard). The APX-Hardness result is based on a linear reduction of Max-3SAT to Block Sorting. We also provide a new lower bound for the problem via a new parametrized problem k-Block Merging.


Robust inversion via semistochastic dimensionality reduction

Abstract: We consider a class of inverse problems where it is possible to aggregate the results of multiple experiments. This class includes problems where the forward model is the solution operator to linear ODEs or PDEs. The tremendous size of such problems motivates dimensionality reduction techniques based on randomly mixing experiments. These techniques break down, however, when robust data-fitting formulations are used, which are essential in cases of missing data, unusually large errors, and systematic features in the data unexplained by the forward model. We survey robust methods within a statistical framework, and propose a semistochastic optimization approach that allows dimensionality reduction. The efficacy of the methods are demonstrated for a large-scale seismic inverse problem using the robust Student's t-distribution, where a useful synthetic velocity model is recovered in the extreme scenario of 60% data missing at random. The semistochastic approach achieves this recovery using 20% of the effort required by a direct robust approach.


Wireless Connectivity and Capacity

Abstract: Given $n$ wireless transceivers located in a plane, a fundamental problem in wireless communications is to construct a strongly connected digraph on them such that the constituent links can be scheduled in fewest possible time slots, assuming the SINR model of interference. In this paper, we provide an algorithm that connects an arbitrary point set in $O(\log n)$ slots, improving on the previous best bound of $O(\log^2 n)$ due to Moscibroda. This is complemented with a super-constant lower bound on our approach to connectivity. An important feature is that the algorithms allow for bi-directional (half-duplex) communication. One implication of this result is an improved bound of $\Omega(1/\log n)$ on the worst-case capacity of wireless networks, matching the best bound known for the extensively studied average-case. We explore the utility of oblivious power assignments, and show that essentially all such assignments result in a worst case bound of $\Omega(n)$ slots for connectivity. This rules out a recent claim of a $O(\log n)$ bound using oblivious power. On the other hand, using our result we show that $O(\min(\log \Delta, \log n \cdot (\log n + \log \log \Delta)))$ slots suffice, where $\Delta$ is the ratio between the largest and the smallest links in a minimum spanning tree of the points. Our results extend to the related problem of minimum latency aggregation scheduling, where we show that aggregation scheduling with $O(\log n)$ latency is possible, improving upon the previous best known latency of $O(\log^3 n)$. We also initiate the study of network design problems in the SINR model beyond strong connectivity, obtaining similar bounds for biconnected and $k$-edge connected structures.


Dictionary Learning for Deblurring and Digital Zoom

Abstract: This paper proposes a novel approach to image deblurring and digital zooming using sparse local models of image appearance. These models, where small image patches are represented as linear combinations of a few elements drawn from some large set (dictionary) of candidates, have proven well adapted to several image restoration tasks. A key to their success has been to learn dictionaries adapted to the reconstruction of small image patches. In contrast, recent works have proposed instead to learn dictionaries which are not only adapted to data reconstruction, but also tuned for a specific task. We introduce here such an approach to deblurring and digital zoom, using pairs of blurry/sharp (or low-/high-resolution) images for training, as well as an effective stochastic gradient algorithm for solving the corresponding optimization task. Although this learning problem is not convex, once the dictionaries have been learned, the sharp/high-resolution image can be recovered via convex optimization at test time. Experiments with synthetic and real data demonstrate the effectiveness of the proposed approach, leading to state-of-the-art performance for non-blind image deblurring and digital zoom.


Hierarchies of Inefficient Kernelizability

Abstract: The framework of Bodlaender et al. (ICALP 2008) and Fortnow and Santhanam (STOC 2008) allows us to exclude the existence of polynomial kernels for a range of problems under reasonable complexity-theoretical assumptions. However, there are also some issues that are not addressed by this framework, including the existence of Turing kernels such as the "kernelization" of Leaf Out Branching(k) into a disjunction over n instances of size poly(k). Observing that Turing kernels are preserved by polynomial parametric transformations, we define a kernelization hardness hierarchy, akin to the M- and W-hierarchy of ordinary parameterized complexity, by the PPT-closure of problems that seem likely to be fundamentally hard for efficient Turing kernelization. We find that several previously considered problems are complete for our fundamental hardness class, including Min Ones d-SAT(k), Binary NDTM Halting(k), Connected Vertex Cover(k), and Clique(k log n), the clique problem parameterized by k log n.


The Query-commit Problem

Abstract: In the query-commit problem we are given a graph where edges have distinct probabilities of existing. It is possible to query the edges of the graph, and if the queried edge exists then its endpoints are irrevocably matched. The goal is to find a querying strategy which maximizes the expected size of the matching obtained. This stochastic matching setup is motivated by applications in kidney exchanges and online dating. In this paper we address the query-commit problem from both theoretical and experimental perspectives. First, we show that a simple class of edges can be queried without compromising the optimality of the strategy. This property is then used to obtain in polynomial time an optimal querying strategy when the input graph is sparse. Next we turn our attentions to the kidney exchange application, focusing on instances modeled over real data from existing exchange programs. We prove that, as the number of nodes grows, almost every instance admits a strategy which matches almost all nodes. This result supports the intuition that more exchanges are possible on a larger pool of patient/donors and gives theoretical justification for unifying the existing exchange programs. Finally, we evaluate experimentally different querying strategies over kidney exchange instances. We show that even very simple heuristics perform fairly well, being within 1.5% of an optimal clairvoyant strategy, that knows in advance the edges in the graph. In such a time-sensitive application, this result motivates the use of committing strategies.


Generalization Strategies for the Verification of Infinite State Systems

Abstract: We present a method for the automated verification of temporal properties of infinite state systems. Our verification method is based on the specialization of constraint logic programs (CLP) and works in two phases: (1) in the first phase, a CLP specification of an infinite state system is specialized with respect to the initial state of the system and the temporal property to be verified, and (2) in the second phase, the specialized program is evaluated by using a bottom-up strategy. The effectiveness of the method strongly depends on the generalization strategy which is applied during the program specialization phase. We consider several generalization strategies obtained by combining techniques already known in the field of program analysis and program transformation, and we also introduce some new strategies. Then, through many verification experiments, we evaluate the effectiveness of the generalization strategies we have considered. Finally, we compare the implementation of our specialization-based verification method to other constraint-based model checking tools. The experimental results show that our method is competitive with the methods used by those other tools. To appear in Theory and Practice of Logic Programming (TPLP).


Parallel Instantiation of ASP Programs: Techniques and Experiments

Abstract: Answer Set Programming (ASP) is a powerful logic-based programming language, which is enjoying increasing interest within the scientific community and (very recently) in industry. The evaluation of ASP programs is traditionally carried out in two steps. At the first step an input program P undergoes the so-called instantiation (or grounding) process, which produces a program P' semantically equivalent to P, but not containing any variable; in turn, P' is evaluated by using a backtracking search algorithm in the second step. It is well-known that instantiation is important for the efficiency of the whole evaluation, might become a bottleneck in common situations, is crucial in several realworld applications, and is particularly relevant when huge input data has to be dealt with. At the time of this writing, the available instantiator modules are not able to exploit satisfactorily the latest hardware, featuring multi-core/multi-processor SMP (Symmetric MultiProcessing) technologies. This paper presents some parallel instantiation techniques, including load-balancing and granularity control heuristics, which allow for the effective exploitation of the processing power offered by modern SMP machines. This is confirmed by an extensive experimental analysis herein reported. To appear in Theory and Practice of Logic Programming (TPLP). KEYWORDS: Answer Set Programming, Instantiation, Parallelism, Heuristics


Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

Abstract: In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4.


Towards a native toplevel for the OCaml language

Abstract: This paper presents the current state of our work on an interactive toplevel for the OCaml language based on the optimizing native code compiler and runtime. Our native toplevel is up to 100 times faster than the default OCaml toplevel, which is based on the byte code compiler and interpreter. It uses Just-In-Time techniques to compile toplevel phrases to native code at runtime, and currently works with various Unix-like systems running on x86 or x86-64 processors.


Using Genetic Algorithm in the Evolutionary Design of Sequential Logic Circuits

Abstract: Evolvable hardware (EHW) is a set of techniques that are based on the idea of combining reconfiguration hardware systems with evolutionary algorithms. In other word, EHW has two sections; the reconfigurable hardware and evolutionary algorithm where the configurations are under the control of an evolutionary algorithm. This paper, suggests a method to design and optimize the synchronous sequential circuits. Genetic algorithm (GA) was applied as evolutionary algorithm. In this approach, for building input combinational logic circuit of each DFF, and also output combinational logic circuit, the cell arrays have been used. The obtained results show that our method can reduce the average number of generations by limitation the search space.


A Novel Elliptic curve cryptography Processor using NoC design

Abstract: In this paper, we propose an elliptic curve key generation processor over GF(2m) and GF(P) with Network-on-Chip (NoC) design scheme based on binary scalar multiplication algorithm. Over the Two last decades, Elliptic Curve Cryptography (ECC) has gained increasing acceptance in the industry and the academic community. This interest is mainly caused by the same level of security with relatively small keys provided by ECC comparing to large key size in Rivest Shamir Adleman (RSA). Parallelism can be utilized in different hierarchy levels as shown in many publications. By using NoC, a new method with the reduced latency of point multiplication (with parallel field arithmetic) is introduced in this paper.


Spider Solitaire is NP-Complete

Abstract: This project investigates the potential of computers to solve complex tasks such as games. The paper proves that the complexity of a generalized version of spider solitaire is NP-Complete and uses much of structure of the proof that FreeCell is NP-Hard in the paper Helmert, M. "Complexity Results for Standard Benchmark Domains in Planning." Artificial Intelligence 143.2 (2003): 219-62. Print. A given decision problem falls in to the class NP-Complete if it is proven to be both in NP and in NP-Hard. To prove that this is the case the paper shows that, not only do the kinds of possible moves that can be reversed prove this, but it is also shown that no spider solitaire game of size n will take more than a polynomial number of moves to complete if such a completion is possible. The paper reduces 3-SAT to SpiderSolitaire (the name used throughout the proof when referring to the generalized version of popular solitaire variant "Spider Solitaire") by showing that any 3-SAT instance can be replicated using an appropriately arranged initial tableau. The example provided reinforces the proof of NP-Hardness and helps to make the proof easier to understand, but the definitive proof lies in the equations providing instruction on how to set up any 3-SAT instance of clause size C as a instance of SpiderSolitaire.


Mirage: Towards Deployable DDoS Defense for Web Applications

Abstract: Distributed Denial of Service (DDoS) attacks form a serious threat to the security of Internet services. However, despite over a decade of research, and the existence of several proposals to address this problem, there has been little progress to date on actual adoption. We present Mirage, a protocol that achieves comparable performance to other DDoS mitigation schemes while providing benefits when deployed only in the server's local network and its upstream ISP, where local business objectives may incentivize deployment. Mirage does not require source end hosts to install any software to access Mirage protected websites. Unlike previous proposals, Mirage only requires functionality from routers that is already deployed in today's routers, though this functionality may need to be scaled depending on the point of deployment. Our approach is that end hosts can thwart the attackers by employing the principle of a moving target: end hosts in our architecture periodically change IP addresses to keep the attackers guessing. Knowledge of an active IP address of the destination end host can act as an implicit authorization to send data. We evaluate Mirage using theoretical analysis, simulations and a prototype implementation on PlanetLab. We find that our design provides a first step towards a deployable, yet effective DDoS defense.


Approximating CSPs with Global Cardinality Constraints Using SDP Hierarchies

Abstract: This work is concerned with approximating constraint satisfaction problems (CSPs) with an additional global cardinality constraints. For example, \maxcut is a boolean CSP where the input is a graph $G = (V,E)$ and the goal is to find a cut $S \cup \bar S = V$ that maximizes the numberof crossing edges, $|E(S,\bar S)|$. The \maxbisection problem is a variant of \maxcut with an additional global constraint that each side of the cut has exactly half the vertices, i.e., $|S| = |V|/2$. Several other natural optimization problems like \minbisection and approximating Graph Expansion can be formulated as CSPs with global constraints. In this work, we formulate a general approach towards approximating CSPs with global constraints using SDP hierarchies. To demonstrate the approach we present the following results: Using the Lasserre hierarchy, we present an algorithm that runs in time $O(n^{poly(1/\epsilon)})$ that given an instance of \maxbisection with value $1-\epsilon$, finds a bisection with value $1-O(\sqrt{\epsilon})$. This approximation is near-optimal (up to constant factors in $O()$) under the Unique Games Conjecture. By a computer-assisted proof, we show that the same algorithm also achieves a 0.85-approximation for \maxbisection, improving on the previous bound of 0.70 (note that it is \uniquegames hard to approximate better than a 0.878 factor). The same algorithm also yields a 0.92-approximation for \maxtwosat with cardinality constraints. For every CSP with a global cardinality constraints, we present a generic conversion from integrality gap instances for the Lasserre hierarchy to a {\it dictatorship test} whose soundness is at most integrality gap. Dictatorship testing gadgets are central to hardness results for CSPs, and a generic conversion of the above nature lies at the core of the tight Unique Games based hardness result for CSPs. \cite{Raghavendra08}


Active Learning with Multiple Views

Abstract: Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.


The Augmented Complex Kernel LMS

Abstract: Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a suitable Wirtinger-like Calculus for general Hilbert Spaces. In this short paper, the extended Wirtinger's calculus is adopted to derive complex kernel-based widely-linear estimation filters. Furthermore, we illuminate several important characteristics of the widely linear filters. We show that, although in many cases the gains from adopting widely linear estimation filters, as alternatives to ordinary linear ones, are rudimentary, for the case of kernel based widely linear filters significant performance improvements can be obtained.


A Near-Optimal Sublinear-Time Algorithm for Approximating the Minimum Vertex Cover Size

Abstract: We give a nearly optimal sublinear-time algorithm for approximating the size of a minimum vertex cover in a graph G. The algorithm may query the degree deg(v) of any vertex v of its choice, and for each 1 <= i <= deg(v), it may ask for the i-th neighbor of v. Letting VC_opt(G) denote the minimum size of vertex cover in G, the algorithm outputs, with high constant success probability, an estimate VC_estimate(G) such that VC_opt(G) <= VC_estimate(G) <= 2 * VC_opt(G) + epsilon*n, where epsilon is a given additive approximation parameter. We refer to such an estimate as a (2,epsilon)-estimate. The query complexity and running time of the algorithm are ~O(avg_deg * poly(1/epsilon)), where avg_deg denotes the average vertex degree in the graph. The best previously known sublinear algorithm, of Yoshida et al. (STOC 2009), has query complexity and running time O(d^4/epsilon^2), where d is the maximum degree in the graph. Given the lower bound of Omega(avg_deg) (for constant epsilon) for obtaining such an estimate (with any constant multiplicative factor) due to Parnas and Ron (TCS 2007), our result is nearly optimal. In the case that the graph is dense, that is, the number of edges is Theta(n^2), we consider another model, in which the algorithm may ask, for any pair of vertices u and v, whether there is an edge between u and v. We show how to adapt the algorithm that uses neighbor queries to this model and obtain an algorithm that outputs a (2,epsilon)-estimate of the size of a minimum vertex cover whose query complexity and running time are ~O(n) * poly(1/epsilon).


Modeling Perceived Relevance for Tail Queries without Click-Through Data

Abstract: Click-through data has been used in various ways in Web search such as estimating relevance between documents and queries. Since only search snippets are perceived by users before issuing any clicks, the relevance induced by clicks are usually called \emph{perceived relevance} which has proven to be quite useful for Web search. While there is plenty of click data for popular queries, very little information is available for unpopular tail ones. These tail queries take a large portion of the search volume but search accuracy for these queries is usually unsatisfactory due to data sparseness such as limited click information. In this paper, we study the problem of modeling perceived relevance for queries without click-through data. Instead of relying on users' click data, we carefully design a set of snippet features and use them to approximately capture the perceived relevance. We study the effectiveness of this set of snippet features in two settings: (1) predicting perceived relevance and (2) enhancing search engine ranking. Experimental results show that our proposed model is effective to predict the relative perceived relevance of Web search results. Furthermore, our proposed snippet features are effective to improve search accuracy for longer tail queries without click-through data.


Optimal Deadline Scheduling with Commitment

Abstract: We consider an online preemptive scheduling problem where jobs with deadlines arrive sporadically. A commitment requirement is imposed such that the scheduler has to either accept or decline a job immediately upon arrival. The scheduler's decision to accept an arriving job constitutes a contract with the customer; if the accepted job is not completed by its deadline as promised, the scheduler loses the value of the corresponding job and has to pay an additional penalty depending on the amount of unfinished workload. The objective of the online scheduler is to maximize the overall profit, i.e., the total value of the admitted jobs completed before their deadlines less the penalty paid for the admitted jobs that miss their deadlines. We show that the maximum competitive ratio is $3-2\sqrt{2}$ and propose a simple online algorithm to achieve this competitive ratio. The optimal scheduling includes a threshold admission and a greedy scheduling policies. The proposed algorithm has direct applications to the charging of plug-in hybrid electrical vehicles (PHEV) at garages or parking lots.


Adaptive Mesh Refinement for Astrophysics Applications with ParalleX

Abstract: Several applications in astrophysics require adequately resolving many physical and temporal scales which vary over several orders of magnitude. Adaptive mesh refinement techniques address this problem effectively but often result in constrained strong scaling performance. The ParalleX execution model is an experimental execution model that aims to expose new forms of program parallelism and eliminate any global barriers present in a scaling-impaired application such as adaptive mesh refinement. We present two astrophysics applications using the ParalleX execution model: a tabulated equation of state component for neutron star evolutions and a cosmology model evolution. Performance and strong scaling results from both simulations are presented. The tabulated equation of state data are distributed with transparent access over the nodes of the cluster. This allows seamless overlapping of computation with the latencies introduced by the remote access to the table. Because of the expected size increases to the equation of state table, this type of table partitioning for neutron star simulations is essential while the implementation is greatly simplified by ParalleX semantics.


On Computing Optimal Locally Gabriel Graphs

Abstract: Delaunay and Gabriel graphs are widely studied geometric proximity structures. Motivated by applications in wireless routing, relaxed versions of these graphs known as \emph{Locally Delaunay Graphs} ($LDGs$) and \emph{Locally Gabriel Graphs} ($LGGs$) were proposed. We propose another generalization of $LGGs$ called \emph{Generalized Locally Gabriel Graphs} ($GLGGs$) in the context when certain edges are forbidden in the graph. Unlike a Gabriel Graph, there is no unique $LGG$ or $GLGG$ for a given point set because no edge is necessarily included or excluded. This property allows us to choose an $LGG/GLGG$ that optimizes a parameter of interest in the graph. We show that computing an edge maximum $GLGG$ for a given problem instance is NP-hard and also APX-hard. We also show that computing an $LGG$ on a given point set with dilation $\le k$ is NP-hard. Finally, we give an algorithm to verify whether a given geometric graph $G=(V,E)$ is a valid $LGG$.


Efficient Encoding of Watermark Numbers as Reducible Permutation Graphs

Abstract: In a software watermarking environment, several graph theoretic watermark methods use numbers as watermark values, where some of these methods encode the watermark numbers as graph structures. In this paper we extended the class of error correcting graphs by proposing an efficient and easily implemented codec system for encoding watermark numbers as reducible permutation flow-graphs. More precisely, we first present an efficient algorithm which encodes a watermark number $w$ as self-inverting permutation $\pi^*$ and, then, an algorithm which encodes the self-inverting permutation $\pi^*$ as a reducible permutation flow-graph $F[\pi^*]$ by exploiting domination relations on the elements of $\pi^*$ and using an efficient DAG representation of $\pi^*$. The whole encoding process takes O(n) time and space, where $n$ is the binary size of the number $w$ or, equivalently, the number of elements of the permutation $\pi^*$. We also propose efficient decoding algorithms which extract the number $w$ from the reducible permutation flow-graph $F[\pi^*]$ within the same time and space complexity. The two main components of our proposed codec system, i.e., the self-inverting permutation $\pi^*$ and the reducible permutation graph $F[\pi^*]$, incorporate important structural properties which make our system resilient to attacks.


Feasible Automata for Two-Variable Logic with Successor on Data Words

Abstract: We introduce an automata model for data words, that is words that carry at each position a symbol from a finite alphabet and a value from an unbounded data domain. The model is (semantically) a restriction of data automata, introduced by Bojanczyk, et. al. in 2006, therefore it is called weak data automata. It is strictly less expressive than data automata and the expressive power is incomparable with register automata. The expressive power of weak data automata corresponds exactly to existential monadic second order logic with successor +1 and data value equality \sim, EMSO2(+1,\sim). It follows from previous work, David, et. al. in 2010, that the nonemptiness problem for weak data automata can be decided in 2-NEXPTIME. Furthermore, we study weak B\"uchi automata on data omega-strings. They can be characterized by the extension of EMSO2(+1,\sim) with existential quantifiers for infinite sets. Finally, the same complexity bound for its nonemptiness problem is established by a nondeterministic polynomial time reduction to the nonemptiness problem of weak data automata.


Getting routers out of the core: Building an optical wide area network with "multipaths"

Abstract: We propose an all-optical networking solution for a wide area network (WAN) based on the notion of multipoint-to-multipoint lightpaths that, for short, we call "multipaths". A multipath concentrates the traffic of a group of source nodes on a wavelength channel using an adapted MAC protocol and multicasts this traffic to a group of destination nodes that extract their own data from the confluent stream. The proposed network can be built using existing components and appears less complex and more efficient in terms of energy consumption than alternatives like OPS and OBS. The paper presents the multipath architecture and compares its energy consumption to that of a classical router-based ISP network. A flow-aware dynamic bandwidth allocation algorithm is proposed and shown to have excellent performance in terms of throughput and delay.


Two-Way Automata Making Choices Only at the Endmarkers

Abstract: The question of the state-size cost for simulation of two-way nondeterministic automata (2NFAs) by two-way deterministic automata (2DFAs) was raised in 1978 and, despite many attempts, it is still open. Subsequently, the problem was attacked by restricting the power of 2DFAs (e.g., using a restricted input head movement) to the degree for which it was already possible to derive some exponential gaps between the weaker model and the standard 2NFAs. Here we use an opposite approach, increasing the power of 2DFAs to the degree for which it is still possible to obtain a subexponential conversion from the stronger model to the standard 2DFAs. In particular, it turns out that subexponential conversion is possible for two-way automata that make nondeterministic choices only when the input head scans one of the input tape endmarkers. However, there is no restriction on the input head movement. This implies that an exponential gap between 2NFAs and 2DFAs can be obtained only for unrestricted 2NFAs using capabilities beyond the proposed new model. As an additional bonus, conversion into a machine for the complement of the original language is polynomial in this model. The same holds for making such machines self-verifying, halting, or unambiguous. Finally, any superpolynomial lower bound for the simulation of such machines by standard 2DFAs would imply L<>NL. In the same way, the alternating version of these machines is related to L =? NL =? P, the classical computational complexity problems.


Performance improvement of the software development project using the Value Management approach

Abstract: Improving performance and delivering value for customers have become a central theme in business. The software industry has become an increasingly important sector for the economy growth in Tunisia. This study aims to show how using Value Management in the Tunisian software industry for project analysis gives new insight about true project value and performance. This new approach is considered as an appropriate tool for guiding the process of making decisions. It offers tools in order to analyze the service value from the customer and organization perspectives. The results showed that the VM allows to have better performance in the software development project by linking customer satisfaction and cost analysis. The present case shows to service managers how they can benchmark project function to reduce their costs and improve resource allocation taking into consideration what customers consider important during their overall service experience. It can identify best professional practices, orient decisions to improve service value


Predicting User Actions in Software Processes

Abstract: This paper describes an approach for user (e.g. SW architect) assisting in software processes. The approach observes the user's action and tries to predict his next step. For this we use approaches in the area of machine learning (sequence learning) and adopt these for the use in software processes. Keywords: Software engineering, Software process description languages, Software processes, Machine learning, Sequence prediction


Discovering patterns of correlation and similarities in software project data with the Circos visualization tool

Abstract: Software cost estimation based on multivariate data from completed projects requires the building of efficient models. These models essentially describe relations in the data, either on the basis of correlations between variables or of similarities between the projects. The continuous growth of the amount of data gathered and the need to perform preliminary analysis in order to discover patterns able to drive the building of reasonable models, leads the researchers towards intelligent and time-saving tools which can effectively describe data and their relationships. The goal of this paper is to suggest an innovative visualization tool, widely used in bioinformatics, which represents relations in data in an aesthetic and intelligent way. In order to illustrate the capabilities of the tool, we use a well known dataset from software engineering projects.


On spanning tree congestion of Hamming graphs

Abstract: We present a tight lower bound for the spanning tree congestion of Hamming graphs.


An efficient polynomial-time approximation scheme for Steiner forest in planar graphs

Abstract: We give an $O(n \log^3 n)$ approximation scheme for Steiner forest in planar graphs, improving on the previous approximation scheme for this problem, which runs in $O(n^{f(\epsilon)})$ time.


Bayesian Locality Sensitive Hashing for Fast Similarity Search

Abstract: Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. Locality-sensitive hashing (LSH) based methods have become a very popular approach for this problem. However, most such methods only use LSH for the first phase of similarity search - i.e. efficient indexing for candidate generation. In this paper, we present BayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search - performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSH-Lite, which calculates similarities exactly, is also presented. BayesLSH is able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH's output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs and LSH, BayesLSH enables significant speedups, typically in the range 2x-20x for a wide variety of datasets.


Sources of Inter-package Conflicts in Debian

Abstract: Inter-package conflicts require the presence of two or more packages in a particular configuration, and thus tend to be harder to detect and localize than conventional (intra-package) defects. Hundreds of such inter-package conflicts go undetected by the normal testing and distribution process until they are later reported by a user. The reason for this is that current meta-data is not fine-grained and accurate enough to cover all common types of conflicts. A case study of inter-package conflicts in Debian has shown that with more detailed package meta-data, at least one third of all package conflicts could be prevented relatively easily, while another one third could be found by targeted testing of packages that share common resources or characteristics. This paper reports the case study and proposes ideas to detect inter-package conflicts in the future.


Runtime Guarantees for Regression Problems

Abstract: We study theoretical runtime guarantees for a class of optimization problems that occur in a wide variety of inference problems. these problems are motivated by the lasso framework and have applications in machine learning and computer vision. Our work shows a close connection between these problems and core questions in algorithmic graph theory. While this connection demonstrates the difficulties of obtaining runtime guarantees, it also suggests an approach of using techniques originally developed for graph algorithms. We then show that most of these problems can be formulated as a grouped least squares problem, and give efficient algorithms for this formulation. Our algorithms rely on routines for solving quadratic minimization problems, which in turn are equivalent to solving linear systems. Finally we present some experimental results on applying our approximation algorithm to image processing problems.


Polynomial integrality gaps for strong SDP relaxations of Densest k-subgraph

Abstract: The densest k-subgraph (DkS) problem (i.e. find a size k subgraph with maximum number of edges), is one of the notorious problems in approximation algorithms. There is a significant gap between known upper and lower bounds for DkS: the current best algorithm gives an ~ O(n^{1/4}) approximation, while even showing a small constant factor hardness requires significantly stronger assumptions than P != NP. In addition to interest in designing better algorithms, a number of recent results have exploited the conjectured hardness of densest k-subgraph and its variants. Thus, understanding the approximability of DkS is an important challenge. In this work, we give evidence for the hardness of approximating DkS within polynomial factors. Specifically, we expose the limitations of strong semidefinite programs from SDP hierarchies in solving densest k-subgraph. Our results include: * A lower bound of Omega(n^{1/4}/log^3 n) on the integrality gap for Omega(log n/log log n) rounds of the Sherali-Adams relaxation for DkS. This also holds for the relaxation obtained from Sherali-Adams with an added SDP constraint. Our gap instances are in fact Erdos-Renyi random graphs. * For every epsilon > 0, a lower bound of n^{2/53-eps} on the integrality gap of n^{Omega(eps)} rounds of the Lasserre SDP relaxation for DkS, and an n^{Omega_eps(1)} gap for n^{1-eps} rounds. Our construction proceeds via a reduction from random instances of a certain Max-CSP over large domains. In the absence of inapproximability results for DkS, our results show that even the most powerful SDPs are unable to beat a factor of n^{Omega(1)}, and in fact even improving the best known n^{1/4} factor is a barrier for current techniques.


A Comparison of Different Machine Transliteration Models

Abstract: Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models -- grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model -- have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.


Learning Sentence-internal Temporal Relations

Abstract: In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects.


Product Review Summarization based on Facet Identification and Sentence Clustering

Abstract: Product review nowadays has become an important source of information, not only for customers to find opinions about products easily and share their reviews with peers, but also for product manufacturers to get feedback on their products. As the number of product reviews grows, it becomes difficult for users to search and utilize these resources in an efficient way. In this work, we build a product review summarization system that can automatically process a large collection of reviews and aggregate them to generate a concise summary. More importantly, the drawback of existing product summarization systems is that they cannot provide the underlying reasons to justify users' opinions. In our method, we solve this problem by applying clustering, prior to selecting representative candidates for summarization.


Two-Variable Logic with Two Order Relations

Abstract: It is shown that the finite satisfiability problem for two-variable logic over structures with one total preorder relation, its induced successor relation, one linear order relation and some further unary relations is EXPSPACE-complete. Actually, EXPSPACE-completeness already holds for structures that do not include the induced successor relation. As a special case, the EXPSPACE upper bound applies to two-variable logic over structures with two linear orders. A further consequence is that satisfiability of two-variable logic over data words with a linear order on positions and a linear order and successor relation on the data is decidable in EXPSPACE. As a complementing result, it is shown that over structures with two total preorder relations as well as over structures with one total preorder and two linear order relations, the finite satisfiability problem for two-variable logic is undecidable.


A Constraint-Satisfaction Parser for Context-Free Grammars

Abstract: Traditional language processing tools constrain language designers to specific kinds of grammars. In contrast, model-based language specification decouples language design from language processing. As a consequence, model-based language specification tools need general parsers able to parse unrestricted context-free grammars. As languages specified following this approach may be ambiguous, parsers must deal with ambiguities. Model-based language specification also allows the definition of associativity, precedence, and custom constraints. Therefore parsers generated by model-driven language specification tools need to enforce constraints. In this paper, we propose Fence, an efficient bottom-up chart parser with lexical and syntactic ambiguity support that allows the specification of constraints and, therefore, enables the use of model-based language specification in practice.


A Face Recognition Scheme using Wavelet Based Dominant Features

Abstract: In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these highinformative horizontal bands precisely, the horizontal band is segmented into several small spatial modules. Dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.


Handwritten Text Image Authentication using Back Propagation

Abstract: Authentication is the act of confirming the truth of an attribute of a datum or entity. This might involve confirming the identity of a person, tracing the origins of an artefact, ensuring that a product is what it's packaging and labelling claims to be, or assuring that a computer program is a trusted one. The authentication of information can pose special problems (especially man-in-the-middle attacks), and is often wrapped up with authenticating identity. Literary can involve imitating the style of a famous author. If an original manuscript, typewritten text, or recording is available, then the medium itself (or its packaging - anything from a box to e-mail headers) can help prove or disprove the authenticity of the document. The use of digital images of handwritten historical documents has become more popular in recent years. Volunteers around the world now read thousands of these images as part of their indexing process. Handwritten text images of old documents are sometimes difficult to read or noisy due to the preservation of the document and quality of the image [1]. Handwritten text offers challenges that are rarely encountered in machine-printed text. In addition, most problems faced in reading machine- printed text (e.g., character recognition, word segmentation, letter segmentation, etc.) are more severe, in handwritten text. In this paper we Here in this paper we proposed a method for authenticating hand written text images using back propagation algorithm..


A Novel Approach for Pass Word Authentication using Brain -State -In -A Box (BSB) Model

Abstract: Authentication is the act of confirming the truth of an attribute of a datum or entity. This might involve confirming the identity of a person, tracing the origins of an artefact, ensuring that a product is what it's packaging and labelling claims to be, or assuring that a computer program is a trusted one. The authentication of information can pose special problems (especially man-in-the-middle attacks), and is often wrapped up with authenticating identity. Password authentication using Brain-State -In-A Box is presented in this paper. Here in this paper we discuss Brain-State -In-A Box Scheme for Textual and graphical passwords which will be converted in to probabilistic values Password. We observe how to get password authentication Probabilistic values for Text and Graphical image. This study proposes the use of a Brain-State -In-A Box technique for password authentication. In comparison to existing layered neural network techniques, the proposed method provides better accuracy and quicker response time to registration and password changes.


Netrawalm: Network Based Resource Aware Application Layer Multicast for Multiparty Video Conference

Abstract: IP Multicast is one of the most absolute method for large bandwidth Internet applications such as video conference, IPTV, E-Learning and Telemedicine etc., But due to security and management reason IP Multicast is not enabled in Internet backbone routers. To achieve these challenges, lot of Application Layer Multicast (ALM) has been proposed. All the existing protocols such as NICE, ZIGZAG and OMNI are trying to reduce average delay by forming a Multicast tree. But still that problem has not been addressed fully. We are proposing a new protocol called NetRawALM, which will address the average delay, Reliability between nodes, Scalability of conference, Heterogeneity and resilient data distribution for real time multimedia applications by constructing the Network based Resource aware Multicast tree algorithm. This is very dynamic and decentralised. The proposed architecture is a LAN aware; it is used to reduce Internet Traffic.


A Probabilistic Approach for Authenticating Text or Graphical Passwords Using Back Propagation

Abstract: Password authentication is a common approach to the system security and it is also a very important procedure to gain access to user resources. In the conventional password authentication methods a server has to authenticate the legitimate user. In our proposed method users can freely choose their passwords from a defined character set or they can use a graphical image as password and that input will be normalized. Neural networks have been used recently for password authentication in order to overcome pitfall of traditional password authentication methods. In this paper we proposed a method for password authentication using alphanumeric password and graphical password. We used Back Propagation algorithm for both alphanumeric (Text) and graphical password by which the level of security can be enhanced. This paper along with test results show that converting user password in to Probabilistic values enhances the security of the system


Cross Layer Adaptation for QoS in WSN

Abstract: In this paper, we propose QoS aware MAC protocol for Wire- less Sensor Networks and its cross layer extension to network layer for providing QoS in delay sensitive WSN scenarios. In WSNs, there can be two types of traffic one is event driven traffic which requires immedi- ate attention and another is periodic reporting. Event driven traffic is classified as Class I(delay sensitive) traffic and periodic reporting is clas- sified as Class II(Best Effort) Traffic. MAC layer adaptation can take place in terms of (i) Dynamic contention window adjustment per class, (ii) Reducing the delay suffered by difference in Sleep schedules(DSS) of communicating nodes by dynamically adjusting Duty Cycle based on Utilization and DSS delay of class I traffic, (iii) Different DIFS (DCF Inter Frame Spacing) per class, (iv) Adjusting all the three schemes pro- posed above simultaneously. Cross layer extension is also proposed, in which MAC layer uses network layer's next hop information for better adaptation of duty cycle based on DSS delay. Routing protocols can uti- lize MAC layer parameter DSS delay to select the routes which offer least DSS delay latency, there by minimizing the overall end-to-end delay.


A Novel Approach For Intranet Mailing For Providing User Authentication

Abstract: With the explosion of the public Internet and e-commerce, private computers, and computer networks, if not adequately secured, are increasingly vulnerable to damaging attacks. Hackers, viruses, vindictive employees and even human error all represent clear and present dangers to networks. Various antidotes that are in fact inextricable with security issues are - Cryptography, Authentication, Integrity and Non Repudiation, Key Distribution and certification, Access control by implementing Firewalls etc.The main idea of this paper is to overcome the PGP's(Pretty Good Privacy) main limitation of incomplete non-repudiation Service, which increases the degree of security and efficiency of an email message communication through NRR(Non-Repudiation of Receipt) and including PGPs original feature of NRO(Non-Repudiation of Origin), and there it assures new security service of Mutual Non-Repudiation (MNR) .


A Cryptosystem Based on Hilbert Matrix using Cipher Block Chaining Mode

Abstract: Cryptography is the science of using mathematics to encrypt and decrypt data. Cryptography enables you to store sensitive information or transmit it across insecure networks so that it cannot be read by anyone except the intended recipient. While cryptography is the science of securing data, cryptanalysis is the science of analyzing and breaking secure communication. Classical cryptanalysis involves an interesting combination of analytical reasoning, application of mathematical tools and pattern finding. The objectives of the proposed work are to propose a new cryptographic method based on the special matrix called the Hilbert matrix for authentication and confidentiality and to propose a model for confidentiality and authentication using a combination of symmetric and public cryptosystems. Further, it is extended to shared key cryptosystems with the concept of digital enveloping using a session key. In the present work an algorithm for shared key encryption is developed using Hilbert matrix cryptosystem. In this the block chaining modes of operation have been used to tackle the issues of confusion and diffusion.


Hilbert Matrix Based Cryptosystem using a Session Key

Abstract: Cryptography protects users by providing functionality for the encryption of data and authentication of other users. This technology lets the receiver of an electronic message verify the sender, ensures that a message can be read only by the intended person, and assures the recipient that a message has not be altered in transit. Classical cryptanalysis involves an interesting combination of analytical reasoning, application of mathematical tools and pattern finding. The objectives of the proposed work are to propose a new cryptographic method based on the special matrix called the Hilbert matrix for authentication and confidentiality and to propose a model for confidentiality and authentication using shared key cryptosystems with the concept of digital enveloping using a session key. In the present work various algorithms are presented for encryption and authentication based on Hilbert matrix using a session key.


A Comparative Experiment of Several Shape Methods in Recognizing Plants

Abstract: Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.


NP-Hardness and Fixed-Parameter Tractability of Realizing Degree Sequences with Directed Acyclic Graphs

Abstract: In graph realization problems one is given a degree sequence and the task is to decide whether there is a graph whose vertex degrees match to the given sequence. This realization problem is known to be polynomial-time solvable when the graph is directed or undirected. In contrary, we show NP-completeness for the problem of realizing a given sequence of pairs of positive integers (representing indegrees and outdegrees) with a directed acyclic graph, answering an open question of Berger and M\"uller-Hannemann [FCT 2011]. Furthermore, we classify the problem as fixed-parameter tractable with respect to the parameter "maximum degree".


Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and Vein Features

Abstract: This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants.


Blackwell Approachability and Minimax Theory

Abstract: This manuscript investigates the relationship between Blackwell Approachability, a stochastic vector-valued repeated game, and minimax theory, a single-play scalar-valued scenario. First, it is established in a general setting --- one not permitting invocation of minimax theory --- that Blackwell's Approachability Theorem and its generalization due to Hou are still valid. Second, minimax structure grants a result in the spirit of Blackwell's weak-approachability conjecture, later resolved by Vieille, that any set is either approachable by one player, or avoidable by the opponent. This analysis also reveals a strategy for the opponent.


Power comparison of CMOS and adiabatic full adder circuit

Abstract: Full adders are important components in applications such as digital signal processors (DSP) architectures and microprocessors. Apart from the basic addition adders also used in performing useful operations such as subtraction, multiplication, division, address calculation, etc. In most of these systems the adder lies in the critical path that determines the overall performance of the system. In this paper conventional complementary metal oxide semiconductor (CMOS) and adiabatic adder circuits are analyzed in terms of power and transistor count using 0.18UM technology.


Hierarchical QR factorization algorithms for multi-core cluster systems

Abstract: This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed multi-core nodes. These platforms make the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism).


On the Coloring of Grid Wireless Sensor Networks: the Vector-Based Coloring Method

Abstract: Graph coloring is used in wireless networks to optimize network resources: bandwidth and energy. Nodes access the medium according to their color. It is the responsibility of the coloring algorithm to ensure that interfering nodes do not have the same color. In this research report, we focus on wireless sensor networks with grid topologies. How does a coloring algorithm take advantage of the regularity of grid topology to provide an optimal periodic coloring, that is a coloring with the minimum number of colors? We propose the Vector-Based Coloring Method, denoted VCM, a new method that is able to provide an optimal periodic coloring for any radio transmission range and for any h-hop coloring, h>=1. This method consists in determining at which grid nodes a color can be reproduced without creating interferences between these nodes while minimizing the number of colors used. We compare the number of colors provided by VCM with the number of colors obtained by a distributed coloring algorithm with line and column priority assignments. We also provide bounds on the number of colors of optimal general colorings of the infinite grid, and show that periodic colorings (and thus VCM) are asymptotically optimal. Finally, we discuss the applicability of this method to a real wireless network.


Parallel routing in Mobile Ad-Hoc Networks

Abstract: This paper proposes and evaluates a new position-based Parallel Routing Protocol (PRP) for simultaneously routing multiple data packets over disjoint paths in a mobile ad-hoc network (MANET) for higher reliability and reduced communication delays. PRP views the geographical region where the MANET is located as a virtual 2-dimensional grid of cells. Cell-disjoint (parallel) paths between grid cells are constructed and used for building pre-computed routing tables. A single gateway node in each grid cell handles routing through that grid cell reducing routing overheads. Each node maintains updated information about its own location in the virtual grid using GPS. Nodes also keep track of the location of other nodes using a new proposed cell-based broadcasting algorithm. Nodes exchange energy level information with neighbors allowing energy-aware selection of the gateway nodes. Performance evaluation results have been derived showing the attractiveness of the proposed parallel routing protocol from different respects including low communication delays, high packet delivery ratios, high routing path stability, and low routing overheads.


Robust Estimators for Variance-Based Device-Free Localization and Tracking

Abstract: Human motion in the vicinity of a wireless link causes variations in the link received signal strength (RSS). Device-free localization (DFL) systems, such as variance-based radio tomographic imaging (VRTI), use these RSS variations in a static wireless network to detect, locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind and rotating or vibrating machinery, also causes RSS variations which degrade the performance of a DFL system. In this paper, we propose and evaluate two estimators to reduce the impact of the variations caused by intrinsic motion. One estimator uses subspace decomposition, and the other estimator uses a least squares formulation. Experimental results show that both estimators reduce localization root mean squared error by about 40% compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97% of errors less than 1.3 m, more than 60% improvement compared to tracking results from VRTI.


A Contribution to Secure the Routing Protocol "Greedy Perimeter Stateless Routing" Using a Symmetric Signature-Based AES and MD5 Hash

Abstract: This work presents a contribution to secure the routing protocol GPSR (Greedy Perimeter Stateless Routing) for vehicular ad hoc networks, we examine the possible attacks against GPSR and security solutions proposed by different research teams working on ad hoc network security. Then, we propose a solution to secure GPSR packet by adding a digital signature based on symmetric cryptography generated using the AES algorithm and the MD5 hash function more suited to a mobile environment.


A Polylogarithmic-Competitive Algorithm for the k-Server Problem

Abstract: We give the first polylogarithmic-competitive randomized online algorithm for the $k$-server problem on an arbitrary finite metric space. In particular, our algorithm achieves a competitive ratio of O(log^3 n log^2 k log log n) for any metric space on n points. Our algorithm improves upon the deterministic (2k-1)-competitive algorithm of Koutsoupias and Papadimitriou [J.ACM'95] whenever n is sub-exponential in k.


PSA: The Packet Scheduling Algorithm for Wireless Sensor Networks

Abstract: The main cause of wasted energy consumption in wireless sensor networks is packet collision. The packet scheduling algorithm is therefore introduced to solve this problem. Some packet scheduling algorithms can also influence and delay the data transmitting in the real-time wireless sensor networks. This paper presents the packet scheduling algorithm (PSA) in order to reduce the packet congestion in MAC layer leading to reduce the overall of packet collision in the system The PSA is compared with the simple CSMA/CA and other approaches using network topology benchmarks in mathematical method. The performances of our PSA are better than the standard (CSMA/CA). The PSA produces better throughput than other algorithms. On other hand, the average delay of PSA is higher than previous works. However, the PSA utilizes the channel better than all algorithms.


Error Detection and Correction for Distributed Group Key Agreement Protocol

Abstract: Integrating an efficient Error detection and correction scheme with less encoding and decoding complexity to support the distribution of keying material in a secure group communication is an important issue, since the amount of information carried out in the wireless channel is high which produces more errors due to noise available in the communication channel. Moreover, the key must be sent securely to the group members. In this paper, we propose a new efficient group key computation protocol that provides more security and also integrates an encoding method in sender side and decoding method in the receiver side. To achieve security in key computation process, we propose Euler's totient function based Diffie-hellman key distribution protocol. To provide efficient error detection and correction method while distributing the Keying and re-keying information, we introduce tanner graph based encoding stopping set construction algorithm in sender and receiver side of the group communication. Two major operations in this scheme are joining and leaving operations for managing group memberships. The encoding and decoding complexity of this approach is computed in this paper and it is proved that this proposed approach takes less decoding time complexity.


Intuitionistic Completeness of First-Order Logic

Abstract: We establish completeness for intuitionistic first-order logic, iFOL, showing that a formula is provable if and only if its embedding into minimal logic, mFOL, is uniformly valid under the Brouwer Heyting Kolmogorov (BHK) semantics, the intended semantics of iFOL and mFOL. Our proof is intuitionistic and provides an effective procedure Prf that converts uniform minimal evidence into a formal first-order proof. We have implemented Prf. Uniform validity is defined using the intersection operator as a universal quantifier over the domain of discourse and atomic predicates. Formulas of iFOL that are uniformly valid are also intuitionistically valid, but not conversely. Our strongest result requires the Fan Theorem; it can also be proved classically by showing that Prf terminates using Konig's Theorem. The fundamental idea behind our completeness theorem is that a single evidence term evd witnesses the uniform validity of a minimal logic formula F. Finding even one uniform realizer guarantees intuitionistic validity because Prf(F, evd) builds a first-order proof of F, establishing its intuitionistic validity and providing a purely logical normalized realizer. We establish completeness for iFOL as follows. Friedman showed that iFOL can be embedded in minimal logic (mFOL) by his A-transformation, mapping formula F to FA. If F is uniformly valid, then so is FA, and by our completeness theorem, we can find a proof of FA in minimal logic. Then we intuitionistically prove F from FFalse, i.e. by taking False for A and for \bot of mFOL. Our result resolves an open question posed by Beth in 1947.


Fault Tolerant Matrix Pencil Method for Direction of Arrival Estimation

Abstract: Continuing to estimate the Direction-of-arrival (DOA) of the signals impinging on the antenna array, even when a few elements of the underlying Uniform Linear Antenna Array (ULA) fail to work will be of practical interest in RADAR, SONAR and Wireless Radio Communication Systems. This paper proposes a new technique to estimate the DOAs when a few elements are malfunctioning. The technique combines Singular Value Thresholding (SVT) based Matrix Completion (MC) procedure with the Direct Data Domain (D^3) based Matrix Pencil (MP) Method. When the element failure is observed, first, the MC is performed to recover the missing data from failed elements, and then the MP method is used to estimate the DOAs. We also, propose a very simple technique to detect the location of elements failed, which is required to perform MC procedure. We provide simulation studies to demonstrate the performance and usefulness of the proposed technique. The results indicate a better performance, of the proposed DOA estimation scheme under different antenna failure scenarios.


Algorithm that Solves 3-SAT in Polynomial Time

Abstract: The question of whether the complexity class P is equal to the complexity class NP has been a seemingly intractable problem for over 4 decades. It has been clear that if an algorithm existed that would solve the problems in the NP class in polynomial time then P would equal NP. However, no one has yet been able to create that algorithm or to successfully prove that such an algorithm cannot exist. The algorithm that will be presented in this paper solves the 3-satisfiability or 3-CNF-SAT problem, which has been proven to be NP-complete.


Scalable Grid Resource Discovery through Distributed Search

Abstract: This paper proposes a simple and scalable web-based model for grid resource discovery for the Internet. The resource discovery model contains the metadata and resource finder web services. The information of resource finder web services is kept in the repositories that are distributed in the application layer of Internet. The resource finder web services will be discovered by sending queries to the repositories in a similar way as the DNS protocol. The underlying technology for implementation of the two architectures of this model is introduced. These architectures are: Direct and Centralized Web-Based Grid Resource Discovery. The resource discovery time is computed after simulating each of these models in GridSim. By performing scalability tests, we found that when increasing the load on the grid with more users and resources, the cost of our model in comparison to the grid resource discovery time is marginal.


Jellyfish: Networking Data Centers Randomly

Abstract: Industry experience indicates that the ability to incrementally expand data centers is essential. However, existing high-bandwidth network designs have rigid structure that interferes with incremental expansion. We present Jellyfish, a high-capacity network interconnect, which, by adopting a random graph topology, yields itself naturally to incremental expansion. Somewhat surprisingly, Jellyfish is more cost-efficient than a fat-tree: A Jellyfish interconnect built using the same equipment as a fat-tree, supports as many as 25% more servers at full capacity at the scale of a few thousand nodes, and this advantage improves with scale. Jellyfish also allows great flexibility in building networks with different degrees of oversubscription. However, Jellyfish's unstructured design brings new challenges in routing, physical layout, and wiring. We describe and evaluate approaches that resolve these challenges effectively, indicating that Jellyfish could be deployed in today's data centers.


On the strong chromatic index and maximum induced matching of tree-cographs and permutation graphs

Abstract: We show that there exist linear-time algorithms that compute the strong chromatic index and a maximum induced matching of tree-cographs when the decomposition tree is a part of the input. We also show that there exists an efficient algorithm for the strong chromatic index of permutation graphs.


Adaptive Data Stream Management System Using Learning Automata

Abstract: In many modern applications, data are received as infinite, rapid, unpredictable and time- variant data elements that are known as data streams. Systems which are able to process data streams with such properties are called Data Stream Management Systems (DSMS). Due to the unpredictable and time- variant properties of data streams as well as system, adaptivity of the DSMS is a major requirement for each DSMS. Accordingly, determining parameters which are effective on the most important performance metric of a DSMS (i.e., response time) and analysing them will affect on designing an adaptive DSMS. In this paper, effective parameters on response time of DSMS are studied and analysed and a solution is proposed for DSMSs' adaptivity. The proposed adaptive DSMS architecture includes a learning unit that frequently evaluates system to adjust the optimal value for each of tuneable effective. Learning Automata is used as the learning mechanism of the learning unit to adjust the value of tuneable effective parameters. So, when system faces some changes, the learning unit increases performance by tuning each of tuneable effective parameters to its optimum value. Evaluation results illustrate that after a while, parameters reach their optimum value and then DSMS's adaptivity will be improved considerably.


Enhanced Secure Algorithm for Message Communion

Abstract: This paper puts forward a safe mechanism of data transmission to tackle the security problem of information which is transmitted in Internet. The encryption standards such as DES (Data Encryption Standard), AES (Advanced Encryption Standard) and EES (Escrowed Encryption Standard) are widely used to solve the problem of communication over an insecure channel. With advanced technologies in computer hardware and software, these standards seem not to be as secure and fast as one would like. In this paper we propose a encryption technique which provides security to both the message and the secret key achieving confidentiality and authentication. The Symmetric algorithm used has two advantages over traditional schemes. First, the encryption and decryption procedures are much simpler, and consequently, much faster. Second, the security level is higher due to the inherent poly-alphabetic nature of the substitution mapping method used here, together with the translation and transposition operations performed in the algorithm. Asymmetric algorithm RSA is worldwide known for its high security. In this paper a detailed report of the process is presented and analysis is done comparing our proposed technique with familiar techniques


Treating Insomnia, Amnesia, and Acalculia in Regular Expression Matching

Abstract: Regular expressions provide a flexible means for matching strings and they are often used in data-intensive applications. They are formally equivalent to either deterministic finite automata (DFAs) or nondeterministic finite automata (NFAs). Both DFAs and NFAs are affected by two problems known as amnesia and acalculia, and DFAs are also affected by a problem known as insomnia. Existing techniques require an automata conversion and compaction step that prevents the use of existing automaton databases and hinders the maintenance of the resulting compact automata. In this paper, we propose Parallel Finite State Machines (PFSMs), which are able to run any DFA- or NFA-like state machines without a previous conversion or compaction step. PFSMs report, online, all the matches found within an input string and they solve the three aforementioned problems. Parallel Finite State Machines require quadratic time and linear memory and they are distributable. Parallel Finite State Machines make very fast distributed regular expression matching in data-intensive applications feasible.


Array Requirements for Scientific Applications and an Implementation for Microsoft SQL Server

Abstract: This paper outlines certain scenarios from the fields of astrophysics and fluid dynamics simulations which require high performance data warehouses that support array data type. A common feature of all these use cases is that subsetting and preprocessing the data on the server side (as far as possible inside the database server process) is necessary to avoid the client-server overhead and to minimize IO utilization. Analyzing and summarizing the requirements of the various fields help software engineers to come up with a comprehensive design of an array extension to relational database systems that covers a wide range of scientific applications. We also present a working implementation of an array data type for Microsoft SQL Server 2008 to support large-scale scientific applications. We introduce the design of the array type, results from a performance evaluation, and discuss the lessons learned from this implementation. The library can be downloaded from our website at http://voservices.net/sqlarray/


Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments

Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources. To provide cloud computing services economically, it is important to optimize resource allocation under the assumption that the required resource can be taken from a shared resource pool. In addition, to be able to provide processing ability and storage capacity, it is necessary to allocate bandwidth to access them at the same time. This paper proposes an optimal resource allocation method for cloud computing environments. First, this paper develops a resource allocation model of cloud computing environments, assuming both processing ability and bandwidth are allocated simultaneously to each service request and rented out on an hourly basis. The allocated resources are dedicated to each service request. Next, this paper proposes an optimal joint multiple resource allocation method, based on the above resource allocation model. It is demonstrated by simulation evaluation that the proposed method can reduce the request loss probability and as a result, reduce the total resource required, compared with the conventional allocation method. Then, this paper defines basic principles and a measure for achieving fair resource allocation among multiple users in a cloud computing environment, and proposes a fair joint multiple resource allocation method. It is demonstrated by simulation evaluations that the proposed method enables the fair resource allocation among multiple users without a large decline in resource efficiency. Keywords: Cloud computing, joint multiple resource allocation, fairness


A New Energy Efficient Approach Towards WASN Routing with Modified QCS Protocol

Abstract: In today's world Wireless Ad-hoc sensor network, consists of small sensor nodes having limited resources, has a great potential to solve problems in various domain including disaster management. In this paper "QCS-protocol" is modified which was introduced in our previous paper [1] and named as "Modified QCS-protocol". This is the backbone of our Intelligent Energy Efficient Ad-hoc Sensor Network. Two other protocols "Irregular Information Transfer" & "Final Broadcast-Petrol Flow" protocol are also modified to enhance performance of the new version of QCS protocol to run the system properly and to make the network more energy efficient and perfect. The challenges in WASN are- limited node power, Ad-hoc organization of network and reliability. Most of the existing approaches addressed the problems separately, but not in a totality. This paper shows how the network can have unlimited life and all time readiness with overall stability to send information to the base station with minimum power dissipation with the help of multimode "same type" sensor nodes and type categorization of generated information. Moreover an effort is made to give some light to the implementation issues and analyzed overall performance of the network by MATLAB simulation.


CNoA: Challenging Number Approach for uncovering TCP SYN flooding using SYN spoofing attack

Abstract: The challenging number is used for the detection of Spoofing attack. The IP Spoofing is considered to be one of the potentially brutal attack which acts as a tool for the DDoS attack which is considered to be a major threat among security problems in today's internet. These kinds of attack are extremely severe. They bring down business of company drastically. DDoS attack can easily exhaust the computing and communication resources of its victim within a short period of time. There are attacks exploiting some vulnerability or implementation bug in the software implementation of a service to bring that down and some attacks will use all the available resources at the target machine. This deals on attacks that consume all the bandwidth available to the victim machine. While concentrating on the bandwidth attack the TCP SYN flood is the more prominent attack. TCP/IP protocol suite is the most widely used protocol suite for data communication. The TCP SYN flood works by exhausting the TCP connection queue of the host and thus denying legitimate connection request. There are various methods used to detect and prevent this attack, one of which is to block the packet based on SYN flag count from the same IP address. This kind of prevention methods becomes unsuitable when the attackers use the Spoofed IP address. The SYN spoofing becomes a major tool the TCP SYN flooding. For the prevention of this kind of attacks, the TCP specific probing is used in the proposed scheme where the client is requested challenging number while sending the ACK in the three way hand shake. This is very useful to find the Spoofed IP Packets/TCP SYN flood and preventing them.


Data formats for phonological corpora

Abstract: The goal of the present chapter is to explore the possibility of providing the research (but also the industrial) community that commonly uses spoken corpora with a stable portfolio of well-documented standardised formats that allow a high re-use rate of annotated spoken resources and, as a consequence, better interoperability across tools used to produce or exploit such resources.


BSK-WBSN: Biometric Symmetric Keys to Secure Wireless Body Sensors Networks

Abstract: The Wireless Sensors Network (WSN) is an emergent technology resulting from progress of various fields. Many applications of networks WSN are born. One of the applications which have an operational effectiveness relates to the field of health and allows a medical remote support. Miniature wireless sensors, strategically placed on the human body, create a Wireless Body Sensor Network (WBSN) which allows supervising various essential biological signals (rate of heartbeat, pressure, etc). The sensitivity of medical information requires mechanisms of safety. This performance constitutes a challenge for WBSN because of their limitation in resources energy and data-processing. In this paper we propose a new approach to symmetric cryptographic key establishment, based on biometrics physiology. This approach takes into account WBSN constraints and its topology.


Side-payment profitability and interacting eyeball ISPs under convex demand-response modeling congestion-sensitive applications

Abstract: This paper is concerned with the issue of side payments between content providers (CPs) and Internet service (access bandwidth) providers (ISPs) in an Internet that is potentially not neutral. We herein generalize past results modeling the ISP and CP interaction as a noncooperative game in two directions. We consider different demand response models (price sensitivities) for different provider types in order to explore when side payments are profitable to the ISP. Also, we consider convex (non-linear) demand response to model demand triggered by traffic which is sensitive to access bandwidth congestion, particularly delay-sensitive interactive real-time applications. Finally, we consider a model with two competing "eyeball" ISPs with transit pricing of net traffic at their peering point to study the effects of caching remote content.


A Study of Unsupervised Adaptive Crowdsourcing

Abstract: We consider unsupervised crowdsourcing performance based on the model wherein the responses of end-users are essentially rated according to how their responses correlate with the majority of other responses to the same subtasks/questions. In one setting, we consider an independent sequence of identically distributed crowdsourcing assignments (meta-tasks), while in the other we consider a single assignment with a large number of component subtasks. Both problems yield intuitive results in which the overall reliability of the crowd is a factor.


A Behavior-based Approach for Multi-agent Q-learning for Autonomous Exploration

Abstract: The use of mobile robots is being popular over the world mainly for autonomous explorations in hazardous/ toxic or unknown environments. This exploration will be more effective and efficient if the explorations in unknown environment can be aided with the learning from past experiences. Currently reinforcement learning is getting more acceptances for implementing learning in robots from the system-environment interactions. This learning can be implemented using the concept of both single-agent and multiagent. This paper describes such a multiagent approach for implementing a type of reinforcement learning using a priority based behaviour-based architecture. This proposed methodology has been successfully tested in both indoor and outdoor environments.


A Simple Proof of Linear Scaling of End-to-End Probabilistic Bounds using Network Calculus

Abstract: Statistical network calculus is the probabilistic extension of network calculus, which uses a simple envelope approach to describe arrival traffic and service available for the arrival traffic in a node. One of the key features of network calculus is the possibility to describe the service available in a network using a network service envelope constructed from the service envelopes of the individual nodes constituting the network. It have been shown that the end-to-end worst case performance measures computed using the network service envelope is bounded by $ {\cal O} (H) $, where $H$ is the number of nodes traversed by a flow. There have been many attempts to achieve a similar linear scaling for end-to-end probabilistic performance measures but with limited success. In this paper, we present a simple general proof of computing end-to-end probabilistic performance measures using network calculus that grow linearly in the number of nodes ($H$).


Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus)

Abstract: This paper addresses the consensus problem in homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership ("homonymous" means that several processes may have the same identifier). New classes of failure detectors suited to these systems are first defined. Among them, the classes H\Omega\ and H\Sigma\ are introduced that are the homonymous counterparts of the classes \Omega\ and \Sigma, respectively. (Recall that the pair <\Omega,\Sigma> defines the weakest failure detector to solve consensus.) Then, the paper shows how H\Omega\ and H\Sigma\ can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge of the membership. One algorithm solves consensus with <H\Omega,H\Sigma>, while the other uses only H\Omega, but needs a majority of correct processes. Observe that the systems with unique identifiers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H\Omega\ can be implemented with partial synchrony, while the analogous class A\Omega\ defined for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the first proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).


Oblivious Storage with Low I/O Overhead

Abstract: We study oblivious storage (OS), a natural way to model privacy-preserving data outsourcing where a client, Alice, stores sensitive data at an honest-but-curious server, Bob. We show that Alice can hide both the content of her data and the pattern in which she accesses her data, with high probability, using a method that achieves O(1) amortized rounds of communication between her and Bob for each data access. We assume that Alice and Bob exchange small messages, of size $O(N^{1/c})$, for some constant $c\ge2$, in a single round, where $N$ is the size of the data set that Alice is storing with Bob. We also assume that Alice has a private memory of size $2N^{1/c}$. These assumptions model real-world cloud storage scenarios, where trade-offs occur between latency, bandwidth, and the size of the client's private memory.


Indices of Power in Optimal IDS Default Configuration: Theory and Examples

Abstract: Intrusion Detection Systems (IDSs) are becoming essential to protecting modern information infrastructures. The effectiveness of an IDS is directly related to the computational resources at its disposal. However, it is difficult to guarantee especially with an increasing demand of network capacity and rapid proliferation of attacks. On the other hand, modern intrusions often come as sequences of attacks to reach some predefined goals. It is therefore critical to identify the best default IDS configuration to attain the highest possible overall protection within a given resource budget. This paper proposes a game theory based solution to the problem of optimal signature-based IDS configuration under resource constraints. We apply the concepts of indices of power, namely, Shapley value and Banzhaf-Coleman index, from cooperative game theory to quantify the influence or contribution of libraries in an IDS with respect to given attack graphs. Such valuations take into consideration the knowledge on common attack graphs and experienced system attacks and are used to configure an IDS optimally at its default state by solving a knapsack optimization problem.


Putting Instruction Sequences into Effect

Abstract: An attempt is made to define the concept of execution of an instruction sequence. It is found to be a special case of directly putting into effect of an instruction sequence. Directly putting into effect of an instruction sequences comprises interpretation as well as execution. Directly putting into effect is a special case of putting into effect with other special cases classified as indirectly putting into effect.


On the Efficiency of Influence-and-Exploit Strategies for Revenue Maximization under Positive Externalities

Abstract: We study the problem of revenue maximization in the marketing model for social networks introduced by (Hartline, Mirrokni, Sundararajan, WWW '08). We restrict our attention to the Uniform Additive Model and mostly focus on Influence-and-Exploit (IE) marketing strategies. We obtain a comprehensive collection of results on the efficiency and the approximability of IE strategies, which also imply a significant improvement on the best known approximation ratios for revenue maximization. Specifically, we show that in the Uniform Additive Model, both computing the optimal marketing strategy and computing the best IE strategy are $\NP$-hard for undirected social networks. We observe that allowing IE strategies to offer prices smaller than the myopic price in the exploit step leads to a measurable improvement on their performance. Thus, we show that the best IE strategy approximates the maximum revenue within a factor of 0.911 for undirected and of roughly 0.553 for directed networks. Moreover, we present a natural generalization of IE strategies, with more than two pricing classes, and show that they approximate the maximum revenue within a factor of roughly 0.7 for undirected and of roughly 0.35 for directed networks. Utilizing a connection between good IE strategies and large cuts in the underlying social network, we obtain polynomial-time algorithms that approximate the revenue of the best IE strategy within a factor of roughly 0.9. Hence, we significantly improve on the best known approximation ratio for revenue maximization to 0.8229 for undirected and to 0.5011 for directed networks (from 2/3 and 1/3, respectively, by Hartline et al.).


Restricted Parameter Range Promise Set Cover Problems Are Easy

Abstract: Let $({\bf U},{\bf S},d)$ be an instance of Set Cover Problem, where ${\bf U}=\{u_1,...,u_n\}$ is a $n$ element ground set, ${\bf S}=\{S_1,...,S_m\}$ is a set of $m$ subsets of ${\bf U}$ satisfying $\bigcup_{i=1}^m S_i={\bf U}$ and $d$ is a positive integer. In STOC 1993 M. Bellare, S. Goldwasser, C. Lund and A. Russell proved the NP-hardness to distinguish the following two cases of ${\bf GapSetCover_{\eta}}$ for any constant $\eta > 1$. The Yes case is the instance for which there is an exact cover of size $d$ and the No case is the instance for which any cover of ${\bf U}$ from ${\bf S}$ has size at least $\eta d$. This was improved by R. Raz and S. Safra in STOC 1997 about the NP-hardness for ${\bf GapSetCover}_{clogm}$ for some constant $c$. In this paper we prove that restricted parameter range subproblem is easy. For any given function of $n$ satisfying $\eta(n) \geq 1$, we give a polynomial time algorithm not depending on $\eta(n)$ to distinguish between {\bf YES:} The instance $({\bf U},{\bf S}, d)$ where $d>\frac{2 |{\bf S}|}{3\eta(n)-1}$, for which there exists an exact cover of size at most $d$; {\bf NO:} The instance $({\bf U},{\bf S}, d)$ where $d>\frac{2 |{\bf S}|}{3\eta(n)-1}$, for which any cover from ${\bf S}$ has size larger than $\eta(n) d$. The polynomial time reduction of this restricted parameter range set cover problem is constructed by using the lattice.


Prevention of Wormhole Attack in Wireless Sensor Network

Abstract: Ubiquitous and pervasive applications, where the Wireless Sensor Networks are typically deployed, lead to the susceptibility to many kinds of security attacks. Sensors used for real time response capability also make it difficult to devise the resource intensive security protocols because of their limited battery, power, memory and processing capabilities. One of potent form of Denial of Service attacks is Wormhole attack that affects on the network layer. In this paper, the techniques dealing with wormhole attack are investigated and an approach for wormhole prevention is proposed. Our approach is based on the analysis of the two-hop neighbors forwarding Route Reply packet. To check the validity of the sender, a unique key between the individual sensor node and the base station is required to be generated by suitable scheme.


Stratified Outsourcing Theory

Abstract: The terminology of sourcing, outsourcing and insourcing is developed in detail on the basis of the preliminary definitions of outsourcing and insourcing and related activities and competences as given in our three previous papers on business mereology, on the concept of a sourcement, and on outsourcing competence respectively. Besides providing more a detailed semantic analysis we will introduce, explain, and illustrate a number of additional concepts including: principal unit of a sourcement, theme of a sourcement, current sourcement, (un)stable sourcement, and sourcement transformation. A three level terminology is designed: (i) factual level: operational facts that hold for sourcements including histories thereof, (ii) business level: roles and objectives of various parts of the factual level description, thus explaining each partner's business process and business objectives, (iii) contract level: specification of intended facts and intended business models as found at the business level. Orthogonal to these three conceptual levels, are four temporal aspects: history, now (actuality), transformation, and transition. A detailed description of the well-known range of sourcement transformations is given.