OpenRaiser/Intern-Atlas huggingface dataset:
Shedding Light on RFID Distance Bounding Protocols and Terrorist Fraud Attacks
Venue: arXiv cs.CR 2010
Year: 2010

The vast majority of RFID authentication protocols assume the proximity between readers and tags due to the limited range of the radio channel. However, in real scenarios an intruder can be located between the prover (tag) and the verifier (reader) and trick this last one into thinking that the prover is in close proximity. This attack is generally known as a relay attack in which scope distance fraud, mafia fraud and terrorist attacks are included. Distance bounding protocols represent a promising countermeasure to hinder relay attacks. Several protocols have been proposed during the last years but vulnerabilities of major or minor relevance have been identified in most of them. In 2008, Kim et al. [1] proposed a new distance bounding protocol with the objective of being the best in terms of security, privacy, tag computational overhead and fault tolerance. In this paper, we analyze this protocol and we present a passive full disclosure attack, which allows an adversary to discover the long-term secret key of the tag. The presented attack is very relevant, since no security objectives are met in Kim et al.'s protocol. Then, design guidelines are introduced with the aim of facilitating protocol designers the stimulating task of designing secure and efficient schemes against relay attacks. Finally a new protocol, named Hitomi and inspired by [1], is designed conforming the guidelines proposed previously.
A Divide-and-Conquer Scheme for Assigning Roles in Multi-Channel Wireless Mesh Networks
Venue: arXiv cs.NI 2010
Year: 2010

A multi-channel MAC seems to be an interesting approach for improving network throughput by multiplexing transmissions over orthogonal channels. In particular, Molecular MAC has recently proposed to modify the standard IEEE 802.11 DCF access method to use dynamic channel switching for efficient packet forwarding over multiple hops. However, this MAC layer requires role and channel assignment to nodes: some of them use a static channel, while others dynamically switch to neighbor channels on-demand. To assign roles and channels, we extend the notion of the Weakly Connected Dominating Set, the structure already used in clustering. More precisely, we adapt the WCDS structure and introduce new constraints to define what we call a reversible WCDS (r-WCDS), which is particularly suitable for wireless mesh networks operating under Molecular MAC. We propose a divide-and-conquer scheme that partitions the network into clusters with one leader per cluster solving a MILP formulation to assign roles in its cluster. By appropriately defining the roles at the border of clusters, we maintain global connectivity in the r-wcds. Finally, our simulations show that the performance of the propose scheme is close to a centralized algorithm.
Learning Gaussian Mixtures with Arbitrary Separation
Venue: arXiv cs.LG 2010
Year: 2010

In this paper we present a method for learning the parameters of a mixture of $k$ identical spherical Gaussians in $n$-dimensional space with an arbitrarily small separation between the components. Our algorithm is polynomial in all parameters other than $k$. The algorithm is based on an appropriate grid search over the space of parameters. The theoretical analysis of the algorithm hinges on a reduction of the problem to 1 dimension and showing that two 1-dimensional mixtures whose densities are close in the $L^2$ norm must have similar means and mixing coefficients. To produce such a lower bound for the $L^2$ norm in terms of the distances between the corresponding means, we analyze the behavior of the Fourier transform of a mixture of Gaussians in 1 dimension around the origin, which turns out to be closely related to the properties of the Vandermonde matrix obtained from the component means. Analysis of this matrix together with basic function approximation results allows us to provide a lower bound for the norm of the mixture in the Fourier domain. In recent years much research has been aimed at understanding the computational aspects of learning parameters of Gaussians mixture distributions in high dimension. To the best of our knowledge all existing work on learning parameters of Gaussian mixtures assumes minimum separation between components of the mixture which is an increasing function of either the dimension of the space $n$ or the number of components $k$. In our paper we prove the first result showing that parameters of a $n$-dimensional Gaussian mixture model with arbitrarily small component separation can be learned in time polynomial in $n$.
Interference Channels with Destination Cooperation
Venue: arXiv cs.IT 2010
Year: 2010

Interference is a fundamental feature of the wireless channel. To better understand the role of cooperation in interference management, the two-user Gaussian interference channel where the destination nodes can cooperate by virtue of being able to both transmit and receive is studied. The sum-capacity of this channel is characterized up to a constant number of bits. The coding scheme employed builds up on the superposition scheme of Han and Kobayashi (1981) for two-user interference channels without cooperation. New upperbounds to the sum-capacity are also derived.
Decentralized Admission Control for Power-Controlled Wireless Links
Venue: arXiv cs.IT 2010
Year: 2010

This paper deals with the problem of admission control/channel access in power-controlled decentralized wireless networks, in which the quality-of-service (QoS) is expressed in terms of the signal-to-interference ratio (SIR). We analyze a previously proposed admission control algorithm, which was designed to maintain the SIR of operational (active) links above some given threshold at all times (protection of active links). This protection property ensures that as new users attempt to join the network, the already established links sustain their quality. The considered scheme may be thus applicable in some cognitive radio networks, where the fundamental premise is that secondary users may be granted channel access only if it does not cause disturbance to primary users. The admission control algorithm was previously analyzed under the assumption of affine interference functions. This paper extends all the previous results to arbitrary standard interference functions, which capture many important receiver designs, including optimal linear reception in the sense of maximizing the SIR and the worst-case receiver design. Furthermore, we provide novel conditions for protection of active users under the considered control scheme when individual power constraints are imposed on each link. Finally, we consider the possibility of a joint optimization of transmitters and receivers in networks with linear transceivers, which includes linear beamforming in multiple antenna systems. Transmitter optimization is performed alternately with receiver optimization to generate non-decreasing sequences of SIRs. Numerical evaluations show that additional transmitter side optimization has potential for significant performance gains.
Fast Searching in Packed Strings
Venue: arXiv cs.DS 2010
Year: 2010

Given strings $P$ and $Q$ the (exact) string matching problem is to find all positions of substrings in $Q$ matching $P$. The classical Knuth-Morris-Pratt algorithm [SIAM J. Comput., 1977] solves the string matching problem in linear time which is optimal if we can only read one character at the time. However, most strings are stored in a computer in a packed representation with several characters in a single word, giving us the opportunity to read multiple characters simultaneously. In this paper we study the worst-case complexity of string matching on strings given in packed representation. Let $m \leq n$ be the lengths $P$ and $Q$, respectively, and let $σ$ denote the size of the alphabet. On a standard unit-cost word-RAM with logarithmic word size we present an algorithm using time $$ O\left(\frac{n}{\log_σn} + m + \occ\right). $$ Here $\occ$ is the number of occurrences of $P$ in $Q$. For $m = o(n)$ this improves the $O(n)$ bound of the Knuth-Morris-Pratt algorithm. Furthermore, if $m = O(n/\log_σn)$ our algorithm is optimal since any algorithm must spend at least $Ω(\frac{(n+m)\log σ}{\log n} + \occ) = Ω(\frac{n}{\log_σn} + \occ)$ time to read the input and report all occurrences. The result is obtained by a novel automaton construction based on the Knuth-Morris-Pratt algorithm combined with a new compact representation of subautomata allowing an optimal tabulation-based simulation.
From Causal Semantics To Duration Timed Models
Venue: arXiv cs.LO 2010
Year: 2010

The interleaving semantics is not compatible with both action refinement and durational actions. Since many true concurrency semantics are congruent w.r.t. action refinement, notably the causality and the maximality ones, this has challenged us to study the dense time behavior - where the actions are of arbitrary fixed duration - within the causality semantics of Da Costa. We extend the causal transition systems with the clocks and the timed constraints, and thus we obtain an over class of timed automata where the actions need not to be atomic. We define a real time extension of the formal description technique CSP, called duration-CSP, by attributing the duration to actions. We give the operational timed causal semantics of duration-CSP as well as its denotational semantics over the class of timed causal transition systems. Afterwards, we prove that the two semantics are equivalent. Finally we extend the duration-CSP language with a refinement operator $ρ$ - that allows to replace an action with a process - and prove that it preserves the timed causal bisimulation.
Budget Constrained Auctions with Heterogeneous Items
Venue: arXiv cs.GT 2010
Year: 2010

In this paper, we present the first approximation algorithms for the problem of designing revenue optimal Bayesian incentive compatible auctions when there are multiple (heterogeneous) items and when bidders can have arbitrary demand and budget constraints. Our mechanisms are surprisingly simple: We show that a sequential all-pay mechanism is a 4 approximation to the revenue of the optimal ex-interim truthful mechanism with discrete correlated type space for each bidder. We also show that a sequential posted price mechanism is a O(1) approximation to the revenue of the optimal ex-post truthful mechanism when the type space of each bidder is a product distribution that satisfies the standard hazard rate condition. We further show a logarithmic approximation when the hazard rate condition is removed, and complete the picture by showing that achieving a sub-logarithmic approximation, even for regular distributions and one bidder, requires pricing bundles of items. Our results are based on formulating novel LP relaxations for these problems, and developing generic rounding schemes from first principles. We believe this approach will be useful in other Bayesian mechanism design contexts.
Small grid embeddings of 3-polytopes
Venue: arXiv cs.CG 2010
Year: 2010

We introduce an algorithm that embeds a given 3-connected planar graph as a convex 3-polytope with integer coordinates. The size of the coordinates is bounded by $O(2^{7.55n})=O(188^{n})$. If the graph contains a triangle we can bound the integer coordinates by $O(2^{4.82n})$. If the graph contains a quadrilateral we can bound the integer coordinates by $O(2^{5.46n})$. The crucial part of the algorithm is to find a convex plane embedding whose edges can be weighted such that the sum of the weighted edges, seen as vectors, cancel at every point. It is well known that this can be guaranteed for the interior vertices by applying a technique of Tutte. We show how to extend Tutte's ideas to construct a plane embedding where the weighted vector sums cancel also on the vertices of the boundary face.
On Secrecy Capacity Scaling in Wireless Networks
Venue: arXiv cs.IT 2010
Year: 2010

This work studies the achievable secure rate per source-destination pair in wireless networks. First, a path loss model is considered, where the legitimate and eavesdropper nodes are assumed to be placed according to Poisson point processes with intensities $λ$ and $λ_e$, respectively. It is shown that, as long as $λ_e/λ=o((\log n)^{-2})$, almost all of the nodes achieve a perfectly secure rate of $Ω(\frac{1}{\sqrt{n}})$ for the extended and dense network models. Therefore, under these assumptions, securing the network does not entail a loss in the per-node throughput. The achievability argument is based on a novel multi-hop forwarding scheme where randomization is added in every hop to ensure maximal ambiguity at the eavesdropper(s). Secondly, an ergodic fading model with $n$ source-destination pairs and $n_e$ eavesdroppers is considered. Employing the ergodic interference alignment scheme with an appropriate secrecy pre-coding, each user is shown to achieve a constant positive secret rate for sufficiently large $n$. Remarkably, the scheme does not require eavesdropper CSI (only the statistical knowledge is assumed) and the secure throughput per node increases as we add more legitimate users to the network in this setting. Finally, the effect of eavesdropper collusion on the performance of the proposed schemes is characterized.
The Weights in MDS Codes
Venue: arXiv cs.IT 2010
Year: 2010

The weights in MDS codes of length n and dimension k over the finite field GF(q) are studied. Up to some explicit exceptional cases, the MDS codes with parameters given by the MDS conjecture are shown to contain all k weights in the range n-k+1 to n. The proof uses the covering radius of the dual code
On k-Column Sparse Packing Programs
Venue: arXiv cs.DS 2010
Year: 2010

We consider the class of packing integer programs (PIPs) that are column sparse, i.e. there is a specified upper bound k on the number of constraints that each variable appears in. We give an (ek+o(k))-approximation algorithm for k-column sparse PIPs, improving on recent results of $k^2\cdot 2^k$ and $O(k^2)$. We also show that the integrality gap of our linear programming relaxation is at least 2k-1; it is known that k-column sparse PIPs are $Ω(k/ \log k)$-hard to approximate. We also extend our result (at the loss of a small constant factor) to the more general case of maximizing a submodular objective over k-column sparse packing constraints.
Deterministic Construction of Binary, Bipolar and Ternary Compressed Sensing Matrices
Venue: arXiv cs.IT 2010
Year: 2010

In this paper we establish the connection between the Orthogonal Optical Codes (OOC) and binary compressed sensing matrices. We also introduce deterministic bipolar $m\times n$ RIP fulfilling $\pm 1$ matrices of order $k$ such that $m\leq\mathcal{O}\big(k (\log_2 n)^{\frac{\log_2 k}{\ln \log_2 k}}\big)$. The columns of these matrices are binary BCH code vectors where the zeros are replaced by -1. Since the RIP is established by means of coherence, the simple greedy algorithms such as Matching Pursuit are able to recover the sparse solution from the noiseless samples. Due to the cyclic property of the BCH codes, we show that the FFT algorithm can be employed in the reconstruction methods to considerably reduce the computational complexity. In addition, we combine the binary and bipolar matrices to form ternary sensing matrices ($\{0,1,-1\}$ elements) that satisfy the RIP condition.
Rapid self-organised initiation of ad hoc sensor networks close above the percolation threshold
Venue: arXiv cs.NI 2010
Year: 2010

This work shows potentials for rapid self-organisation of sensor networks where nodes collaborate to relay messages to a common data collecting unit (sink node). The study problem is, in the sense of graph theory, to find a shortest path tree spanning a weighted graph. This is a well-studied problem where for example Dijkstra's algorithm provides a solution for non-negative edge weights. The present contribution shows by simulation examples that simple modifications of known distributed approaches here can provide significant improvements in performance. Phase transition phenomena, which are known to take place in networks close to percolation thresholds, may explain these observations. An initial method, which here serves as reference, assumes the sink node starts organisation of the network (tree) by transmitting a control message advertising its availability for its neighbours. These neighbours then advertise their current cost estimate for routing a message to the sink. A node which in this way receives a message implying an improved route to the sink, advertises its new finding and remembers which neighbouring node the message came from. This activity proceeds until there are no more improvements to advertise to neighbours. The result is a tree network for cost effective transmission of messages to the sink (root). This distributed approach has potential for simple improvements which are of interest when minimisation of storage and communication of network information are a concern. Fast organisation of the network takes place when the number $k$ of connections for each node ({\em degree}) is close above its critical value for global network percolation and at the same time there is a threshold for the nodes to decide to advertise network route updates.
Relative $(p,ε)$-Approximations in Geometry
Venue: arXiv cs.CG 2010
Year: 2010

We re-examine the notion of relative $(p,\eps)$-approximations, recently introduced in [CKMS06], and establish upper bounds on their size, in general range spaces of finite VC-dimension, using the sampling theory developed in [LLS01] and in several earlier studies [Pol86, Hau92, Tal94]. We also survey the different notions of sampling, used in computational geometry, learning, and other areas, and show how they relate to each other. We then give constructions of smaller-size relative $(p,\eps)$-approximations for range spaces that involve points and halfspaces in two and higher dimensions. The planar construction is based on a new structure--spanning trees with small relative crossing number, which we believe to be of independent interest. Relative $(p,\eps)$-approximations arise in several geometric problems, such as approximate range counting, and we apply our new structures to obtain efficient solutions for approximate range counting in three dimensions. We also present a simple solution for the planar case.
New Approximation Algorithms for Minimum Enclosing Convex Shapes
Venue: arXiv cs.CG 2010
Year: 2010

Given $n$ points in a $d$ dimensional Euclidean space, the Minimum Enclosing Ball (MEB) problem is to find the ball with the smallest radius which contains all $n$ points. We give a $O(nd\Qcal/\sqrtε)$ approximation algorithm for producing an enclosing ball whose radius is at most $ε$ away from the optimum (where $\Qcal$ is an upper bound on the norm of the points). This improves existing results using \emph{coresets}, which yield a $O(nd/ε)$ greedy algorithm. Finding the Minimum Enclosing Convex Polytope (MECP) is a related problem wherein a convex polytope of a fixed shape is given and the aim is to find the smallest magnification of the polytope which encloses the given points. For this problem we present a $O(mnd\Qcal/ε)$ approximation algorithm, where $m$ is the number of faces of the polytope. Our algorithms borrow heavily from convex duality and recently developed techniques in non-smooth optimization, and are in contrast with existing methods which rely on geometric arguments. In particular, we specialize the excessive gap framework of \citet{Nesterov05a} to obtain our results.
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Venue: arXiv cs.LG 2010
Year: 2010

Conditional Random Fields (CRFs) constitute a popular and efficient approach for supervised sequence labelling. CRFs can cope with large description spaces and can integrate some form of structural dependency between labels. In this contribution, we address the issue of efficient feature selection for CRFs based on imposing sparsity through an L1 penalty. We first show how sparsity of the parameter set can be exploited to significantly speed up training and labelling. We then introduce coordinate descent parameter update schemes for CRFs with L1 regularization. We finally provide some empirical comparisons of the proposed approach with state-of-the-art CRF training strategies. In particular, it is shown that the proposed approach is able to take profit of the sparsity to speed up processing and hence potentially handle larger dimensional models.
Frame Permutation Quantization
Venue: arXiv cs.IT 2010
Year: 2010

Frame permutation quantization (FPQ) is a new vector quantization technique using finite frames. In FPQ, a vector is encoded using a permutation source code to quantize its frame expansion. This means that the encoding is a partial ordering of the frame expansion coefficients. Compared to ordinary permutation source coding, FPQ produces a greater number of possible quantization rates and a higher maximum rate. Various representations for the partitions induced by FPQ are presented, and reconstruction algorithms based on linear programming, quadratic programming, and recursive orthogonal projection are derived. Implementations of the linear and quadratic programming algorithms for uniform and Gaussian sources show performance improvements over entropy-constrained scalar quantization for certain combinations of vector dimension and coding rate. Monte Carlo evaluation of the recursive algorithm shows that mean-squared error (MSE) decays as 1/M^4 for an M-element frame, which is consistent with previous results on optimal decay of MSE. Reconstruction using the canonical dual frame is also studied, and several results relate properties of the analysis frame to whether linear reconstruction techniques provide consistent reconstructions.
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary $β$-Mixing Processes
Venue: arXiv cs.LG 2010
Year: 2010

Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for margin classifiers: there have been recent contributions showing how practical these bounds can be either to perform model selection (Ambroladze et al., 2007) or even to directly guide the learning of linear classifiers (Germain et al., 2009). However, there are many practical situations where the training data show some dependencies and where the traditional IID assumption does not hold. Stating generalization bounds for such frameworks is therefore of the utmost interest, both from theoretical and practical standpoints. In this work, we propose the first - to the best of our knowledge - Pac-Bayes generalization bounds for classifiers trained on data exhibiting interdependencies. The approach undertaken to establish our results is based on the decomposition of a so-called dependency graph that encodes the dependencies within the data, in sets of independent data, thanks to graph fractional covers. Our bounds are very general, since being able to find an upper bound on the fractional chromatic number of the dependency graph is sufficient to get new Pac-Bayes bounds for specific settings. We show how our results can be used to derive bounds for ranking statistics (such as Auc) and classifiers trained on data distributed according to a stationary ß-mixing process. In the way, we show how our approach seemlessly allows us to deal with U-processes. As a side note, we also provide a Pac-Bayes generalization bound for classifiers learned on data from stationary $φ$-mixing distributions.
Performance of Opportunistic Epidemic Routing on Edge-Markovian Dynamic Graphs
Venue: arXiv cs.NI 2010
Year: 2010

Connectivity patterns in intermittently-connected mobile networks (ICMN) can be modeled as edge-Markovian dynamic graphs. We propose a new model for epidemic propagation on such graphs and calculate a closed-form expression that links the best achievable delivery ratio to common ICMN parameters such as message size, maximum tolerated delay, and link lifetime. These theoretical results are compared to those obtained by replaying a real-life contact trace.