Effectively Nonblocking Consensus Procedures Can Execute Forever - a Constructive Version of FLP

Abstract: The Fischer-Lynch-Paterson theorem (FLP) says that it is impossible for processes in an asynchronous distributed system to achieve consensus on a binary value when a single process can fail; it is a widely cited theoretical result about network computing. All proofs that I know depend essentially on classical (nonconstructive) logic, although they use the hypothetical construction of a nonterminating execution as a main lemma. FLP is also a guide for protocol designers, and in that role there is a connection to an important property of consensus procedures, namely that they should not block, i.e. reach a global state in which no process can decide. A deterministic fault-tolerant consensus protocol is effectively nonblocking if from any reachable global state we can find an execution path that decides. In this article we effectively construct a nonterminating execution of any such protocol. That is, given any effectively nonblocking protocol P and a natural number n, we show how to compute the n-th step of an infinitely indecisive computation of P. From this fully constructive result, the classical FLP follows as a corollary as well as a stronger classical result, called here Strong FLP. Moreover, the construction focuses attention on the important role of nonblocking in protocol design. An interesting consequence of the constructive proof is that we can, in principle, build an undefeatable attacker for a consensus protocol that is provably correct, indeed because it is provably correct. We can do this in practice on certain kinds of networks.


Syntactic Complexity of Star-Free Languages

Abstract: The syntactic complexity of a regular language is the cardinality of its syntactic semigroup. The syntactic complexity of a subclass of regular languages is the maximal syntactic complexity of languages in that subclass, taken as a function of the state complexity of these languages. We study the syntactic complexity of star-free regular languages, that is, languages that can be constructed from finite languages using union, complement and concatenation. We find tight upper bounds on the syntactic complexity of languages accepted by monotonic and partially monotonic automata. We introduce "nearly monotonic" automata, which accept star-free languages, and find a tight upper bound on the syntactic complexity of languages accepted by such automata. We conjecture that this bound is also an upper bound on the syntactic complexity of star-free languages.


Max-Throughput for (Conservative) k-of-n Testing

Abstract: We define a variant of k-of-n testing that we call conservative k-of-n testing. We present a polynomial-time, combinatorial algorithm for the problem of maximizing throughput of conservative k-of-n testing, in a parallel setting. This extends previous work of Kodialam and Condon et al., who presented combinatorial algorithms for parallel pipelined filter ordering, which is the special case where k=1 (or k = n). We also consider the problem of maximizing throughput for standard k-of-n testing, and show how to obtain a polynomial-time algorithm based on the ellipsoid method using previous techniques.


Packing-Based Approximation Algorithm for the k-Set Cover Problem

Abstract: We present a packing-based approximation algorithm for the $k$-Set Cover problem. We introduce a new local search-based $k$-set packing heuristic, and call it Restricted $k$-Set Packing. We analyze its tight approximation ratio via a complicated combinatorial argument. Equipped with the Restricted $k$-Set Packing algorithm, our $k$-Set Cover algorithm is composed of the $k$-Set Packing heuristic \cite{schrijver} for $k\geq 7$, Restricted $k$-Set Packing for $k=6,5,4$ and the semi-local $(2,1)$-improvement \cite{furer} for 3-Set Cover. We show that our algorithm obtains a tight approximation ratio of $H_k-0.6402+\Theta(\frac{1}{k})$, where $H_k$ is the $k$-th harmonic number. For small $k$, our results are 1.8667 for $k=6$, 1.7333 for $k=5$ and 1.5208 for $k=4$. Our algorithm improves the currently best approximation ratio for the $k$-Set Cover problem of any $k\geq 4$.


One, None and One Hundred Thousand Profiles: Re-imagining the Pirandellian Identity Dilemma in the Era of Online Social Networks

Abstract: Uno, Nessuno, Centomila ("One, No One and One Hundred Thousand") is a classic novel by Italian playwright Luigi Pirandello. Published in 1925, it recounts the tragedy of Vitangelo Moscarda, a man who struggles to reclaim a coherent and unitary identity for himself in the face of an inherently social and multi-faceted world. What would Moscarda identity tragedy look like today? In this article we transplant Moscarda's identity play from its offline setting to the contemporary arena of social media and online social networks. With reference to established theories on identity construction, performance, and self-presentation, we re-imagine how Moscarda would go about defending the integrity of his selfhood in the face of the discountenancing influences of the online world.


Learning Topic Models by Belief Propagation

Abstract: Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interests and touches on many important applications in text mining, computer vision and computational biology. This paper represents LDA as a factor graph within the Markov random field (MRF) framework, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly-used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great successes in learning LDA, the proposed BP is competitive in both speed and accuracy as validated by encouraging experimental results on four large-scale document data sets. Furthermore, the BP algorithm has the potential to become a generic learning scheme for variants of LDA-based topic models. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representation.


Large-scale Complex IT Systems

Abstract: This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that identifies the major challenges and issues in the development of large-scale complex, software-intensive systems. Central to this is the notion that we cannot separate software from the socio-technical environment in which it is used.


cuIBM -- A GPU-accelerated Immersed Boundary Method

Abstract: A projection-based immersed boundary method is dominated by sparse linear algebra routines. Using the open-source Cusp library, we observe a speedup (with respect to a single CPU core) which reflects the constraints of a bandwidth-dominated problem on the GPU. Nevertheless, GPUs offer the capacity to solve large problems on commodity hardware. This work includes validation and a convergence study of the GPU-accelerated IBM, and various optimizations.


A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier

Abstract: In this paper we demonstrate that two common problems in Machine Learning---imbalanced and overlapping data distributions---do not have independent effects on the performance of SVM classifiers. This result is notable since it shows that a model of either of these factors must account for the presence of the other. Our study of the relationship between these problems has lead to the discovery of a previously unreported form of "covert" overfitting which is resilient to commonly used empirical regularization techniques. We demonstrate the existance of this covert phenomenon through several methods based around the parametric regularization of trained SVMs. Our findings in this area suggest a possible approach to quantifying overlap in real world data sets.


Approximation Algorithms for Variable-Sized and Generalized Bin Covering

Abstract: We consider the Generalized Bin Covering (GBC) problem: We are given $m$ bin types, where each bin of type $i$ has profit $p_i$ and demand $d_i$. Furthermore, there are $n$ items, where item $j$ has size $s_j$. A bin of type $i$ is covered if the set of items assigned to it has total size at least the demand $d_i$. In that case, the profit of $p_i$ is earned and the objective is to maximize the total profit. To the best of our knowledge, only the cases $p_i = d_i = 1$ (Bin Covering) and $p_i = d_i$ (Variable-Sized Bin Covering (VSBC)) have been treated before. We study two models of bin supply: In the unit supply model, we have exactly one bin of each type, i.\,e., we have individual bins. By contrast, in the infinite supply model, we have arbitrarily many bins of each type. Clearly, the unit supply model is a generalization of the infinite supply model. To the best of our knowledge the unit supply model has not been studied yet. Our results for the unit supply model hold not only asymptotically, but for all instances. This contrasts most of the previous work on \prob{Bin Covering}. We prove that there is a combinatorial 5-approximation algorithm for GBC with unit supply, which has running time $\bigO{nm\sqrt{m+n}}$. Furthermore, for VSBC we show that the natural and fast Next Fit Decreasing ($\NFD$) algorithm is a 9/4-approximation in the unit supply model. The bound is tight for the algorithm and close to being best-possible. We show that there is an AFPTAS for VSBC in the \emph{infinite} supply model.


Using In-Memory Encrypted Databases on the Cloud

Abstract: Storing data in the cloud poses a number of privacy issues. A way to handle them is supporting data replication and distribution on the cloud via a local, centrally synchronized storage. In this paper we propose to use an in-memory RDBMS with row-level data encryption for granting and revoking access rights to distributed data. This type of solution is rarely adopted in conventional RDBMSs because it requires several complex steps. In this paper we focus on implementation and benchmarking of a test system, which shows that our simple yet effective solution overcomes most of the problems.


Universal adaptive self-stabilizing traversal scheme: random walk and reloading wave

Abstract: In this paper, we investigate random walk based token circulation in dynamic environments subject to failures. We describe hypotheses on the dynamic environment that allow random walks to meet the important property that the token visits any node infinitely often. The randomness of this scheme allows it to work on any topology, and require no adaptation after a topological change, which is a desirable property for applications to dynamic systems. For random walks to be a traversal scheme and to answer the concurrence problem, one needs to guarantee that exactly one token circulates in the system. In the presence of transient failures, configurations with multiple tokens or with no token can occur. The meeting property of random walks solves the cases with multiple tokens. The reloading wave mechanism we propose, together with timeouts, allows to detect and solve cases with no token. This traversal scheme is self-stabilizing, and universal, meaning that it needs no assumption on the system topology. We describe conditions on the dynamicity (with a local detection criterion) under which the algorithm is tolerant to dynamic reconfigurations. We conclude by a study on the time between two visits of the token to a node, which we use to tune the parameters of the reloading wave mechanism according to some system characteristics.


Near Optimality in Covering and Packing Games by Exposing Global Information

Abstract: Covering and packing problems can be modeled as games to encapsulate interesting social and engineering settings. These games have a high Price of Anarchy in their natural formulation. However, existing research applicable to specific instances of these games has only been able to prove fast convergence to arbitrary equilibria. This paper studies general classes of covering and packing games with learning dynamics models that incorporate a central authority who broadcasts weak, socially beneficial signals to agents that otherwise only use local information in their decision-making. Rather than illustrating convergence to an arbitrary equilibrium that may have very high social cost, we show that these systems quickly achieve near-optimal performance. In particular, we show that in the public service advertising model, reaching a small constant fraction of the agents is enough to bring the system to a state within a log n factor of optimal in a broad class of set cover and set packing games or a constant factor of optimal in the special cases of vertex cover and maximum independent set, circumventing social inefficiency of bad local equilibria that could arise without a central authority. We extend these results to the learn-then-decide model, in which agents use any of a broad class of learning algorithms to decide in a given round whether to behave according to locally optimal behavior or the behavior prescribed by the broadcast signal. The new techniques we use for analyzing these games could be of broader interest for analyzing more general classic optimization problems in a distributed fashion.


IR-based Communication and Perception in Microrobotic Swarms

Abstract: In this work we consider development of IR-based communication and perception mechanisms for real microrobotic systems. It is demonstrated that a specific combination of hardware and software elements provides capabilities for navigation, objects recognition, directional and unidirectional communication. We discuss open issues and their resolution based on the experiments in the swarm of microrobots "Jasmine".


Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments

Abstract: Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. In opposition, the so-called local methods enforce connectivity but lack robustness to deal with challenging situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. In this paper, we address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional content agree with potential line segments. As a result, our method, which we call STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in several situations where current methods fail.


Optimization, Randomized Approximability, and Boolean Constraint Satisfaction Problems

Abstract: We give a unified treatment to optimization problems that can be expressed in the form of nonnegative-real-weighted Boolean constraint satisfaction problems. Creignou, Khanna, Sudan, Trevisan, and Williamson studied the complexity of approximating their optimal solutions whose optimality is measured by the sums of outcomes of constraints. To explore a wider range of optimization constraint satisfaction problems, following an early work of Marchetti-Spaccamela and Romano, we study the case where the optimality is measured by products of constraints' outcomes. We completely classify those problems into three categories: PO problems, NPO-hard problems, and intermediate problems that lie between the former two categories. To prove this trichotomy theorem, we analyze characteristics of nonnegative-real-weighted constraints using a variant of the notion of T-constructibility developed earlier for complex-weighted counting constraint satisfaction problems.


Computing the Hermite Form of a Matrix of Ore Polynomials

Abstract: Let R=F[D;sigma,delta] be the ring of Ore polynomials over a field (or skew field) F, where sigma is a automorphism of F and delta is a sigma-derivation. Given a an m by n matrix A over R, we show how to compute the Hermite form H of A and a unimodular matrix U such that UA=H. The algorithm requires a polynomial number of operations in F in terms of both the dimensions m and n, and the degree of the entries in A. When F=k(z) for some field k, it also requires time polynomial in the degree in z, and if k is the rational numbers Q, it requires time polynomial in the bit length of the coefficients as well. Explicit analyses are provided for the complexity, in particular for the important cases of differential and shift polynomials over Q(z). To accomplish our algorithm, we apply the Dieudonne determinant and quasideterminant theory for Ore polynomial rings to get explicit bounds on the degrees and sizes of entries in H and U.


Towards a Coalgebraic Interpretation of Propositional Dynamic Logic

Abstract: The interpretation of propositional dynamic logic (PDL) through Kripke models requires the relations constituting the interpreting Kripke model to closely observe the syntax of the modal operators. This poses a significant challenge for an interpretation of PDL through stochastic Kripke models, because the programs' operations do not always have a natural counterpart in the set of stochastic relations. We use rewrite rules for building up an interpretation of PDL. It is shown that each program corresponds to an essentially unique irreducible tree, which in turn is assigned a predicate lifting, serving as the program's interpretation. The paper establishes and studies this interpretation. It discusses the expressivity of probabilistic models for PDL and relates properties like logical and behavioral equivalence or bisimilarity to the corresponding properties of a Kripke model for a closely related non-dynamic logic of the Hennessy-Milner type.


Contradiction measures and specificity degrees of basic belief assignments

Abstract: In the theory of belief functions, many measures of uncertainty have been introduced. However, it is not always easy to understand what these measures really try to represent. In this paper, we re-interpret some measures of uncertainty in the theory of belief functions. We present some interests and drawbacks of the existing measures. On these observations, we introduce a measure of contradiction. Therefore, we present some degrees of non-specificity and Bayesianity of a mass. We propose a degree of specificity based on the distance between a mass and its most specific associated mass. We also show how to use the degree of specificity to measure the specificity of a fusion rule. Illustrations on simple examples are given.


Learning where to Attend with Deep Architectures for Image Tracking

Abstract: We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways: identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-Restricted Boltzmann Machines. At each point in time the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies which operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a Gaussian Process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.


Parallel Sparse Matrix-Matrix Multiplication and Indexing: Implementation and Experiments

Abstract: Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. Here we show that SpGEMM also yields efficient algorithms for general sparse-matrix indexing in distributed memory, provided that the underlying SpGEMM implementation is sufficiently flexible and scalable. We demonstrate that our parallel SpGEMM methods, which use two-dimensional block data distributions with serial hypersparse kernels, are indeed highly flexible, scalable, and memory-efficient in the general case. This algorithm is the first to yield increasing speedup on an unbounded number of processors; our experiments show scaling up to thousands of processors in a variety of test scenarios.


New Principles of Coordination in Large-scale Micro- and Molecular-Robotic Groups

Abstract: Micro- and molecular-robotic systems act as large-scale swarms. Capabilities of sensing, communication and information processing are very limited on these scales. This short position paper describes a swarm-based minimalistic approach, which can be applied for coordinating collective behavior in such systems.


Generalised Object Detection and Semantic Analysis: Casino Example using Matlab

Abstract: Matlab version 7.1 had been used to detect playing cards on a Casino table and the suits and ranks of these cards had been identified. The process gives an example of an application of computer vision to a problem where rectangular objects are to be detected and the information content of the objects are extracted out. In the case of playing cards, it is the suit and rank of each card. The image processing system is done in two passes. Pass 1 detects rectangular shapes and template matched with a template of the left and right edges of the cards. Pass 2 extracts the suit and rank of the cards by matching the top left portion of the card that contains both rank and suit information, with stored templates of ranks and suits of the playing cards using a series of if-then statements.


WebCloud: Recruiting web browsers for content distribution

Abstract: We are at the beginning of a shift in how content is created and exchanged over the web. While content was previously created primarily by a small set of entities, today, individual users -- empowered by devices like digital cameras and services like online social networks -- are creating content that represents a significant fraction of Internet traffic. As a result, content today is increasingly generated and exchanged at the edge of the network. Unfortunately, the existing techniques and infrastructure that are still used to serve this content, such as centralized content distribution networks, are ill-suited for these new patterns of content exchange. In this paper, we take a first step towards addressing this situation by introducing WebCloud, a content distribution system for online social networking sites that works by re- purposing web browsers to help serve content. In other words, when a user browses content, WebCloud tries to fetch it from one of that user's friend's browsers, instead of from the social networking site. The result is a more direct exchange of content ; essentially, WebCloud leverages the spatial and temporal locality of interest between social network users. Because WebCloud is built using techniques already present in many web browsers, it can be applied today to many social networking sites. We demonstrate the practicality of WebCloud with microbenchmarks, simulations, and a prototype deployment.


Dynamic Deferral of Workload for Capacity Provisioning in Data Centers

Abstract: Recent increase in energy prices has led researchers to find better ways for capacity provisioning in data centers to reduce energy wastage due to the variation in workload. This paper explores the opportunity for cost saving utilizing the flexibility from the Service Level Agreements (SLAs) and proposes a novel approach for capacity provisioning under bounded latency requirements of the workload. We investigate how many servers to be kept active and how much workload to be delayed for energy saving while meeting every deadline. We present an offline LP formulation for capacity provisioning by dynamic deferral and give two online algorithms to determine the capacity of the data center and the assignment of workload to servers dynamically. We prove the feasibility of the online algorithms and show that their worst case performance are bounded by a constant factor with respect to the offline formulation. We validate our algorithms on a MapReduce workload by provisioning capacity on a Hadoop cluster and show that the algorithms actually perform much better in practice compared to the naive `follow the workload' provisioning, resulting in 20-40% cost-savings.


Fast approximation of matrix coherence and statistical leverage

Abstract: The statistical leverage scores of a matrix $A$ are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recently-popular problems such as matrix completion and Nystr\"{o}m-based low-rank matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary $n \times d$ matrix $A$, with $n \gg d$, and that returns as output relative-error approximations to all $n$ of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of $n$ and $d$) in $O(n d \log n)$ time, as opposed to the $O(nd^2)$ time required by the na\"{i}ve algorithm that involves computing an orthogonal basis for the range of $A$. Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas. Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with $n \approx d$, and the extension to streaming environments.


Digital (co)homology modules and digital Pontryagin algebras

Abstract: In the current study, we explore digital homology and cohomology modules, and investigate their fundamental properties on pointed digital images. We also examine pointed digital Hopf spaces and base point preserving digital Hopf functions between the pointed digital Hopf spaces with suitable digital multiplications, and explore the digital primitive homology and cohomology classes, the digital Pontryagin algebras and coalgebras on the digital Hopf spaces as digital images.


A Dynamic Stabbing-Max Data Structure with Sub-Logarithmic Query Time

Abstract: In this paper we describe a dynamic data structure that answers one-dimensional stabbing-max queries in optimal $O(\log n/\log\log n)$ time. Our data structure uses linear space and supports insertions and deletions in $O(\log n)$ and $O(\log n/\log \log n)$ amortized time respectively. We also describe a $O(n(\log n/\log\log n)^{d-1})$ space data structure that answers $d$-dimensional stabbing-max queries in $O((\log n/\log\log n)^{d})$ time. Insertions and deletions are supported in $O((\log n/\log\log n)^d\log\log n)$ and $O((\log n/\log\log n)^d)$ amortized time respectively.


Monitoring Breathing via Signal Strength in Wireless Networks

Abstract: This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call "BreathTaking". We show that although an individual link cannot reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.3 breaths per minute (bpm) RMS error. Use of directional antennas is shown to improve robustness to motion near the network.


A linear-time algorithm for the strong chromatic index of Halin graphs

Abstract: We show that there exists a linear-time algorithm that computes the strong chromatic index of Halin graphs.


A Faster Grammar-Based Self-Index

Abstract: To store and search genomic databases efficiently, researchers have recently started building compressed self-indexes based on grammars. In this paper we show how, given a straight-line program with $r$ rules for a string (S [1..n]) whose LZ77 parse consists of $z$ phrases, we can store a self-index for $S$ in $\Oh{r + z \log \log n}$ space such that, given a pattern (P [1..m]), we can list the $\occ$ occurrences of $P$ in $S$ in $\Oh{m^2 + \occ \log \log n}$ time. If the straight-line program is balanced and we accept a small probability of building a faulty index, then we can reduce the $\Oh{m^2}$ term to $\Oh{m \log m}$. All previous self-indexes are larger or slower in the worst case.


On-Demand Multicasting in Ad-hoc Networks: Performance Evaluation of AODV, ODMRP and FSR

Abstract: Adhoc networks are characterized by connectivity through a collection of wireless nodes and fast changing network topology. Wireless nodes are free to move independent of each other which makes routing much difficult. This calls for the need of an efficient dynamic routing protocol. Mesh-based multicast routing technique establishes communications between mobile nodes of wireless adhoc networks in a faster and efficient way. In this article the performance of prominent on-demand routing protocols for mobile adhoc networks such as ODMRP (On Demand Multicast Routing Protocol), AODV (Adhoc on Demand Distance Vector) and FSR (Fisheye State Routing protocol) was studied. The parameters viz., average throughput, packet delivery ration and end-to-end delay were evaluated. From the simulation results and analysis, a suitable routing protocol can be chosen for a specified network. The results show that the ODMRP protocol performance is remarkably superior as compared with AODV and FSR routing protocols. Keywords: MANET, Multicast Routing, ODMRP, AODV, FSR.


Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach

Abstract: Organization, scalability and routing have been identified as key problems hindering viability and commercial success of mobile ad hoc networks. Clustering of mobile nodes among separate domains has been proposed as an efficient approach to address those issues. In this work, we introduce an efficient distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution mainly addresses cluster stability, manageability and energy efficiency issues. Also, unlike existing active clustering methods, our algorithm relieves the network from the unnecessary burden of control messages broadcasting, especially for relatively static network topologies. This is achieved through adapting broadcast period according to mobile nodes mobility pattern. The efficiency, scalability and competence of our algorithm against alternative approaches have been demonstrated through simulation results.


The SeaLion has Landed: An IDE for Answer-Set Programming---Preliminary Report

Abstract: We report about the current state and designated features of the tool SeaLion, aimed to serve as an integrated development environment (IDE) for answer-set programming (ASP). A main goal of SeaLion is to provide a user-friendly environment for supporting a developer to write, evaluate, debug, and test answer-set programs. To this end, new support techniques have to be developed that suit the requirements of the answer-set semantics and meet the constraints of practical applicability. In this respect, SeaLion benefits from the research results of a project on methods and methodologies for answer-set program development in whose context SeaLion is realised. Currently, the tool provides source-code editors for the languages of Gringo and DLV that offer syntax highlighting, syntax checking, and a visual program outline. Further implemented features are support for external solvers and visualisation as well as visual editing of answer sets. SeaLion comes as a plugin of the popular Eclipse platform and provides itself interfaces for future extensions of the IDE.


Lowest-ID with Adaptive ID Reassignment: A Novel Mobile Ad-Hoc Networks Clustering Algorithm

Abstract: Clustering is a promising approach for building hierarchies and simplifying the routing process in mobile ad-hoc network environments. The main objective of clustering is to identify suitable node representatives, i.e. cluster heads (CHs), to store routing and topology information and maximize clusters stability. Traditional clustering algorithms suggest CH election exclusively based on node IDs or location information and involve frequent broadcasting of control packets, even when network topology remains unchanged. More recent works take into account additional metrics (such as energy and mobility) and optimize initial clustering. However, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon reach battery exhaustion. Herein, we introduce an efficient distributed clustering algorithm that uses both mobility and energy metrics to provide stable cluster formations. CHs are initially elected based on the time and cost-efficient lowest-ID method. During clustering maintenance phase though, node IDs are re-assigned according to nodes mobility and energy status, ensuring that nodes with low-mobility and sufficient energy supply are assigned low IDs and, hence, are elected as CHs. Our algorithm also reduces control traffic volume since broadcast period is adjusted according to nodes mobility pattern: we employ infrequent broadcasting for relative static network topologies, and increase broadcast frequency for highly mobile network configurations. Simulation results verify that energy consumption is uniformly distributed among network nodes and that signaling overhead is significantly decreased.


A Lightweight and Flexible Mobile Agent Platform Tailored to Management Applications

Abstract: Mobile Agents (MAs) represent a distributed computing technology that promises to address the scalability problems of centralized network management. A critical issue that will affect the wider adoption of MA paradigm in management applications is the development of MA Platforms (MAPs) expressly oriented to distributed management. However, most of available platforms impose considerable burden on network and system resources and also lack of essential functionality. In this paper, we discuss the design considerations and implementation details of a complete MAP research prototype that sufficiently addresses all the aforementioned issues. Our MAP has been implemented in Java and tailored for network and systems management applications.


The Complexity of Nash Equilibria in Stochastic Multiplayer Games

Abstract: We analyse the computational complexity of finding Nash equilibria in turn-based stochastic multiplayer games with omega-regular objectives. We show that restricting the search space to equilibria whose payoffs fall into a certain interval may lead to undecidability. In particular, we prove that the following problem is undecidable: Given a game G, does there exist a Nash equilibrium of G where Player 0 wins with probability 1? Moreover, this problem remains undecidable when restricted to pure strategies or (pure) strategies with finite memory. One way to obtain a decidable variant of the problem is to restrict the strategies to be positional or stationary. For the complexity of these two problems, we obtain a common lower bound of NP and upper bounds of NP and PSPACE respectively. Finally, we single out a special case of the general problem that, in many cases, admits an efficient solution. In particular, we prove that deciding the existence of an equilibrium in which each player either wins or loses with probability 1 can be done in polynomial time for games where the objective of each player is given by a parity condition with a bounded number of priorities.


Tying up the loose ends in fully LZW-compressed pattern matching

Abstract: We consider a natural generalization of the classical pattern matching problem: given compressed representations of a pattern p[1..M] and a text t[1..N] of sizes m and n, respectively, does p occur in t? We develop an optimal linear time solution for the case when both p and t are compressed using the LZW method. This improves the previously known O((n+m)log(n+m)) time solution of Gasieniec and Rytter, and essentially closes the line of research devoted to studying LZW-compressed exact pattern matching.


I Know Where You are and What You are Sharing: Exploiting P2P Communications to Invade Users' Privacy

Abstract: In this paper, we show how to exploit real-time communication applications to determine the IP address of a targeted user. We focus our study on Skype, although other real-time communication applications may have similar privacy issues. We first design a scheme that calls an identified targeted user inconspicuously to find his IP address, which can be done even if he is behind a NAT. By calling the user periodically, we can then observe the mobility of the user. We show how to scale the scheme to observe the mobility patterns of tens of thousands of users. We also consider the linkability threat, in which the identified user is linked to his Internet usage. We illustrate this threat by combining Skype and BitTorrent to show that it is possible to determine the file-sharing usage of identified users. We devise a scheme based on the identification field of the IP datagrams to verify with high accuracy whether the identified user is participating in specific torrents. We conclude that any Internet user can leverage Skype, and potentially other real-time communication systems, to observe the mobility and file-sharing usage of tens of millions of identified users.


Implementing Continuation based language in GCC

Abstract: We have implemented C like Continuation based programming language. Continuation based C, CbC was implemented using micro-C on various architecture, and we have tried several CbC programming experiments. Here we report new implementation of CbC compiler based on GCC 4.2.3. Since it contains full C capability, we can use CbC and C in a mixture.


Kara: A System for Visualising and Visual Editing of Interpretations for Answer-Set Programs

Abstract: In answer-set programming (ASP), the solutions of a problem are encoded in dedicated models, called answer sets, of a logical theory. These answer sets are computed from the program that represents the theory by means of an ASP solver and returned to the user as sets of ground first-order literals. As this type of representation is often cumbersome for the user to interpret, tools like ASPVIZ and IDPDraw were developed that allow for visualising answer sets. The tool Kara, introduced in this paper, follows these approaches, using ASP itself as a language for defining visualisations of interpretations. Unlike existing tools that position graphic primitives according to static coordinates only, Kara allows for more high-level specifications, supporting graph structures, grids, and relative positioning of graphical elements. Moreover, generalising the functionality of previous tools, Kara provides modifiable visualisations such that interpretations can be manipulated by graphically editing their visualisations. This is realised by resorting to abductive reasoning techniques. Kara is part of SeaLion, a forthcoming integrated development environment (IDE) for ASP.


Algorithms for Constructing Overlay Networks For Live Streaming

Abstract: We present a polynomial time approximation algorithm for constructing an overlay multicast network for streaming live media events over the Internet. The class of overlay networks constructed by our algorithm include networks used by Akamai Technologies to deliver live media events to a global audience with high fidelity. We construct networks consisting of three stages of nodes. The nodes in the first stage are the entry points that act as sources for the live streams. Each source forwards each of its streams to one or more nodes in the second stage that are called reflectors. A reflector can split an incoming stream into multiple identical outgoing streams, which are then sent on to nodes in the third and final stage that act as sinks and are located in edge networks near end-users. As the packets in a stream travel from one stage to the next, some of them may be lost. A sink combines the packets from multiple instances of the same stream (by reordering packets and discarding duplicates) to form a single instance of the stream with minimal loss. Our primary contribution is an algorithm that constructs an overlay network that provably satisfies capacity and reliability constraints to within a constant factor of optimal, and minimizes cost to within a logarithmic factor of optimal. Further in the common case where only the transmission costs are minimized, we show that our algorithm produces a solution that has cost within a factor of 2 of optimal. We also implement our algorithm and evaluate it on realistic traces derived from Akamai's live streaming network. Our empirical results show that our algorithm can be used to efficiently construct large-scale overlay networks in practice with near-optimal cost.


Approximate Distance Oracles with Improved Preprocessing Time

Abstract: Given an undirected graph $G$ with $m$ edges, $n$ vertices, and non-negative edge weights, and given an integer $k\geq 1$, we show that for some universal constant $c$, a $(2k-1)$-approximate distance oracle for $G$ of size $O(kn^{1 + 1/k})$ can be constructed in $O(\sqrt km + kn^{1 + c/\sqrt k})$ time and can answer queries in $O(k)$ time. We also give an oracle which is faster for smaller $k$. Our results break the quadratic preprocessing time bound of Baswana and Kavitha for all $k\geq 6$ and improve the $O(kmn^{1/k})$ time bound of Thorup and Zwick except for very sparse graphs and small $k$. When $m = \Omega(n^{1 + c/\sqrt k})$ and $k = O(1)$, our oracle is optimal w.r.t.\ both stretch, size, preprocessing time, and query time, assuming a widely believed girth conjecture by Erd\H{o}s.


Three Cases of Connectivity and Global Information Transfer in Robot Swarms

Abstract: In this work we consider three different cases of robot-robot interactions and resulting global information transfer in robot swarms. These mechanisms define cooperative properties of the system and can be used for designing collective behavior. These three cases are demonstrated and discussed based on experiments in a swarm of microrobots "Jasmine".


Actor Continuation Passing: Efficient and Extensible Request Routing for Event-Driven Architectures

Abstract: The logic for handling of application requests to a staged, event-driven architecture is often distributed over different portions of the source code. This complicates changing and understanding the flow of events in the system. The article presents an approach that extracts request handling logic from regular stage functionality into a set of request scripts. These scripts are executed step-wise by sending continuations that encapsulate their request's current execution state to stages for local processing and optional forwarding of follow-up continuations. A new internal domain specific language (DSL) that aims to simplify writing of request scripts is described along with its implementation for the scala actors library. Evaluation results indicate that request handling with actor continuations performs about equally or better compared to using separate stages for request handling logic for scripts of at least 3 sequential steps.


Offering A Product Recommendation System in E-commerce

Abstract: This paper proposes a number of explicit and implicit ratings in product recommendation system for Business-to-customer e-commerce purposes. The system recommends the products to a new user. It depends on the purchase pattern of previous users whose purchase pattern is close to that of a user who asks for a recommendation. The system is based on weighted cosine similarity measure to find out the closest user profile among the profiles of all users in database. It also implements Association rule mining rule in recommending the products. Also, this product recommendation system takes into consideration the time of transaction of purchasing the items, thus eliminating sequence recognition problem. Experimental result shows for implicit rating, the proposed method gives acceptable performance in recommending the products. It also shows introduction of association rule improves the performance measure of recommendation system.


Adding Logical Operators to Tree Pattern Queries on Graph-Structured Data

Abstract: As data are increasingly modeled as graphs for expressing complex relationships, the tree pattern query on graph-structured data becomes an important type of queries in real-world applications. Most practical query languages, such as XQuery and SPARQL, support logical expressions using logical-AND/OR/NOT operators to define structural constraints of tree patterns. In this paper, (1) we propose generalized tree pattern queries (GTPQs) over graph-structured data, which fully support propositional logic of structural constraints. (2) We make a thorough study of fundamental problems including satisfiability, containment and minimization, and analyze the computational complexity and the decision procedures of these problems. (3) We propose a compact graph representation of intermediate results and a pruning approach to reduce the size of intermediate results and the number of join operations -- two factors that often impair the efficiency of traditional algorithms for evaluating tree pattern queries. (4) We present an efficient algorithm for evaluating GTPQs using 3-hop as the underlying reachability index. (5) Experiments on both real-life and synthetic data sets demonstrate the effectiveness and efficiency of our algorithm, from several times to orders of magnitude faster than state-of-the-art algorithms in terms of evaluation time, even for traditional tree pattern queries with only conjunctive operations.


Enabling Adaptive Rate and Relay Selection for 802.11 Mobile Ad Hoc Networks

Abstract: Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Moreover, for an 802.11 network that employs geographic opportunistic routing with adaptive rate and relay selection, we propose a novel method to reduce the effect of the feedback delay at the MAC layer in the presence of Rayleigh fading. This method exploits channel reciprocity and fading prediction and does not require significant modification to the existing 802.11 frame structure. Extensive network simulations demonstrate that the proposed approach significantly improves the throughput, delay, and packet delivery ratio for high mobile velocities relative to previously proposed approaches that employ outdated CSI at the transmitter.


On the complexity of computing with zero-dimensional triangular sets

Abstract: We study the complexity of some fundamental operations for triangular sets in dimension zero. Using Las-Vegas algorithms, we prove that one can perform such operations as change of order, equiprojectable decomposition, or quasi-inverse computation with a cost that is essentially that of modular composition. Over an abstract field, this leads to a subquadratic cost (with respect to the degree of the underlying algebraic set). Over a finite field, in a boolean RAM model, we obtain a quasi-linear running time using Kedlaya and Umans' algorithm for modular composition. Conversely, we also show how to reduce the problem of modular composition to change of order for triangular sets, so that all these problems are essentially equivalent. Our algorithms are implemented in Maple; we present some experimental results.


Social choice rules driven by propositional logic

Abstract: Several rules for social choice are examined from a unifying point of view that looks at them as procedures for revising a system of degrees of belief in accordance with certain specified logical constraints. Belief is here a social attribute, its degrees being measured by the fraction of people who share a given opinion. Different known rules and some new ones are obtained depending on which particular constraints are assumed. These constraints allow to model different notions of choiceness. In particular, we give a new method to deal with approval-disapproval-preferential voting.


Designing a CPU model: from a pseudo-formal document to fast code

Abstract: For validating low level embedded software, engineers use simulators that take the real binary as input. Like the real hardware, these full-system simulators are organized as a set of components. The main component is the CPU simulator (ISS), because it is the usual bottleneck for the simulation speed, and its development is a long and repetitive task. Previous work showed that an ISS can be generated from an Architecture Description Language (ADL). In the work reported in this paper, we generate a CPU simulator directly from the pseudo-formal descriptions of the reference manual. For each instruction, we extract the information describing its behavior, its binary encoding, and its assembly syntax. Next, after automatically applying many optimizations on the extracted information, we generate a SystemC/TLM ISS. We also generate tests for the decoder and a formal specification in Coq. Experiments show that the generated ISS is as fast and stable as our previous hand-written ISS.


On the confluence of lambda-calculus with conditional rewriting

Abstract: The confluence of untyped \lambda-calculus with unconditional rewriting is now well un- derstood. In this paper, we investigate the confluence of \lambda-calculus with conditional rewriting and provide general results in two directions. First, when conditional rules are algebraic. This extends results of M\"uller and Dougherty for unconditional rewriting. Two cases are considered, whether \beta-reduction is allowed or not in the evaluation of conditions. Moreover, Dougherty's result is improved from the assumption of strongly normalizing \beta-reduction to weakly normalizing \beta-reduction. We also provide examples showing that outside these conditions, modularity of confluence is difficult to achieve. Second, we go beyond the algebraic framework and get new confluence results using a restricted notion of orthogonality that takes advantage of the conditional part of rewrite rules.


Innocent strategies as presheaves and interactive equivalences for CCS (expanded version)

Abstract: Seeking a general framework for reasoning about and comparing programming languages, we derive a new view of Milner's CCS. We construct a category E of 'plays', and a subcategory V of 'views'. We argue that presheaves on V adequately represent 'innocent' strategies, in the sense of game semantics. We equip innocent strategies with a simple notion of interaction. We then prove decomposition results for innocent strategies, and, restricting to presheaves of finite ordinals, prove that innocent strategies are a final coalgebra for a polynomial functor derived from the game. This leads to a translation of CCS with recursive equations. Finally, we propose a notion of 'interactive equivalence' for innocent strategies, which is close in spirit to Beffara's interpretation of testing equivalences in concurrency theory. In this framework, we consider analogues of fair testing and must testing. We show that must testing is strictly finer in our model than in CCS, since it avoids what we call 'spatial unfairness'. Still, it differs from fair testing, and we show that it coincides with a relaxed form of fair testing.


Argument filterings and usable rules in higher-order rewrite systems

Abstract: The static dependency pair method is a method for proving the termination of higher-order rewrite systems a la Nipkow. It combines the dependency pair method introduced for first-order rewrite systems with the notion of strong computability introduced for typed lambda-calculi. Argument filterings and usable rules are two important methods of the dependency pair framework used by current state-of-the-art first-order automated termination provers. In this paper, we extend the class of higher-order systems on which the static dependency pair method can be applied. Then, we extend argument filterings and usable rules to higher-order rewriting, hence providing the basis for a powerful automated termination prover for higher-order rewrite systems.


Fault-Tolerant Aggregation: Flow-Updating Meets Mass-Distribution

Abstract: Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.


Mitigation of Random Query String DoS via Gossip

Abstract: This paper presents a mitigation scheme to cope with the random query string Denial of Service (DoS) attack, which is based on a vulnerability of current Content Delivery Networks (CDNs). The attack exploits the fact that edge servers composing a CDN, receiving an HTTP request for a resource with an appended random query string never saw before, ask the origin server for a (novel) copy of the resource. Such characteristics can be employed to take an attack against the origin server by exploiting edge servers. Our strategy adopts a simple gossip protocol executed by edge servers to detect the attack. Based on such a detection, countermeasures can be taken to protect the origin server and the CDN against the attack. We provide simulation results that show the viability of our approach.


Asymetric Pavlovian Populations

Abstract: Population protocols have been introduced by Angluin et al. as a model of networks consisting of very limited mobile agents that interact in pairs but with no control over their own movement. A collection of anonymous agents, modeled by finite automata, interact pairwise according to some rules that update their states. Predicates on the initial configurations that can be computed by such protocols have been characterized as semi-linear predicates. In an orthogonal way, several distributed systems have been termed in literature as being realizations of games in the sense of game theory. We investigate under which conditions population protocols, or more generally pairwise interaction rules, correspond to games. We show that restricting to asymetric games is not really a restric- tion: all predicates computable by protocols can actually be computed by protocols corresponding to games, i.e. any semi-linear predicate can be computed by a Pavlovian population multi-protocol.


Periodic sequences with stable $k$-error linear complexity

Abstract: The linear complexity of a sequence has been used as an important measure of keystream strength, hence designing a sequence which possesses high linear complexity and $k$-error linear complexity is a hot topic in cryptography and communication. Niederreiter first noticed many periodic sequences with high $k$-error linear complexity over GF(q). In this paper, the concept of stable $k$-error linear complexity is presented to study sequences with high $k$-error linear complexity. By studying linear complexity of binary sequences with period $2^n$, the method using cube theory to construct sequences with maximum stable $k$-error linear complexity is presented. It is proved that a binary sequence with period $2^n$ can be decomposed into some disjoint cubes. The cube theory is a new tool to study $k$-error linear complexity. Finally, it is proved that the maximum $k$-error linear complexity is $2^n-(2^l-1)$ over all $2^n$-periodic binary sequences, where $2^{l-1}\le k<2^{l}$.


An Algorithm for Computing $m$-Tight Error Linear Complexity of Sequences over $GF(p^{m})$ with Period $p^{m}$

Abstract: The linear complexity (LC) of a sequence has been used as a convenient measure of the randomness of a sequence. Based on the theories of linear complexity, $k$-error linear complexity, the minimum error and the $k$-error linear complexity profile, the notion of $m$-tight error linear complexity is presented. An efficient algorithm for computing $m$-tight error linear complexity is derived from the algorithm for computing $k$-error linear complexity of sequences over GF($p^{m}$) with period $p^n$, where $p$ is a prime. The validity of the algorithm is shown. The algorithm is also realized with C language, and an example is presented to illustrate the algorithm.


A Simple Linear-Space Data Structure for Constant-Time Range Minimum Query

Abstract: We revisit the range minimum query problem and present a new O(n)-space data structure that supports queries in O(1) time. Although previous data structures exist whose asymptotic bounds match ours, our goal is to introduce a new solution that is simple, intuitive, and practical without increasing costs for query time or space.


Pushdown Abstractions of JavaScript

Abstract: We design a family of program analyses for JavaScript that make no approximation in matching calls with returns, exceptions with handlers, and breaks with labels. We do so by starting from an established reduction semantics for JavaScript and systematically deriving its intensional abstract interpretation. Our first step is to transform the semantics into an equivalent low-level abstract machine: the JavaScript Abstract Machine (JAM). We then give an infinite-state yet decidable pushdown machine whose stack precisely models the structure of the concrete program stack. The precise model of stack structure in turn confers precise control-flow analysis even in the presence of control effects, such as exceptions and finally blocks. We give pushdown generalizations of traditional forms of analysis such as k-CFA, and prove the pushdown framework for abstract interpretation is sound and computable.


Smart Grid Information Security (IS) Functional Requirement

Abstract: It is important to implement safe smart grid environment to enhance people's lives and livelihoods. This paper provides information on smart grid IS functional requirement by illustrating some discussion points to the sixteen identified requirements. This paper introduces the smart grid potential hazards that can be referred as a triggering factor to improve the system and security of the entire grid. The background of smart information infrastructure and the needs for smart grid IS is described with the adoption of hermeneutic circle as methodology. Grid information technology and security-s session discusses that grid provides the chance of a simple and transparent access to different information sources. In addition, the transformation between traditional versus smart grid networking trend and the IS importance on the communication field reflects the criticality of grid IS functional requirement identification is introduces. The smart grid IS functional requirements described in this paper are general and can be adopted or modified to suit any smart grid system. This paper has tutorial contents where some related backgrounds were provided, especially for networking community, covering the cyber security requirement of smart grid information infrastructure.


Vertical Handover decision schemes using SAW and WPM for Network selection in Heterogeneous Wireless Networks

Abstract: Seamless continuity is the main goal and challenge in fourth generation Wireless networks (FGWNs), to achieve seamless connectivity "HANDOVER" technique is used,Handover mechanism are mainly used when a mobile terminal(MT) is in overlapping area for service continuity. In Heterogeneous wireless networks main challenge is continual connection among the different networks like WiFi, WiMax, WLAN, WPAN etc. In this paper, Vertical handover decision schemes are compared, Simple Additive Weighting method (SAW) and Weighted product model (WPM) are used to choose the best network from the available Visitor networks(VTs) for the continuous connection by the mobile terminal. In our work we mainly concentrated to the handover decision phase and to reduce the processing delay in the period of handover. In this paper both SAW and WPM methods are compared with the Qos parameters of the mobile terminal (MT) to connect with the best network. Keywords: Handover, Vertical handover decision schemes, Simple additive weighting, Weight product method.


Empirical study of sensor observation services server instances

Abstract: The number of Sensor Observation Service (SOS) instances available online has been increasing in the last few years. The SOS specification standardises interfaces and data formats for exchanging sensor-related in-formation between information providers and consumers. SOS in conjunction with other specifications in the Sensor Web Enablement initiative, at-tempts to realise the Sensor Web vision, a worldwide system where sensor networks of any kind are interconnected. In this paper we present an empirical study of actual instances of servers implementing SOS. The study focuses mostly in which parts of the specification are more frequently included in real implementations, and how exchanged messages follows the structure defined by XML Schema files. Our findings can be of practical use when implementing servers and clients based on the SOS specification, as they can be optimized for common scenarios.


A Probabilistic Approach to Pronunciation by Analogy

Abstract: The relationship between written and spoken words is convoluted in languages with a deep orthography such as English and therefore it is difficult to devise explicit rules for generating the pronunciations for unseen words. Pronunciation by analogy (PbA) is a data-driven method of constructing pronunciations for novel words from concatenated segments of known words and their pronunciations. PbA performs relatively well with English and outperforms several other proposed methods. However, the best published word accuracy of 65.5% (for the 20,000 word NETtalk corpus) suggests there is much room for improvement in it. Previous PbA algorithms have used several different scoring strategies such as the product of the frequencies of the component pronunciations of the segments, or the number of different segmentations that yield the same pronunciation, and different combinations of these methods, to evaluate the candidate pronunciations. In this article, we instead propose to use a probabilistically justified scoring rule. We show that this principled approach alone yields better accuracy (66.21% for the NETtalk corpus) than any previously published PbA algorithm. Furthermore, combined with certain ad hoc modifications motivated by earlier algorithms, the performance climbs up to 66.6%, and further improvements are possible by combining this method with other methods.


About a Discrete Cellular Soliton (computer simulation)

Abstract: For the first time a mathematical object is presented - a reversible cellular Automaton - with many paradoxical qualities, the main ones among them are: a frequent quickly return to its original state, the presence of a large number of conservation laws and paradoxical "fuzzy" symmetries, which connects the current position of the automaton with its signature Main Integral.


Surface Split Decompositions and Subgraph Isomorphism in Graphs on Surfaces

Abstract: The Subgraph Isomorphism problem asks, given a host graph G on n vertices and a pattern graph P on k vertices, whether G contains a subgraph isomorphic to P. The restriction of this problem to planar graphs has often been considered. After a sequence of improvements, the current best algorithm for planar graphs is a linear time algorithm by Dorn (STACS '10), with complexity $2^{O(k)} O(n)$. We generalize this result, by giving an algorithm of the same complexity for graphs that can be embedded in surfaces of bounded genus. At the same time, we simplify the algorithm and analysis. The key to these improvements is the introduction of surface split decompositions for bounded genus graphs, which generalize sphere cut decompositions for planar graphs. We extend the algorithm for the problem of counting and generating all subgraphs isomorphic to P, even for the case where P is disconnected. This answers an open question by Eppstein (SODA '95 / JGAA '99).


An Adaptive Technique using Advanced Encryption Standard to Implement Hard Disk Security

Abstract: The main objective of the paper is to study and develop an efficient method for Hard Disk Drive(HDD) Security using Full Disk Encryption (FDE) with Advanced Encryption Standards(AES) for data security specifically for Personal Computers(PCS) and Laptops . The focus of this work is to authenticate and protect the content of HDD from illegal use. The paper proposes an adaptive methods for protecting a HDD based on FDE. The proposed method is labeled as DiskTrust. FDE encrypts entire content or a single volume on your disk. DiskTrust implements Symmetric key cryptography with, Advanced Encryption Standards. Finally, the applicability of these methodologies for HDD security will be evaluated on a set of data files with different key sizes.


Reduction in X does not agree with Intersection and Union Types (Extended abstract)

Abstract: This paper defines intersection and union type assignment for the calculus X, a substitution free language that enjoys the Curry-Howard correspondence with respect to Gentzen's sequent calculus for classical logic. We show that this notion is closed for subject-expansion, and show that it needs to be restricted to satisfy subject-reduction as well, making it unsuitable to define a semantics.


Explicit Approximations of the Gaussian Kernel

Abstract: We investigate training and using Gaussian kernel SVMs by approximating the kernel with an explicit finite- dimensional polynomial feature representation based on the Taylor expansion of the exponential. Although not as efficient as the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms of the number of features, we show how this polynomial representation can provide a better approximation in terms of the computational cost involved. This makes our "Taylor features" especially attractive for use on very large data sets, in conjunction with online or stochastic training.


Memristive fuzzy edge detector

Abstract: Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this problem by proposing new method for the implementation of those fuzzy inference systems which use fuzzy rule base to make inference. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing some new concepts like fuzzy minterms. Although many applications can be realized through the use of our proposed system, in this study we show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. Our memristive fuzzy edge detector (implemented in analog form) compared with other common edge detectors has this advantage that it can extract edges of any given image all at once in real-time.


Semantic Predicate Types and Approximation for Class-based Object Oriented Programming

Abstract: We apply the principles of the intersection type discipline to the study of class-based object oriented programs and; our work follows from a similar approach (in the context of Abadi and Cardelli's Varsigma-object calculus) taken by van Bakel and de'Liguoro. We define an extension of Featherweight Java, FJc and present a predicate system which we show to be sound and expressive. We also show that our system provides a semantic underpinning for the object oriented paradigm by generalising the concept of approximant from the Lambda Calculus and demonstrating an approximation result: all expressions to which we can assign a predicate have an approximant that satisfies the same predicate. Crucial to this result is the notion of predicate language, which associates a family of predicates with a class.


Outlier detection in default logics: the tractability/intractability frontier

Abstract: In default theories, outliers denote sets of literals featuring unexpected properties. In previous papers, we have defined outliers in default logics and investigated their formal properties. Specifically, we have looked into the computational complexity of outlier detection problems and proved that while they are generally intractable, interesting tractable cases can be singled out. Following those results, we study here the tractability frontier in outlier detection problems, by analyzing it with respect to (i) the considered outlier detection problem, (ii) the reference default logic fragment, and (iii) the adopted notion of outlier. As for point (i), we shall consider three problems of increasing complexity, called Outlier-Witness Recognition, Outlier Recognition and Outlier Existence, respectively. As for point (ii), as we look for conditions under which outlier detection can be done efficiently, attention will be limited to subsets of Disjunction-free propositional default theories. As for point (iii), we shall refer to both the notion of outlier of [ABP08] and a new and more restrictive one, called strong outlier. After complexity results, we present a polynomial time algorithm for enumerating all strong outliers of bounded size in an quasi-acyclic normal unary default theory. Some of our tractability results rely on the Incremental Lemma that provides conditions for a deafult logic fragment to have a monotonic behavior. Finally, in order to show that the simple fragments of DL we deal with are still rich enough to solve interesting problems and, therefore, the tractability results that we prove are interesting not only on the mere theoretical side, insights into the expressive capabilities of these fragments are provided, by showing that normal unary theories express all NL queries, hereby indirectly answering a question raised by Kautz and Selman.


TrackMeNot: Enhancing the privacy of Web Search

Abstract: Most search engines can potentially infer the preferences and interests of a user based on her history of search queries. While search engines can use these inferences for a variety of tasks, including targeted advertisements, such tasks do impose an serious threat to user privacy. In 2006, after AOL disclosed the search queries of 650,000 users, TrackMeNot was released as a simple browser extension that sought to hide user search preferences in a cloud of queries. The first versions of TrackMeNot, though used extensively in the past three years, was fairly simplistic in design and did not provide any strong privacy guarantees. In this paper, we present the new design and implementation of TrackMeNot, which address many of the limitations of the first release. TrackMeNot addresses two basic problems. First, using a model for characterizing search queries, TrackMeNot provides a mechanism for obfuscating the search preferences of a user from a search engine. Second, TrackMeNot prevents the leakage of information revealing the use of obfuscation to a search engine via several potential side channels in existing browsers such as clicks, cookies etc. Finally, we show that TrackMeNot cannot be detected by current search bot detection mechanisms and demonstrate the effectiveness of TrackMeNot in obfuscating user interests by testing its efficiency on a major search engine.


Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video

Abstract: We describe an approach for segmenting an image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional, that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach.


Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

Abstract: This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.


An Approach for Message Hiding using Substitution Techniques and Audio Hiding in Steganography

Abstract: A crypto system can be used to encrypt messages sent between two communicating parties so that an eavesdropper who overhears the encrypted messages will not be able to decode them. The paper mainly concentrates on the method in which the substitution technique of steganography can been used to hide data in a 24-bit bitmap file. Popular audio hiding techniques based on methods of steganography is also discussed here.


Parameterized Complexity of Firefighting Revisited

Abstract: The Firefighter problem is to place firefighters on the vertices of a graph to prevent a fire with known starting point from lighting up the entire graph. In each time step, a firefighter may be permanently placed on an unburned vertex and the fire spreads to its neighborhood in the graph in so far no firefighters are protecting those vertices. The goal is to let as few vertices burn as possible. This problem is known to be NP-complete, even when restricted to bipartite graphs or to trees of maximum degree three. Initial study showed the Firefighter problem to be fixed-parameter tractable on trees in various parameterizations. We complete these results by showing that the problem is in FPT on general graphs when parameterized by the number of burned vertices, but has no polynomial kernel on trees, resolving an open problem. Conversely, we show that the problem is W[1]-hard when parameterized by the number of unburned vertices, even on bipartite graphs. For both parameterizations, we additionally give refined algorithms on trees, improving on the running times of the known algorithms.


Probabilistic prototype models for attributed graphs

Abstract: This contribution proposes a new approach towards developing a class of probabilistic methods for classifying attributed graphs. The key concept is random attributed graph, which is defined as an attributed graph whose nodes and edges are annotated by random variables. Every node/edge has two random processes associated with it- occurence probability and the probability distribution over the attribute values. These are estimated within the maximum likelihood framework. The likelihood of a random attributed graph to generate an outcome graph is used as a feature for classification. The proposed approach is fast and robust to noise.


From X to Pi; Representing the Classical Sequent Calculus in the Pi-calculus

Abstract: We study the Pi-calculus, enriched with pairing and non-blocking input, and define a notion of type assignment that uses the type constructor "arrow". We encode the circuits of the calculus X into this variant of Pi, and show that all reduction (cut-elimination) and assignable types are preserved. Since X enjoys the Curry-Howard isomorphism for Gentzen's calculus LK, this implies that all proofs in LK have a representation in Pi.


Note on a simple type system for non-interference

Abstract: We consider CCS with value passing and elaborate a notion of noninterference for the process calculi, which matches closely that of the programming language. The idea is to view channels as information carriers rather than as "events", so that emitting a secret on output channel can be considered safe, while inputting a secret may lead to some kind of leakage. This is in contrast with the standard notion of noninterference for the process calculi where any causal dependency of low-level action from any high-level action is forbidden.


Elementary Components of the Quadratic Assignment Problem

Abstract: The Quadratic Assignment Problem (QAP) is a well-known NP-hard combinatorial optimization problem that is at the core of many real-world optimization problems. We prove that QAP can be written as the sum of three elementary landscapes when the swap neighborhood is used. We present a closed formula for each of the three elementary components and we compute bounds for the autocorrelation coefficient.


Automatic transcription of 17th century English text in Contemporary English with NooJ: Method and Evaluation

Abstract: Since 2006 we have undertaken to describe the differences between 17th century English and contemporary English thanks to NLP software. Studying a corpus spanning the whole century (tales of English travellers in the Ottoman Empire in the 17th century, Mary Astell's essay A Serious Proposal to the Ladies and other literary texts) has enabled us to highlight various lexical, morphological or grammatical singularities. Thanks to the NooJ linguistic platform, we created dictionaries indexing the lexical variants and their transcription in CE. The latter is often the result of the validation of forms recognized dynamically by morphological graphs. We also built syntactical graphs aimed at transcribing certain archaic forms in contemporary English. Our previous research implied a succession of elementary steps alternating textual analysis and result validation. We managed to provide examples of transcriptions, but we have not created a global tool for automatic transcription. Therefore we need to focus on the results we have obtained so far, study the conditions for creating such a tool, and analyze possible difficulties. In this paper, we will be discussing the technical and linguistic aspects we have not yet covered in our previous work. We are using the results of previous research and proposing a transcription method for words or sequences identified as archaic.


Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D Rigid-Body Motion Registration

Abstract: Motivated by an emerging theory of robust low-rank matrix representation, in this paper, we introduce a novel solution for online rigid-body motion registration. The goal is to develop algorithmic techniques that enable a robust, real-time motion registration solution suitable for low-cost, portable 3-D camera devices. Assuming 3-D image features are tracked via a standard tracker, the algorithm first utilizes Robust PCA to initialize a low-rank shape representation of the rigid body. Robust PCA finds the global optimal solution of the initialization, while its complexity is comparable to singular value decomposition. In the online update stage, we propose a more efficient algorithm for sparse subspace projection to sequentially project new feature observations onto the shape subspace. The lightweight update stage guarantees the real-time performance of the solution while maintaining good registration even when the image sequence is contaminated by noise, gross data corruption, outlying features, and missing data. The state-of-the-art accuracy of the solution is validated through extensive simulation and a real-world experiment, while the system enjoys one to two orders of magnitude speed-up compared to well-established RANSAC solutions. The new algorithm will be released online to aid peer evaluation.


Inapproximability of Treewidth, One-Shot Pebbling, and Related Layout Problems

Abstract: We study the approximability of a number of graph problems: treewidth and pathwidth of graphs, one-shot black (and black-white) pebbling costs of directed acyclic graphs, and a variety of different graph layout problems such as minimum cut linear arrangement and interval graph completion. We show that, assuming the recently introduced Small Set Expansion Conjecture, all of these problems are hard to approximate within any constant factor.


Beyond pixels and regions: A non local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images

Abstract: A patch-based non-local restoration and reconstruction method for preprocessing degraded document images is introduced. The method collects relative data from the whole input image, while the image data are first represented by a content-level descriptor based on patches. This patch-equivalent representation of the input image is then corrected based on similar patches identified using a modified genetic algorithm (GA) resulting in a low computational load. The corrected patch-equivalent is then converted to the output restored image. The fact that the method uses the patches at the content level allows it to incorporate high-level restoration in an objective and self-sufficient way. The method has been applied to several degraded document images, including the DIBCO'09 contest dataset with promising results.


Couillard: Parallel Programming via Coarse-Grained Data-Flow Compilation

Abstract: Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading) introduces a user-defined coarse-grained parallel data-flow model, where programmers identify code blocks, called super-instructions, to be run in parallel and connect them in a data-flow graph. TALM has been implemented as a hybrid Von Neumann/data-flow execution system: the \emph{Trebuchet}. We have observed that TALM's usefulness largely depends on how programmers specify and connect super-instructions. Thus, we present \emph{Couillard}, a full compiler that creates, based on an annotated C-program, a data-flow graph and C-code corresponding to each super-instruction. We show that our toolchain allows one to benefit from data-flow execution and explore sophisticated parallel programming techniques, with small effort. To evaluate our system we have executed a set of real applications on a large multi-core machine. Comparison with popular parallel programming methods shows competitive speedups, while providing an easier parallel programing approach.


Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation

Abstract: Virtualization has become commonplace in modern data centers, often referred as "computing clouds". The capability of virtual machine live migration brings benefits such as improved performance, manageability and fault tolerance, while allowing workload movement with a short service downtime. However, service levels of applications are likely to be negatively affected during a live migration. For this reason, a better understanding of its effects on system performance is desirable. In this paper, we evaluate the effects of live migration of virtual machines on the performance of applications running inside Xen VMs. Results show that, in most cases, migration overhead is acceptable but cannot be disregarded, especially in systems where availability and responsiveness are governed by strict Service Level Agreements. Despite that, there is a high potential for live migration applicability in data centers serving modernInternet applications. Our results are based on a workload covering the domain of multi-tier Web 2.0 applications.


Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition

Abstract: This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of midlevel features, we develop a spectral embedding approach to latent semantic learning based on L1-graph, without the need to tune any parameter for graph construction as a key step of manifold learning. More importantly, we construct the L1-graph with structured sparse representation, which can be obtained by structured sparse coding with its structured sparsity ensured by novel L1-norm hypergraph regularization over mid-level features. In the new embedding space, we learn latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for human action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both L1-graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the superior performance of our method.


An O(n^2) Time Algorithm for Alternating B\"uchi Games

Abstract: Computing the winning set for B{\"u}chi objectives in alternating games on graphs is a central problem in computer aided verification with a large number of applications. The long standing best known upper bound for solving the problem is $\tilde{O}(n \cdot m)$, where $n$ is the number of vertices and $m$ is the number of edges in the graph. We are the first to break the $\tilde{O}(n\cdot m)$ bound by presenting a new technique that reduces the running time to $O(n^2)$. This bound also leads to an $O(n^2)$ algorithm time for computing the set of almost-sure winning vertices in alternating games with probabilistic transitions (improving an earlier bound of $\tilde{O}(n\cdot m)$) and in concurrent graph games with constant actions (improving an earlier bound of $O(n^3)$). We also show that the same technique can be used to compute the maximal end-component decomposition of a graph in time $O(n^2)$. Finally, we show how to maintain the winning set for B{\"u}chi objectives in alternating games under a sequence of edge insertions or a sequence of edge deletions in O(n) amortized time per operation. This is the first dynamic algorithm for this problem.


Natural hand gestures for human identification in a Human-Computer Interface

Abstract: The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.


Testing first-order properties for subclasses of sparse graphs

Abstract: We present a linear-time algorithm for deciding first-order (FO) properties in classes of graphs with bounded expansion, a notion recently introduced by Nesetril and Ossona de Mendez. This generalizes several results from the literature, because many natural classes of graphs have bounded expansion: graphs of bounded tree-width, all proper minor-closed classes of graphs, graphs of bounded degree, graphs with no subgraph isomorphic to a subdivision of a fixed graph, and graphs that can be drawn in a fixed surface in such a way that each edge crosses at most a constant number of other edges. We deduce that there is an almost linear-time algorithm for deciding FO properties in classes of graphs with locally bounded expansion. More generally, we design a dynamic data structure for graphs belonging to a fixed class of graphs of bounded expansion. After a linear-time initialization the data structure allows us to test an FO property in constant time, and the data structure can be updated in constant time after addition/deletion of an edge, provided the list of possible edges to be added is known in advance and their simultaneous addition results in a graph in the class. All our results also hold for relational structures and are based on the seminal result of Nesetril and Ossona de Mendez on the existence of low tree-depth colorings.


A New Approach to Design Graph Based Search Engine for Multiple Domains Using Different Ontologies

Abstract: Search Engine has become a major tool for searching any information from the World Wide Web (WWW). While searching the huge digital library available in the WWW, every effort is made to retrieve the most relevant results. But in WWW majority of the Web pages are in HTML format and there are no such tags which tells the crawler to find any specific domain. To find more relevant result we use Ontology for that particular domain. If we are working with multiple domains then we use multiple ontologies. Now in order to design a domain specific search engine for multiple domains, crawler must crawl through the domain specific Web pages in the WWW according to the predefined ontologies.


Analysis of first prototype universal intelligence tests: evaluating and comparing AI algorithms and humans

Abstract: Today, available methods that assess AI systems are focused on using empirical techniques to measure the performance of algorithms in some specific tasks (e.g., playing chess, solving mazes or land a helicopter). However, these methods are not appropriate if we want to evaluate the general intelligence of AI and, even less, if we compare it with human intelligence. The ANYNT project has designed a new method of evaluation that tries to assess AI systems using well known computational notions and problems which are as general as possible. This new method serves to assess general intelligence (which allows us to learn how to solve any new kind of problem we face) and not only to evaluate performance on a set of specific tasks. This method not only focuses on measuring the intelligence of algorithms, but also to assess any intelligent system (human beings, animals, AI, aliens?,...), and letting us to place their results on the same scale and, therefore, to be able to compare them. This new approach will allow us (in the future) to evaluate and compare any kind of intelligent system known or even to build/find, be it artificial or biological. This master thesis aims at ensuring that this new method provides consistent results when evaluating AI algorithms, this is done through the design and implementation of prototypes of universal intelligence tests and their application to different intelligent systems (AI algorithms and humans beings). From the study we analyze whether the results obtained by two different intelligent systems are properly located on the same scale and we propose changes and refinements to these prototypes in order to, in the future, being able to achieve a truly universal intelligence test.


Application of distances between terms for flat and hierarchical data

Abstract: In machine learning, distance-based algorithms, and other approaches, use information that is represented by propositional data. However, this kind of representation can be quite restrictive and, in many cases, it requires more complex structures in order to represent data in a more natural way. Terms are the basis for functional and logic programming representation. Distances between terms are a useful tool not only to compare terms, but also to determine the search space in many of these applications. This dissertation applies distances between terms, exploiting the features of each distance and the possibility to compare from propositional data types to hierarchical representations. The distances between terms are applied through the k-NN (k-nearest neighbor) classification algorithm using XML as a common language representation. To be able to represent these data in an XML structure and to take advantage of the benefits of distance between terms, it is necessary to apply some transformations. These transformations allow the conversion of flat data into hierarchical data represented in XML, using some techniques based on intuitive associations between the names and values of variables and associations based on attribute similarity. Several experiments with the distances between terms of Nienhuys-Cheng and Estruch et al. were performed. In the case of originally propositional data, these distances are compared to the Euclidean distance. In all cases, the experiments were performed with the distance-weighted k-nearest neighbor algorithm, using several exponents for the attraction function (weighted distance). It can be seen that in some cases, the term distances can significantly improve the results on approaches applied to flat representations.


A Semantic Analysis of Key Management Protocols for Wireless Sensor Networks

Abstract: We propose a simple timed broadcasting process calculus for modelling wireless network protocols. The operational semantics of our calculus is given in terms of a labelled transition semantics which is used to derive a standard (weak) bi-simulation theory. Based on our simulation theory, we reformulate Gorrieri and Martinelli's timed Generalized Non-Deducibility on Compositions (tGNDC) scheme, a well-known general framework for the definition of timed properties of security protocols. We use tGNDC to perform a semantic analysis of three well-known key management protocols for wireless sensor networks: \mu TESLA, LEAP+ and LiSP. As a main result, we provide a number of attacks to these protocols which, to our knowledge, have not yet appeared in the literature.


Nerio: Leader Election and Edict Ordering

Abstract: Coordination in a distributed system is facilitated if there is a unique process, the leader, to manage the other processes. The leader creates edicts and sends them to other processes for execution or forwarding to other processes. The leader may fail, and when this occurs a leader election protocol selects a replacement. This paper describes Nerio, a class of such leader election protocols.